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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Yue, Qi | Deng, Zhibin | Hu, Bin | Tao, Yuan
Article Type: Research Article
Abstract: The two-sided matching (TSM) decision-making is an interdisciplinary research field encompassing management science, behavioral science, and computer science, which are widely applied in various industries and everyday life, generating significant economic and social value. However, in the decision-making process of real-world TSM, the complexity of the decision-making problem and environment lead to the preference information provided by the two-sided agents being ambiguous and uncertain. The purpose of this study is to develop a new fair and stable matching methodology to resolve the TSM problem with multiple hesitant fuzzy element (HFE) information. The decision-making process is as follows. First, the TSM …problem with four kinds of HFEs is described. To solve this problem, the HFE value of each index is normalized and then is transformed into the closeness degree by using the bidirectional projection technology. Second, based on the closeness degree, the weight of each index is calculated by using the Critic method. Then, the agent satisfaction is obtained by aggregating the closeness and the weights. Next, a fair and stable TSM model to maximizing agent satisfactions under the constraints of one-to-one stable matching is constructed. The best TSM scheme can be obtained by solving the TSM model. Finally, an example of logistics technology cooperation is provided to verify the effectiveness and feasibility of the presented model and methodology. The proposed methodology develops a novel fuzzy information presentation tool and constructs a TSM model considering the fairness and stability, which is of great significance to investigate the TSM decision-making and the resolution of real-life TSM problems under the uncertain and fuzzy environments. One future research direction is to consider multiple psychological and behavioral factors of two-sided agents in TSM problems. Show more
Keywords: Two-sided matching, fairness and stability, hesitant fuzzy element (HFE), bidirectional projection technology, critic, multiobjective programming model
DOI: 10.3233/JIFS-232520
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3045-3069, 2024
Authors: Nasef, Mohammed M. | El Kafrawy, Passent M. | Hashim, Amal
Article Type: Research Article
Abstract: Computational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the …proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity. Show more
Keywords: Particle swarm optimization, P systems, mitochondrial fusion model, mutual dynamic rules
DOI: 10.3233/JIFS-223804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3071-3083, 2024
Authors: Nghiem, Thi Bich Ha | Chu, Ta-Chung
Article Type: Research Article
Abstract: Selecting a sustainable facility location is a crucial strategy for manufacturing companies to achieve long-term success in today’s competitive environment. Various quantitative and qualitative criteria with different importance in a multiple level structure must be considered and aggregated to assist the company in decision-making. How to determine these criteria weights and select the sustainable manufacturing facility locations have become research questions. To resolve this problem, this paper proposes a total distance ranking approach to fuzzy analytic hierarchical process (AHP)-based multiple criteria decision-making (MCDM) method. Currently, the membership functions of fuzzy weighted ratings in the MCDM with a multilevel structure cannot …be developed. A ranking method is needed to defuzzify those fuzzy numbers used for fuzzy AHP and qualitative criteria in the MCDM for better executing decision-making procedure. The total distance ranking method related to the centroid on x-axis, centroid on y-axis and the distances of centroids point to the two tangent lines of a fuzzy number are considered in the proposed ranking approach. Formulas of the proposed approach will be presented, and some properties will be investigated to derive formulas for trapezoidal and triangular fuzzy numbers. A comparison with relevant ranking methods will be made to show the advantages of the proposed ranking approach. The proposed ranking approach is then applied to defuzzify the fuzzy numbers used in fuzzy AHP and linguistic values under qualitative criteria to obtain the criteria weights under multi-level structure and crisp values under qualitative criteria, respectively. The final scores of alternatives can be obtained by aggregating crisp criteria values and their corresponding weights by simple additive weighting method to obtain the ranking result. A numerical example will be conducted to show the effectiveness of the proposed model. Finally, a comparison with Best-Worst method (BWM) will be presented to show the persuasiveness of the proposed method. Show more
Keywords: Total distance, tangent line, fuzzy AHP, MCDM, sustainable manufacturing facility location
DOI: 10.3233/JIFS-223962
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3085-3115, 2024
Authors: Li, Wenjuan | Yi, Xiduo
Article Type: Research Article
Abstract: Under the rapid process of urbanization, many early renovated urban villages have also encountered many problems. Due to the rapid development of urban construction and the continuous changes in spatial functions, early renovated urban villages have already encountered problems such as unreasonable commercial distribution, lack of parking spaces, reduced commercial vitality, and commercial activities crowding out affecting the normal lives of villagers. There is a serious contradiction between the need for development and the quality of life of villagers. Due to the fixed nature of architectural space, only by fully understanding the essential morphological characteristics of the space can we …find the optimal solutions for different space usage functions, and obtain the matching of the optimal solutions in the existing space requirements. The social ecological evaluation of the spatial form of old urban blocks is a multi-attribute group decision making (MAGDM). Recently, the grey relational analysis (GRA) and CRITIC method has been used to cope with MAGDM issues. The dual probabilistic linguistic term sets (DPLTSs) are used as a tool for characterizing uncertain information during the social ecological evaluation of the spatial form of old urban blocks. In this manuscript, the dual probabilistic linguistic GRA (DPL-GRA) method is built to solve the MAGDM under DPLTSs. The CRITIC method is used to obtain the attributes weights. In the end, a numerical case study for social ecological evaluation of the spatial form of old urban blocks is given to validate the proposed method. Show more
Keywords: Multi-attribute group decision making (MAGDM), dual probabilistic linguistic term sets, GRA, CRITIC, social ecological evaluation
DOI: 10.3233/JIFS-233165
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3117-3127, 2024
Authors: Dhanyashree, | Meera, K.N. | Broumi, Said
Article Type: Research Article
Abstract: An L (p 1 , p 2 , p 3 , … , p m )- labeling of a graph G , has the vertices of G assigned with non-negative integers, such that the vertices at distance j should have at least p j as their label difference. If m = 3 and p 1 = 3, p 2 = 2, p 3 = 1, it is called an L (3, 2, 1)-labeling which is widely studied in the literature. In this paper, we define an L (3, 2, 1)-path coloring of G as a labeling g : V (G …) → Z + such that between every pair of vertices there exists at least one path P where in the labeling restricted to this path is an L (3, 2, 1)-labeling. Among the labels assigned to any vertex of G under g , the maximum label is called the span of g . The L (3, 2, 1)-connection number of a graph G , denoted by k 3c (G ) is defined as the minimum value of span of g taken over all such labelings g . We call graphs with the special property that k 3c (G ) = |V (G ) | as L (3, 2, 1)-path graceful. In this paper, we obtain k 3c (G ) of graphs that possess a Hamiltonian path and carry forward the discussion to certain classes of graphs which do not possess a Hamiltonian path, which is novel to this paper. Although different kinds of labeling are studied in the literature with different mathematical constraints imposed, the idea of showing the existence of a graph with a given number as its minimum labeling number has rarely been addressed. We show that given any positive integer, there always exists an L (3, 2, 1)-path graceful graph with the given integer as its k 3c (G ), thus addressing the inverse question. Finally exploiting the fact that there is no gap on the k 3c (G ) number line, we give an application of path colorings for secure communication on social networking sites. Efforts to deploy graph coloring in task scheduling, interference-free transmission, etc have been dealt by earlier researchers. In this paper, we deploy the L (3, 2, 1)-path coloring technique defined by us for secure communication in social networks, which has not been dealt with so far. Show more
Keywords: Multi level distance labeling, L (h, k)-labeling, L (3, 2, 1)-labeling, path coloring, social networks
DOI: 10.3233/JIFS-222784
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3129-3139, 2024
Authors: Hussain, Abrar | Latif, Sajid | Ullah, Kifayat | Garg, Harish | Al-Quran, Ashraf
Article Type: Research Article
Abstract: Multiple-attribute group decision-making (MAGDM) technique is often used to make decisions when several optimal options are under consideration. It can be difficult to select a reasonable optimal option for the decision maker under consideration of insufficient information. The theory of Hamy mean (HM) operators are used to express correlation among different input arguments and provide a smooth approximation during the decision-making process. Recently, Aczel Alsina aggregating expressions gained a lot of attention from numerous mathematicians under different fuzzy circumstances. This article aims to illustrate the notion of a Pythagorean fuzzy (PyF) set (PyFS) with some restricted constraints, such as a …sum of the square of truth membership value and falsity membership value. We developed a series of new approaches under consideration of the HM tools, including PyF Aczel Alsina Hamy mean (PyFAAHM), and PyF Aczel Alsina weighted Hamy mean (PyFAAWHM) operators. Further, we also extend the theory of Dual Hamy mean (DHM) operators and derived a series of new methodologies such as PyF Aczel Alsina Dual Hamy mean (PyFAADHM) and PyF Aczel Alsina weighted Dual Hamy mean (PyFAAWDHM) operators. To demonstrate the flexibility of our derived approaches, we illustrate an application of a multinational company considering the MAGDM technique. An experimental case study is also illustrated to evaluate a reasonable option from a group of options. We see the advantages and compatibility of our findings by comparing the results of existing approaches with the results of currently discussed methodologies. Show more
Keywords: Pythagorean fuzzy value, operations of Aczel Alsina aggregation expressions, Hamy mean aggregation models, multi-attribute group decision-making system
DOI: 10.3233/JIFS-232691
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3141-3171, 2024
Authors: Xie, Bin
Article Type: Research Article
Abstract: In the information age, teachers are no longer the only source of information for students, and the problems of the traditional lecture mode are becoming more and more obvious. Especially in the process of teaching English in colleges and universities, students’ personalized and diversified needs for English learning are becoming more and more obvious, and if traditional theoretical lectures or theoretical indoctrination are continued, students’ needs and goals will be affected. As a new form of teaching, blended teaching can integrate the advantages of classroom teaching and online teaching and improve the overall quality and effectiveness of English teaching under …the premise of changing the orientation of teachers and students. The English blended teaching quality evaluation is a multiple-attribute group decision-making (MAGDM) problem involving multiple qualitative and quantitative attributes. In this paper, on basis of EDAS technique, a novel spherical fuzzy number EDAS (SFN-EDAS) technique based on SFN Hamming distance (SFNHD) and SFN Jaccard similarity measure (SFNJSM) is built for dealing with MAGDM. Moreover, the MEREC technique with SFNHD and SFNJSM is extended to SFSs to acquire the attribute weights. Finally, SFN-EDAS technique is used for English blended teaching quality evaluation to prove practicability of the developed SFN-EDAS technique and SFN-EDAS technique is compared with existing techniques to further demonstrate its superiority. Hence, the research main achievements for this paper are outlined: (1) the novel EDAS technique is extended to the SFSs environment based on the SFNHD and SFNJSM; (2) the MEREC technique is extended to SFSs to acquire the attribute weights based on the SFNHD and SFNJSM; (3) a novel spherical fuzzy number EDAS (SFN-EDAS) technique based on the SFNHD and SFNJSM is built for dealing with MAGDM; (4) an empirical example and several comparative analysis for English blended teaching quality evaluation is offered to demonstrate the SFN-EDAS technique. Show more
Keywords: Multiple-attribute group decision-making (MAGDM), spherical fuzzy sets, EDAS technique, MEREC technique, teaching quality evaluation
DOI: 10.3233/JIFS-233458
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3173-3189, 2024
Authors: Umamaheswaran, S. | Mannar Mannan, J. | Karthick Raghunath, K.M. | Dharmarajlu, Santhi Muttipoll | Anuratha, M.D.
Article Type: Research Article
Abstract: The IoMT (Internet of Medical Things) has allowed for uninterrupted, critical patient observation, improved diagnosis precision, and efficient therapy. However, despite the usefulness of such medical things (devices), they also raise a lot of confidentiality and security issues since they provide potential entry points for hackers to exploit. Therefore, there is a pressing need for a technique for detecting network intrusions that combines precision, flexibility, and consistency. Addressing diverse information sources is challenging for finding negligible intrusions in sophisticated network systems, a core problem for current Intrusion Detection Systems (IDS). In this research, we propose a deep learning-based method for …efficient network IDS in cases when data is unevenly distributed. Therefore, to address the poor identification rate of intrusions, we present a unique CGAN-CNN (Conditional Generative Adversarial Network-Convolutional Neural Network) IDS approach that oversamples from the unbalanced information based on the CGAN paradigm to overcome the functional deterioration induced by such unbalanced data, especially during intrusion detection. In addition, the sub-networks’ critic and generator each get additional constraints as part of the CGAN’s standard operating procedure, which helps to reduce the amount of leeway in the convergence process and speeds up the impact of convergence. To validate the effectiveness of the suggested model, we conducted an investigation using the most contemporary publicly available datasets, namely NIDS (Network Intrusion Detection System), and the CICDDoS2019 (Canadian Institute for Cybersecurity Distributed Denial of Service 2019) dataset from the Canadian Institution for Cybersecurity, and for healthcare-oriented image datasets Kaggle, respectively. The experimental findings validated the superiority of the CGAN-CNN approach described in this research. Notified as more trustworthy indications, F1-score and precision performed at 97.88%, and 97.15%, respectively. Show more
Keywords: Deep learning, IDS, dataset, balancing, attacking, intrusions, generator, discriminator, CGAN, detection, evaluation
DOI: 10.3233/JIFS-233649
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3191-3207, 2024
Authors: Aruna Sri, P. | Santhi, V.
Article Type: Research Article
Abstract: This study addresses challenges in land use and cover identification using remote sensing (RS) imagery, focusing on the Uppal region. By leveraging deep learning models, particularly an optimized ResNext-50 architecture, we aim to enhance efficiency and accuracy in classifying land features. Our approach integrates Landsat-8 and hyper-spectral satellite data, utilizing preprocessing techniques like dark subtraction, stacking, merging, and spectral enhancement. Principal Component Analysis (PCA) is applied to streamline high-dimensional feature sets obtained from pre-processed spectral data. We further employ hybrid NSCT-FDCT fusion for integrating Landsat-8 and hyperspectral images. The resulting fused image is fed into our classification process, utilizing the …modified ResNext50 (Deep Learning Architecture) model with Reptile Search Optimization for weight link optimization. Notably, our proposed method achieves impressive outcomes: 97% accuracy, 96% sensitivity, 99% specificity, 3% error, 97% precision, and a 95% Matthew Correlation Coefficient. This demonstrates the efficacy of our approach in predicting diverse land covers within the Uppal region, showcasing the potential of Landsat-8 and Hyper-spectral data for accurate land use and cover identification. Show more
Keywords: Landsat-8, hyper-spectral, land cover, hybrid NSCT-FDCT, reptile search algorithm, resnext-50
DOI: 10.3233/JIFS-232891
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3209-3229, 2024
Authors: Jeya Sutha, M. | Ramesh Dhanaseelan, F. | Felix Nes Mabel, M. | Vijumon, V.T.
Article Type: Research Article
Abstract: Association rule mining (ARM) is an important research issue in the field of data mining that aims to find relations among different items in binary databases. The conventional ARM algorithms consider the frequency of the items in binary databases, which is not sufficient for real time applications. In this paper, a novel hash table based Type-2 fuzzy mining algorithm (T2FM) with an efficient pruning strategy is presented for discovering multiple fuzzy frequent itemsets from quantitative databases. The algorithm employs a hash table based structure for efficient storage and retrieval of item/itemset which reduces the search efficiency to O(1) or constant …time. Previously, type-2 based Apriori and FP-growth based fuzzy frequent itemsets mining were proposed, which required large amounts of computation and a greater number of candidate generation and processing. Meanwhile, the proposed approach reduces a huge amount of computation by finding the common keys before the actual intersection operation takes place. An efficient pruning strategy is proposed to avoid unpromising candidates in order to speed up the computations. Several experiments are carried out to verify the efficiency of the approach in terms of runtime and memory for different minimum support threshold and the results show that the designed approach provides better performance compared to the state-of-the-art algorithms. Show more
Keywords: Data mining, type-2 fuzzy set, quantitative dataset, fuzzy frequent itemsets, multiple linguistic terms
DOI: 10.3233/JIFS-232918
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3231-3244, 2024
Authors: Lakshmana Kumar, R. | Jayanthi, S. | Muthu, BalaAnand | Sivaparthipan, C.B.
