Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Pi, Feng | Tian, Shengwei | Pei, Xinjun | Chen, Peng | Wang, Xin | Wang, Xiaowei

Article Type: Research Article

Abstract: With the development of the Internet of Things (IoT), mobile devices are playing an increasingly important role in our daily lives. There are various malware threats present in these mobile devices, which can steal users’ personal information. Some malware exploits Inter-Component Communication (ICC) to execute malicious activities for unauthorized data access and system control, enabling communication between different components within an app and between different apps. In this paper, we propose an Adaptive Transformer-based malware framework (named AdaTrans) that combines sensitive Application Programming Interface (API)- and ICC-related features. The framework first extracts sensitive function call subgraphs (SFCS) to reflect the …caller-callee relationships, and then utilizes ICC interactions to reveal hidden communication patterns in malicious activities. Moreover, we propose a novel adaptive Transformer model to detect malicious behaviors. We evaluate our framework on real-world datasets and demonstrate that AdaTrans consistently outperforms other existing state-of-the-art systems. Show more

Keywords: Internet of things, ICC, Malware detection, transformer

DOI: 10.3233/JIFS-233556

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11439-11452, 2023

Authors: Lyu, Yucheng | Mo, Yuanbin | Yue, Songqing | Hong, Lila

Article Type: Research Article

Abstract: Optimization problems in the field of industrial engineering usually involve massive amounts of information and complex scheduling process with the characteristics of high-dimension and non-convexity, which bring many challenges to finding an optimal solution. We proposed an improved beetle swarm optimization (IBSO) algorithm demonstrating the potential to solve different problems of path planning in static environment with good performance. Firstly, the algorithm is an upgrade of the original beetle antennae search (BAS) algorithm and the search strategy is improved by replacing a single beetle by multiple beetles. Secondly, the global search ability gets enhanced, and the diversity of optimization is …improved through introducing nonlinear sinusoidal disturbance with Levy flight mechanism in beetles’ position. Finally, the search performance of beetle swarm is improved by simulating the characteristics of employment bees to search for a better solution near the honey source field in the Artificial Bee Colony (ABC) algorithm. Our experiment results show that IBSO algorithm can achieve higher search efficiency and wider search ranges through well balancing the advantages of local search and fast optimization of the BAS algorithm with the global search of the improved mechanism. The IBSO algorithm has shown the potential to provide a new solution for several optimization problems in path planning in static environment. Show more

Keywords: Path planning, beetle antennae search algorithm, Levy flight, artificial bee colony algorithm

DOI: 10.3233/JIFS-224163

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11453-11479, 2023

Authors: Zhang, Lei | Gu, Yue | Xia, Pengfei | Wei, Chuyuan | Yang, Chengwei

Article Type: Research Article

Abstract: Knowledge graphs are knowledge bases that represent entities and relations in the objective world through graph structure, and they have a boosting effect on many artificial intelligence tasks. To facilitate the development of downstream artificial intelligence tasks, knowledge graph embedding (KGE) is proposed. It aims to express semantic information for each entity and relation in the knowledge graph within a low-dimensional space. However, when it comes to semantic hierarchy, multiple relation patterns and multi-fold relational structures in knowledge graphs, most of the existing models tend to focus on only one or two aspects, often neglecting the importance of considering all …three simultaneously. Therefore, we propose a new knowledge graph embedding model, Hierarchical relation and Entity Rotation-based Multi-Feature knowledge graph Embedding (HERotMFE). Concerning hierarchical relation rotation and entity rotation, it can represent semantic hierarchy, multiple relation patterns and multi-fold relations simultaneously. Self-attention mechanism is used to learn the weights of the two-part rotation to further enhance the model’s performance. According to the findings of the experiments, HERotMFE outperforms existing models on most metrics and achieves state-of-the-art results. Show more

Keywords: Knowledge graph embedding, multi-feature, multi-fold relation, hierarchical relation, self-attention mechanism

DOI: 10.3233/JIFS-231774

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11481-11493, 2023

Authors: Zhou, Xueling | Sun, Lei | Wei, Cuiping

Article Type: Research Article

Abstract: With the advancement of technology and growing social demand, large scale group decision making has gained significant importance in the field of decision making. Clustering analysis plays a crucial role in enhancing the efficiency of large scale group decision making processes. Linguistic evaluation is more in line with people’s cognitive and expressive habits. The hesitant fuzzy linguistic term set (HFLTS) offers more flexibility in expressing evaluation information. This paper is dedicated to designing a fuzzy C -means clustering model that is specifically applicable for the hesitant fuzzy linguistic preference relation (HFLPR). The objective function of the model is built based …on the dissimilarity between HFLPRs and the initial cluster centers to obtain the fuzzy membership matrix and cluster centers. Since initializing cluster centers is a crucial step to produce a reasonable cluster result, three methods are proposed for generating initial centers for HFLPRs. The first and second methods are improvements over existing approaches that dealt with the clustering problem with numerical values. The third approach considers both the preference relation of preferring an alternative and the distribution of the actual preference relations. Based on this, a fuzzy C -means clustering algorithm with HFLPR is designed to obtain the cluster centers and membership matrix for there types of initializing clustering centers. Finally, based on the quality and speed of clustering, a numerical example and comparative analyses illustrate that the proposed clustering algorithm is efficient and effective. Show more

Keywords: Large scale group decision making, Fuzzy C-means clustering, Hesitant fuzzy linguistic term set, Probabilistic linguistic term set

DOI: 10.3233/JIFS-224098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11495-11514, 2023

Authors: Muthulakshmi, V. | Hemapriya, N.

Article Type: Research Article

Abstract: The advent of deep learning techniques has ignited interest in medical image processing. The proposed work in this paper suggests one of the edge technologies in deep learning, which is recommended, based on a Radiomics feature extraction model for the effective detection of Kaposi sarcoma, a vascular skin lesion expression that indicates the most prevalent cancer in AIDS patients. This work investigates the role and impact of medical image fusion on deep feature learning based on ensemble learning in the medical domain. The model is crafted wherein the pre-built ResNet50 (Residual network) and Visual Geometry Group (VGG16) are fine-tuned and …an ensemble learning approach is applied. The pre-defined CNN was incrementally regulated to determine the appropriate standards for classification efficiency improvements. Our findings show that layer-by-layer fine-tuning can improve the performance of middle and deep layers. This work would serve the purpose of masking and classification of skin lesion images, primarily sarcoma using an ensemble approach. Our proposed assisted framework could be deployed in assisting radiologists by classifying Kaposi sarcoma as well as other related skin lesion diseases, based on the positive classification findings. Show more

Keywords: Kaposi sarcoma, vascular skin lesions, ensemble learning, ResNet50, VGG16, radiomics

DOI: 10.3233/JIFS-230426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11515-11534, 2023

Authors: Ma, Chao | Yager, Ronald R. | Liu, Jing | Yatsalo, Boris | Garg, Harish | Senapati, Tapan | Jin, LeSheng

Article Type: Research Article

Abstract: Uncertainty exists in numerous evaluation and decision making problems and therefore it also provides space for the subjective preferences of decision makers to affect the aggregation and evaluation results. Recently, relative basic uncertain information is proposed to further generalize basic uncertain information, but currently there is no research on how to apply this type of uncertainty in both theory and practices. There is also a paucity of decision methodology about how to build systematic preference involved decision model considering this new type of uncertainty. The relative basic uncertain information can serve as a general frame to enable the possibility for …simultaneously handling heterogeneous uncertain information including interval information, basic uncertain information, and relative basic uncertain information. Different types of bipolar subjective preferences commonly should be taken into consideration in practical decision making. With the individual heterogeneous uncertain information and the involved two types of subjective preferences, namely bipolar preferences for uncertainties and bipolar optimism-pessimism preferences, the evaluation and decision making become more complex. This work proposes a systematic intersubjective decision model which can effectively and reasonably deal with the decision scenario with such complex uncertainty, in which Yager preference induced weights allocation is applied. Some novel preference conversion and transformation functions, specified techniques, and the related decision making procedures and sub-modules are proposed and analyzed. An application is also presented to showthe practicality of the proposed decision models and related conversion and transformation functions. Show more

Keywords: Basic uncertain information, decision making, information fusion, relative basic uncertain information, uncertain decision making, Yager induced weights allocation

DOI: 10.3233/JIFS-231395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11535-11547, 2023

