Journal of Intelligent & Fuzzy Systems - Volume 45, issue 3

Authors: Yu, Junqi | Su, Yucong | Feng, Chunyong | Cheng, Renyin | Hou, Shuai

Article Type: Research Article

Abstract: Global path planning is one of the key technologies for airport energy station inspection robots to achieve autonomous navigation. Due to the complexity of airport energy station buildings with numerous mechanical and electrical equipment and narrow areas, planning an optimal global path remains a challenge. This paper aimed to study global path planning for airport energy station inspection robots using an improved version of the Grey Wolf Optimizer (IGWO) algorithm. Firstly, the initialization process of the Grey Wolf Optimizer algorithm selects several grey wolf individuals closer to the optimal solution as the initial population through the lens imaging reverse learning …strategy. The algorithm introduces nonlinear convergence factors in the control parameters, and adds an adaptive adjustment strategy and an elite individual reselection strategy to the location update to improve the search capability and to avoid falling into local optima. Benchmark function and global path planning simulation experiments were carried out in MATLAB to test the proposed algorithm’s effectiveness. The results showed that compared to other swarm intelligent optimization algorithms, the proposed algorithm outperforms them in terms of higher convergence speed and optimization accuracy. Friedman’s test ranked this algorithm first overall. The algorithm outperforms others in terms of average path length, standard deviation of path length, and running time. Show more

Keywords: Airport energy station, inspection robot, global path planning, improved grey wolf optimizer

DOI: 10.3233/JIFS-230894

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4483-4500, 2023

Authors: Priyadharshini, S. | Mahapatra, Ansuman

Article Type: Research Article

Abstract: With the advances in video technology, the advent of spherical video (360° video) recorded using an omnidirectional camera offers a limitless field-of-view (FoV) to the viewers. However, they suffer from the fear of missing out (FOMO) because they can only see a particular FoV at a time. Reviewing a long recorded surveillance video i.e., 24 hours a day is a time-consuming process due to temporal and spatial redundancy. A solution to this problem is to compactly represent the video synopsis by shifting the objects along the time domain. Using a multi-camera setup for surveillance creates blind spots. This problem is …solved by using a spherical camera. Therefore, in this paper, we focus on creating and visualizing the video synopsis recorded by the spherical camera. The optimization algorithm plays a key role in condensing the recorded video. Hence, a novel spherical video synopsis optimization framework has been introduced to generate compact videos that eliminate FOMO. The synopsis is generated by shifting objects on the temporal axis and displays them simultaneously by optimizing multiple constraints. It minimizes activity loss, virtual collisions, temporal inconsistencies, and synopsis video length by preserving interactions between objects. The proposed multiobjective optimization includes a new constraint to restrict the number of objects displayed per frame due to the limitation of the human visual system. Direction-based visualization methods have been proposed to improve the viewer’s experience without FOMO. Comparative performance of the proposed framework using the latest metaheuristic optimization algorithms with existing video synopsis optimization algorithms is performed. It is found that chronological disorder ratio and overall virtual collision are minimized effectively through the recent metaheuristics optimization algorithms compared to the related works on video synopsis. Show more

Keywords: Display constraint, object-based video synopsis, optimization, panoramic surveillance video, spherical video synopsis

DOI: 10.3233/JIFS-232227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4501-4516, 2023

Authors: Xuan, Cho Do | Nguyen, Hoa Dinh

Article Type: Research Article

Abstract: Advanced persistent threat (APT) attacking campaigns have been a common method for cyber-attackers to attack and exploit end-user computers (workstations) in recent years. In this study, to enhance the effectiveness of the APT malware detection, a combination of deep graph networks and contrastive learning is proposed. The idea is that several deep graph networks such as Graph Convolution Networks (GCN), Graph Isomorphism Networks (GIN), are combined with some popular contrastive learning models like N-pair Loss, Contrastive Loss, and Triplet Loss, in order to optimize the process of APT malware detection and classification in endpoint workstations. The proposed approach consists of …three main phases as follows. First, the behaviors of APT malware are collected and represented as graphs. Second, GIN and GCN networks are used to extract feature vectors from the graphs of APT malware. Finally, different contrastive learning models, i.e. N-pair Loss, Contrastive Loss, and Triplet Loss are applied to determine which feature vectors belong to APT malware, and which ones belong to normal files. This combination of deep graph networks and contrastive learning algorithm is a novel approach, that not only enhances the ability to accurately detect APT malware but also reduces false alarms for normal behaviors. The experimental results demonstrate that the proposed model, whose effectiveness ranges from 88% to 94% across all performance metrics, is not only scientifically effective but also practically significant. Additionally, the results show that the combination of GIN and N-pair Loss performs better than other combined models. This provides a base malware detection system with flexible parameter selection and mathematical model choices for optimal real-world applications. Show more