Article Type: Research Article
Abstract: The proliferation of mobile technology has given rise to a multitude of applications, among them those designed with malicious intent, aimed at compromising the integrity of mobile devices (MDs). To combat this issue, this study introduces an innovative anomaly application detection system leveraging Federated Learning in conjunction with a Hyperbolic Tangent Radial-Deep Belief Network (FL-HTR-DBN). This system operates through two distinct phases: training and testing. During the training phase, the system first extracts log files and transforms them into a structured format, harnessing the power of the Hadoop System. Subsequently, these structured logs are converted into vector representations using the …Updating Gate-BERT (UG-BERT) technique, thereby facilitating feature extraction. These features are then annotated utilizing the Symmetric Kullback Leibler Divergence squared Euclidean distance-based K Means (SKLD-SED K Means) algorithm. The FL-HTR-DBN model is subsequently trained using these labelled features. The detected anomalies are hashed and securely stored within an index tree, alongside their corresponding hashed Media Access Control (MAC) addresses. In the testing phase, log files are cross-referenced with the hashed index tree to identify potential anomalies. Notably, this novel approach outperforms many valuable outcomes in comparison with the existing approaches ConAnomaly, QLLog and LogCAD in terms of precision 97.5, recall 97.1, accuracy 95.9, F-measure 93.9, sensitivity 94.8 and specificity 95.9. Show more
Keywords: Updating gate –BERT (UG-BERT), symmetric kullback leibler divergence squared euclidean distance-based K means (SKLD-SED K Means), federated learning based hyperbolic tangent radial - deep belief network (FL-HTR-DBN)
DOI: 10.3233/JIFS-233361
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3245-3258, 2024
Authors: Yuvashri, Prakash | Saraswathi, Appasamy
Article Type: Research Article
Abstract: Every decision-making process particularly those involving real-life issues is disproportionately plagued by uncertainty. It is also unavoidable and obvious. Since its conception are several ways for representing uncertainty have been proposed by numerous academics to cope with uncertainty. Fuzzy sets and hierarchical such as picture fuzzy sets stand out among them as excellent representation techniques for modeling uncertainty. However, there are several significant drawbacks to the current uncertainty modeling techniques. Due to its vast versatility and benefits we here embrace the idea of the spherical fuzzy set, an extension of the picture fuzzy set. On the other hand amid uncertainty …in real life the multi-objective plays a critical role. In this research paper determining a Multi-Objective Linear Programming Problem of Spherical fuzzy sets serves to stimulate nous. The score function corresponding to the degree positive, negative and neutral is the foundation upon which the suggested approach is developed. Additionally we apply the suggested strategy to the solution of the multi-objective linear programming problem to demonstrate its superiority through certain numerical examples. Maximization or Minimizing of the cost is the primary goal of the multi-objective linear programming problem. Using an explicitly defined score function the suggested solution transformed the Spherical Fuzzy Multi-Objective Linear Programming Problem into a Crisp Multi-Objective Linear Programming Problem (CMOLPP). We establish some theorems to show that the efficient solution of CMOLPP is likewise an efficient solution of SFMOLPP. The CMOLPP is then further simplified into a single-objective Linear Programming Problem (LPP) thus we revamp the modified Zimmermann’s approach in the environment of a nonlinear membership function with the aid of the suggested technique. It is possible to simply solve this single-objective LPP using any software or standard LPP algorithm. The suggested approach achieves the fuzzy optimum result without altering the nature of the issue. An application of the suggested approach has been used to illustrate it and its results have been distinguished from those of other preexisting methods found in the literature. To determine the importance of the suggested technique which adjudicate thorough theorem and result analysis is conducted. Show more
Keywords: Crisp solution, spherical fuzzy number, spherical fuzzy multi-objective linear programming problem, spherical fuzzy optimal solution
DOI: 10.3233/JIFS-233441
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3259-3280, 2024
Authors: Zekrifa, Djabeur Mohamed Seifeddine | Lamani, Dharmanna | Chaitanya, Gogineni Krishna | Kanimozhi, K.V. | Saraswat, Akash | Sugumar, D. | Vetrithangam, D. | Koshariya, Ashok Kumar | Manjunath, Manthur Sreeramulu | Rajaram, A.
Article Type: Research Article
Abstract: Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the …Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future. Show more
Keywords: Autoencoder-generative adversarial network (AE-GAN)
DOI: 10.3233/JIFS-235582
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3281-3294, 2024
Authors: Sakthipriya, R. | Suja, K.
Article Type: Research Article
Abstract: The purpose of this article is to study the notion of statistical limit superior(SLS) and statistical limit inferior(SLI) in non-Archimedean(NA) L -fuzzy normed spaces( L -FNS). The concept of SLS and SLI is examined and extended to SLS and SLI in NA L -FNS. Moreover, the analogue of some results between SLS and SLI over NA L -FNS have been discussed. And also, it is proved that a bounded sequence is statistically convergent over NA L -FNS. Throughout this article, K …denotes a complete, non-trivially valued, non-Archimedean fields(NAF). Show more
Keywords: Statistical limit superior and statistical limit inferior, 𝔏-fuzzy normed space, non-Archimedean fields
DOI: 10.3233/JIFS-224359
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3295-3306, 2024
Authors: Wu, Yuhang | Jiao, Xu | Hao, Qingbo | Xiao, Yingyuan | Zheng, Wenguang
Article Type: Research Article
Abstract: The next Point-of-Interest (POI) recommendation, in recent years, has attracted an extensive amount of attention from the academic community. RNN-based methods cannot establish effective long-term dependencies among the input sequences when capturing the user’s motion patterns, resulting in inadequate exploitation of user preferences. Besides, the majority of prior studies often neglect high-order neighborhood information in users’ check-in trajectory and their social relationships, yielding suboptimal recommendation efficacy. To address these issues, this paper proposes a novel Double-Layer Attention Network model, named DLAN. Firstly, DLAN incorporates a multi-head attention module that can combine first-order and high-order neighborhood information in user check-in trajectories, …thereby effectively and parallelly capturing both long- and short-term preferences of users and overcoming the problem that RNN-based methods cannot establish long-term dependencies between sequences. Secondly, this paper designs a user similarity weighting layer to measure the influence of other users on the target users leverage the social relationships among them. Finally, comprehensive experiments are conducted on user check-in data from two cities, New York (NYC) and Tokyo (TKY), and the results demonstrate that DLAN achieves a performance in Accuracy and Mean Reverse Rank enhancement by 8.07% -36.67% compared to the state-of-the-art method. Moreover, to investigate the effect of dimensionality and the number of heads of the multi-head attention mechanism on the performance of the DLAN model, we have done sufficient sensitivity experiments. Show more
Keywords: Point-of-interest recommendation, user preferences, attention network, social information
DOI: 10.3233/JIFS-232491
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3307-3321, 2024
Authors: Li, Aiguo | Feng, Rongrong
Article Type: Research Article
Abstract: In security system, high utility pattern mining of a large number of users mobile trajectories is helpful to analyze user behavior patterns, and enhance the internal prevention of the security system.Currently, the frequent pattern mining for mobile trajectory in security systems do not take into account the differences in the safety levels between staying points and the frequency of their occurrence. Existing high utility mobile trajectory pattern mining methods are unable to discover staying points that meet specific user requirements. To solve these problems, a constraint-based high utility mobile trajectory mining algorithm is proposed (names as HUIM-ILC-ACO). It takes into …account the user’s stay time, stay frequency, and the safety level of each stopping point and calculates the utility value of each staying point by incorporating these factors through weighted computation. Based on this, the algorithm combines ant colony optimization with length constraints and item constraints to construct a method for mining high utility mobile trajectory patterns that better align with user interests. Experimental results on real datasets and a target mobile trajectory RFID dataset show that proposed algorithm is efficient in terms of runtime and pattern quantity, and it can effectively mine a pattern set that is closer to user interests. Show more
Keywords: Security system, ant colony algorithm, mobile trajectory pattern mining, high utility itemset
DOI: 10.3233/JIFS-233967
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3323-3338, 2024
Authors: Ma, Qianxia | Zhu, Xiaomin | Bai, Kaiyuan | Pu, Qian | Zhang, Runtong
Article Type: Research Article
Abstract: Multi-attribute group decision-making (MAGDM) is one of the research hotspots in human cognitive and decision-making theory. However, there are still challenges to the existing MAGDM methods in modeling uncertain linguistics of decision-makers’ (DMs’) cognitive information and objectively obtaining weights. Therefore, this paper aims to develop a new MAGDM method considering incomplete known weight information under spherical uncertain linguistic sets (SULSs) to model uncertain information in MAGDM problems. The method mainly includes the following aspects. Firstly, a new concept, which enables an intuitive evaluation of neutral membership and hesitancy degrees at the linguistic evaluation, has been is first developed for capturing …the more uncertain information. Secondly, the cosine similarity measure (CSM) and cross-entropy measure (CEM) are widely used to measure ambiguous information because of their robustness of measurement results. The CSM and CEM are extended to SULSs to calculate the DMs’ and attributes weights quantitively, respectively. Thirdly, in terms of effective integration of fuzzy information to obtain more accurate decision results, the Hamy mean (HM) and dual Hamy mean (DHM) operators are valued due to their consideration of the interrelationships between inputs. Two extension operators, named spherical fuzzy uncertain linguistic weight HM and DHM, are proposed to integrate spherical fuzzy uncertain linguistic information in the third stage. In the experiment, a decision case is presented to illustrate the applicability of the proposed method, and results show the effectiveness, flexibility and advantages of the proposed method are demonstrated by numerical examples and comparative analysis. Show more
Keywords: Multi-attribute group decision-making, spherical uncertain linguistic set, Hamy mean, cosine similarity measure, cross-entropy measure
DOI: 10.3233/JIFS-235044
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3339-3361, 2024
Authors: Songhua, Huan
Article Type: Research Article
Abstract: The development of an accurate electricity demand forecasting model is of paramount importance for promoting global energy efficiency and sustainability. Nonetheless, the presence of outliers and inappropriate model training can result in suboptimal performance. To tackle these challenges, this study explores the potential of Convolutional Neural Network (CNN) and active learning theory as forecasting solutions, offering high efficiency and advantages for long time series. In this study, a hybrid model that combines Isolation Forest (IF), Outlier Reconstruction (OR), CNN and Random Forest (RF) is conducted to mitigate computational complexity and enhance the accuracy of electricity demand forecasting in the presence …of outliers. IF is employed to detect outliers in electricity demand time series, while OR is used to reconstruct subsequences based on calendrical heterogeneity for training. CNN is applied for both training and forecasting, and the final output is combined using RF. The effectiveness of the proposed IF-OR-CNN-RF model is validated using electricity data collected from recent sources in Australia at different sampling frequency. The experimental results demonstrate that, in comparison with other popular CNN-based electricity demand forecasting models, IF-OR-CNN-RF model outperforms with significantly improved performance metrics. Specifically, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R-squared values are 77.92, 179.18 and 0.9769 in 5-minute frequency; 162.67, 353.96 and 0.9775 in 10-minute frequency; 841.27, 1374.79 and 0.9622 in 30-minute frequency; 2746.01, 3824.00 and 0.9262 in 60-minute frequency; 9106.08, 12269.04 and 0.8044 in 120-minute frequency. IF-OR-CNN-RF model represents a valuable framework for future electricity demand forecasting, particularly in scenarios involving outliers. Show more
Keywords: Outlier reconstruction, deep learning, electricity demand, forecasting model, calendrical heterogeneity
DOI: 10.3233/JIFS-235218
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3363-3394, 2024
Authors: Sucharitha, G. | sankardass, Veeramalai | Rani, R. | Bhat, Nagaraj | Rajaram, A.
Article Type: Research Article
Abstract: Although difficult, robust and reliable synchronization of multimodal medical pictures has several practical uses. For instance, in MR-TRUS fusing guided prostate treatments, picture registration between the two modalities is essential. However, due to the significant variety in image appearance and correlation, MR-TRUS picture registration remains a challenging issue. In this research, we suggest employing deep convolutional neural networks (CNN) i.e. three dimensional CNN U-NET (3D-Conv-Net) to develop a resemblance measure for MR-TRUS registration. Finally, for the second-order optimal of the taught measure, we apply a composite optimisation method that searches the solution space for an appropriate starting point. We also …use a multi-stage process to improve the optimisation metric. Show more
Keywords: Image registration, convolutional neural networks, multimodal image fusion, and prostate cancer
DOI: 10.3233/JIFS-235744
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3395-3409, 2024
Authors: Sagar, Maloth | Vanmathi, C.
Article Type: Research Article
Abstract: Machine learning techniques commonly used for intrusion detection systems (IDSs face challenges due to inappropriate features and class imbalance. A novel IDS comprises four stages: Pre-processing, Feature Extraction, Feature Selection, and Detection. Initial pre-processing balances input data using an improved technique. Features (statistical, entropy, correlation, information gain) are extracted, and optimal ones selected using Improved chi-square. Intrusion detection is performed by a hybrid model combining Bi-GRU and CNN classifiers, with optimized weight parameters using SI-BMO. The outputs from both classifiers are averaged for the result. The SI-BMO-based IDS is compared with conventional techniques Blue Monkey Optimization (BMO), Grasshopper Optimization Algorithm …(GOA), Deer Hunting Optimization (DHO), Poor Rich Optimization (PRO), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) for performance evaluation. Show more
Keywords: Intrusion detection system, Improved class imbalance processing, bi-directional gated recurrent unit (Bi-GRU), convolutional neural network (CNN), self-improved blue monkey optimization (SI-BMO), cyber-physical systems (CPS)
DOI: 10.3233/JIFS-236400
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3411-3427, 2024
Authors: Meenakshi, K. | Revathi, M. | Harsha, Sanda Sri | Tamilarasi, K. | Shanthi, T.S. | Sugumar, D. | Suriyakrishnaan, K. | Uma Maheswari, B. | Rajaram, A.
Article Type: Research Article
Abstract: A new era in communication has been ushered in by MANET networks, in which users (nodes) interact with one another through a self-configuring network of handheld devices linked by wireless links. Nodes are capable of participating and enthusiastic about sending packets to other nodes. Consequently, the need for a routing protocol materializes. The most difficult aspect is dealing with the network’s dynamic topology as a result of node mobility. This is because limited resources like storage space, battery life, and bandwidth require a protocol that can quickly adapt to topology changes while periodically updating messages. On the other hand, security …is another important aspect of routing since the involvement of attackers will exhaust the network resources. This paper addresses the main issue of designing a routing protocol that handles all the adversaries and achieves better efficiency. For that, we proposed a Hybrid Machine Learning (HyML) model which evaluates the. Initially, the network is segregated by the Secure Stable Clustering (SSC) approach which first verifies the node’s legacy and forms clusters based on stability. The HyML is designed by combining two important ML techniques such as ANN and fuzzy-C Means (FCM) algorithm. The ANN model learns multiple attributes of the trust value and computes the cumulative trust score. Next, FCM determines the node position upon trust score. After the computation of the trust value, optimal route selection is performed by the Spider Monkey Optimization (SMO) technique. The overall work is evaluated through comprehensive simulations based on network longevity, throughput, energy usage, PDR, attack detection efficiency, and delay. Show more
Keywords: Hybrid machine learning, ANN, routing attacks, MANET
DOI: 10.3233/JIFS-231918
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3429-3445, 2024
Authors: Al-Qatf, Majjed | Hawbani, Ammar | Wang, XingFu | Abdusallam, Amr | Alsamhi, Saeed | Alhabib, Mohammed | Curry, Edward
Article Type: Research Article
Abstract: Visual attention has emerged as a prominent approach for improving the effectiveness of image captioning, as it enables the decoder network to focus selectively on the most salient regions in the image content, thereby facilitating the generation of precise and informative captions. Although visual attention achieves the improvement, the small numerical values of its input have a negative impact on its softmax, decreasing its effectiveness. To address this limitation, we propose a refined visual attention (RVA) framework that internally reweights visual attention by leveraging the language context of previously generated words. We first feed the language context into a fully …connected layer to obtain appropriate dimensions for the visual features. Then, we use a sigmoid function to obtain a probability distribution to reweight the softmax’s input by applying the multiplication process. Experiments conducted on the MS COCO dataset demonstrate that RVA outperforms traditional visual attention and other existing image captioning methods, highlighting its effectiveness in enhancing the accuracy and informativeness of image captions. Show more
Keywords: Visual attention, refined visual attention, image captioning
DOI: 10.3233/JIFS-233004
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3447-3459, 2024
Authors: Sathya, S. | Senthil Murugan, J. | Surendran, S. | Sundar, R.