Authors: Gong, Kaixin | Ma, Weimin | Ren, Zitong | Wang, Jia

Article Type: Research Article

Abstract: Large-scale group decision-making (LSGDM) issues are increasingly prevalent in modern society across various domains. The preference information has emerged as a widely adopted approach to tackle LSGDM problems. However, a significant challenge lies in facilitating consensus among decision-makers (DMs) with diverse backgrounds while considering their hesitation and psychological behavior. Consequently, there is a pressing need to establish a novel model that enables DMs to evaluate alternatives with heterogeneous preference relations (HPRs). To this end, this research presents a new consensus-building method to address LSGDM problems with HPRs. First, a novel approach for solving collective priority weight is introduced based on …cosine similarity and prospect theory. In particular, a new cosine similarity measure is defined for HPRs. Subsequently, a consensus index is provided to gauge the consensus level among DMs by considering their psychological behavior and risk attitudes. Further, a consensus-reaching model is developed to address LSGDM with HPRs. Finally, an instance of supplier selection is presented to demonstrate the practicality and efficacy of the proposed method. Show more

Keywords: Large-scale group decision-making, prospect theory, heterogeneous preference relations, consensus reaching, risk attitudes

DOI: 10.3233/JIFS-231456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11549-11566, 2023

Authors: Huang, Feidan | Deng, Zexi | Cao, Fasheng

Article Type: Research Article

Abstract: Concepts of generalized commutative intuitionistic fuzzy finite state machines and generalized switching intuitionistic fuzzy finite state machines are proposed in this paper, and properties of these two kinds of intuitionistic fuzzy finite state machines are investigated. By using the conditions associated with generalized commutativity, a kind of congruences of intuitionistic fuzzy finite state machines is defined. Homomorphic properties of generalized commutative intuitionistic fuzzy finite state machine are also discussed. Moreover, products of generalized commutative intuitionistic fuzzy finite state machines and products of generalized switching intuitionistic fuzzy finite state machines are studied, and some related properties are proved.

Keywords: Intuitionistic fuzzy finite state machine, generalized commutativity, congruence, homomorphism, product

DOI: 10.3233/JIFS-231549

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11567-11583, 2023

Authors: Wang, Liming | Liu, Yingming | Pang, Xinfu | Wang, Qimin | Wang, Xiaodong

Article Type: Research Article

Abstract: A low-carbon economic scheduling method based on a Q-learning-based multiobjective memetic algorithm (Q-MOMA) is proposed to improve the economy of cogeneration system scheduling and reduce carbon emission. First, the model incorporates a carbon capture device, a heat storage device, and a demand response mechanism to enhance the system’s flexibility and wind power consumption. In addition, the Q-MOMA algorithm combines global and local search and uses a Q-learning algorithm to dynamically adjust the crossover and mutation probabilities to improve the algorithm’s searchability. Finally, the fuzzy membership function method is used to make a multiobjective decision, which balances the economy and low …carbon of the system, and a compromise scheduling scheme is given. The effectiveness of the proposed model and solution method is verified through the simulation calculation of the improved system and compared with the simulation results of various optimization algorithms. The simulation results show that the proposed model can improve the wind power consumption space and the system’s economy and reduce carbon emissions. The Q-MOMA algorithm has a relatively better optimization ability in the low-carbon economic scheduling of the cogeneration system. Show more

Keywords: Bi-objective optimization, carbon capture, demand response, memetic algorithm, Q-learning

DOI: 10.3233/JIFS-231824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11585-11600, 2023

Authors: Wenjun, Zhou | Jianmin, Ma

Article Type: Research Article

Abstract: In the speaker verification task based on Gaussian Mixture Model-Universal Background Model (GMM-UBM), by constructing the UBM as a tree structure, the kernel Gaussians suitable for different speakers can be quickly selected, which speeds up the modeling of speaker acoustic space by GMM. The tree-based kernel selection algorithm (TBKS) introduces a beam-width, which increases the candidate range of kernels and improves the kernel selection accuracy. In this paper, we improve the TBKS algorithm by introducing a recall rate to adjust the number of nodes recalled in each layer of the tree structure. This adjustment refines the quantity and resolution of …Gaussian distributions in various subspaces within the acoustic space, compensating for the loss caused by discarding some significant Gaussians erroneously. Speaker verification experiments are carried out based on the Aishell2 dataset. The results reveal that the modified TBKS algorithm reduces EER by 7.5% relatively and increses computational reduction factor to 42.93, enhancing both recognition accuracy and speed. In addition, the test speech is spliced into different lengths and common environmental noise is added to verify the universality of the improved algorithm. Show more

Keywords: Speaker verification, fast scoring, gaussian mixture model, tree-based kernel selection

DOI: 10.3233/JIFS-232304

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11601-11611, 2023

Authors: Sermakani, A.M. | Paulraj, D.

Article Type: Research Article

Abstract: The contemporary development of cloud is a next generation federated cloud technology envisioned by virtualization to enable cost-efficient usage of computing resources. The resources are intended on scalability as data grows enormously with on demand services. Federated cloud is an efficient networked computing environment that can adopt infrastructure which aims for virtual unlimited pool during on demand services. The challenging task for federated cloud includes managing workloads of individual cloud, progressing virtual machine volumes, cost utilization, fair load distribution. In order to addresses these challenges, this approach uses “Optimized Bit Matrix based Node Acquisition for Federated cloud (BMNF)”. The framework …process two different approaches: managing bit matrix and fulfilling load distribution in federated cloud based on cost aware workloads. The formation of bit matrix designed by each member in cloud services that validates load availability status. Load distribution factor concentrates on fair allocation with cost aware policy at individual level. BMNF policy segregates the request among various clouds by analyzing bits patterns. In addition to load distribution using bit matrix, it also focuses on improving cost utilization and targets with better quality of load distribution. The proposed working model is highly efficient with computation and communication overhead for federated cloud. Show more

Keywords: Cloud computing, load distribution, virtualization, federated cloud, virtual machine allocation, quality of service

DOI: 10.3233/JIFS-232897

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11613-11627, 2023

Authors: Minh, K.D. | Nguyen, X.H. | Nguyen, V.P.

Article Type: Research Article

Abstract: With the rapid expansion of artificial intelligence (AI) and machine learning, the evaluation of AI cloud platforms has become a critical research topic. Given the availability of many platforms, selecting the best AI cloud services that can satisfy the requirements and budget of an organization is crucial. Several solutions, each with its advantages and disadvantages, are available. In this study, a combinative-distance-based assessment approach was proposed in probabilistic linguistic hesitant fuzzy sets (PLHFSs) to accommodate the multiple characteristics of group decision-making. The original data were normalized using a standardized process that integrated numerous methodologies. Furthermore, under PLHFSs, the statistical variance …approach was used to generate the weighted objective of the vector of assessment criteria. Finally, an AI cloud platform evaluation and comparison analysis case study was used to validate the feasibility of this method. Show more

Keywords: Combinative-distance-based assessment (CODAS) method, probabilistic linguistic hesitant fuzzy sets (PLHFSs), AI cloud platform evaluation, multiple attribute decision-making (MADM), fuzzy environments

DOI: 10.3233/JIFS-232546

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11629-11646, 2023

Authors: Wang, Xin-Fan | Zhang, Li-Na | Zhou, Huan | Wang, Xue-Bin

Article Type: Research Article

Abstract: The intuitionistic uncertain linguistic information aggregation problems considering different priority levels of criteria are investigated. Firstly, we extended the prioritized averaging (PA) operator to intuitionistic uncertain linguistic environment, defined two new prioritized aggregation operators called the intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator and the intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator, and established various desirable properties of the proposed operators. Secondly, we developed a multi-criteria decision making (MCDM) approach based on the IULPWA operator (or the IULPWG operator) to deal with the MCDM problems in which the criterion values take the form of intuitionistic uncertain linguistic numbers …(IULNs) and the criteria are in different priority levels. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed method, and a comparison analysis is conducted to make clear the differences among the IULPWA operator, the IULPWG operator, the intuitionistic uncertain linguistic number weighted averaging (IULNWA) operator and the intuitionistic uncertain linguistic weighted geometric average (IULWGA) operator. Show more

Keywords: Multi-criteria decision making (MCDM), intuitionistic uncertain linguistic number (IULN), intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator, intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator

DOI: 10.3233/JIFS-223829

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11647-11661, 2023

Authors: Hajmirfattahtabrizi, Mahboobehalsadat | Feylizadeh, Mohammad Reza | Song, Huaming