Keywords: APT malware detection, end-point workstations, event ID, deep graph networks, contrastive learning

DOI: 10.3233/JIFS-231548

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4517-4533, 2023

Authors: Zhao, Hu | Hu, Xia | Chen, Gui-Xiu

Article Type: Research Article

Abstract:  In order to give a characterization of the product of (L , M )-fuzzy convex structures, the notion of convex (L , M )-fuzzy hull operators is presented, it is proved that the category of (L , M )-fuzzy convex structures and the category of convex (L , M )-fuzzy hull operators are isomorphic. In particular, the lattices structure of convex (L , M )-fuzzy hull operators and a new characterization of the product of (L , M )-fuzzy convex structures are given.

Keywords: (L, M)-fuzzy convex structures, (L, M)-fuzzy weak hull operators, Sayed’s (L, M)-fuzzy hull operators, convex (L, M)-fuzzy hull operators, product

DOI: 10.3233/JIFS-231909

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4535-4545, 2023

Authors: Wang, Lu

Article Type: Research Article

Abstract: After entering the 21st century, China’s national economy has shown a rapid growth momentum, the comprehensive transportation system has been continuously improved, the road traffic infrastructure has made remarkable achievements, and the modern logistics industry has also risen rapidly and grown rapidly, which has greatly changed the market demand for road transport hubs. The road transport hub is the main node of the road transport network, the hub of passenger and freight distribution of road transport, and the organizational center for the interconnection of road transport and other transport modes and the development of comprehensive transport. Highway transportation hub is …an important part of highway transportation infrastructure and plays an important role in highway transportation. The planning scheme evaluation of highway transportation hub is a multi-attribute decision making (MADM). This paper intends to propose a MADM methodology based on cross-entropy (CE) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for planning scheme evaluation of highway transportation hub. First of all, this paper extends the cross entropy method under the IVIFSs to propose the interval-valued intuitionistic fuzzy number CE(IVIFN-CE) method, it enlarges the application range of the CE method. Secondly, a new MADM model for planning scheme evaluation of highway transportation hub based on IVIFN-CE algorithm is proposed. Show more

Keywords: Multi-attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), CRITIC method cross-entropy (CE), planning scheme evaluation

DOI: 10.3233/JIFS-232668

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4547-4558, 2023

Authors: Tahir, Zaigham | Khan, Hina | Alamri, Faten S. | Aslam, Muhammad

Article Type: Research Article

Abstract: The current work is one step in filling a large void in the research left by the advent of neutrosophic Statistics (NS), a philosophized variant of classical statistics (CS). The philosophy of NS deals with techniques for investigating data that is ambiguous, hazy, or uncertain. The traditional techniques of estimation utilizing auxiliary information work under specific determinate data, which in the case of neutrosophic data may lead to mistakes (over/ under-estimation). This study presents a generalized neutrosophic ratio-type exponential estimator (NRTEE) for estimating location parameters and achieving the lowest mean square error (MSE) possible for interval neutrosophic data (IND). The …offered NRTEE helps to deal with the uncertainty and ambiguity of data. Unlike typical estimators, its findings are not single-valued but rather in interval form, which reduces the possibility of over-or under-estimation caused by single crisp outcomes and also increases the likelihood of the parameter dwelling in the interval. It improves the efficiency of the estimator since we have an estimated interval that contains the unknown value of the population mean with a minimal MSE. The suggested NRTEE’s efficiency is further addressed by utilizing real-life IND of temperature and simulations. A comparison is also performed to establish the superiority of the proposed estimator over the traditional estimators. The limits are calculated and discussed in cases when our suggested estimator is always efficient. The suggested estimator is the most efficient of all estimators and outperformed all others on both IND and classical data. Show more

Keywords: Neutrosophic statistics, classical statistics, estimation, ratio estimators, bias, mean square error

DOI: 10.3233/JIFS-223539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4559-4583, 2023

Authors: Qian, Yurong | Shao, Jinxin | Zhang, Zhe | Leng, Hongyong | Ma, Mengnan | Li, Zichen

Article Type: Research Article

Abstract: In traditional user portrait construction methods, static word vectors can extract only shallow semantic representations, which cannot manage word polysemy. Moreover, the common clustering algorithm K-means has the problems of initial K values and unstable initial centroid selection. A Bert-CK model based on Bert and CK-means+ is proposed. First, Bert is used to extract semantic and syntactic text features at various levels, and word vectors and sentence vectors are obtained according to the context. Then, the CK-means+ algorithm is improved based on canopy and mean calculation. Next, the K value and initial centroid are determined. The sentence vectors are input …to CK-means+ to obtain user classification and topic features. Finally, semantic features and topic features are fused and classified. CK-means+ is evaluated on the Sogou user portrait dataset. The experimental results verify that Bert-CK is better than the baseline model. Show more