Article Type: Research Article
Abstract: Oil spills in maritime areas pose a serious environmental risk, wreaking havoc on marine ecosystems, coastal habitats, and local residents. An accurate and timely evaluation of oil spill occurrences and extent is critical for effective pollution control and mitigation. In this study, we present a novel and cutting-edge approach for analyzing oil-spilled images using Deep Attention Transformer Nets (DATN) with Collective Intelligence (CI), with the goal of reducing pollution in the marine environment. This method takes advantage of deep learning capability, notably the incorporation of transformer-based attention processes, to improve the identification and measurement of oil spills in satellite and …aerial images. The DATN model is intended to learn complicated features from images automatically, capturing complex patterns associated with oil spills and their surrounding context. The model chooses focus on key regions and add spatial links by using attention mechanisms, allowing for a more comprehensive understanding of the environmental influence. We thoroughly test DATN performance using a variety of datasets encompassing various oil spill scenarios and environmental circumstances. The results show that DATN surpasses standard approaches and other deep learning models in recognizing oil spill regions, with excellent accuracy, precision, and recall rates. Furthermore, the model has strong generalization capabilities across a wide range of image sources and situations. Show more
Keywords: Oil spill detection, deep attention transformer nets, aerial imagery, pollution mitigation, neural networks
DOI: 10.3233/JIFS-235657
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3461-3473, 2024
Authors: Slimani, Hicham | El Mhamdi, Jamal | Jilbab, Abdelilah
Article Type: Research Article
Abstract: A significant concern is the economic impact of agricultural diseases on the world’s crop production. The disease significantly reduces agricultural production across the world. Loss of nutrients caused by parasite infection of leaves, pods, and roots–the pathogenic agent that causes fava bean rust disease–decreases crop health. This work addresses this requirement by offering an innovative deep-learning model approach for early identification and classification of fava bean rust disease. The suggested method uses the effectiveness of modern YOLO-based object detection architectures like You Only Look Once –Neural Architecture Search (YOLO-NAS) L, YOLO-NASM, and YOLO-NASS, Faster Region-based Convolutional Neural Network (Faster R-CNN), …and RetinaNet. An inclusive dataset of 3296 images of various lighting and background situations was selected for extensive model training. Each model underwent thorough training and adjusted parameters through careful experimentation. The models’ comparative studies found significant performance differences. The precision for YOLO-NASL was 82.10%; for YOLO-NASM, it was 84.80%; for YOLO-NASS, it was 83.90%; for Faster R-CNN, it was 75.51%; and for RetinaNet, it was 73.74%. According to the evaluation, model complexity and detection accuracy are directly correlated. YOLO-NASL, YOLO-NASM, and YOLO-NASS showed remarkable mean average precision values of 90.90%, 94.10%, and 92.60%, respectively, and became highly functional models. The fastest model was YOLO-NASS. Its satisfying recognition speed made real-time detection possible in particular applications. The YOLO-NASM model, which shows an extraordinary state-of-the-art performance, represents the pinnacle of our work. Its mean average precision (mAP@0.5) was 94.10%, with notable values of 90.84%, 96.96%, and 84.80% for the F1-score, Recall, and precision, respectively. This investigation addresses a critical need in agricultural disease management, aligning with broader global efforts toward sustainable agriculture. Our studies add to the knowledge about precision agriculture and inspire practical, long-lasting disease management techniques in the agricultural industry. The real-time performance of the system will need to be improved, and satellite imagery integration may be considered in the future to provide more comprehensive coverage. Show more
Keywords: Deep learning, YOLO-NAS, fava bean, rust disease, CNN, crops disease
DOI: 10.3233/JIFS-236154
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3475-3489, 2024
Authors: Yin, Xiang | Guan, Li | Li, Bing | Huang, Qing | Lin, Huijie
Article Type: Research Article
Abstract: We provide a strategy for minimizing losses and redistributing loads in distribution systems while emergency repairs are being made. The proposed approach takes advantage of the preexisting, network-accessible, and Power Companies’ Adoption of Residential Energy Storage Batteries devices. Batteries are expected to be used increasingly often to deal with a few of the growing challenges with renewable, among them the infamous duck curve difficulty, as renewable energy sources that are widely dispersed, like photovoltaic (PV) and wind turbines, become more popular. The proposed approach may be implemented using signals in reaction to demand. To demonstrate its value, we provide a …method for concurrent simulation for designing and analyzing strategies for optimizing a distribution that benefits from the synergy-connected smart grid, intelligent structures, and decentralized battery systems to reduce overall energy consumption and costs while enhancing power management. The suggested method is created and verified inside of the Smart Builds co-simulation environment. From what we can tell from simulations, energy storage devices provide interim relief for line-outage-affected distribution networks. Show more
Keywords: Distributed generation (DG), distribution system, power system, photovoltaic (PV), mat power, distributed energy resource (DER), battery energy storage systems (BESS), load balance
DOI: 10.3233/JIFS-236323
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3491-3503, 2024
Authors: Zhao, Hongyan
Article Type: Research Article
Abstract: China has now embraced the information era, which has had a significant impact on everyday life, employment, and educational practices. Information technology has also had a significant impact on the growth of the education sector, resulting in a fast-paced and resource-rich setting for student interaction. Through the network platform, various interactive software can improve students’ learning methods, especially language teaching software. English audio-visual speaking is software for training English language listening and speaking, which can carry out relevant oral activities and topic discussions according to the imported materials. As a result, you can assist pupils in using the vocabulary and …knowledge associated with the subject, which will increase their interest in learning. English teachers can fully prepare for speaking and listening tasks in the classroom by using audio-visual speaking. At the same time, through the learning of TV and movie trailers, English audio-visual speaking can provide readers with background knowledge, which is ready for readers to fully understand the language and content in the video materials. Based on information technology, this paper constructs English audio-visual and oral mobile teaching software, which depends on interactive digital media algorithms. Through the mobile teaching software for English audio-visual speaking, students can form good English listening and reading habits, which will provide important help for English language learning.First, this essay examines the value and benefits of mobile applications for providing English instruction orally and visually, which might help to illustrate the need for software development. The research then suggests various algorithms for English that are related to audio, visual, and oral input that can detect, assess, and correct students’ learning mistakes. Finally, this work develops the fundamental methodology of the audio-visual and verbal mobile software for instruction in English. Show more
Keywords: Interactive digital media algorithm, English audio-visual speaking, mobile teaching software
DOI: 10.3233/JIFS-233741
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3505-3515, 2024
Authors: Xu, Xiaosheng
Article Type: Research Article
Abstract: The current conventional water resources management planning method realizes the optimal allocation of water resources by constructing a function aiming at economic benefits; it causes poor model planning repercussions as a result of the disregard of comprehensive benefits. In this regard, a hydrological model-based water resource management planning method for climate change is proposed. By combining geological conditions, hydrological conditions and other climate change factors, a hydrological model is constructed to calculate watershed flows, and the hydrological model is used to divide the watershed scale and hydrological response units. A multi-objective function planning model is constructed with economic and ecological …benefits as the objective functions. The proposed approach is tested in trials and shown to provide advantages for thorough planning. The results of the study demonstrate that the algorithm has a high value of extensive benefit when the recommended strategy is utilized for the optimum allocation of water resources, and has a more preferable optimal allocation consequence. Show more
Keywords: Hydrological modeling, climate change, water resources, management planning, optimal allocation
DOI: 10.3233/JIFS-233939
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3517-3526, 2024
Authors: Hu, Jianbin
Article Type: Research Article
Abstract: This study intends to solve the problems brought on by regional differences in the distribution of educational resources, inadequate growth of schools, and various levels of informationization in university education. Because of the complicated functional framework that is now in place, university life is unsafe for both teachers and students. The issue is further complicated by challenges with maintaining several cards for one person, a lack of seamless software and educational platform integration, and multiple obstacles between data and users. Information inequality is exacerbated by inadequate learning resources, and development is hampered by the lack of efficient teacher-student feedback mechanisms. …It can also be difficult to accurately manage a huge group of people. This study uses the web and artificial intelligence (AI) technology to create comprehensive, succinct, effective, and high-performing college instruction information technology in order to address these difficulties. Irrespective of the time or day, the system seeks to serve teachers and students while managing a sizable influx of visitors. Throughout the development process, the system is actively optimized and improved by the research. The experiment’s findings illustrate a robust interface function via practical assessment. Usability assessments show that the feedback is better than the previous system, with response times being reduced. Additionally, the updated system shows a typical reduction in overall electrical usage. Show more
Keywords: Informationization, education, network, teacher-student feedback
DOI: 10.3233/JIFS-235050
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3527-3544, 2024
Authors: Li, Yajie | Cai, Xirui
Article Type: Research Article
Abstract: In recent years, significant progress has been made in the study of Chinese vocabulary acquisition, and the research content and scope have gradually expanded. Chinese vocabulary is the foundation for understanding and using language, and any language has developed on the basis of Chinese vocabulary. Many studies have shown that there are many factors that affect the acquisition of Chinese vocabulary, and the impact on Chinese vocabulary acquisition is also different. Among them, the influencing factors of Chinese vocabulary acquisition have gradually become a research hotspot, and a large number of related empirical studies have emerged. Based on the big …data technology and the random effect model, this paper comprehensively analyzes whether the influencing factors of Chinese vocabulary acquisition as a second language are significant and investigates the effects of internal and external influencing factors, delayed post-test, specific factors and experimental interval time on the learning effect. External factors significantly impact Chinese vocabulary acquisition, including learning style, input factors and input methods. Through the subgroup analysis of the experimental interval, we find that the effect of Chinese vocabulary acquisition decreases with the extension of the experimental interval. Therefore, this paper holds an inverse relationship between the experimental interval and the development of Chinese vocabulary acquisition. Show more
Keywords: Chinese, second language, Chinese vocabulary, big data technology, analysis of influencing factors
DOI: 10.3233/JIFS-235515
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3545-3556, 2024
Authors: Du, Pei
Article Type: Research Article
Abstract: To protect the historical and cultural heritage, the application of self-organizing mapping networks and genetic algorithms in the restoration of ancient architectural murals is studied. The results show that the average repair time for different types of mural paintings is less than 60 seconds, and the shortest repair time is only 17.81 seconds. The evaluation effect of the research model is good, and the comprehensive efficiency evaluation of the mural restoration work is improved by about 40.42%. The repair system has excellent performance, and the algorithm has high feasibility and effectiveness. The impact of restoring murals is substantial, and the …extent of restoration is highly consequential for the restoration of ancient architectural murals. Show more
Keywords: Ancient architectural murals, self-organizing networks, genetic algorithms, automatic annotation, restoration strategies
DOI: 10.3233/JIFS-235769
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3557-3568, 2024
Authors: Li, Tao | Liu, C. | Qu, Xingle | Guo, Linjia | Fang, Jiangping
Article Type: Research Article
Abstract: The conventional evaluation methods for the state of agricultural environmental geological system mainly use the support vector regression (SVR) model to process the evaluation samples, which is vulnerable to the influence of the sensitive loss function, resulting in the high difference of the evaluation entropy. Therefore, a new evaluation method for the state of agricultural environmental geological system needs to be designed based on the optimized particle swarm optimization algorithm. That is to say, combining with the evolution process of regional agricultural environmental geology, the accurate state evaluation target is selected, the state evaluation system of agricultural environmental geology system …is constructed, and the state evaluation model of agricultural environmental geology system is designed combined with the optimized particle swarm optimization algorithm, so as to complete the state evaluation of geological system. The results demonstrated the suggested methodology assesses the state of an agricultural environmental geological system. Key factors included soil texture (0.254), soil nutrient (0.118), and soil pH (0.256). It showed that the designed evaluation method of agricultural environmental geological system state based on optimized particle swarm optimization algorithm has good evaluation effect, reliability and certain application value, and has made certain contributions to the formulation of reasonable agricultural ecological protection scheme. Show more
Keywords: Optimized particle swarm optimization, agriculture, environmental science, geology, system, status, evaluation method
DOI: 10.3233/JIFS-236184
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3569-3576, 2024
Authors: Rajalakshmi, K. | Priyan, S. Vishnu | Inbakumar, J. Parivendhan | Kumar, C.
Article Type: Research Article
Abstract: The distribution system plays a pivotal role in connecting power generation sources to vital facilities like nuclear reactors. In this intricate network, losses occur while supplying electricity, demanding a reduction for enhanced performance. The quality of power reaching the nuclear plant is imperative due to the susceptibility of sensitive equipment to poor power conditions. This study presents a reconfiguration strategy to bolster dependability and curtail power losses in distribution networks. Leveraging the Modified Genetic Optimization Algorithm (MGOA), the reconfiguration conundrum is tactfully addressed to determine optimal switch operation schemes. The MGOA-based reconfiguration not only minimizes energy wastage but also refines …voltage profiles, elevating operational efficiency. The effectiveness of this approach is substantiated through its successful application to radial distribution systems comprising 33, 69, and 136 buses. Embracing diverse scenarios encompassing normal and abnormal operating states, as well as varying loads, the method’s robustness is showcased. The validity of the proposed methodology is reinforced by comprehensive simulation results, underscoring its reliability and potential for real-world implementation. Show more
Keywords: Distribution network reconfiguration, genetic algorithm firework algorithm, runner-root model, fuzzy shuffled frog-leaping algorithm, grey wolf optimizer and PSO method
DOI: 10.3233/JIFS-233917
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3577-3591, 2024
Authors: He, Jianshe | Chen, Zhong
Article Type: Research Article
Abstract: Dynamical systems that exhibit a high degree of sensitivity to the parameters of their initial states are referred to as chaotic. Natural selection and the process of evolution are the models that inspire a group of optimization algorithms collectively referred to as evolutionary algorithms (EA). EA is quite beneficial when handling difficult optimization difficulties, especially in situations where traditional procedures are either not practical or insufficient. The resolution of goal conflicts is accomplished through multi-objective optimization (MOO). The study proposed using chaotic systems and evolutionary algorithms to address the issue of multi-objective optimization.An initially chaotic time series of wind speed …predictions was gathered from three locations in Penglai, China. The preprocessing of these data was carried out using Z-score normalization. We suggested using multi-objective particle swarm optimization (MOPSO) to gather information. Before the suggested design can be applied to the MOPSO of the chaotic system itself, it is required to evaluate the architecture of the proposed that will be utilized, the functioning of the chaotic systems, and the problems in the design of the system. Studies using currently available methods demonstrate that the proposed method outperforms all parameter measurements in terms of 15bits of throughput, active power loss 6.4812 MVA, 0.6495 voltages, 6.8% of RMSE, 0.8% of MAPE, and 0.1 sec of time. The finding of combining evolutionary algorithms with chaotic systems yields a powerful and effective framework for addressing multi-objective optimization problems, which bodes well for practical implementations in fields like building design, economics, and time management. Show more
Keywords: Multi-objective optimization (MOO), problem-solving, Z-score normalization, particle swarm optimization (PSO), chaotic systems
DOI: 10.3233/JIFS-236000
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3593-3603, 2024
Authors: Zhang, Zhao Zhao | Pan, Hao Ran | Zhu, Ying Qin
Article Type: Research Article
Abstract: Modular neural networks (MNNs) have garnered substantial attention in the field of nonlinear system modeling. However, even though MNNs require fewer hyperparameters due to their hierarchical structure compared to traditional NNs, determining the optimal module arrangement remains challenging. To address these issues, a novel approach named fuzzy modular neural networks (FMNN) is introduced. This method employs conditional fuzzy clustering and incremental radial basis function (RBF) neural networks to automatically construct sub-modules within the MNN framework. The resultant sub-modules are chosen utilizing a distance-based fuzzy integrative strategy, effectively diminishing the necessity for manual intervention. To showcase the superiority of the FMNN …approach, a series of experiments are carried out employing three benchmark examples. These experiments encompass a comparison of modeling accuracy against other extensively employed neural network models. The experimental findings illustrate that FMNN surpasses alternative neural network models in terms of model precision. Show more
Keywords: Automated modeling, fuzzy clustering, modular neural network, radial basis function network
DOI: 10.3233/JIFS-232396
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3605-3621, 2024
Authors: Zhang, Jiarui | Ling, Bingo Wing-Kuen
Article Type: Research Article
Abstract: The patients with the nasopharyngeal cancer are required to breath through their mouth after performing the surgery. Hence, it is required to perform the breathing site classification and employs the classification results to indicate whether the patients breath correctly or not. Nevertheless, there is currently no such a medical aided tool in the market. To address this issue, this paper extracts both the mel frequency cepstral coefficients (MFCCs) based features and the gammatone frequency cepstral coefficients (GFCCs) based features as well as employs the random forest as the classifier for performing the breathing site classification. The data lasted for a …few minutes acquired from 10 volunteers are employed to demonstrate the effectiveness of our proposed method. The computer numerical simulation results show that the average accuracy, the average specificity and the average sensitivity yielded by our proposed method are 95.30±2.00%, 93.27±3.87% and 97.15±1.87%, respectively. Although this paper proposes a method based on the fusion of two types of the acoustic features for classifying different breathing sites, the computer numerical simulation results show that our proposed method outperforms the common respiration or speech processing based methods. Besides, our proposed method is also compared to a series of relevant methods. It is found that our proposed method achieves the highest classification results at the majority signal to noise ratios among the state of the arts methods. Show more
Keywords: Nasopharyngeal cancer, mel frequency cepstral coefficients, gammatone frequency cepstral coefficients, random forest, breathing site classification
DOI: 10.3233/JIFS-235446
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3623-3634, 2024
Authors: Mukiri, RajaKumari | Burra, Vijaya Babu
Article Type: Research Article
Abstract: The convergence of healthcare and deep learning has engendered transformative solutions for myriad medical challenges. Amid the COVID-19 pandemic, innovative strategies are imperative to mitigate the propagation of misinformation and myths, which can exacerbate the crisis. This study embarks on a pioneering research quest, harnessing advanced deep learning methodologies, including the novel Vision Transformer (ViT) model and state-of-the-art (SOTA) models, to predict and quell the dissemination of rumors within the COVID-19 milieu. By synergizing the capabilities of Vision Transformers (ViTs) with cutting-edge SOTA models, the proposed approach strives to elevate the precision of information disseminated through traditional and digital media …platforms, thereby cultivating informed decision-making and public awareness. Central to this inquiry is the development of a bespoke vision transformer architecture, adeptly tailored to scrutinize CT images associated with COVID-19 cases. This model adeptly captures intricate patterns, anomalies, and features within the images, facilitating precise virus detection. Extending beyond conventional methodologies, the model adroitly harnesses the scalability and hierarchical learning intrinsic to deep learning frameworks. It delves into spatial relationships and finer intricacies within CT scans. An extensive dataset of COVID-19-related CT images, encompassing diverse instances, stages, and severities, is meticulously curated to fully exploit the innovative potential of the vision transformer model. Thorough training, validation, and testing refine the model’s predictive prowess. Techniques like data augmentation and transfer learning bolster generalization and adaptability for real-world scenarios. The efficacy of this research is gauged through comprehensive assessments, encompassing sensitivity, specificity, and prediction accuracy. Comparative analyses against existing methods underscore the superior performance of the novel model, highlighting its transformative influence on predicting and mitigating rumor propagation during the COVID-19 pandemic. Enhanced interpretability sheds light on the decision-making process, augmenting the model’s utility within real-world decision support systems. By harnessing the transformative capabilities of vision transformers and synergizing them with advanced SOTA models, this study offers a robust solution to counter the dissemination of misinformation during the pandemic. The model’s proficiency in discerning intricate patterns in COVID-19-related CT scans signifies a pivotal leap toward combating the infodemic. This endeavor culminates in more precise public health communication and judicious decision-making, ushering in a new era of leveraging cutting-edge deep learning for societal well-being amidst the challenges posed by the COVID-19 era. Show more
Keywords: Healthcare, deep learning, COVID-19, vision transformer, rumor prediction, misinformation
DOI: 10.3233/JIFS-236842
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3635-3648, 2024
Authors: Yamuna, K.S. | Thirunavukkarasu, S. | Manjunatha, B. | Karthikeyan, B.