Article Type: Research Article

Abstract: In the past two years, 2020-2022, the developing construction industry has been a huge issue according to the negative effect of Covid-19 with the increasing pandemic situation in cities and areas. In Covid-19 pandemic situation, the cement manufacturing industry has been crucial and needed more scrutiny. As cement is the second significant component after water in concrete and construction industry. Meanwhile, locating a cement plant in a special area of the city is challenging and affecting more by local communities and other involved environmental factors. The location selection decisions need to grow by environmental, economic, technical and social attributes. This …study aims to present the site suitability decisions through a case study of locating a new manufacturing plant for cement production in Tehran surrounding, Iran. In this process, some required technical and tactical criteria are deserved for evaluating and suitability of the plant through decision-makers for cement manufacturing. All the feasible industrial alternative locations were evaluated under various criteria and regarding the Covid-19 pandemic’s negative impact to identify the most appropriate location for the cement industry. The authors proposed two Multi-Criteria Decision Attributes (MCDA) methods of MacBeth and COmplex PRoportional ASsessment (COPRAS) to evaluate and select the most suitable location for site suitability of the cement plant in this problem. Though the MacBeth method does not need to calculate weights of the Geographical Information System (GIS) criteria, the COPRAS method determined and used BWM (Best-Worst Method) as the weighing method. In sum, the comparison of the two methods was obtained according to the given results and ranks of volunteer cement suppliers for site suitability of the cement plant. Show more

Keywords: Cement plant, site suitability, GIS, BWM, COPRAS, entropy, MacBeth

DOI: 10.3233/JIFS-224534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11663-11678, 2023

Authors: Prabakaran, G. | Jayanthi, K.

Article Type: Research Article

Abstract: Coronavirus 2019 (COVID-19) is a severe disease in respiratory syndrome. Early identification and efficient treatment of COVID-19 are not presented which provides ineffective treatment. This research develops an efficient system for early detection and segmentation of COVID-19 severity with the consideration of CT images. To overcome the abovementioned drawbacks, we develop the optimized Mask R-CNN method to train and test the dataset to classify and segment the COVID-19 disease. The proposed technique contains three phases which are, pre-processing, segmentation, and severity analysis. Initially, the patient’s CT images are collected from a different clinic. Then, the noise present in the images …is detached with a Gaussian filter. Then, the pre-processed images are given to the optimized mask region-based convolution neural network (OMRCNN) classifier to detect, classify and segment the image. After segmentation, the severity of the disease is examined. To enhance the performance of the mask RCNN classifier, the parameter is efficiently chosen by using the adaptive red deer algorithm. In the adaptive red deer algorithm, the levy flight is utilized to enhance the updating process. The performance of the proposed technique is analyzed based on various metrics. Show more

Keywords: COVID-19 segmentation, detection, recurrent neural network, gaussian filter adaptive red deer algorithm, and severity analysis

DOI: 10.3233/JIFS-230312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11679-11693, 2023

Authors: Gulistan, Muhammad | Pedrycz, Witold | Yaqoob, Naveed

Article Type: Research Article

Abstract: We explore switching techniques between q-fractional fuzzy sets (qFr sets) and various other classes of fuzzy sets to establish connections and provide a comprehensive framework. In particular, we examine the relationships between qFr sets and interval-valued fuzzy sets (IVFS), type 2 fuzzy sets(T2FS), intuitionistic fuzzy sets(IFS), Pythagorean fuzzy sets(PFS), q-rung orthopair fuzzy sets (q-ROFS), and linear diophantine fuzzy sets(LDFS). By examining these interconnections, we aim to understand better qFr sets and their applications in a wide range of fuzzy systems. It is possible to convert qFr sets into other fuzzy set models using the derived switching techniques, facilitating the utilization …of existing methods and algorithms. The versatility of qFr sets, combined with the bridging techniques presented, holds promise for addressing complex problems in decision-making, pattern recognition, and other applications where uncertainty and imprecision play significant roles. Through case studies and practical applications, we illustrate the effectiveness and usefulness of the proposed switching techniques, showcasing their potential impact on real-world scenarios. Show more

Keywords: q-fractional fuzzy sets, fuzzy set, interval-valued fuzzy sets, type 2 fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, q-rung orthopair fuzzy sets, linear diophantine fuzzy sets, switching techniques, uncertainty, imprecision

DOI: 10.3233/JIFS-233563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11695-11706, 2023

Authors: Ponniah, Krishna Kumar | Retnaswamy, Bharathi

Article Type: Research Article

Abstract: The Internet of Things (IoT) integrated Cloud (IoT-Cloud) has gotten much attention in the past decade. This technology’s rapid growth makes it even more critical. As a result, it has become critical to protect data from attackers to maintain its integrity, confidentiality, protection, privacy, and the procedures required to handle it. Existing methods for detecting network anomalies are typically based on traditional machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and so on. Although these methods can produce some outstanding results, they have low accuracy and rely heavily on manual traffic feature design, which has …become obsolete in the age of big data. To overcome such drawbacks in intrusion detection (ID), this paper proposes a new deep learning (DL) model namely Morlet Wavelet Kernel Function included Long Short-Term Memory (MWKF-LSTM), to recognize the intrusions in the IoT-Cloud environment. Initially, to maintain a user’s privacy in the network, the SHA-512 hashing mechanism incorporated a blockchain authentication (SHABA) model is developed that checks the authenticity of every device/user in the network for data uploading in the cloud. After successful authentication, the data is transmitted to the cloud through various gateways. Then the intrusion detection system (IDS) using MWKF-LSTM is implemented to identify the type of intrusions present in the received IoT data. The MWKF-LSTM classifier comes up with the Differential Evaluation based Dragonfly Algorithm (DEDFA) optimal feature selection (FS) model for increasing the performance of the classification. After ID, the non-attacked data is encrypted and stored in the cloud securely utilizing Enhanced Elliptical Curve Cryptography (E2 CC) mechanism. Finally, in the data retrieval phase, the user’s authentication is again checked to ensure user privacy and prevent the encrypted data in the cloud from intruders. Simulations and statistical analysis are performed, and the outcomes prove the superior performance of the presented approach over existing models. Show more

Keywords: Internet of Things (IoT), deep learning, cloud computing, data security, IoT authentication, intrusion detection system, Elliptical Curve Cryptography

DOI: 10.3233/JIFS-221873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11707-11724, 2023

Authors: Nunsanga, Morrel V.L. | Pakray, Partha | Devi, Toijam Sonalika | Singh, L. Lolit Kr

Article Type: Research Article

Abstract: The process of associating words with their relevant parts of speech is known as part-of-speech (POS) tagging. It takes a substantial amount of well-organized data or corpora and significant target language research to obtain good performance for a tagger. Mizo is a language that needs more research attention in computational linguistics due to its under-resourced nature. The limited availability of corpora and relevant literature adds complexity to the task of assigning POS labels to Mizo text. This paper explores two methods to potentially improve the Hidden Markov Model (HMM)-based POS tagger for the Mizo language. The proposed taggers are compared …with the baseline HMM tagger and the N-gram taggers on the designed Mizo corpus, which consists of 72,077 manually tagged tokens. The experimental results proved that the two proposed taggers enhanced the HMM-based Mizo POS tagger, achieving 81.52% and 84.29% accuracy, respectively. Moreover, a comprehensive analysis of the performance of the suggested hybrid tagger was conducted, yielding a weighted average precision, recall, and F1-score of 83.09%, 77.88%, and 79.64% respectively. Show more

Keywords: Hybrid POS tagger, rule-based POS tagger, N-gram tagger, Mizo POS tagger, Hidden Markov Model

DOI: 10.3233/JIFS-224220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11725-11736, 2023

Authors: Srihari, Pasala | Harikiran, Jonnadula | Sai Chandana, B. | Surendra Reddy, Vinta

Article Type: Research Article

Abstract: Recognizing human activity is the process of using sensors and algorithms to identify and classify human actions based on the data collected. Human activity recognition in visible images can be challenging due to several factors of the lighting conditions can affect the quality of images and, consequently, the accuracy of activity recognition. Low lighting, for example, can make it difficult to distinguish between different activities. Thermal cameras have been utilized in earlier investigations to identify this issue. To solve this issue, we propose a novel deep learning (DL) technique for predicting and classifying human actions. In this paper, initially, to …remove the noise from the given input thermal images using the mean filter method and then normalize the images using with min-max normalization method. After that, utilizing Deep Recurrent Convolutional Neural Network (DRCNN) technique to segment the human from thermal images and then retrieve the features from the segmented image So, here we choose a fully connected layer of DRCNN as the segmentation layer is utilized for segmentation, and then the multi-scale convolutional neural network layer of DRCNN is used to extract the features from segmented images to detect human actions. To recognize human actions in thermal pictures, the DenseNet-169 approach is utilized. Finally, the CapsNet technique is used to classify the human action types with Elephant Herding Optimization (EHO) algorithm for better classification. In this experiment, we select two thermal datasets the LTIR dataset and IITR-IAR dataset for good performance with accuracy, precision, recall, and f1-score parameters. The proposed approach outperforms “state-of-the-art” methods for action detection on thermal images and categorizes the items. Show more