Keywords: User profile, bert, canopy, K-means, text classification

DOI: 10.3233/JIFS-224531

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4585-4597, 2023

Authors: Kumari, Rani | Ramachandran, Prakash

Article Type: Research Article

Abstract: The deformation of speech caused by glottic vocal tract is an early bio marker for Parkinson’s disease. A novel idea of Line Spectral Frequency trajectory spectrum image representation of the speech signals of the subjects in Deep Convolution Neural Network is proposed for Parkinson’s disease classification in which the convolution layer automatically learn the features from the input images and no separate feature calculation stage in required. The human vocal tract that produces a short phonetics is assumed as an all-pole Infinite impulse response system and the Line spectral frequency trajectory spectrum images represents the poles of the system and …reflects the voice defects due to Parkinson’s disease. It is shown that the proposed method outperforms the existing state of the art work for two different utterance tasks one for sustained phonation and another for natural running speech dataset. It is demonstrated that the Deep Convolution Neural Network results in a training accuracy of 92.5% for sustained phonation dataset and training accuracy of 99.18% for King’s college running speech dataset. The validation accuracies for both the datasets are 100%. The proposed work is much better than another recent benchmark work in which Mel Frequency Cepstral Coefficient parameters are used in machine learning for Parkinson’s disease detection in running speech. The high performance of the proposed method for King’s college running speech dataset which is collected through mobile device voice recordings, gains attention. Rigorous performance analysis is performed for running speech dataset by using separate isolated test set for repeated 50 trials and the performance metrics are F1 score of 99.37%, sensitivity of 100%, precision of 98.75% and specificity of 99.27%. Show more

Keywords: Deep convolution neural network, line spectral frequency, Parkinson’s disease, running speech, sustained phonation

DOI: 10.3233/JIFS-230183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4599-4615, 2023

Authors: Muhsen, Yousif Raad | Husin, Nor Azura | Zolkepli, Maslina Binti | Manshor, Noridayu

Article Type: Research Article

Abstract:  The Fuzzy-Weighted Zero-Inconsistency (FWZIC) and Fuzzy-Decision-by-Opinion-Score-Method (FDOSM) are considered the recent advance methods. FDOSM generates a ranking for possible alternatives, while FWZIC produces a weight for criterion. Keeping up with the stream of academic publications on the FDOSM and FWZIC methods is complicated. This study aims to provide a comprehensive review of the literature on the latest advanced methods of MCDM in order to reorganize the findings of the previous literature and provide decisive evidence for ongoing research and future studies. Based on previous literature, the current study used the Prisma method to collect data from multiple databases such as …IEEE Xplore®, ScienceDirect, and Web of Science. There were 45 papers discovered relevant to this subject; however, only 23 studies were relevant for the FDOSM & FWZIC study. The results included theoretical and practical implications. Theoretically, additions of new aggregation operators or usage of new fuzzy sets in the FDOSM & FWZIC model to solve the uncertainty problem are the key obstacles. Practically, agriculture and architectural fields are considered to be a hotspot of research. Finally, a number of potential points for future research to develop methods with high certainty and low ambiguity are presented. Show more

Keywords: Multi-criteria decision-making, fuzzy set, FWZIC, FDOSM

DOI: 10.3233/JIFS-230803

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4617-4638, 2023

Authors: Hsu, Pi-Shan | Huang, Chien-Chung | Sung, Wei-Ying | Tsai, Han-Ying | Wu, Zih-Xin | Lin, Ting-Yu | Lin, Kuo-Ping | Liu, Gia-Shie

Article Type: Research Article

Abstract: This study attempts to develop the adaptive neuro-fuzzy inference system (ANFIS) with biogeography-based optimization (BBO) (ANFIS-BBO) for a case study of the actual number of COVID-19 vaccinations in a medical center, considering the variables of the date and time of vaccination, the brand of vaccine, and the number of open appointments on the government network platform in Taiwan. The COVID-19 has brought about a great burden on the health and economy of the world since the end of 2019. Many scholars have proposed a prediction model for the number of confirmed cases and deaths. However, there is still a lack …of research in the prediction model for mass vaccination. In this study, ANFIS-BBO is developed to predict the number of COVID-19 vaccination, and three other forecasting models, support vector machines (SVM), least-square support vector machines (LSSVM) and general regression neural network (GRNN) are employed for forecasting the same data sets. Empirical results show that the ANFIS-BBO with trapezoidal membership function model can achieve better performance than other methods and provide robust predictions for the actual number of COVID-19 mass vaccination. Show more

Keywords: COVID-19, mass vaccination, adaptive neuro-fuzzy inference system, biogeography-based optimization, prediction

DOI: 10.3233/JIFS-231165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4639-4650, 2023

Authors: Sasirekha, N. | Karuppaiah, Jayakumar | Shekhar, Himanshu | Naga Saranya, N.