Article Type: Research Article
Abstract: Lung sound (LS) signals are a vital source of information for the identification of pulmonary disorders. Heart sound (HS) is the most common contaminant of lung sounds during auscultation from the chest walls. This directly affects the efficiency of lung sound processing in diagnosing lung diseases. In this work, Adaptive Variational Mode Decomposition (AVMD) technique is proposed to remove heart sound contaminants from lung sounds. The proposed AVMD method initially breakdown the noisy lung sound signal into a collective of bandlimited modes called variational mode functions (VMF). Then, based on the frequency spectrum, the HS is filtered out from the …LS. The real time lung sound data is collected from 95 participants and the performance of VMD technique is evaluated using the statistical metrics measures. Thus, the proposed topology exhibits Higher SNR (29.6587dB, lowest Root Mean Square (RMSE) of 0.0102, lowest normalized Mean Absolute Error (nMAE) of 0.0336, and highest percentage in correlation coefficient Factor (CCF) of 99.79% respectively. These experimental results are found to be superior and outperform all other recently proposed techniques. Show more
Keywords: Variational mode decomposition (VMD), adaptive VMD, lung sound signals, heart sound signals
DOI: 10.3233/JIFS-231127
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3649-3657, 2024
Authors: Elamrani Abou Elassad, Dauha | Elamrani Abou Elassad, Zouhair | Ed-dahbi, Abdel Majid | El Meslouhi, Othmane | Kardouchi, Mustapha | Akhloufi, Moulay
Article Type: Research Article
Abstract: The concept of endorsing AI in embedded systems is growing in all sectors including the development of Accident Avoidance Systems. Although real-time road crash prediction is vital for enhancing road user safety, there has been limited focus on the analysis of real-time crash events within ensemble and deep learning fused systems. The main aim of this paper is to design an advanced Accident Avoidance System established on a deep learning and ensemble fusion strategy in order to acquire more performant crash predictions. As such, four highly optimized models for crash prediction have been designed based on the popular ensemble techniques: …CatBoost, AdaBoost and Bagging and the deep learning CNN. Additionally, four categories of features, including driver inputs, vehicle kinematics, driver states and weather conditions, were measured during the execution of various driving tasks performed on a driving simulator. Moreover, given the infrequent nature of crash events, an imbalance-control procedure was adopted using the SMOTE and ADASYN techniques. The highest performances results have been acquired using CatBoost along with ADASYN on almost all the adopted metrics during the different weather conditions, and more than 50% of all crashes have occurred in rainy weather conditions, whereas 31% have been exhibited in fog patterns. The sensitivity analysis results indicate that the fusing all the acquired features has the highest impact on the prediction performance. To our knowledge, there has been a limited interest, if not at all, at adopting a fused ensemble deep learning system examining the real-time impact of the adopted features’ combinations on the prediction of road crashes while taking into account class imbalance. The findings provide new insights into crash prediction and emphasize the relevance of the explanatory features which can be endorsed in designing efficient Accident Avoidance Systems. Show more
Keywords: Accident avoidance system, machine learning, class-imbalance, ensemble learning, deep learning, sensitivity analysis
DOI: 10.3233/JIFS-232446
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3659-3676, 2024
Authors: Wang, Jing | Yu, Liying | Rong, Yuan
Article Type: Research Article
Abstract: Quality function deployment (QFD) is a customer-driven product development technique that converts customer requirements (CRs) into design attributes (DAs) of a product and service. Nevertheless, in real situations, the traditional QFD method has been found that possesses some deficiencies, such as the accuracy assessment of relationships between CRs and DAs, and the inter-relationships among DAs. To fill in the above gaps, this study develops a new QFD approach by a CoCoSo-based ranking method under Pythagorean fuzzy environment. To begin with, an extended Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL) method is proposed to identify the relationships within DAs. Second, …the aggregation method of the weighted average method and objective penalty function are propounded to construct the programming models for calculating the importance of DAs under Pythagorean fuzzy setting. Third, a new CoCoSo-based ranking method for Pythagorean triangular fuzzy numbers (PTrFNs) is proposed to obtain the ranking of DAs. Lastly, a case regarding “Ping An Health” mobile medical App is carried out to verify the effectiveness and superiority of the proposed QFD approach. The results show that the top DA is perceptibility. Therefore, perceptibility should be focus on firstly in the “Ping An Health” App design, such as system fluency, interface comfort and network stability. Additionally, the results show that the new QFD can express experts’ hesitant assessment information, deal with the interrelations among DAs, and yield more precise rankings of DAs in QFD. Show more
Keywords: Quality function deployment, DEMATEL method, CoCoSo-based ranking method, mobile medical App
DOI: 10.3233/JIFS-233229
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3677-3700, 2024
Authors: Wang, Cong | Teng, Yue | Zhang, Tianhang
Article Type: Research Article
Abstract: Establishing a closed-loop system that could facilitate the reusing, renovation, and recycling of the various garbage products generated by this business could prove significant value to the particular business chains involved. A system of shipping that is mindful of the surroundings and takes accountability regarding all the relevant money, sustainable, and societal concerns. The sustainability Closed-Loop Supply-Chain Networks (CLSCN) architecture and the marketplace are brought together in the present article, which serves as the study’s primary part in the body of knowledge. As a result, an optimization with multiple objectives paradigm has been offered to arrive at their choices regarding …position, allocations, and stock in relation to the challenge under consideration. The goals of the optimized model, derived from the triple bottom line strategy, are aimed at lowering overall expenditure and emissions of CO2 as much as possible while increasing the number of employment possibilities. In this study, we have proposed Hybrid electromagnetism with a genetic algorithm (HEGA) and compare our proposal with the existing methods. The obtained results show that the proposed model integrated with HEGA gives significant improvements with significant outcomes in terms of sensitivity (97%), specificity (95%), transportation cost (30%), and computational time (5.3s). This knowledge serves as a driving force behind the development of CLSN in the sector to establish a viable and affordable approach. Show more
Keywords: Supply chain (SC), Closed-Loop Supply Chain Network (CLSCN), industries, fuzzy logic, Hybrid electromagnetism (HE)
DOI: 10.3233/JIFS-236612
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3701-3712, 2024
Authors: Lei, Fan | Cai, Qiang | Wang, Hongjun | Wei, Guiwu | Mo, Zhiwen
Article Type: Research Article
Abstract: Urban fire accident is a common dangerous accident in urban sudden accidents, which threatens the safety of people’s lives and property. For this reason, in recent years, all cities have incorporated the prevention and emergency management of urban fire accidents into their urban development planning, and actively improved their fire accident emergency management capabilities. However, how to evaluate the urban fire accident emergency management capacity of each city to ensure that people’s lives and property are protected to the greatest extent is an urgent problem to be considered and solved. Therefore, this paper defines a class of probabilistic double hierarchy …linguistic Heronian mean (PDHLHM) operator, probabilistic double hierarchy linguistic Power Heronian mean (PDHLPHM) operators, and their dual operators that can reflect the relationship between two attributes during aggregation. Taking urban fire accident risk monitoring and early warning capability, fire infrastructure and communication system, fire-fighting and rescue capability, recovery and reconstruction capability as evaluation attributes, the probabilistic double hierarchy linguistic weight Power Heronian mean (PDHLWPHM) operator model and the probabilistic double hierarchy linguistic weight Power geometric Heronian mean (PDHLWPGHM) operator model are constructed for group decision-making. In addition, the idempotence, boundedness and monotonicity of these operators are studied, and the sensitivity of the parameters involved in the operator model is analyzed. Finally, the new model proposed in this paper is compared with the existing model to verify its scientificity. Show more
Keywords: Group decision-making, probabilistic double hierarchy linguistic term set (PDHLTS), PDHLWPHM operator and PDHLWPGHM operator, urban fire emergency management capability
DOI: 10.3233/JIFS-230485
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3713-3760, 2024
Authors: Zhao, Chen | Sun, Lijun | Li, Gang | Tang, Yiming
Article Type: Research Article
Abstract: Relevancy transformation operators (RET operators) have been widely used in fuzzy systems modelling and the construction of weighted aggregation functions. Several construction methods of RET operators based on different aggregation functions such as t-norm, t-conorm and copula, have been proposed. In this paper, the attention is paid to the expression of RET operators, which is an important feature from an application the point of view. Polynomial RET operators are introduced as those RET operators in the form of polynomial functions of two variables. A complete characterisation of polynomial RET operators of degree less than 4 are presented.
Keywords: Relevancy transformation operators, Polynomial functions, Fuzzy systems, Monotonicity
DOI: 10.3233/JIFS-231017
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3761-3771, 2024
Authors: Wang, Jiaguo | Li, Wenheng | Lei, Chao | Yang, Meng | Pei, Yang
Article Type: Research Article
Abstract: Recently, actor-critic architectures such as deep deterministic policy gradient (DDPG) are able to understand higher-level concepts for searching rich reward, and generate complex actions in continuous action space, and widely used in practical applications. However, when action space is limited and has dynamic hard margins, training DDPG can be problematic and inefficiency. Since real-world actuators always have margins and interferences, after initialization, the actor network is likely to be stuck at a local optimal point on action space margin: actor gradient orients to the outside of action space but actuators stop at the margin. If the hard margins are complex, …dynamic and unknown to the DDPG agent, it is unable to use penalty functions to recover from local optimum. If we enlarge the random process for local exploration, the training could be in potential risk of failure. Therefore, simply relying on gradient of critic network to train the actor network is not a robust method in real environment. To solve this problem, in this paper we modify DDPG to deep comparative policy (DCP). Rather than leveraging critic-to-actor gradient, the core training process of DCP is regulated by a T-fold compare among random proposed adjacent actions. The performance of DDPG, DCP and related algorithms are tested and compared in two experiments. Our results show that, DCP is effective, efficient and qualified to perform all tasks that DDPG can perform. More importantly, DCP is less likely to be influenced by the action space margins, DCP can provide more safety in avoiding training failure and local optimum, and gain more robustness in applications with dynamic hard margins in the action space. Another advantage is that, complex penalty for margin touching detection is not required, the reward function can always be brief and short. Show more
Keywords: Actor-critic, deep reinforcement learning, intelligent agent, iterative learning
DOI: 10.3233/JIFS-233747
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3773-3788, 2024
Authors: Mukesh Krishnan, M. | Thanga Ramya, S. | Ramar, K.
Article Type: Research Article
Abstract: Unusual crowd activity detection is a challenging problem in surveillance video applications because feature extraction is difficult process in crowded scenes. The main objective of this research work is to detect unusual crowd activities and to detect unusual splits of moving objects. Various methods have been employed to address these challenges. However, there is still a lack of appropriate handling of this problem due to frames having occlusion, noise, and congestion. This paper proposes a novel clustering approach to detect unusual crowd activities. The proposed method consists of five phases including foreground extraction, foreground enhancement, foreground estimation, clustering crowds, and …the Unusual Crowd Activities (UCA) model. The UCA model can find unusual crowd activities and unusual splits of moving objects using the Laplacian Matrix formulation. Two public datasets viz. PETS 2009 and UMN dataset are used for evaluating the proposed methodology. To estimate the effectiveness of the proposed work, several unusual event detection methods are compared with the proposed work results. The experimental results revealed that the proposed method gives better results than the existing methods. Show more
Keywords: Unusual event detection, crowd detection, crowd clustering, and unusual crowd activities model
DOI: 10.3233/JIFS-233833
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3789-3798, 2024
Authors: Li, Tao | Wang, Xiaolong | Li, Xinkun | Jia, Xinyu | Wu, Lijie | Yang, Weihong
Article Type: Research Article
Abstract: Tunnel stability is mainly concerned with the object of symmetric tunnels, shallow buried unsymmetric(SBU) tunnels should also be emphasized as the focus of the computational analysis of tunnel engineering. It is especially important to solve the expressions of ultimate support force and damage surface function for SBU tunnels. In this paper, considering the effect of unsymmetrical action, based on the Hoke-Brown(H-B) damage criterion, the optimal upper bound(UB) solution expression is derived by using the limit analysis method. The expression can be used to express the support force and collapse pattern of a SBU rectangular tunnel. The results show that q …1 and q 2 decrease with the increase of parameters A and σ c , and increase with the increase of parameters B , γ , and h . q 1 increases with the increase of α , and vice versa for q 2 . The range of damage surface decreases with increasing parameter A , σ c and increases with increasing parameter B , γ , d , h . After the feasibility study and results analysis, it is concluded that the results obtained in this study are consistent with common engineering knowledge. The training results using Feedforward neural network verify the feasibility of the method for SBU tunnels and can be generalized for shallow buried(SB) symmetrical tunnels. The proposed method can provide a theoretical basis for the support design of SBU tunnels. Show more
Keywords: Shallow buried unsymmetrical tunnels, Hoke-Brown criterion, collapsed rock mass, limit analysis method
DOI: 10.3233/JIFS-234766
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3799-3809, 2024
Authors: Arulkumar, V. | Sandana Karuppan, A. | Alex, Sini Anna | Lathamanju, R.
Article Type: Research Article
Abstract: In an era marked by the widespread adoption of cloud services, individuals and businesses face the daunting task of navigating a complex landscape to make informed choices. The inherent opacity of the cloud service environment underscores the need for methods that can effectively handle imprecise information. This research presents a novel and superior approach to aid customers in selecting the most suitable cloud services. Our work introduces a distinctive fuzzy decision-making paradigm, surpassing current methodologies. We leverage an innovative analytic hierarchy process technique to quantify the semantic similarity between concepts and employ a fuzzy ontology to elucidate the uncertain relationships …among database items, facilitating precise service matching. Furthermore, we present a multi-faceted evaluation framework for ranking cloud services. To substantiate the efficacy of our similarity matching based on the fuzzy ontology, we conduct comprehensive testing. The results of our experiments provide compelling evidence of the viability and effectiveness of the proposed method. This research offers a valuable contribution to the challenging realm of cloud service selection, empowering individuals and organizations to make well-informed decisions amidst the cloud service abundance. Show more
Keywords: Semantic information retrieval, fuzzy ontology, ontology, the Semantic Web
DOI: 10.3233/JIFS-235130
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3811-3826, 2024
Authors: Cheng, Long | Wang, Lei | Cai, Jingcao
Article Type: Research Article
Abstract: For solving the distributed assembly flow shop scheduling problem with fuzzy processing time (FDAPFSP), a regional biogeography-based optimization algorithm (RBBO) is proposed to minimize the maximum fuzzy completion time. The mathematical model is provided. In RBBO, all habitats are divided into regions based on the habitat suitability index, and the habitats of each region are subject to cross-regional migration and replacement procedures. A critical factory optimization strategy is developed to enhance local search capability. Taguchi method is used to determine the parameters of RBBO. In ten FDAPFSP instances, comparative testing of RBBO algorithm with various heuristic and swarm intelligence algorithms …are conducted. The computation results show that in ten FDAPFSP cases, the proposed RBBO outperforms other algorithms in nine out of ten FDAPFSP cases. Show more
Keywords: Fuzzy scheduling, distributed scheduling, permutation flow-shop, regional biogeography-based optimization algorithm
DOI: 10.3233/JIFS-235854
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3827-3841, 2024
Authors: Vidhya, K. | Krishnamoorthi, K.