Keywords: Human action recognition, deep recurrent convolutional neural network, thermal images, classification, CapsNet, feature extraction, DenseNet-169

DOI: 10.3233/JIFS-230505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11737-11755, 2023

Authors: Xiao, Jian | Meng, Linglong | Wu, Kaiyin

Article Type: Research Article

Abstract: A supplier portrait generation method based on Big data analysis and deep learning was proposed to help users make reasonable decisions in core links such as procurement and contract signing. This method establishes a label element analysis model for each level in the vertical label system of power supply enterprises, and divides it into target layer, standard layer, and solution layer based on the logic and attributes of the elements, and establishes a hierarchical structure. Compare the index labels of each level with the labels of the upper and lower levels by considering the logical relationship and correlation between each …level. Utilize deep learning algorithms to sort hierarchically, and use a multidimensional structural model to represent and fuse portrait labels of power supply enterprises. Based on the imaging results of supplier vertical rating, combined with objective factors such as material production cycle, supply cycle, market supply and demand, price fluctuations, etc., it helps power enterprises effectively predict the supplier’s performance ability. The simulation results show that the reliability of the power supply enterprise portrait generated by this method is high, and the credibility of the portrait identification system for all levels of power supply enterprises is high. This supplier portrait method can effectively improve the supplier management capabilities of power enterprises. Show more

Keywords: Deep learning BCCM, multi-aspect, electricity supplier, portrait generation, information management

DOI: 10.3233/JIFS-230722

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11757-11767, 2023

Authors: Zhu, Yiping | Huang, Jiajia | Zhu, Yi | Guo, Yang

Article Type: Research Article

Abstract: Online teaching platforms have developed into mainstream knowledge learning and exchange platform. The research on the quality evaluation of online teaching platforms and the construction of an applicable and scientific evaluation index system model can help explore the key factors affecting the quality of online teaching platforms and provide some references for evaluating online teaching platforms and improving online teaching quality. This study combines the rough set theory (RS) with the BP (Back Propagation) neural networks to build an RS-BP neural network model to evaluate the quality of online teaching platforms. Firstly, an initial online teaching platform quality evaluation index …system is constructed based on knowledge transfer theory from four aspects: course content, knowledge transmitter, knowledge receiver and teaching platform. Then, 12 core evaluation indicators were generated by attribute reduction using rough set theory, and the weights of each core indication were determined. The normalized data input was then trained, validated, and tested to generate a rough set neural network quality evaluation model for online teaching platforms. After that, three representative online education platforms of content, interaction and compatibility are selected for empirical research. The accuracy of the model is first tested by the error between the simulated and output values, after which the core metric scores and the overall scores are calculated for the three types of platforms. The empirical results demonstrate that the model has certain advantages in terms of index simplification and adaptive training when evaluating online teaching platforms, as well as strong operability and practicality. The evaluation results show that the content online teaching platform has the highest comprehensive score, followed by the compatible and interactive online teaching platforms. According to the index scores, the quality of the course content, stage assessments, and contact between professors and students were identified as major elements influencing the quality of the online teaching platform. Finally, suggestions for optimization for each of the three types of online teaching platforms were made based on the core indicators and their weights, as well as the scores and characteristics of the three types of online teaching platforms, with the goal of improving the quality of online teaching platforms. Show more

Keywords: Knowledge transfer, online teaching platform, rough set theory, BP neural network

DOI: 10.3233/JIFS-231381

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11769-11789, 2023

Authors: Wen, Shuting | Wen, Fangcheng

Article Type: Research Article

Abstract: Culture and tourism development through public services rely on accumulated big data and overall country/ province development. Accumulated data relies on various cultures, people, places, etc. attributes for which a heterogeneous and multi-faced analysis is required. This article introduces a Development-focused Data Handling Process (D-DHP) for providing insights through culture and tourism accumulated information. The proposed process relies on heterogeneous data attributes for identifying economic and society-based development stagnancies. The data analysis is performed for identifying missing sequences and invariable information that shows development stagnancies. The stagnancy rates between successive quarters (per annum) are accounted for identifying development drops. If …such drops are identified, the accumulated data outputs are analyzed through classification learning. In this classification, the development and drop-associated data are split for an independent analysis. This analysis helps to replace the mode of development focusing on tourism or culture or both based on dependability. The classification process is updated based on the replaced information for further improvements across various accumulated data inputs. Therefore, the proposed process is viable in identifying development-focused information from the accumulated data. Show more

Keywords: Big data, classification learning, culture and tourism, public services

DOI: 10.3233/JIFS-232318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11791-11806, 2023

Authors: Chen, Xin | Wang, Yan | Li, Fuzhen

Article Type: Research Article

Abstract: A singular system, assumed to possess both regularity and freedom from impulses, is categorized as a causal system. Noncausal systems (NSs) are a class of singular systems anticipated to exhibit regularity. This study focuses on investigating zero-sum games (ZSGs) in the context of NSs. We introduce recurrence equations grounded in Bellman’s optimality principle. The saddle-point solution for multistage two-player ZSGs can be obtained by solving these recurrence equations. This methodology has demonstrated its effectiveness in addressing two-player ZSGs involving NSs. Analytical expressions that characterize saddle-point solutions for two types of two-player ZSGs featuring NSs, encompassing both linear and quadratic control …scenarios, are derived in this paper. To enhance clarity, we provide an illustrative example that effectively highlights the utility of our results. Finally, we apply our methodology to analyze a ZSG in the realm of environmental management, showcasing the versatility of our findings. Show more

Keywords: Zero-sum game, noncausal system, saddle-point solution, recurrence equations

DOI: 10.3233/JIFS-232401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11807-11833, 2023

Authors: Sun, Xiujing

Article Type: Research Article

Abstract: With the rapid development and application of internet technology, cross-border e-commerce (CBEC) has begun to popularize globally and play an important role in China’s foreign trade. The Chinese government has successively introduced multiple policies and regulations to strongly support its rapid development. Compared to the booming trend of CBEC, the development of its supply chain is slightly lacking in momentum, which has formed a certain obstacle to the overall development of CBEC. The supply chain is the foundation of successful CBEC transactions, and the foundation of the supply chain is logistics. The primary task to improve the backwardness of supply …chain development is to solve logistics problems. Therefore, while enjoying the dividends brought by the rapid development of CBEC, international logistics enterprises should continuously improve their logistics service capabilities, effectively evaluate their service quality, and then identify problems based on the evaluation results, analyze and improve them. The service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is a classical multiple attribute group decision making (MAGDM). The Spherical fuzzy sets (SFSs) provide more free space for DMs to portray uncertain information during the service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain. Therefore, this paper expands the partitioned Maclaurin symmetric mean (PPMSM) operator and IOWA operator to SFSs based on the power average (PA) technique and construct induced spherical fuzzy weighted power partitioned MSM (I-SFWPPMSM) technique. Subsequently, a novel MAGDM method is constructed based on I-SFWPPMSM technique and SFNWG technique under SFSs. Finally, a numerical example for service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is employed to verify the constructed method, and comparative analysis with some existing techniques to testy the validity and superiority of the I-SFWPPMSM technique. Show more

Keywords: MAGDM, Spherical fuzzy sets (SFSs), I-SFWPPMSM operator, Service quality evaluation

DOI: 10.3233/JIFS-233384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11835-11851, 2023

Authors: Liu, Wenxiu | Xu, Lijun | Zhou, Yijia | Yu, Bo

Article Type: Research Article

Abstract: In this paper, we propose two novel Alternating Direction Method of Multipliers (ADMM) algorithms for the sparse portfolio problem via sorted ℓ1 -norm penalization (SLOPE). The first algorithm (FADMM) is presented by adding a prediction-correction step to the classic ADMM framework. Since the problem is not strongly convex, the second fast ADMM (FADMMR) is proposed by utilizing both prediction-correction step and restarting rules. Numerical experiments show that the FADMMR algorithm converges faster than the FADMM algorithm and ADMM algorithm when tuning parameters are relatively small. On the other hand, when tuning parameters are relative large, the FADMM algorithm performs better …than the FADMMR algorithm and ADMM algorithm. The FADMM algorithm and FADMMR algorithm converge faster than the ADMM algorithm in terms of convergence time for different sizes of tuning parameters. For large-scale portfolio problem, the proposed algorithms have highly performance as well. Finally, empirical analysis on five datasets of stocks index show that the proposed algorithms are efficient and superior for solving sparse portfolio problems via SLOPE. Show more

Keywords: Fast ADMM, fast ADMM with restart, sparse portfolio, sort ℓ1-norm penalty

DOI: 10.3233/JIFS-234381

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11853-11872, 2023

Authors: Jin, Huilong | Du, Ruiyan | Wen, Tian | Zhao, Jia | Shi, Lei | Zhang, Shuang

Article Type: Research Article

Abstract: Compared with other facial expression recognition, classroom facial expression recognition should pay more attention to the feature extraction of a specific region to reflect the attention of students. However, most features are extracted with complete facial images by deep neural networks. In this paper, we proposed a new expression recognition based on attention mechanism, where more attention would be paid in the channel information which have much relationship with the expression classification instead of depending on all channel information. A new classroom expression classification has also been concluded with considering the concentration. Moreover, activation function is modified to reduce the …number of parameters and computations, at the same time, dropout regularization is added after the pool layer to prevent overfitting of the model. The experiments show that the accuracy of our method named Ixception has an maximize improvement of 5.25% than other algorithms. It can well meet the requirements of the analysis of classroom concentration. Show more

Keywords: Deep learning, classroom facial expression recognition, attention mechanism, activation function, dropout regularization

DOI: 10.3233/JIFS-235541

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11873-11882, 2023

Authors: Luo, Dang | Ambreen, Muffarah | Latif, Assad | Wang, Xiaolei | Samreen, Mubbarra | Muhammad, Aown

Article Type: Research Article

Abstract: Almost all cities of Pakistan are economically affected by the electricity shortage due to the continuously increasing demand for electricity. To correctly forecast the seasonal fluctuations of the electricity consumption of Lahore city in Pakistan, we proposed the SDGPM(1,1,N) model, which is a seasonal discrete grey polynomial model combined with seasonal adjustment. We conducted an empirical analysis using the proposed model based on the seasonal electricity consumption data of Lahore city in Pakistan from 2014 to 2021. The findings from the SDGPM (1,1,N) model are compared with those collected through the original grey model DGPM(1,1,N) and other eight models. The …comparison’s findings demonstrated that the SDGPM(1,1,N) model is indeed capable of correctly identifying seasonal fluctuations of electricity consumption in Lahore city and its prediction accuracy is significantly higher than the original DGPM(1,1,N) model and the other seven models. The SDGPM(1,1,N) model’s forecast findings for Lahore from 2022 to 2025 indicate that the city’s energy consumption is expected to rise marginally, although there will still be significant seasonal fluctuations. It is predicted that the annual electricity consumption from 2022 to 2025 will be 26249, 26749, 27928, and 28136 with an annual growth rate of 7.18%. This forecast can provide policymakers ahead start in planning to ensure that supply and demand are balanced. Show more

Keywords: Seasonal factor, Lahore electricity forecasting, seasonal discrete grey polynomial model, seasonal DGPM(1, 1,N)

DOI: 10.3233/JIFS-231106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11883-11894, 2023

Authors: Sangeetha, J. | Priyanka, M. | Jayakumar, C.

Article Type: Research Article

Abstract: Audio Event Detection (AED) and classification of acoustic events has become a notable task for machines to interpret the auditory information around us. Nevertheless, it has been a difficult and cumbersome task to extract the most basic characteristics of acoustic events that encapsulate the fundamental elements of the audio events. Previous works on audio event classification utilized supervised pre-training as well as meta-learning approaches that happened to depend on labeled data therefore facing instability. Deep Learning is progressing in an increasingly mature direction, and the application of deep learning methods to detect acoustic event has become more and more sought …after. The proposed hybrid method called Greedy Regression-based Convolutional Neural Network and Differential Convex Bidirectional Gated Recurrent Unit (GRCNN-DCBGRU) is introduced to learn a vector representation of an audio sequence for Audio Event Classification (AEC). Differential Convex Bidirectional Gated Recurrent Unit is analogous to long short-term memory and involves time-cyclic long-term dependencies with a lesser processing complexity. The model first extracts acoustic features from the sound event dataset through a Differential Convex Bidirectional Gated Recurrent Unit employing Gabor Filter bank features and then extracts the local static acoustic features through the Greedy Regression-based Convolutional Neural Network by utilizing Mel Frequency Cepstral Coefficients (MFCC). Finally, the Differential Convex Meta-Learning classifier is used for the final acoustic event classification. Extensive evaluation on large-size publicly available acoustic event database like Findsounds2016 will be performed in Python programming language to demonstrate the efficiency of the proposed method for the AEC task. To demonstrate the visualizations of individual modules and their influence on overall representation learning for AEC tasks, several parameters like audio detection time, audio detection accuracy, precision, and recall are measured. Show more

Keywords: Audio event detection, audio event classification, deep learning, greedy regression, convolutional neural network, differential convex, bidirectional gated recurrent unit

DOI: 10.3233/JIFS-232561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11895-11908, 2023

Authors: Tan, Mindong | Qu, Liangdong

Article Type: Research Article

Abstract: Oral English teaching quality evaluation is a complex nonlinear relationship, which is affected by many factors and has low accuracy. Aiming at the problem, a teaching quality evaluation method based on a BP neural network optimized by the improved crow search algorithm (ICSA) is proposed. First, ICSA is put forward and five algorithms are used to compare with the proposed algorithm on 10 benchmarks functions. The results show that ICSA outperforms the other five algorithms on 10 functions. Second, a feature selection method based on the improved binary crow search algorithm (BICSA) is used to select teaching quality evaluation indexes, …and 10 standard datasets from the UCI repository are used for testing experiments. Finally, an oral English teaching evaluation model based on BP neural network is designed, in which BICSA is used for feature selection and ICSA is used to optimize the initial weights of the BP neural network. In the experiment, we designed 5 first-grade indexes and 15 second-grade indexes, and then we collects 23 groups of oral English teaching quality data. BICSA selected 10 features from a set of 15 features. Experimental results show that this method can effectively evaluate the quality of oral English teaching with high accuracy and real-time performance. Show more

Keywords: BP neural network, crow search algorithm, feature selection, oral English teaching, quality evaluation

DOI: 10.3233/JIFS-222455

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11909-11924, 2023

Authors: Srivatsun, G. | Thivaharan, S.

Article Type: Research Article

Abstract: Writing is a crucial component of the language requirement and is an effective method for correctly reflecting language proficiency. Manually evaluating Tamil language exams becomes time-consuming and costly for standardized language administrators as they grow in popularity. Numerous studies on computerized English assessment systems have been conducted in recent years. Due to Tamil text’s complicated grammatical structures, less research has been done on computerized evaluation methods. In this research, we present a Tamil review comment analysis system using a novel multivariate naïve Bayes classifier (mv  - NB ) where the comments are acquired from an online social network and performed training …using the database for further analysis. Experiments show that the graded Kappa of 0.4239, error rate of 2.55 and precision of 85% was achieved on the online dataset by our contents grading system, which is superior in grading compared to the other widely used machine learning algorithms training on big datasets. Our findings are promising. Additionally, our contents analysis may provide beneficial criticism on Tamil writing on YouTube posts including comments, spelling errors and morphological issues that help to analyze thelanguage correlation. Show more

Keywords: Writing, Tamil content, grading system, reviews, morphological issues

DOI: 10.3233/JIFS-222504

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11925-11936, 2023

Authors: Cheng, Shumin | Zhou, Yan | Bao, Yanling

Article Type: Research Article

Abstract: With the increasing diversification and complexity of information, it is vital to mine effective knowledge from information systems. In order to extract information rapidly, we investigate attribute reduction within the framework of dynamic incomplete decision systems. Firstly, we introduce positive knowledge granularity concept which is a novel measurement on information granularity in information systems, and further give the calculation method of core attributes based on positive knowledge granularity. Then, two incremental attribute reduction algorithms are presented for incomplete decision systems with multiple objects added and deleted on the basis of positive knowledge granularity. Furthermore, we adopt some numerical examples to …illustrate the effectiveness and rationality of the proposed algorithms. In addition, time complexity of the two algorithms are conducted to demonstrate their advantages. Finally, we extract five datasets from UCI database and successfully run the algorithms to obtain corresponding reduction results. Show more

Keywords: Incomplete decision system, positive knowledge granularity, incremental attribute reduction