Article Type: Research Article
Abstract: In this manuscript, a hybrid approach is proposed for multi-functional grid connected photovoltaic (PV) interleaved inverter using power quality(PQ) enhancement. The proposed method is the integration of Spherical Evolution Search Algorithm (LSE) and Wild Horse Optimizer (WHO), thus it is called LSE-WHO method. The key objective of the proposed method lessens the DC voltage fluctuation and enhances the PQ. At the grid side, the interleaved inverter is used and it consists of 4 legs and every leg has a power electronic switch and a diode. Because of the structure of interleaved inverter, the shoot-through effect overcomes. The system performance is …improved by the utilization of interleaved inverter. The operation of proposed method is divided into 2 parts, like harmonics reduction and power harvesting. The LSE method is used to improve the maximal power of photovoltaic and the WHO method is used to lessen the harmonics distortion and eliminated the DC-link voltage fluctuation by double band hysteresis current controller (DBHCC). The switching losses are low because the DBHCC gives lesser switching frequency. Then, the LSE-WHO method is done in MATLAB, and its performance is compared to the existing methods. From the simulation, it conclude that the LSE-WHO method provides the THD as 2.12% and improves the PQ. Show more
Keywords: Power quality, power harvesting, grid connected PV, interleaved inverter, DC voltage fluctuation, switching losses, harmonic
DOI: 10.3233/JIFS-221561
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3843-3865, 2024
Authors: Al-Essa, Laila A. | Khan, Zahid | Alduais, Fuad S.
Article Type: Research Article
Abstract: The logistic distribution is frequently encountered to model engineering, industrial, healthcare and other wide range of scientific data. This work introduces a flexible neutrosophic logistic distribution (LDN ) constructed using the neutrosophic framework. The LDN is considered to be ideal for evaluating and quantifying the uncertainties included in processing data. The suggested distribution offers greater flexibility and superior fit to numerous commonly used metrics for assessing survival, such as the hazard function, reliability function, and survival function. The mode, skewness, kurtosis, hazard function, and moments of the new distribution are established to determine its properties. The theoretical findings are …experimentally proven by numerical studies on simulated data. It is observed that the suggested distribution provides a better fit than the conventional model for data involving imprecise, vague, and fuzzy information. The maximum likelihood technique is explored to estimate the parameters and evaluate the performance of the method for finite sample sizes under the neutrosophic context. Finally, a real dataset on childhood mortality rates is considered to demonstrate the implementation methodology of the proposed model. Show more
Keywords: Uncertain data, neutrosophic probability, neutrosophic distribution, uncertain estimators, Monte Carlo simulation
DOI: 10.3233/JIFS-233357
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3867-3880, 2024
Authors: Liu, Yicheng | Hu, Zewei | Nie, Haiwen
Article Type: Research Article
Abstract: With the rapid economic development and high concentration of urban population, people’s income level and quality of life continue to improve, resulting in more and more crowded scenes caused by people going out. Especially in urban commercial centers, transportation hubs, sports venues during important events, tourist attractions, etc., crowd gatherings occur frequently. However, accidents involving crowd gatherings in public places occur frequently, causing heavy casualties and property losses. Therefore, for crowd recognition, this paper proposes a new method to accurately estimate the number of dense crowds. In this method, a density map with accurate pedestrian locations is first generated using …the focal inverse distance transform and used as ground truth labels for network training. Then, a multi-scale feature fusion algorithm based on residual network is designed, combining spatial and channel attention mechanisms to improve the accuracy and stability of crowd density estimation. In dense crowds, the phenomenon of overlapping and occlusion of people is very common and serious, making it difficult for existing pedestrian detection methods to distinguish each individual and accurately count the flow of people. To solve this problem, this paper proposes a density map-based method that uses a local maximum detection strategy and a K-nearest neighbor algorithm to convert the density map into the corresponding dense head bounding box. This method can effectively reduce the impact of occlusion and improve the accuracy of people counting. In order to further improve the estimation accuracy, a pattern recognition density peak clustering algorithm is introduced to study the clustered crowds. By treating the head bounding box as an element point, the distance between each element point is calculated, and the density of each point is calculated. Then perform clustering to find the cluster center with the highest density in each class. Finally, by comparing the density of each cluster center with the corresponding density threshold and adopting the corresponding decision-making method, the accuracy of people counting is further improved. Show more
Keywords: Deep learning, residual networks, public places, crowd recognition, clustering
DOI: 10.3233/JIFS-236811
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3881-3893, 2024
Authors: Feng, Kan | Yang, Ke | Shi, Haopeng | Jia, Najuan | Zhang, Pingjuan
Article Type: Research Article
Abstract: Overload service in the communication network of smart substation will cause congestion, resulting in low overload service throughput, high congestion rate and long congestion control time in the average smart substation. A congestion control method for overloaded services in smart substations with high concurrent users is proposed. According to the characteristics of overload service request of smart substation, the mathematical model of the algorithm is defined by describing the overload service request of smart substation on the basis of network topology model. Combined with the wavelength rotation strategy, the congestion rate of overloaded services in smart substations is reduced, and …the throughput rate of overloaded services in smart substations is improved. Considering the factors of high concurrent users, by judging and feeding back the congestion of the overloaded services of smart substations, the congestion control of overloaded services of smart substations under high concurrent users is realized. The experimental results show that the proposed method has better effect and scalability in the congestion control of the overloaded service of the smart substation, and can effectively shorten the congestion control time of the overloaded service of the smart substation. Show more
Keywords: High concurrent users, smart substation, wavelength rotation strategy, overload service, congestion control, D_WA algorithm, overload service congestion model, congestion degree value, interest packet forwarding rate
DOI: 10.3233/JIFS-224276
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3895-3906, 2024
Authors: Wu, Zhongyi | Liu, Weidong | Zheng, Weijie
Article Type: Research Article
Abstract: This research presents a novel model for optimizing process information in manufacturing steps through the utilization of Process Constituent Elements (PCE), with the aim of enhancing the effectiveness of product process information design. To achieve this objective, a systematic analysis is conducted on six dimensions: input, output, resources, value-adding activities, environment, and process control and inspection content. In addition, specific attributes of PCE are investigated, and an improved FP-growth algorithm is employed to extract the optimized structural expressions of typical PCE, thus determining specific expression requirements. The PCE and their attribute relationships are organized into modular mapping rules, resulting in …an optimized representation structure based on a polychromatic set approach. The effectiveness of this approach is quantitatively assessed by developing a comprehensive quality indicator evaluation system for process information and using a fuzzy comprehensive evaluation model for analysis. Show more
Keywords: Process constituent elements, process design, optimization, data mining, process quality, fuzzy evaluation
DOI: 10.3233/JIFS-231198
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3907-3932, 2024
Authors: Siva Senthil, D. | Sivarani, T.S.
Article Type: Research Article
Abstract: Detecting abnormal events in surveillance involves identifying unexpected behavior through video analysis. This involves recognizing patterns or deviations from normal behavior and taking actions to mitigate potential risks. However, the distribution of data can change over time, leading to concept drift, which can make it challenging to accurately detect abnormal events. To address this issue, a new approach using a global density network (GDN) has been proposed. The GDN allows for more efficient identification of object distributions in surveillance videos, leading to improved accuracy in abnormal event detection. The proposed method combines features extracted by a backbone network with a …global density joined network (GDJN), which refines density features using dilated convolutional networks. A multistage long short-term memory (LSTM) network is then used to classify abnormal events. The experimental results are conducted on two datasets, UMN and UCSD Ped2. The achieved F1 scores were 93.42 and 94.46 respectively, with corresponding AUC values of 93.5 and 94.8. Show more
Keywords: Keywords:Video analysis, abnormal event detection, GDN, GDJN, LSTM, deep learning
DOI: 10.3233/JIFS-232177
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3933-3944, 2024
Authors: Li, Weidong | Fan, Jinsheng | Li, Zhenying | Wang, Chisheng | Zhang, Xuehai | Duan, Jinlong
Article Type: Research Article
Abstract: The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow …discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance. Show more
Keywords: ANFIS, ANN, bacterial foraging optimization algorithm, modeling, suspended sediment
DOI: 10.3233/JIFS-232277
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3945-3961, 2024
Authors: Sahapudeen, Farjana Farvin | Krishna Mohan, S.
Article Type: Research Article
Abstract: Patients with lung cancer can only be diagnosed and treated surgically. Early detection of lung cancer through medical imaging could save numerous lives. Adding advanced techniques to conventional tests that offer high accuracy in diagnosing lung cancer is essential. U-Net has excelled in diversified tasks involving the segmentation of medical image datasets. A significant challenge remains in determining the ideal combination of hyper parameters for designing an optimized U-Net for detailed image segmentation. In our work, we suggested a technique for automatically generating evolutionary U-Nets to detect and segregate lung cancer anomalies. We used three distinct datasets, namely the LIDC-IRDC …Dataset, Luna 16 Dataset, and Kaggle Dataset, for training the proposed work on lung images. Our results, examined with six distinct evaluation criteria used for medical image segmentation, consistently demonstrated the highest performance. More specifically, the GA-UNet outperforms conventional approaches in terms of an impressive accuracy rate of 97.5% and a Dice similarity coefficient (DSC) of 92.3%. Show more
Keywords: Genetic programming, deep learning, attention blocks, residual network, UNets, optimized U-Net
DOI: 10.3233/JIFS-233006
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3963-3974, 2024
Authors: Demirtaş, Naime | Dalkılıç, Orhan | Riaz, Muhammad | Al-Quran, Ashraf
Article Type: Research Article
Abstract: Introduction: The soft set theory has drawn the attention of many researchers, particularly for dealing with uncertainty in decision-making problems. Despite its remarkable advantages, the soft set theory has only been used to tackle decision-making problems that aim to choose the best option. However, there exist different forms of decision-making problems that involve different forms of uncertainty. Methods: In this study, we present various algorithms based on the soft set theory in order to handle the cases where one has different uncertainty forms in decision-making problems. Some new concepts such as object code, personal object code, parameter significance …weight and new distance measures have been introduced to the literature for the construction of these algorithms. Furthermore, we show the application results of those algorithms and provide several examples. Results and Conclusions: As a result, a comparison among the application results of the algorithms implies that the best objects might not always yield the most efficient outcomes. Show more
Keywords: Soft set, D-metric space, parametric distance, algorithm, decision making
DOI: 10.3233/JIFS-234481
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3975-3985, 2024
Authors: Li, Zhixin | Liu, Hao | Huan, Zhan | Liang, Jiuzhen
Article Type: Research Article
Abstract: Human activity recognition (HAR) plays a crucial role in remotely monitoring the health of the elderly. Human annotation is time-consuming and expensive, especially for abstract sensor data. Contrastive learning can extract robust features from weakly annotated data to promote the development of sensor-based HAR. However, current research mainly focuses on the exploration of data augmentation methods and pre-trained models, disregarding the impact of data quality on label effort for fine-tuning. This paper proposes a novel active contrastive coding model that focuses on using an active query strategy to evenly select small, high-quality samples in downstream tasks to complete the update …of the pre-trained model. The proposed uncertainty-based balanced query strategy mines the most indistinguishable hard samples according to the data posterior probability in the unlabeled sample pool, and imposes class balance constraints to ensure equilibrium in the labeled sample pool. Extensive experiments have shown that the proposed method consistently outperforms several state-of-the-art baselines on four mainstream HAR benchmark datasets (UCI, WISDM, MotionSense, and USCHAD). With approximately only 10% labeled samples, our method achieves impressive F1-scores of 98.54%, 99.34%, 98.46%, and 87.74%, respectively. Show more
Keywords: Contrastive learning, active learning, human activity recognition, hard sample mining, mobile medical system
DOI: 10.3233/JIFS-234804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3987-3999, 2024
Authors: Zeng, Zengpei
Article Type: Research Article
Abstract: Visual communication design, as a type of artistic and three-dimensional design behavior, helps to spread visual behavior by designing it. The rapid development of new media technology has provided rich channels and vast space for visual communication design, and the elements and modes of visual communication design are constantly being updated, better promoting the development of visual communication technology. The Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities based on the cultivation of innovative and creative abilities is a multiple-attribute decision-making (MADM). In this paper, some calculating laws on IVIFSs, Hamacher sum, …Hamacher product are introduced, and the induced interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (I-IVIFHIHWA) operator is proposed based on the interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (IVIFHIHWA) operator and induced ordered weighted averaging (I-OWA) operator. Meanwhile, some ideal properties of I-IVIFHIHWA operator are studied. Then, the I-IVIFHIHWA operator is employed to cope with the MADM under IVIFSs. Finally, an example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities is employed to test the I-IVIFHIHWA operator. Thus, the main research aim of this paper is concluded as follows: [1 ] the I-IVIFHIHWA operator is constructed based on classical IOWA operator; [2 ] the I-IVIFHIHWA operator is put forward to cope with the MADM under IVIFSs; [3 ] an empirical example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities has been put forward to show the I-IVIFHIHWA operator. Show more
Keywords: Multiple-attribute decision-making (madm), interval-valued intuitionistic fuzzy sets (ivifss), i-ivifhihwa operator, quality evaluation
DOI: 10.3233/JIFS-235960
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4001-4013, 2024
Authors: Li, Fang | Li, Pengfei
Article Type: Research Article
Abstract: Currently, the digital economy continues to deepen its development, and it has become a consensus among all sectors as the direction of global future development. Digital finance, as a fleet in the wave of digital economy, is rapidly heading towards the sunny shore of benefiting the public and serving entities driven by the digital technology engine. Xinwang Bank is a fast boat in the digital finance fleet, always adhering to the principle of technology, building an open platform, and actively promoting the construction of an open, shared, and secure digital credit ecosystem from three levels: institutional, industry, and ecological, to …assist in the development of the digital economy. The digital commercial bank security evaluation is a classical multiple attribute group decision making (MAGDM) problems. Recently, the Evaluation based on Distance from Average Solution (EDAS) method has been employed to manage MAGDM issues. The intuitionistic fuzzy sets (IFSs) are used as a tool for portraying uncertain information during the digital commercial bank security evaluation. In this paper, the intuitionistic fuzzy nunmber EDAS (IFN-EDAS) method is cultivated to manage the MAGDM based on Hamming distance and Euclidean distance under IFSs. In the end, a numerical case study for digital commercial bank security evaluation is supplied to validate the proposed method. The main contributions of this paper are outlined: (1) the EDAS method has been extended to IFSs based on Hamming distance and Euclidean distance; (2) the CRITIC method is used to derive weight based on Hamming distance and Euclidean distance under IFSs. (3) the IFN-EDAS method based on Hamming distance and Euclidean distance is founded to manage the MAGDM based on the Hamming distance and Euclidean distance under IFSs; (4) a numerical case study for digital commercial bank security evaluation and some comparative analysis is supplied to validate the proposed method. Show more