DOI: 10.3233/JIFS-230349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11937-11947, 2023

Authors: Wei, Qiuyue | Yang, Dong | Zhang, Mingjie

Article Type: Research Article

Abstract: Aspect-based sentiment analysis is a fine-grained task in the field of sentiment analysis. Various GCN approaches have recently emerged to work on this, but many approaches ignored the critical role of aspectual word information and the effect of noise. In view of this situation, we propose an aspect-based word embedding graph convolutional network (AWEGCN) model. In order to make good use of the aspect information and distinguish the contextual information that is more important for a particular aspect, the aspect information is embedded in the output of the hidden layer. To reduce the noise effect when multiple aspect words appear …in a sentence, after going through the bidirectional graph convolutional network, the aspect information is embedded. A specific contextual representation is computed through an attention mechanism, which is used as the final classification feature. Experiments show that our model achieves impressive performance on five public datasets, and we also apply BERT and XLNet pre-trained models to this task and obtain advanced results that validate the effectiveness of our model. Show more

Keywords: Aspect-level sentiment classification, aspect word embeddings, graph convolutional networks, attention mechanisms

DOI: 10.3233/JIFS-230537

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11949-11962, 2023

Authors: Cheng, Tao | Cheng, Hua | Fang, Yiquan | Liu, Yufei | Gao, Caiting

Article Type: Research Article

Abstract: As prototype-based Few-Shot Learning methods, Prototypical Network generates prototypes for each class in a low-resource state and classify by a metric module. Therefore, the quality of prototypes matters but they are inaccurate from the few support instances, and the domain-specific information of training data are harmful to the generalizability of prototypes. We propose a C onceptual P rototype (CP), which contains both rich instance and concept features. The numerous query data can inspire the few support instances. An interactive network is designed to leverage the interrelation between support set and query-detached set to acquire a rich Instance Prototype which is …typical on the whole data. Besides, class labels are introduced to prototype by prompt engineering, which makes it more conceptual. The label-only concept makes prototype immune to domain-specific information in training phase to improve its generalizability. Based on CP, C onceptual P rototypical C ontrastive L earning (CPCL) is proposed where PCL brings instances closer to its corresponding prototype and pushes away from other prototypes. “2-way 5-shot” experiments show that CPCL achieves 92.41% accuracy on ARSC dataset, 2.30% higher than other prototype-based models. Meanwhile, the 0-shot performance of CPCL is comparable to Induction Network in the 5-shot way, indicating that our model is adequate for 0-shot tasks. Show more

Keywords: Prototypical network, text classification, Few-Shot learning, prompt learning

DOI: 10.3233/JIFS-231570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11963-11975, 2023

Authors: AlAlaween, Wafa’ H. | AlAlawin, Abdallah H. | AbuHamour, Saif O. | Gharaibeh, Belal M.Y. | Mahfouf, Mahdi | Alsoussi, Ahmad | AbuKaraky, Ashraf E.

Article Type: Research Article

Abstract: Right-first-time production enables manufacturing companies to be profitable as well as competitive. Ascertaining such a concept is not as straightforward as it may seem in many industries, including 3D printing. Therefore, in this research paper, a right-first-time framework based on the integration of fuzzy logic and multi-objective swarm optimization is proposed to reverse-engineer the radial based integrated network. Such a framework was elicited to represent the fused deposition modelling (FDM) process. Such a framework aims to identify the optimal FDM parameters that should be used to produce a 3D printed specimen with the desired mechanical characteristics right from the first …time. The proposed right-first-time framework can determine the optimal set of the FDM parameters that should be used to 3D print parts with the required characteristics. It has been proven that the right-first-time model developed in this paper has the ability to identify the optimal set of parameters successfully with an average error percentage of 4.7%. Such a framework is validated in a real medical case by producing three different medical implants with the desired mechanical characteristics for a 21-year-old patient. Show more

Keywords: Fuzzy logic, particle swarm optimization, radial based integrated network, right-first-time production

DOI: 10.3233/JIFS-232135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11977-11991, 2023

Authors: Uganya, G. | Bommi, R.M. | Muthu Krishnammal, P. | Vijayaraj, N.

Article Type: Research Article

Abstract: Internet of things (IoT) is a recent developing technology in the field of smart healthcare. But it is difficult to transfer the patient’s health record as a centralized network. So, “blockchain technology” has excellent consideration due to its unique qualities such as decentralized network, openness, irreversible data, and cryptography functions. Blockchain technology depends on cryptography hash techniques for safe transmission. For increased security, it transforms the variable size inputs into a constant length hash result. Current cryptographic hash algorithms with digital signatures are only able to access keys up to a size of 256 bytes and have concerns with single …node accessibility. It just uses the bits that serve as the key to access the data. This paper proposes the “Revised Elliptic Curve Cryptography Multi-Signature Scheme” (RECC-MSS) for multinode availability to find the nearest path for secure communications with the medical image as keys. Here, the input image key can be converted into an array of data that can be extended up to 512 bytes of size. The performance of the proposed algorithm is analyzed with other cryptography hash functions like Secure Hashing Algorithms (SHAs) such as “SHA224”, “SHA256”, “SHA384”, “SHA512”, “SHA3-224”, “SHA3-256”, “SHA3-384”, “SHA3-512”, and “Message Digest5” (MD5) by “One-way ANOVA” test in terms of “accuracy”, “throughput” and “time complexity”. The proposed scheme with ECC achieved the throughput of 17.07 kilobytes per 200 nano seconds, 93.25% of accuracy, 1.5 nanoseconds latency of signature generation, 1.48 nanoseconds latency of signature verification, 1.5 nanoseconds of time complexity with 128 bytes of hash signature. The RECC-MSS achieved the significance of 0.001 for accuracy and 0.002 for time complexity which are less than 0.05. From the statistical analysis, the proposed algorithm has significantly high accuracy, high throughput and less time complexity than other cryptography hash algorithms. Show more

Keywords: Internet of Things, blockchain technology, multi-signature, Secure Hash Algorithm, Revised Elliptic Curve Cryptography, medical image

DOI: 10.3233/JIFS-232802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11993-12012, 2023

Authors: Zhang, Guowei | Tang, Yutong | Tang, Hulin | Li, Wuzhi | Wang, Li

Article Type: Research Article

Abstract: Unmanned sorting technology can significantly improve the transportation efficiency of the logistics industry, and package detection technology is an important component of unmanned sorting. This paper proposes a lightweight deep learning network called EPYOLO, in which a lightweight self-attention feature extraction backbone network named EPnet is also designed. It also reduces the Floating-Point Operations (FLOPs) and parameter count during the feature extraction process through an improved Contextual Transformer-slim (CoTs) self-attention module and GSNConv module. To balance network performance and obtain semantic information for express packages of different sizes and shapes, a multi-scale pyramid structure is adopted using the Feature Pyramid …Network (FPN) and the Path Aggregation Network (PAN). Finally, comparative experiments were conducted with the state-of-the-art (SOTA) model by using a self-built dataset of express packages by using a self-built dataset of express packages, results demonstrate that the mean Average Precision (mAP) of the EPYOLO network reaches 98.8%, with parameter quantity only 11.63% of YOLOv8 s and FLOPs only 9.16% of YOLOv8 s. Moreover, compared to the YOLOv8 s network, the EPYOLO network shows superior detection performance for small targets and overlapping express packages. Show more

Keywords: Object detection, express package detection, lightweight, deep learning

DOI: 10.3233/JIFS-232874

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12013-12025, 2023

Authors: Li, Yue | Mao, Liang

Article Type: Research Article

Abstract: Automatic detection of defects in mature litchi plays a vital role in the classification of fruit grades. The existing method mainly relies on manual, it is difficult to meet the needs of different varieties of litchi various types of commodity packaging, and there are problems such as low efficiency, high cost and poor quality of goods. To address the above problems, this paper proposes an improved You Only Look Once(YOLO)v7 algorithm for the automatic detection of post-harvest mature litchi epidermal defects. First, a dataset of litchi defects (black spot, fall off, crack) was constructed, in which the train and test …sets had 4133 and 516; Next, A Simple Parameter-Free Attention(SimAM) mechanism is introduced into the original YOLOv7 backbone network, while GSconv is used in the neck instead of convolution, and the shallow network is used instead of the deep network for lateral linking, finally, the Mish function is used as the activation function. Experimental results show the precious and mAP of the original YOLOv7 are 87.66% and 88.98%, and those of the improved YOLOv7 are 91.56% and 93.42%, improvements of 3.9% and 4.44%. A good foundation is laid for the automated classification of ripe litchi after harvesting. Show more

Keywords: YOLOv7, litchi epidermal defects, SimAM, GSconv, shallow networks

DOI: 10.3233/JIFS-233440

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12027-12036, 2023

Authors: He, Ping | Chen, Jingfang

Article Type: Research Article

Abstract: In this paper, a question answering method is proposed for educational knowledge bases (KBQA) using a question-aware graph convolutional network (GCN). KBQA provides instant tutoring for learners, improving their learning interest and efficiency. However, most open domain KBQA methods model question sentences and candidate answer entities independently, limiting their effectiveness. The proposed method extracts description information and query entity sets for a specific question, processes them with Transformer and pre-trained embeddings of the knowledge base, and extracts a subgraph of candidate answer sets from the knowledge base. The node information is updated by GCN with two attention mechanisms expressed by …the question description and query entity set, respectively. The query description information, query entity set, and candidate entity representation are fused to calculate the score and predict the answer. Experiments on MOOC Q&A dataset show that the proposed method outperforms benchmark models. Show more

Keywords: Educational knowledge base, data-driven intelligent education, question answering method, Graph convolutional network (GCN), prediction accuracy

DOI: 10.3233/JIFS-233915

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12037-12048, 2023

Authors: Wang, Yashao

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-234605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12049-12063, 2023

Authors: Zhao, Jie | Wang, Shuo | Wu, Haotian

Article Type: Research Article

Abstract: To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2 ) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In …addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOC UC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOC B was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified. Show more

Keywords: DC microgrid, model predictive control, mixed energy storage, objective function, secondary planning

DOI: 10.3233/JIFS-234849

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12065-12077, 2023

Authors: Maguluri, Lakshmana Phaneendra | Vinya, Viyyapu Lokeshwari | Goutham, V. | Uma Maheswari, B. | Kumar, Boddepalli Kiran | Musthafa, Syed | Manikandan, S. | Srivastava, Suraj | Munjal, Neha

Article Type: Research Article

Abstract: Depression is a prevalent mental health disorder that affects people of all ages and origins; therefore, early detection is essential for timely intervention and support. This investigation proposes a novel method for detecting melancholy in young, healthy individuals by analysing their gait and balance patterns. In order to accomplish this, a comprehensive system is designed that incorporates cutting-edge technologies such as a Barometric Pressure Sensor, Beck Depression Inventory (BDI), and t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. The system intends to capitalize on the subtle motor and physiological changes associated with melancholy, which may manifest in a person’s gait and balance. …The Barometric Pressure Sensor is used to estimate variations in altitude and vertical velocity, thereby adding context to the evaluation. The mood states of participants are evaluated using the BDI, a well-established psychological assessment instrument that provides insight into their emotional health. Integrated and pre-processed data from the Barometric Pressure Sensor, BDI responses, and gait and balance measurements. The t-SNE algorithm is then used to map the high-dimensional data into a lower-dimensional space while maintaining the local structure and identifying underlying patterns within the dataset. The t-SNE algorithm improves visualization and pattern recognition by reducing the dimensionality of the data, allowing for a more nuanced analysis of depression-related markers. As the proposed system combines objective physiological measurements with subjective psychological assessments, it has the potential to advance the early detection and prediction of depression in young, healthy individuals. The results of this exploratory study have implications for the development of non-intrusive and easily accessible instruments that can assist healthcare professionals in identifying individuals at risk and implementing targeted interventions. Show more

Keywords: Depression, barometric pressure sensor, beck depression inventory, t-SNE, mental health

DOI: 10.3233/JIFS-235058

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12079-12093, 2023

Authors: Vidhya, R. | Banavath, Dhanalaxmi | Kayalvili, S. | Naidu, Swarna Mahesh | Charles Prabu, V. | Sugumar, D. | Hemalatha, R. | Vimal, S. | Vidhya, R.G.

Article Type: Research Article

Abstract: Early Alzheimer’s disease detection is essential for facilitating prompt intervention and enhancing the quality of care provided to patients. This research presents a novel strategy for the diagnosis of Alzheimer’s disease that makes use of sophisticated sampling methods in conjunction with a hybrid model of deep learning. We use stratified sampling, ADASYN (Adaptive Synthetic Sampling), and Cluster- Centroids approaches to ensure a balanced representation of Alzheimer’s and non-Alzheimer’s cases during model training in order to meet the issues posed by imbalanced data distributions in clinical datasets. This allows us to solve the challenges posed by imbalanced data distributions in clinical …datasets. A strong hybrid architecture is constructed by combining a Residual Neural Network (ResNet) with Residual Neural Network (ResNet) units. This architecture makes the most of both the feature extraction capabilities of ResNet and the capacity of LSTM to capture temporal dependencies. The findings demonstrate that the model is superior to traditional approaches to machine learning and single-model architectures in terms of accuracy, sensitivity, and specificity. The hybrid deep learning model demonstrates exceptional capabilities in identifying early indicators of Alzheimer’s disease with a high degree of accuracy, which paves the way for early diagnosis and treatment. In addition, an interpretability study is carried out in order to provide light on the decision-making process underlying the model. This helps to contribute to a better understanding of the characteristics and biomarkers that play a role in the identification of Alzheimer’s disease. In general, the strategy that was provided provides a promising foundation for accurate and reliable Alzheimer’s disease identification. It does this by harnessing the capabilities of hybrid deep learning models and sophisticated sampling approaches to improve clinical decision support and, as a result, eventually improve patient outcomes. Show more

Keywords: Alzheimer’s disease, Residual Neural Network (ResNet), Residual Neural Network (ResNet), Cluster Centroids, stratified sampling, ADASYN (Adaptive Synthetic Sampling)

DOI: 10.3233/JIFS-235059

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12095-12109, 2023

Authors: Nandhini, Ramesh Sneka | Lakshmanan, Ramanathan

Article Type: Research Article

Abstract: Cyber-physical systems (CPS) play a pivotal role in various critical applications, ranging from industrial automation to healthcare monitoring. Ensuring the reliability and accuracy of sensor data within these systems is of paramount importance. This research paper presents a novel approach for enhancing fault detection in sensor data within a cyber-physical system through the integration of machine learning algorithms. Specifically, a hybrid ensemble methodology is proposed, combining the strengths of AdaBoost and Random Forest with Rocchio’s algorithm, to achieve robust and accurate fault detection. The proposed approach operates in two phases. In the first phase, AdaBoost and Random Forest classifiers are …trained on a diverse dataset containing normal and faulty sensor data to develop individual base models. AdaBoost emphasizes misclassified instances, while Random Forest focuses on capturing complex interactions within the data. In the second phase, the outputs of these base models are fused using Rocchio’s algorithm, which exploits the similarities between faulty instances to improve fault detection accuracy. Comparative analyses are conducted against individual classifiers and other ensemble methods to validate the effectiveness of the hybrid approach. The results demonstrate that the proposed approach achieves superior fault detection rates. Additionally, the integration of Rocchio’s algorithm significantly contributes to the refinement of the fault detection process, effectively leveraging the strengths of AdaBoost and Random Forest. In conclusion, this research offers a comprehensive solution to enhance fault detection capabilities in cyber-physical systems by introducing a novel ensemble framework. By synergistically combining AdaBoost, Random Forest, and Rocchio’s algorithm, the proposed methodology provides a robust mechanism for accurately identifying sensor data anomalies, thus bolstering the reliability and performance of cyber-physical systems across a multitude of critical applications. Show more

Keywords: Cyber-physical systems, fault detection, sensor data, ensemble learning, random forest

DOI: 10.3233/JIFS-235809

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12111-12122, 2023

Authors: Ng, Grace Yee Lin | Ang, Kim Loon | Tan, Shing Chiang | Ong, Chia Sui | Ngeow, Yun Fong