Keywords: Multiple attribute group decision making (MAGDM), intuitionistic fuzzy sets (IFSs), EDAS method, CRITIC method, digital commercial bank security evaluation
DOI: 10.3233/JIFS-236058
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4015-4027, 2024
Authors: Zhang, Bin
Article Type: Research Article
Abstract: In recent years, e-commerce live streaming and short video marketing supported by big data and artificial intelligence technology have flourished, adding new sales models for e-commerce products to mass consumption, promoting the multimodal development of the e-commerce industry, giving new impetus and connotation to economic and social development, and being an effective means to achieve high-quality development in the new era. The effectiveness evaluation of short video marketing strategies is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM technique and Combined Compromise Solution (CoCoSo) technique has been employed to cope with MAGDM issues. The interval-valued Pythagorean fuzzy sets …(IVPFSs) are employed as a tool for characterizing uncertain information during the effectiveness evaluation of short video marketing strategies. In this paper, the interval-valued Pythagorean fuzzy Exponential TODIM (ExpTODIM) (IVPF-ExpTODIM-CoCoSo) technique is constructed to solve the MAGDM under IVPFSs. In the end, a numerical case study for effectiveness evaluation of short video marketing strategies is given to validate the proposed technique. The main contributions of this paper are outlined: (1) the Exp-TODIM and CoCoSo technique has been extended to IVPFSs; (2) Information Entropy is employed to manage the weight values under IVPFSs. (3) the IVPF-ExpTODIM-CoCoSo technique is founded to implement the MAGDM under IVPFSs; (4) a numerical case study for effectiveness evaluation of short video marketing strategies and some comparative analysis is supplied to verify the IVPF-ExpTODIM-CoCoSo technique. Show more
Keywords: Multiple attribute group decision making (MAGDM), interval-valued Pythagorean fuzzy sets (IVPFSs), Exponential TODIM (ExpTODIM) technique, CoCoSo technique, effectiveness evaluation
DOI: 10.3233/JIFS-236767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4029-4042, 2024
Authors: Ma, Junwen | Bi, Wenhao | Mao, Zeming | Zhang, An | Tang, Changhong
Article Type: Research Article
Abstract: The weaponized unmanned aerial vehicle (UAV) swarms have posed a significant threat to maritime civilian and military installations. For effective defense deployment, threat assessment has become a critical part of maritime defense decision-making. However, due to the uncertainty of threat information and the ignorance of decision-makers’ psychological behaviors, there are great challenges in obtaining a reliable and accurate threat assessment result to assist in maritime defense decision-making. To this end, this paper proposes an integrated threat assessment method for maritime defense against UAV swarms based on improved interval type-2 fuzzy best-worst method (IT2FBWM), prospect theory and VIKOR (VlseKriterijumska Optimizacija I …Kompromisno Resenje, in Serbian). Firstly, the improved IT2FBWM is designed by introducing interval type-2 fuzzy set (IT2FS) and entropy-based information to obtain attribute weights with high reliability. Then, the hybrid fuzzy scheme covering IT2FS and interval number is constructed to express the uncertainty of different types of threat information. Next, VIKOR is extended to hybrid fuzzy environment and combined with prospect theory to consider the influence of psychological behaviors of decision-makers. Finally, the improved IT2FBWM and extended VIKOR are integrated to determine the threat ranking of targets and the priority defense targets. A case study of maritime threat assessment is provided to illustrate the performance of the proposed method. Moreover, sensitivity and comparative experiments were conducted, and the results indicate that the proposed method not only obtain the reliable threat assessment result but also outperforms the other methods in terms of attribute weight determination, decision preference consideration and decision mechanism. Show more
Keywords: Threat assessment, interval type-2 fuzzy, best-worst method, prospect theory, multi-attribute decision-making
DOI: 10.3233/JIFS-231675
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4043-4061, 2024
Authors: Cai, Buqing | Tian, Shengwei | Yu, Long | Long, Jun | Zhou, Tiejun | Wang, Bo
Article Type: Research Article
Abstract: With the rapid growth of Internet penetration, identifying emergency information from network news has become increasingly significant for emergency monitoring and early warning. Although deep learning models have been commonly used in Chinese Named Entity Recognition (NER), they require a significant amount of well-labeled training data, which is difficult to obtain for emergencies. In this paper, we propose an NER model that combines bidirectional encoder representations from Transformers (BERT), bidirectional long-short-term memory (BILSTM), and conditional random field (CRF) based on adversarial training (ATBBC) to address this issue. Firstly, we constructed an emergency dataset (ED) based on the classification and coding …specifications of the national emergency platform system. Secondly, we utilized the BERT pre-training model with adversarial training to extract text features. Finally, BILSTM and CRF were used to predict the probability distribution of entity labels and decode the probability distribution into corresponding entity labels.Experiments on the ED show that our model achieves an F1-score of 85.39% on the test dataset, which proves the effectiveness of our model. Show more
Keywords: Named Entity Recognition, BERT, BILSTM, CRF, Adversarial Training
DOI: 10.3233/JIFS-232385
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4063-4076, 2024
Authors: Wang, Chuantao | Wang, Xiumin | Zhai, Jiliang | Shao, Shuo
Article Type: Research Article
Abstract: In recent years, UNet and its derivative networks have gained widespread recognition as major methods of medical image segmentation. However, networks like UNet often struggle with Point-of-Care (POC) healthcare applications due to their high number of parameters and computational complexity. To tackle these challenges, this paper introduces an efficient network designed for medical image segmentation called MCU-Net, which leverages ConvNeXt to enhance UNet. 1) Based on ConvNeXt, MCU-Net proposes the MCU Block, which employs techniques such as large kernel convolution, depth-wise separable convolution, and an inverted bottleneck design. To ensure stable segmentation performance, it also integrates global response normalization (GRN) …layers and Gaussian Error Linear Unit (GELU) activation functions. 2) Additionally, MCU-Net introduces an enhanced Multi-Scale Convolution Attention (MSCA) module after the original UNet’s skip connections, emphasizing medical image features and capturing semantic insights across multiple scales. 3)The downsampling process replaces pooling layers with convolutions, and both upsampling and downsampling stages incorporate batch normalization (BN) layers to enhance model stability during training. The experimental results demonstrate that MCU-Net, with a parameter count of 2.19 million and computational complexity of 19.73 FLOPs, outperforms other segmentation models. The overall performance of MCU-Net in medical image segmentation surpasses that of other models, achieving a Dice score of 91.8% and mIoU of 84.7% on the GlaS dataset. When compared to UNet on the BUSI dataset, MCU-Net shows an improvement of 2% in Dice and 2.9% in mIoU. Show more
Keywords: Convolution neural network, deep learning, medical image processing, semantic segmentation
DOI: 10.3233/JIFS-233232
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4077-4092, 2024
Authors: Ragul Vignesh, M. | Srihari, K. | Karthik, S.
Article Type: Research Article
Abstract: The rapid development of Internet of Things (IoT) technology has enabled the emergence of the Internet of Medical Things (IoMT), especially in body area network applications. To protect sensitive medical data, it is essential to ensure privacy preservation and detect intrusions in this context. This study proposes a novel intrusion detection system that protects the privacy of IoMT networks, specifically in the context of body area networks. For feature extraction, the system employs a recurrent U-Net autoencoder algorithm, which effectively captures temporal dependencies in IoMT data. In addition, privacy is protected through the combination of data anonymization techniques and data …classification using Principal Component Analysis (PCA). Combining the recurrent U-Net autoencoder algorithm, privacy preservation mechanisms, and PCA-based data classification, the proposed system architecture comprises the U-Net autoencoder algorithm. The proposed method is superior to existing approaches in terms of accuracy, precision, recall, F-measure, and classification loss, as demonstrated by experimental evaluations. This research contributes to the field of privacy protection and intrusion detection in IoMT, specifically in body area network applications. Show more
Keywords: Biomedical, Internet of Medical Things, intrusion detection, privacy preservation, recurrent neural networks, U-Net
DOI: 10.3233/JIFS-234441
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4093-4104, 2024
Authors: Liu, Yongfei
Article Type: Research Article
Abstract: The improved Sparse Signal Reconstruction (SR) algorithm for Trusted Artificial Intelligence (AI) and Distributed Compressed Sensing (DCS) technology was thoroughly investigated. The study verified its effectiveness and advantages in trusted AI and DCS systems, which have significant implications for enhancing the credibility, security, and performance of signal processing and AI algorithms. The reconstruction performance was evaluated using Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), and Least Absolute Shrinkage and Selection Operator (LASSO). The analysis primarily focused on runtime, refactoring errors, and the number of successful reconstruction attempts. When K = 4, K = 6, K = 8, and K = 10, OMP outperformed BP and LASSO in terms …of successful reconstructions, demonstrating better performance and higher reconstruction precision. Show more
Keywords: Trusted artificial intelligence, distributed compressed sensing technology, sparse signal reconstruction algorithm, orthogonal matching pursuit, basis pursuit, least absolute shrinkage and selection operator
DOI: 10.3233/JIFS-234771
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4105-4118, 2024
Authors: Luo, Minxia | Gu, Xiaojing | Li, Wenling
Article Type: Research Article
Abstract: As the theory of picture fuzzy sets has been developed, more information in life can be expressed in mathematical terms. Similarity measure is a special tool for quantifying the similarity between two sets, so studying similarity measure on picture fuzzy sets has become a trending topic. This new research direction has drawn a great deal of attention from experts and has led to a number of important results which have led to significant results in a number of practical applications. By examining these new findings, we discovered that there are many studies on similarity measure of picture fuzzy sets, some …of them are deficient in solving certain problems, and such similarity measures can lead to the calculation of unreasonable data in practical applications, affecting the final results. Secondly, there is still room for research similarity measures on exponential functions. Considering these two aspects, we propose two new similarity measures based on exponential function, which not only satisfy the axiomatic definition of similarity measures, but also show reasonable computational results in practical applications. Show more
Keywords: Picture fuzzy set, similarity measure, pattern recognition, degree of confidence
DOI: 10.3233/JIFS-235571
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4119-4126, 2024
Authors: Mao, Cui
Article Type: Research Article
Abstract: With the acceleration of economic globalization, enterprises are facing fierce competition and huge challenges, requiring deep financial management transformation. In this context, the integration of industry and finance has gradually demonstrated extremely important value. The integration of industry and finance can not only effectively improve the efficiency of financial management, prevent business risks, and improve operational efficiency, but also enhance the comprehensive ability of enterprise financial management, providing a more flexible, transparent, and efficient financial management system for enterprises. The operational quality evaluation of industry-finance integration enterprises under lean management accounting is a multiple-attribute decision-making (MADM). In this paper, some …calculating laws on IVIFSs, Hamacher sum, Hamacher product are introduced, and the interval-valued intuitionistic fuzzy Hamacher interactive power averaging (IVIFHIPA) technique is proposed based on the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive weighted averaging (IVIFHIWA) technique and power average (PA) technique. Meanwhile, some ideal properties of IVIFHIPA technique are studied. Then, the IVIFHIPA technique is employed to cope with the MADM under IVIFSs. Finally, an example for operational quality evaluation of industry-finance integration enterprises under lean management accounting is employed to test the IVIFHIPA technique. Thus, the main research aim of this paper is concluded as follows: (1) the IVIFHIPA technique is constructed based on IVIFHIWA technique and classical power average (PA) technique; (2) the IVIFHIPA technique is put forward to cope with the MADM under IVIFSs; (3) an empirical example for operational quality evaluation of industry-finance integration enterprises under lean management accounting has been put forward to show the IVIFHIPA technique. Show more
Keywords: Multi-attribute decision making (MADM), Interval-valued intuitionistic fuzzy sets (IVIFSs), IVIFHIPA technique, operational quality evaluation
DOI: 10.3233/JIFS-235820
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4127-4146, 2024
Authors: Banitalebi, S. | Ahn, S.S. | Borzooei, R.A.
Article Type: Research Article
Abstract: Recently, the neutrosophic graph has been introduced as an extension of fuzzy graphs and intuitionistic fuzzy graphs, which offers more compatibility and flexibility than these two types in modeling and structuring many actual issues. In this article, using neutrosophic highly strong arc, the new notions of (totally) special irregular, highly special irregular, strongly special irregular, neighborly special irregular and special arc-irregular of neutrosophic graphs are stated. Finally, one of their utilizations relevant to offering a fixed optimization model in decision making in diverse conditions is presented. In fact,we present a decision-making problem in real-world applied example which discusses the factors …influencing a companys efficiency. The presented model is, in fact, a factor-based model wherein the impact score of each factor is divided into two types of direct and indirect influences, in which the concept of neutrosophic special dominating set plays a significant role. Show more
Keywords: Neutrosophic graph, special irregular neurosophic graph, special homomorphism, special isomorphism
DOI: 10.3233/JIFS-221785
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4147-4157, 2024
Authors: Dong, Yumin | Che, Xuanxuan | Fu, Yanying | Liu, Hengrui | Sun, Lina
Article Type: Research Article
Abstract: Previously, single classification models were mainly studied to classify human protein cell images, i.e., to identify a certain protein based on a set of different cells. However, a classifier can identify only one protein, in fact, a single cell usually consists of multiple proteins, and the proteins are not completely independent of each other. In this paper, we build a human protein cell classification model by multi-label learning. The logical relationship and distribution characteristics among the labels are analyzed to determine the different proteins contained in a set of different cells (i.e., containing multiple elements in the output space). In …this paper, using human protein image data, we conducted comparison experiments on pre-trained Xception and InceptionResnet V2 to optimize the two models in terms of data augmentation, channel settings, and model structure. The results show that the Optimized InceptionResnet V2 model achieves high performance in the classification task. The final accuracy of the Optimized InceptionResnet V2 model we obtained reached 96.1%, which is a 2.82% improvement relative to that before the optimized model. Show more
Keywords: Human protein atlas images data set, multi-label learning, deep convolutional neural network
DOI: 10.3233/JIFS-223464
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4159-4172, 2024
Authors: Kamber, Eren | Baskak, Murat
Article Type: Research Article
Abstract: In this study, it is aimed to integrate CODAS method with circular intuitionistic fuzzy sets as a new solution method for MCDM problems. Containing a radius notation with degrees of central membership and non-membership degrees is the main advantage of circular intuitionistic fuzzy in decision making. On the other side, Combinative Distance-based Assessment (CODAS) method contains many advantages such as basing on two types of distance calculations (Euclidean and Taxicab distances) comparing with other MCDM methods. When the advantages of circular intuitionistic fuzzy sets and CODAS method are considered, proposed circular intuitionistic fuzzy CODAS method (CIFS-CODAS) presents many superiorities compared …to other MCDM techniques. By this way, an application for green logistics park location selection will be handled by using CIFS-CODAS to show the validity of the methodology. After, a comparative analysis with intuitionistic fuzzy CODAS (IFS-CODAS), intuitionistic fuzzy TOPSIS (IFS-TOPSIS) and intuitionistic fuzzy EDAS (IFS-EDAS) methods will be performed for green logistics park location selection problem to confirm the robustness of presented method. Green logistics and Green Deal are also emphasized considering environmental factors as a scope of the article. Finally, the results will be evaluated in the context of the logistics sector and green logistics. Show more
Keywords: Green logistics, circular intuitionistic fuzzy sets, fuzzy, CODAS method, location selection
DOI: 10.3233/JIFS-231843
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4173-4189, 2024
Authors: Tamizharasi, A. | Ezhumalai, P.