Article Type: Research Article

Abstract: Multilocus variable number tandem repeat analysis (MLVA) utilizes short DNA repeat polymorphism in genomes, which is termed variable number tandem repeat (VNTR), to differentiate closely related organisms. One research challenge is to find an optimal set of VNTR to distinguish different members accurately. An intuitive method is to use an exhaustive search method. However, this method is not an efficient way to find optimal solutions from a dataset comprising many attributes (loci) due to the curse of dimensionality. In this study, metaheuristic methods are proposed to find an optimal set of loci combination. Basic genetic algorithm (BGA) and modified genetic …algorithm (MGA) were proposed in our previous work for this purpose. However, they require prior knowledge from an experienced user to specify the minimum number of loci for achieving good results. To impose no such expertise requirement for parameter setting, a GA with Duplicates (GAD), which allows the inclusion of duplicated loci in a chromosome (potential solution) during the search process, is developed. The study also investigates the search performance of a hybrid metaheuristic method, namely quantum-inspired differential evolution (QDE). Hunter-Gaston Discriminatory Index (HGDI) is used to indicate the discriminatory power of a loci combination. Two Mycobacterium tuberculosis MLVA datasets obtained from a public portal and a local laboratory respectively, are used. The results obtained by using exhaustive search and metaheuristic methods are first compared, followed by a performance comparison among BGA, MGA, GAD, and QDE by a statistical approach. The best-performing GA method (i.e., GAD) and QDE are selected for a performance comparison with several recent metaheuristic methods using both MLVA datasets by a statistical approach. The statistical results show that both GAD and QDE could achieve higher HGDI than the recent methods using a small but informative set of loci combination. Show more

Keywords: Variable number tandem repeat (VNTR), multiple locus VNTR analysis (MLVA), genotyping, metaheuristic algorithms, genetic algorithm

DOI: 10.3233/JIFS-231367

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12123-12142, 2023

Authors: Yaqoot, Iqra | Riaz, Muhammad | Al-Quran, Ashraf | Tehreem,

Article Type: Research Article

Abstract: This research work proposes a novel approach for multi stage decision analysis (MSDA) using innovative concepts of cubic intuitionistic fuzzy set (CIFS) theory. The paper introduces CIF-technique for order preference by similarity to ideal solution (TOPSIS) as a robust method for MSDA problems, particularly for the diagnosis of epilepsy disorders. To achieve this goal, new similarity measures (SMs) are developed for CIFS, including the Cosine angle between two vectors, a new distance measure, and the Cosine function, presented as three different types of Cosine similarity measures. The proposed CIF-TOPSIS approach is found to be suitable for precise value performance ratings …and is expected to be a viable approach for case studies in the diagnosis of epilepsy disorders. The efficiency and reliability of the proposed MSDA methods is efficiently carried through numerical examples and comparative analysis. Show more

Keywords: CIF information, CIF-TOPSIS, similarity, measures, epilepsy disorders, multi stage decision analysis

DOI: 10.3233/JIFS-232085

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12143-12166, 2023

Authors: Zhou, Zilong | Yu, Yue | Song, Chaoyang | Liu, Zhen | Shi, Manman | Zhang, Jingxiang

Article Type: Research Article

Abstract: Reducing noise in CT images and extracting key features are crucial for improving the accuracy of medical diagnoses, but it remains a challenging problem due to the complex characteristics of CT images and the limitations of existing methods. It is worth noting that multiple views can provide a richer representation of information compared to a single view, and the unique advantages of the wavelet transform in feature analysis. In this study, a novel Multi-View Weighted Feature Fusion algorithm called MVWF is proposed to address the challenge of enhancing CT image recognition utilizing wavelet transform and convolutional neural networks. In the …proposed approach, the wavelet transform is employed to extract both detailed and primary features of CT images from two views, including high frequency and low frequency. To mitigate information loss, the source domain is also considered as a view within the multi-view structure. Furthermore, AlexNet is deployed to extract deeper features from the multi-view structure. Additionally, the MVWF algorithm introduces a balance factor to account for both specific information and global information in CT images. To accentuate significant multi-view features and reduce feature dimensionality, random forest is used to assess feature importance followed by weighted fusion. Finally, CT image recognition is accomplished using the SVM classifier. The performance of the MVWF algorithm has been compared with classical multi-view algorithms and common single-view methods on COVID-CT and SARS-COV-2 datasets. The experimental results indicate that an average improvement of 6.8% in CT image recognition accuracy can be achieved by utilizing the proposed algorithm. Particularly, the MVF algorithm and MVWF algorithm have attained AUC values of 0.9972 and 0.9982, respectively, under the SARS-COV-2 dataset, demonstrating outstanding recognition performance. The proposed algorithms can capture more robust and comprehensive high-quality feature representation by considering feature correlations across views and feature importance based on Multi-view. Show more

Keywords: Multi-view, CT image recognition, feature fusion, wavelet transform, random forest

DOI: 10.3233/JIFS-233373

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12167-12183, 2023

Authors: Xu, Le | Wang, Jinghua | Kuang, Ciwei | Xu, Yong

Article Type: Research Article

Abstract: The 0-1 grid method is commonly used to divide a fire building into fully passable and fully impassable areas. Firefighters are only able to perform rescue tasks in the fully passable areas. However, in an actual building fire environment, there are three types of areas: fully impassable areas (areas blocked by obstacles or with heavy smoke and fire), fully passable areas, and partially passable areas (areas without obstacles or fire, but with some smoke risk). Due to the urgency of rescue, firefighters can consider conducting rescue tasks in both fully passable and partially passable areas to save valuable rescue time. …To address this issue, we propose a three-value grid method, which classifies the fire environment into fully impassable areas, fully passable areas, and partially passable areas, represented by 1, 0, and 0.5, respectively. Considering that the ACO algorithm is prone to local optimum, we propose an enhanced ant colony algorithm (EACO) to solve the fire rescue path planning problem. The EACO introduces an adaptive heuristic function, a new pheromone increment strategy, and a pheromone segmentation rule to predict the shortest rescue path in the fire environment. Moreover, the EACO takes into account both the path length and the risk to balance rescue effectiveness and safety. Experiments show that the EACO obtains the shortest rescue path, which demonstrates its strong path planning capability. The three-value grid method and the path planning algorithm take reasonable application requirements into account. Show more

Keywords: Fire rescue, path planning, 0-1 grid method, three-value grid method, EACO

DOI: 10.3233/JIFS-233862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12185-12200, 2023

Authors: Deng, Qiao

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-234396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12201-12212, 2023

Authors: Xing, Yu-Xuan | Wang, Jie-Sheng | Zhang, Shi-Hui | Bao, Yin-Yin | Zheng, Yue | Zhang, Yun-Hao

Article Type: Research Article

Abstract: The p-Hub allocation problem is a classic problem in location assignment, which aims to optimize the network by placing Hub devices and allocating each demand node to the corresponding Hub. A mutation Transit search (TS) algorithm with the introduction of the black hole swallowing strategy was proposed to solve the p-Hub allocation problem. Firstly, the mathematical model for the p-Hub allocation problem is established. Six mutation operators specifically designed for p-Hub allocation problem are introduced to enhance the algorithm’s ability to escape local optima. Additionally, the black hole swallowing strategy was incorporated into TS algorithm so as to accelerate its …convergence rate while ensuring sufficient search in the solution space. The improved TS algorithm was applied to optimize three p-Hub location allocation problems, and the simulation results are compared with those of the basic TS algorithm. Furthermore, the improved TS algorithm is compared with the Honey Badger Algorithm (HBA), Sparrow Search Algorithm (SSA), Harmony Search Algorithm (HS), and Particle Swarm Optimization (PSO) to solve three of p-Hub allocation problems. Finally, the impact of the number of Hubs on the cost of three models was studied, and the simulation results validate the effectiveness of the improved TS algorithm. Show more

Keywords: p-Hub allocation problem, transit search algorithm, black hole strategy, mutation operator

DOI: 10.3233/JIFS-234695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12213-12232, 2023

Authors: Migdadi, Hatim Solayman | Al-Olaimat, Nesreen M.

Article Type: Research Article

Abstract: In this paper, a new extension of the standard Rayleigh distribution called the Power Rayleigh distribution (PRD) is investigated for the accelerated life test (ALT) using the geometric process (GP) under Type-I censored data. Point estimates of the formulated model parameters are obtained via the likelihood estimation approach. In addition, interval estimates are obtained based on the asymptotic normality of the derived estimators. To evaluate the performance of the obtained estimates, a simulation study of 4, 5 and 6 levels of stress is conducted for ALT in different combinations of sample sizes and censored times. Simulation results indicated that point …estimates are very close to their initial true values, have small relative errors, are robust and are efficient for estimating the model parameters. Similarly, the interval estimates have small lengths and their coverage probabilities are almost converging to their 95% nominated significance level. The estimation procedure is also improved by the approach of finding optimum values of the acceleration factor to have optimum values for the reliability function at the specified design stress level. This work confirms that PRD has the superiority to model the lifetimes in ALT using GP under any censoring scheme and can be effectively used in reliability and survival analysis. Show more

Keywords: Accelerated life test, geometric process, power ryleigh distribution, maximum likelihood estimation, optimum test plan

DOI: 10.3233/JIFS-232084

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12233-12242, 2023