Article Type: Research Article
Abstract: A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm …(WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost. Show more
Keywords: Context encoder, pre-processing, FCM, WOA, HWOCSA
DOI: 10.3233/JIFS-232700
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4191-4207, 2024
Authors: Dong, Yue-Fang | Fu, Wei-wei | Zhou, Zhe | Shi, Guo-Hua
Article Type: Research Article
Abstract: Relative pupillary afferent disorder (RAPD) plays a crucial role in diagnosing optic nerve dysfunction. This paper introduces an innovative equipment design with a high-speed pupil detection algorithm and a binocular independent stimulation optical path. The proposed algorithm utilizes the grayscale characteristics of the pupil region to achieve rapid and accurate pupil detection and tracking. Initially, a pupil threshold is estimated using eigenvalues, enabling the calculation of the pupil centroid. Subsequently, leveraging the unique characteristics of the pupil region, a dynamic tracking algorithm, a second-order partial derivative threshold algorithm, and a pupil diameter extraction algorithm are employed to precisely locate the …centroid. By incorporating a binocular independent stimulus light path design, the algorithm overcomes limitations associated with the current measurement equipment. The experimental results demonstrate the algorithm’s high robustness and fast detection speed, meeting the tracking speed requirement of 1250 frames per second for a single eye. These advancements have the potential to significantly enhance the diagnosis and assessment of optic nerve dysfunction. Show more
Keywords: RAPD, pupil detection, gray level features, dynamic tracking
DOI: 10.3233/JIFS-232752
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4209-4218, 2024
Authors: Ma, Xiaoqin | Liu, Jianming | Wang, Pei | Yu, Wenchang | Hu, Huanhuan
Article Type: Research Article
Abstract: Feature selection can remove data noise and redundancy and reduce computational complexity, which is vital for machine learning. Because the difference between nominal attribute values is difficult to measure, feature selection for hybrid information systems faces challenges. In addition, many existing feature selection methods are susceptible to noise, such as Fisher, LASSO, random forest, mutual information, rough-set-based methods, etc. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. Firstly, a new distance incorporating decision attributes is defined, and then a relation between fuzzy evidence theory and fuzzy β covering with an anti-noise …mechanism is established. Based on fuzzy belief and fuzzy plausibility, two robust feature selection algorithms for hybrid data are proposed in this framework. Experiments on 10 datasets of various types have shown that the proposed algorithms achieved the highest classification accuracy 11 times out of 20 experiments, significantly surpassing the performance of the other 6 state-of-the-art algorithms, achieved dimension reduction of 84.13% on seven UCI datasets and 99.90% on three large-scale gene datasets, and have a noise tolerance that is at least 6% higher than the other 6 state-of-the-art algorithms. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability. Show more
Keywords: Feature selection, fuzzy β covering, fuzzy belief, fuzzy plausibility, hybrid information systems
DOI: 10.3233/JIFS-233070
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4219-4242, 2024
Authors: Lu, Tianjun | Zhong, Xian | Zhong, Luo
Article Type: Research Article
Abstract: Convolutional neural networks (CNNs) have received significant attention for change detection (CD) on multimodal remote sensing images, but they struggle to capture global cues due to the locality of convolution operations. In contrast, the transformer can learn global semantic information by dividing the input image into patches, adding position encodings, and utilizing the self-attention mechanism. Motivated by this, we propose mSwinUNet, a novel end-to-end multi-modal model with swin-transformer-based and U-shaped siamese network architectures for supervised CD using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Imager (MSI) data. mSwinUNet contains multi-modal encoder with difference module, bottleneck, and fused decoder, and …all of them are based on swin transformer. Firstly, tokenized multi-modal bitemporal image patches are fed into multiple Siamese encoder branches to extract multi-level multi-modal difference feature maps in parallel. Subsequently, the last level multi-modal difference maps are fused to generate the smallest scale change map in the bottleneck. Then, the hierarchical decoder incorporates patch expansion and fusion operations to fuse multi-scale difference and change maps, effectively recuperating the details of the change information. Finally, the last patch expansion and a linear projection are applied to output the final change map, which preserves the identical spatial resolution as the input image. Extensive experiments have shown that mSwinUNet outperforms several the state-of-the-art multi-modal CD methods on OSCD dataset and the corresponding Sentinel-1 SAR data. Show more
Keywords: Change detection (CD), multi-modal siamese network, swin transformer, remote sensing image
DOI: 10.3233/JIFS-233868
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4243-4252, 2024
Authors: Chen, Wenda | Shi, Cao
Article Type: Research Article
Abstract: Accurate segmentation of knee cartilage in MR images is crucial for early diagnosis and treatment of knee conditions. Manual segmentation is time-consuming, leading researchers to explore automatic deep learning methods. However, the choice between 2D and 3D networks for organ segmentation remains debated. In this paper, we propose a hybrid 2D and 3D deep neural network approach, named UVNet, which combines the strengths of both techniques to enhance segmentation performance. Within this network structure, the 3D segmentation network serves as the backbone for feature extraction, while the 2D segmentation network functions as an information supplement network. Local and global MIP …images are generated by employing various maximum intensity projection modes of knee MRI volumes as input for the information supplement network. By constructing a local and global MIP feature fusion module, the supplementary information obtained from the 2D segmentation network is fully integrated into the backbone network. We assess the quality of the proposed method using the Osteoarthritis Initiative (OAI) dataset and the 2010 Grand Challenge Knee Image Segmentation (SKI-10) dataset, comparing it to the Baseline Network and other advanced 2D and 3D segmentation methods. The experiments demonstrate that UVNet achieves competitive performance in the aforementioned two cartilage segmentation tasks. Show more
Keywords: Convolutional neural network, maximum intensity projection, segmentation of knee cartilage
DOI: 10.3233/JIFS-234050
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4253-4264, 2024
Authors: Wu, Rong | Yu, Long | Tian, Shengwei | Long, Jun | Zhou, Tiejun | Wang, Bo
Article Type: Research Article
Abstract: Event Detection (ED) has long struggled with the ambiguous definition of event categories, making it challenging to accurately classify events. Previous endeavors aimed to tackle this problem by constructing prototypes for specific event categories. However, they overlooked potential correlations among instances of distinct event categories, resulting in trigger misclassifications across event types. In this research, we introduce KEPA-CRF to train enhanced event prototypes and address the issue of limited samples in few-shot event detection. By integrating external knowledge from the Glove knowledge base into the model training process, we augment synonymous examples, mitigating the problem of insufficient samples in few-shot …event detection. Additionally, through prototype adversarial training, we contrast prototypes of different event categories to augment the representational capabilities of prototype vectors. Experimental results showcase that our approach attains superior performance on the benchmark dataset FewEvent, surpassing comparative models with reduced training time. Show more
Keywords: Few-shot event detection, PA-CRF, Contrast Learning, Glove
DOI: 10.3233/JIFS-234368
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4265-4275, 2024
Authors: Sikkandar, Mohamed Yacin | Sabarunisha Begum, S. | Algamdi, Musaed Saadullah | Alanazi, Ahmed Bakhit | Alotaibi, Mashhor Shlwan N. | Alenazi, Nadr Saleh F. | AlMutairy, Habib Fallaj | Almutairi, Abdulaziz Fallaj | Almutairi, Mohammed Sulaiman
Article Type: Research Article
Abstract: Alzheimer’s disease (AD) is the predominant aetiology of dementia among the elderly population, accounting for about 60–70% of all instances of cognitive decline. Diffusion tensor imaging (DTI) is a contemporary methodology that enables the cartography of alterations in the microstructure of white matter (WM) in neurological diseases. Nevertheless, the effort of analysing substantial amounts of medical pictures poses significant challenges, prompting researchers to shift their focus towards machine learning. This approach encompasses a collection of computer algorithms that possess the ability to autonomously adjust their output to align with the desired goal. This work proposed the use of a combined …approach using Hidden Markov Model (HMM) and MR-DTI, where Diffusion Tensor Imaging (DTI) is employed as a magnetic resonance imaging technique. The purpose of this method is to forecast the occurrence of AD. Furthermore, the statistical analysis demonstrated a significant correlation between microstructural WM changes with both output in the patient groups and cognitive functioning. This finding suggests that these abnormalities in WM might potentially serve as a biomarker for AD. The proposed method is named as Graphcut Hidden MorkovModel (Graph_HMM) is evaluated on ADNI database with statistical analysis and found that it achieves 99.8% of accuracy, 96.4% of sensitivity, 97.4% of specificity and 12.3% of MSE. Show more
Keywords: Hidden Morkov Model, Alzhemier disease, prediction, segmentation, diffusion tensor imaging (DTI), statistical analysis
DOI: 10.3233/JIFS-234613
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4277-4289, 2024
Authors: Chinnamuniyandi, Maharajan | Chandran, Sowmiya | Xu, Changjin
Article Type: Research Article
Abstract: This research investigates the presence of unique solutions and quasi-uniform stability for a class of fractional-order uncertain BAM neural networks utilizing the Banach fixed point concept, the contraction mapping principle, and analysis techniques. In order to guarantee the equilibrium point of fractional-order BAM neural networks with undetermined parameters, some new adequate criteria are devised, and both time delays result in quasi-uniform stability. The acquired results, which are simple to verify in practice, enhance and extend several earlier research works in some ways. Finally, two illustrative examples are provided to show the value of the suggested outcomes.
Keywords: BAM neural networks, quasi-uniform stability, caputo fractional-order differential equation, uncertain parameters, linear matrix inequality
DOI: 10.3233/JIFS-234744
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4291-4313, 2024
Authors: Saranya, K. | Paulraj, M. | Hema, C.R. | Nithya, S.
Article Type: Research Article
Abstract: Exploring and finding Significant features for colour visualization tasks using the EEG signals is crucial in developing a robust Brain-machine Interface (BMI). The visually evoked potential carries multiple pieces of information, and finding its best feature is a tedious task. The main objective of this research is to concentrate on various linear and non-linear features which classifies the visually evoked potential when visualizing various colours for a certain period with reduced computational time and with higher accuracy. The feature extraction techniques utilized for extracting the features of EEG signals while visualizing various colours are Power Spectral Intensity (PSI), Spectral Entropy …(SE), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA). The extracted features were classified using the Multiclass classifier using one vs rest technique Support Vector Machine algorithm. The result shows that the MFDFA method with multiclass classifier combination has achieved 97.4 percent of classification accuracy when compared with other features. Show more
Keywords: Electroencephalogram (EEG), Brain Machine Interface (BMI), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA)
DOI: 10.3233/JIFS-235469
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4315-4324, 2024
Authors: Ma, Xiuqin | Sun, Huanling | Qin, Hongwu | Wang, Yibo | Zheng, Yan
Article Type: Research Article
Abstract: When handling complex uncertainty information for multi-attribute decision-making (MADM) problems, interval-valued Fermatean fuzzy sets (IVFFSs) are a novel and powerful tool with a wide range of prospective applications. However, existing MADM methods based on IVFFS ignore associations between attributes and are vulnerable to extreme values. Thus, this research proposes a novel MADM method based on IVFFSs. First, taking into consideration attribute relationships, we propose interval-valued Fermatean fuzzy Bonferroni mean (IVFFBM) operators and interval-valued Fermatean fuzzy weighted Bonferroni mean (IVFFWBM) operators based on IVFFSs. Further, interval-valued Fermatean fuzzy power Bonferroni mean (IVFFPBM) operator and interval-valued Fermatean fuzzy weighted power Bonferroni mean …(IVFFWPBM) operator are suggested considering the impact of extreme values. Secondly, Attribute weights are a key component of MADM. A novel method for determining attribute weights based on fuzzy entropy is developed. Finally, a novel MADM approach is proposed based on the proposed operator and weight determination method. Experimental results on one real-life case demonstrate the superiority and effectiveness of our method. Show more
Keywords: Interval-valued fermatean fuzzy set, bonferroni mean operator, multi-attribute decision making
DOI: 10.3233/JIFS-235495
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4325-4345, 2024
Authors: Gao, Wenlong | Zhi, Minqian | Ke, Yongsong | Wang, Xiaolong | Zhuo, Yun | Liu, Anping | Yang, Yi
Article Type: Research Article
Abstract: Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the …mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy. Show more
Keywords: Keywords. Bayesian network, structure learning, HC algorithm, PSO algorithm, DE algorithm
DOI: 10.3233/JIFS-236454
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4347-4359, 2024
Authors: Guo, Hairu | Wang, Jin’ge | Liu, Yongli | Zhang, Yudong
Article Type: Research Article
Abstract: The Aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm’s convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO’s …viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO’s robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design. Show more
Keywords: Aquila optimization algorithm, optimization function, kapur entropy, threshold optimization, pressure vessel design
DOI: 10.3233/JIFS-236804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4361-4380, 2024
Authors: Nippatla, V. Ramanaiah | Mandava, Srihari
Article Type: Research Article
Abstract: The main contribution of this review work is to show how various control techniques are used to manage the speed of Permanent Magnet Synchronous Motor (PMSM). The PMSM’s are mostly used in electric vehicles, electric traction and high performance industrial drive applications. In this article conventional sensorless techniques are compared with machine learning techniques such as fuzzy logic, artificial neural network and neuro-fuzzy controllers to control the speed of PMSM drive based on vector control approach. The benefits of machine learning techniques used in sensorless PMSM drive are easy to design, less execution time and fast access speed control. The …various controlling techniques used in controller along with its complexity, advantages and drawbacks are discussed in this article. The above mentioned controlling techniques are implemented and simulated by using MATLAB R2019b/Simulink software based on sensorless Model Reference Adaptive System (MRAS) with the help of Field Oriented Control (FOC) strategy of PMSM drive. By comparing the all sensorless controlling techniques in simulation study, it is identified that the combination of neuro-fuzzy controller gives the best speed control performance than other controllers. Show more
Keywords: Field oriented control, fuzzy logic control, neuro-fuzzy control, PMSM drive, sensorless control
DOI: 10.3233/JIFS-222164
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4381-4395, 2024
Authors: Chen, Siting | You, Cuiling | Wu, Nan | Huang, Yan
Article Type: Research Article
Abstract: Cross-efficiency evaluation is an extension of data envelopment analysis (DEA), which can effectively distinguish between decision-making units (DMUs) through self- and peer-evaluation. The cross-efficiency of each DMU in a set of DMUs is measured in terms of intervals when the input–output data are represented by the number of intervals. Based on the interval cross-efficiency matrix, the interval entropy is defined in terms of the likelihood. Then, considering the influence of peer evaluation, the interval conditional cross-efficiency entropy is proposed and an aggregation model of the interval conditional cross-efficiency entropy is presented to create a ranking index for DMUs. Finally, a …simple example is provided to illustrate the effectiveness of the proposed method, which is applied to the evaluation of forest carbon sink efficiency in China. The results indicate that the final cross-efficiencies of all 30 provinces range from 0 to 0.6. Among these provinces, those with a relatively high efficiency include Guangdong, Guizhou, Hainan, Shandong, and Qinghai. Show more
Keywords: Data envelopment analysis, interval data, cross-efficiency, entropy, likelihood
DOI: 10.3233/JIFS-223071
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4397-4415, 2024
Authors: Huang, Zhengwei | Liu, Huayuan | Duan, Chen | Min, Jintao
Article Type: Research Article
Abstract: In the E-commerce environment, conversations between customers and businesses contain lots of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. For conversational sentiment analysis, most existing approaches take contextual information into account. On this basis, we focus on the degree of association between utterances, which can more effectively capture overall and useful sentiment information in conversation. For this purpose, we propose a hybrid model to recognize customer sentiment in conversation. The model obtains utterance vectors with sentiment information through Sentiment Knowledge …Enhanced Pre-training (SKEP), then uses the bidirectional long short-term memory network (BiLSTM) to generate contextual semantic information, and further obtains customer sentiment information by applying the self-attention mechanism to focus on the degree of association between utterances. The experimental results on the JD Dialog dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation. Show more
Keywords: Customer sentiment recognition, bidirectional long short-term memory network, self-attention mechanism, sentiment knowledge enhanced pre-training
DOI: 10.3233/JIFS-224183
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4417-4428, 2024
Authors: Ghiduk, Ahmed S. | Hashim, Marwa
Article Type: Research Article
Abstract: Mutation testing can evaluate the quality of the test inputs, generate test data, and simulate any test coverage criterion. Genetic algorithms and harmony search have been applied to reduce the cost of generating test inputs. Although hybridizing search algorithms enhances the efficiency of searching the solution domain, there is a shortage of applying the hybrid search techniques in mutation testing. This paper merges the genetic and harmony search algorithms to effectively generate test data to kill higher-order mutants. In addition, the performance of the proposed technique will be evaluated and compared with a stand-alone genetic algorithm and a stand-alone harmony …search algorithm through an empirical study using a set of benchmark programs. The experimental study shows that the proposed technique outperformed the compared algorithms, reaching a higher killing ratio, where the proposed approach kills 92.8% of higher-order mutants for all tested programs. In comparison, GA kills 88.7%, and HA kills 86.6%. Besides, the proposed algorithm overcame the compared algorithm in reaching a targeted killing ratio faster than the compared algorithms. HGA reduced the execution time for each program with a reduction ratio ranging from 58.9% to 89.8%. Show more
Keywords: Genetic algorithm, harmony search algorithm, higher-order mutation testing, test-data generation
DOI: 10.3233/JIFS-230226
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4429-4452, 2024
Authors: Liu, Xia | Chen, Benwei
Article Type: Research Article
Abstract: This paper defines an improved similarity degree based on inclusion degree as well as advanced information system based on interval coverage and credibility, and thus an attribute reduction framework embodying 4×2 = 8 reduct algorithms is systematically constructed for application and optimization in interval-valued decision systems. Firstly, a harmonic similarity degree is constructed by introducing interval inclusion degree and harmonic average mechanism, which has better semantic interpretation and robustness. Secondly, interval credibility degree and coverage degree are defined for information fusion, and they are combined to propose a δ -fusion condition entropy. The improved condition entropy achieves the information reinforcement and integrity …by dual quantization fusion of credibility and coverage, and it obtains measure development from granularity monotonicity to non-monotonicity. In addition, information and joint entropies are also constructed to obtain system equations. Furthermore, 8 reduct algorithms are designed by using attribute significance for heuristic searches. Finally, data experiments show that our five novel reduct algorithms are superior to the three contrast algorithms on classification performance, which also further verify the effectiveness of proposed similarity degree, information measures and attribute reductions. Show more
Keywords: Attribute reductions, interval-valued decision systems, information measurements, δ-fusion condition entropy, harmonic similarity degree, interval coverage degree and credibility degree
DOI: 10.3233/JIFS-231950
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4453-4466, 2024
Authors: Zhang, Xiang | Huang, Jianhua | Fang, Liting | Li, Qian
Article Type: Research Article
Abstract: Selecting suppliers for prefabricated components (PCs) involves a complex decision-making process, frequently relying on ambiguous information and subjective judgment. However, most existing methods use precise values to portray indicator information and overlook the uncertainty of weights and the subjective preferences of decision-makers (DMs). In order to address these limits, this paper proposes a novel approach to select suppliers of PCs. Initially, an evaluation index system for suppliers is established through literature analysis and a questionnaire survey. The system comprises six layers: product quality, price, service level, comprehensive ability, supply ability, and environmental sustainability. The group decision matrix is then constructed …using the set-valued statistical method and the prospect theory. The index weights are determined by a combination weighting method. Next, the cobweb model is introduced to analyze the disparity between the alternative and ideal solutions, describing their similarities in terms of area and shape. Lastly, cobweb similarity is employed instead of comprehensive distance, combined with the minimum sum of squares criterion, to improve the closeness algorithm and contrast the alternatives. The results demonstrate that this method facilitates a comprehensive evaluation of the benefits and drawbacks of various alternatives from diverse perspectives. Furthermore, it allows flexible adjustments based on the risk preferences of DMs, ensuring accurate and reliable decision results. Show more
Keywords: Select suppliers, risk preference, prospect theory, cobweb model, cobweb similarity
DOI: 10.3233/JIFS-232027
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4467-4479, 2024
Authors: Xu, Yi | Zhou, Meng
Article Type: Research Article
Abstract: As an important extension of classical rough sets, local rough set model can effectively process data with noise. How to effectively calculate three approximation regions, namely positive region, negative region and boundary region, is a crucial issue of local rough sets. Existing calculation methods for approximation regions are based on conditional probability, the time complexity is O (|X ||U ||C |). In order to improve the computational efficiency of three approximation regions of local rough sets, we propose a double-local conditional probability based fast calculation method. First, to improve the computational efficiency of equivalence class, we define the double-local equivalence …class. Second, based on the double-local equivalence class, we define the double-local conditional probability. Finally, given the probability thresholds and a local equivalence class, the monotonicity of double-local conditional probability is proved, on this basis, a double-local conditional probability based fast calculation method for approximation regions of local rough sets is proposed, and the time complexity is O (MAX (|X |2 |C |, |X ||X C ||C |)). Experimental results based on 9 datasets from UCI demonstrate the effectiveness of the proposed method. Show more
Keywords: Local rough sets, approximation regions, double-local equivalence class, double-local conditional probability
DOI: 10.3233/JIFS-232767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4481-4493, 2024
Authors: Shi, Dingpu | Zhou, Jincheng | Wu, Feng | Wang, Dan | Yang, Duo | Pan, Qingna
Article Type: Research Article
Abstract: How to better grasp students’ learning preferences in the environment of rapid development of engineering and science and technology so as to guide them to high-quality learning is one of the important research topics in the field of educational technology research today. In order to achieve this goal, this paper utilizes the LDA (Latent Dirichlet Allocation) model for text mining of the survey results on the basis of a survey on students’ self-perception evaluation. The results show that the LDA model is capable of extracting terms from text, fuzzy identifying groups of students at different levels and presenting potential logical …relationships between the groups, and further analyzing the learning preferences of students at different levels for IT courses. Based on the student’s learning needs, this paper proposes recommendations for developing students’ learning effectiveness. The LDA method proposed in this paper is a feasible and effective method for assessing students’ learning dynamics as it generates cognitive content about students’ learning and allows for the timely discovery of students’ learning expectations and cutting-edge dynamics. Show more
Keywords: Latent Dirichlet Allocation model, educational data mining, self-perceptions, network modeling
DOI: 10.3233/JIFS-232971
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4495-4509, 2024
Authors: Wang, Hui | Liu, Ensheng | Wei, Hokai
Article Type: Research Article
Abstract: A machine for tunnel boring machine (TBM ) is recognized as productive equipment for tunnel construction. A dependable and precise tunnel boring machine’s performance (such as penetration rate (ROP )) prediction could reduce the cost and help choose the suitable construction method. Hence, this research develops new integrated artificial intelligence methods, i.e., biogeography-based multilayer perceptron neural network (BMLP ) and biogeography-based support vector regression (BSVR ), to forecast TBM PR . Using the biogeography-based optimization (BBO ) algorithm aims to improve the developed model’s performance by determining the optimized neuron number of hidden layers for MLP models and the …ideal values of the essential variables of SVR method. The results show that advanced methods can productively make a nonlinear relation among the ROP and its forecasters to obtain a satisfying forecast. Amongst the BMLP models with several hidden substrates, BM 5L with five hidden substrates could attain the total ranking score (TRS ) greatest rate, with root mean squared error (RMSE ) and coefficient of determination (R 2 ) equal to 0.017 and 0.9969. Simultaneously, the BSVR was the supreme model because of the fewer RMSE (0.00497 m /hr ) and a larger R 2 (0.999) compared with BMLP models. Overall, the acquired TRS s show that the BSVR outperforms the BMLP in terms of performance. As a consequence, the BSVR model may have been chosen as the suggested model if it had been able to accurately forecast the observed value even better than BM 5L . Show more
Keywords: Tunnel boring machine, penetration rate, biogeography-based multilayer perceptron neural network (BMLP), biogeography-based support vector regression (BSVR)
DOI: 10.3233/JIFS-232989
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4511-4528, 2024
Authors: Lin, Xiangyi | Luo, Hongyun | Lian, Yinghuan
Article Type: Research Article
Abstract: This research mainly evaluates the synergistic effect of “dual carbon” and high-quality economic development from four aspects: carbon reduction, pollution reduction, green expansion, and economic growth. Firstly, an indicator system of synergistic effect evaluation is constructed, and a FOPA-Cloud evaluation model is proposed based on the FOPA (Fuzzy Ordinal Priority Approach) and Cloud model. Based on the evaluation of experts’ language variables, it is calculated that a province’s “dual carbon” and high-quality economic development generally belong to a high-level synergistic effect. However, further improvement is still needed in reducing carbon, pollution reduction, and green expansion. The tedious work of pairwise …comparison can be overcome in the FOPA-Cloud model. Optimizing and solving to determine the weight of each indicator can not only determine the overall level but also analyze specific reasons, which can provide a basis for improving the synergistic effect of “dual carbon” and high-quality economic development. Show more
Keywords: Carbon peaking and carbon neutrality, high quality development, fuzzy ordinal priority approach, cloud model
DOI: 10.3233/JIFS-233119
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4529-4541, 2024
Authors: Yu, Ming | Liu, Jiali | Liu, Yi | Yan, Gang
Article Type: Research Article
Abstract: Most existing RGB-D salient object detection (SOD) methods extract features of both modalities in parallel or adopt depth features as supplementary information for unidirectional interaction from depth modality to RGB modality in the encoder stage. These methods ignore the influence of low-quality depth maps, and there is still room for improvement in effectively fusing RGB features and depth features. To address the above problems, this paper proposes a Feature Interaction Network (FINet), which performs bi-directional interaction through feature interaction module (FIM) in the encoder stage. The feature interaction module is divided into two parts: depth enhancement module (DEM) filters the …noise in the depth features through the attention mechanism; and cross enhancement module (CEM) effectively interacts RGB features and depth features. In addition, this paper proposes a two-stage cross-modal fusion strategy: high-level fusion adopts the semantic information of high level for coarse localization of salient regions, and low-level fusion makes full use of the detailed information of low level through boundary fusion, and then we progressively refine high-level and low-level cross-modal features to obtain the final saliency prediction map. Extensive experiments show that the proposed model achieves better performance than eight state-of-the-art models on five standard datasets. Show more
Keywords: RGB-D salient object detection, feature interaction, depth enhancement module, cross enhancement module, cross-modal fusion
DOI: 10.3233/JIFS-233225
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4543-4556, 2024
Authors: Jiang, Guangtian | Song, Anbin
Article Type: Research Article
Abstract: The dual probabilistic linguistic term sets (DPLTSs) are more effective than PLTSs in solving the problem of multi-attribute group decision-making (MAGDM). In this paper, an improved TOPSIS method is developed combining the TOPSIS method and projection measure of DPLTS to supplement the existing research. Firstly, considering the mathematical characteristics of DPLTS, this paper defines the concepts of the module, cosine function, and projection of DPLTS, and then proves the mathematical properties of the cosine function. Secondly, considering the uncertainty of decision-making problems, the weight-solving models are established respectively under the condition that the weight information is completely unknown and partially …known. Furthermore, a novel DPLPrj-TOPSIS approach is established based on the projection measure proposed. It involves integrating experts’ DPLTS evaluations, normalizing different DPLTSs, calculating alternatives’ relative closeness and score, etc. Secondly, the proposed method’s feasibility is demonstrated through a case study that entails selecting network promotion plans for food manufacturers. Finally, the proposed method’s effectiveness and validity are verified by comparing and analyzing it with the traditional TOPSIS method based on a distance measure and other existing decision methods. Show more
Keywords: Dual probabilistic linguistic term sets, multi-attribute group decision-making, technique for order preference by similarity to an ideal solution (TOPSIS), closeness degree
DOI: 10.3233/JIFS-233234
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4557-4572, 2024
Authors: Camgoz Akdag, Hatice | Menekse, Akin | Sahin, Fatih
Article Type: Research Article
Abstract: Cervical cancer is entirely preventable if diagnosed at an early stage; however, the current rate of cervical cancer screening participation is not very adequate, and early detection approaches are still open and demanding. Evaluating the risk levels of potential patients in a practical and economic way is crucial to direct risky candidates to screening and establishing potential treatments to conquer the disease. In this study, a machine learning-integrated fuzzy multi-criteria decision-making (MCDM) methodology is proposed to assess the cervical cancer risk levels of patients. In this context, based on behavioral criteria obtained from the publicly accessible cervical cancer behavior risk …data set from the UCI repository, the risk levels of patients are evaluated. The proposed methodology is established in three stages: In the first stage, using a machine learning technique, i.e., feature selection, the most effective criteria for predicting cervical cancer risk are selected. In the second stage, the criteria for importance through intercriteria correlation (CRITIC) method is used to assign objective importance levels to the criteria. In the third stage, the cervical cancer risk levels of candidate patients are prioritized using the technique for order preference by similarity to the ideal solution (TOPSIS) and, alternatively, the evaluation based on distance from the average solution (EDAS) techniques. The proposed methodology is developed in an interval-valued Pythagorean fuzzy atmosphere for quantifying the uncertainty in the nature of the problem. This study demonstrates that the feature selection algorithm can be efficiently utilized to determine the fundamental criteria of an MCDM problem and to aid in the early identification of cervical cancer. Show more
Keywords: Cervical cancer, machine learning, feature selection, pythagorean fuzzy, MCDM
DOI: 10.3233/JIFS-234647
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4573-4592, 2024
Authors: Shankari, R. | Leena Jasmine, J.S. | Mary Joans, S.
Article Type: Research Article
Abstract: Breast cancer poses a significant health risk for women, demanding early detection to mitigate its mortality impact. Leveraging the power of Deep Learning (DL) in medical imaging, this paper introduces a hybrid model that integrates YOLOv7 and Half UNet for feature extraction. YOLOv7 identifies and localizes potential cancerous regions, while Half UNet focuses on extracting pertinent features with its encoder-decoder structure. The fusion of these discriminative features, coupled with feature selection via Coati Optimization, ensures a comprehensive and optimized dataset. The selected features then feed into the CatBoost classification algorithm, refining parameters iteratively for precise predictions and minimizing the loss …function. Evaluation metrics, including precision, recall, specificity, and accuracy, demonstrate the model’s superior performance. Notably, the proposed model surpasses existing methods in early-stage breast cancer detection. Beyond numerical metrics, its significance lies in the potential to positively impact patient outcomes and increase survival rates. By amalgamating cutting-edge DL techniques, the model excels in identifying intricate patterns crucial for early cancer detection. The efficient fusion of YOLOv7 and Half UNet, coupled with feature optimization through Coati Optimization, sets this model apart. This research contributes to the evolving landscape of medical imaging and DL applications, emphasizing the potential for enhanced breast cancer diagnosis and improved patient prognoses. Show more
Keywords: Breast cancer prediction, YoloV7 model, HalfUNet feature extraction, feature Selection, cat Boost model, performance metrics
DOI: 10.3233/JIFS-235116
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4593-4607, 2024
Authors: Chen, Nongtian | Chen, Kai | Sun, Youchao
Article Type: Research Article
Abstract: The reliability level of general aviation fleet system directly affects the economic benefits and safe operation of general aviation fleet. In order to effectively evaluate the reliability level of general aviation fleet, using the entropy weight variable fuzzy recognition and 1D-CNN depth learning reliability evaluation method. Firstly, taking the Cessna 172 general aviation fleet as the research object, refers to the maintenance statistical analysis of general aviation fleet reliability data, and classifies the fleet reliability evaluation indexes according to the ATA100 chapter standard. Combined with index importance analysis and Delphi expert investigation, 14 key items are extracted as reliability evaluation …indexes of general aviation fleet. Secondly, using entropy weight method to obtain indexes weight objectively, and the evaluation level membership function is constructed based on variable fuzzy recognition method. Finally, a reliability evaluation model based on 1D-CNN deep learning method was established. Through training and testing the reliability data evaluation model of general aviation fleet, and comparing with the results of evaluation methods such as support vector machines. The results show that the recognition rate of the 1D-CNN deep learning method based on entropy weight variable fuzzy recognition can reach 91.95%, verifying the objective effectiveness of the evaluation method. Show more
Keywords: General aviation fleet, reliability evaluation, variable fuzzy recognition, 1D-CNN deep learning
DOI: 10.3233/JIFS-235280
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4609-4619, 2024
Authors: Ravindra Krishna Chandar, V. | Baskaran, P. | Mohanraj, G. | Karthikeyan, D.
Article Type: Research Article
Abstract: Unmanned robotics and autonomous systems (URAS) are integral components of contemporary Cyber-Physical Systems (CPS), allowing vast applications across many domains. However, due to uncertainties and ambiguous data in real-world environments, ensuring robust and efficient decision-making in URAS is difficult. By capturing and reasoning with linguistic data, fuzzy logic has emerged as a potent tool for addressing such uncertainties. Deep Iterative Fuzzy Pooling (DIFP) is a novel method proposed in this paper for improving decision-making in URAS within CPS. The DIFP integrates the capabilities of deep learning and fuzzy logic to effectively pool and aggregate information from multiple sources, thereby facilitating …more precise and trustworthy decision-making. This research presents the architecture and operational principles of DIFP and demonstrates its efficacy in various URAS scenarios through extensive simulations and experiments. The proposed method demonstrated a high-performance level, with an accuracy of 98.86%, precision of 95.30%, recall of 97.32%, F score of 96.26%, and a notably low false positive rate of 4.17%. The results show that DIFP substantially improves decision-making performance relative to conventional methods, making it a promising technique for enhancing the autonomy and dependability of URAS in CPS. Show more
Keywords: Unmanned robotics, autonomous systems, cyberphysical systems, decision-making, fuzzy logic, deep learning, iterative fuzzy pooling, information aggregation, uncertainty handling, reliability, autonomy
DOI: 10.3233/JIFS-235721
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4621-4639, 2024
Authors: Gao, Miaomiao
Article Type: Research Article
Abstract: To improve the effect of intelligent teaching in music classrooms, this paper combines the advanced music waveform iterative reconstruction algorithm to analyze the integration and reconstruction of the music curriculum. Aiming at the problem that the projection matrix occupies a large space and takes a long time to calculate in iterative reconstruction, a fast and real-time incremental method for generating a music wave matrix is proposed. The improved method avoids the judgment and comparison calculations performed by the incremental method when calculating the length and number of each voxel that the ray passes through. The research results show that the …music curriculum integration and reconstruction model based on the advanced music waveform iterative reconstruction algorithm can effectively improve the teaching effect of modern music classrooms. Show more
Keywords: Advanced iteration, reconstruction algorithm, music curriculum, integration, reconstruction
DOI: 10.3233/JIFS-236169
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4641-4655, 2024
Authors: Venkata Krishna, G.P.C. | Vivekananda Reddy, D.
Article Type: Research Article
Abstract: Ensuring data security in cloud computing is crucial due to the growing reliance on cloud-based services. Hybrid cryptography and image steganography have emerged as robust techniques to enhance data confidentiality in the cloud. In this research paper, we propose a novel algorithm, “Machine Learning-Enhanced Hybrid Cryptography and Image Steganography,” integrating these methods to provide comprehensive data protection. The algorithm employs key generation, encryption, steganography, cloud storage, data retrieval, and machine learning-based attack detection to defend against advanced cyber threats. Our experimentation demonstrates the algorithm’s effectiveness in detecting DoS attacks, data breaches, and data leakage attempts using SVM, Neural Network, Isolation …Forest, and Random Forest models. The proposed approach offers broad applicability, fortifying data security and fostering further advancements in cloud security research. Show more
Keywords: Data security, hybrid cryptography, security
DOI: 10.3233/JIFS-236229
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4657-4667, 2024
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