Article Type: Research Article

Abstract: Cancer is a devastating disease that has far-reaching effects on our culture and economy, in addition to the human lives it takes. Regarding budgetary responsibility, investing just in cancer treatment is not an option. Early diagnosis is a crucial part of the remedy that sometimes gets overlooked. Malignancy is often diagnosed and evaluated using Histopathology Images (HI), which are widely accepted as the gold standard in the field. Yet, even for experienced pathologists, analysing such images is challenging, which raises concerns of inter- and intra-observer variability. The analysis also requires a substantial investment of time and energy. One way that …such an examination may be sped up is by making use of computer-assisted diagnostics devices. The purpose of this research is to create a comprehensive cancer detection system using images of breast and prostate histopathology stained with haematoxylin and eosin (H&E). Proposed here is work on improving colour normalisation methods, constructing an integrated model for nuclei segmentation and multiple objects overlap resolution, introducing and evaluating multi-level features for extracting relevant histopathological image and interpretable information, and developing classification algorithms for tasks such as cancer diagnosis, tumor identification, and tumor class labelling. Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network which obtained statistical kappa value for breast cancer histopathology images shows a high degree of consistency in the classification task, with a range of 0.610.80 for benign and low grades and a range of 0.811.0 for medium and high rates. The Support Vector Machine (SVM), on the other hand, shows an almost perfect degree of consistency (0.811.0) across the several breast cancer picture classifications (benign, low, medium, and high). Show more

Keywords: Breast cancer, Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network, computer-assisted diagnostic systems, histopathology images

DOI: 10.3233/JIFS-231480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4651-4667, 2023

Authors: Sahayaraj, L. Remegius Praveen | Muthurajkumar, S.

Article Type: Research Article

Abstract: Preserving the integrity of log data and using the same for forensic analysis is one of the prime concerns of cloud-oriented applications. Since log data collates sensitive information, providing confidentiality and privacy is of at most importance. For data auditors, maintaining the integrity of the log data is a prime concern. Existing models focus on providing models and frameworks that relies on any third-party entity or the cloud service provider (CSP) to handle the logs, which lacks in securing the integrity due to the presence of the external entities. Sole dependence on CSP is a major flaw together with a …drawback, since the CSP itself is prone to data theft alliance. In this paper, we instantiate a mechanism which maintains the integrity of the log without compromising the performance efficiency of the system. The influence of machine learning classification techniques is leveraged in order to efficiently classify the log data before it is processed. Progressively the log data integrity is maintained through the proposed Propagated Chain of Log Blocks (PCLB), the Hybrid Vector Committed BST (HVCBST) and lightweight Multikey Hybrid Storage (MKHS) structures. The results of the implemented systems have proven to be efficient and tamper proof compared to the existing systems and can be easily rendered in any private or public cloud deployments. Show more

Keywords: Data integrity, cloud, security, log, block chain, encryption, decryption

DOI: 10.3233/JIFS-224585

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4669-4687, 2023

Authors: Hou, Jia-Ning | Zhang, Min | Wang, Jie-Sheng | Wang, Yu-Cai | Song, Hao-Ming

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-230081

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4689-4714, 2023

Authors: Zhang, Yiwen | Zhang, Li | Dong, Yunchun | Chu, Jun | Wang, Xing | Ying, Zuobin

Article Type: Research Article

Abstract: Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and …predicts ratings more precisely by utilizing the first two phases’ outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model. Show more

Keywords: Knowledge graph, rating prediction, collaborative filtering

DOI: 10.3233/JIFS-230795

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4715-4724, 2023

Authors: Zhang, Fang | Wang, Hongjuan | Wang, Lukun | Wang, Yue

Article Type: Research Article

Abstract: Human body pose transfer is to transform the character image from the source image pose to the target pose. In recent years, the research has achieved great success in transforming the human body pose from the source image to the target image, but it is still insufficient in the detailed texture of the generated image. To solve the above problems, a new two-stage TPIT network model is proposed to process the detailed texture of the pose-generated image. The first stage is the source image self-learning module, which extracts the source image features by learning the source image itself and further …improves the appearance details of pose-generated image. The other stage is to change the pose of the figure gradually from the source image pose to the target pose. Then, by learning the feature correlation between source and target images through cross-modal attention, texture transmission between images is promoted to generate finer-grained details of the generated image. A large number of experiments show that the model has superior performance on the Market-1501 and DeepFashion datasets, especially in the quantitative and qualitative evaluation of Market-1501, which is superior to other advanced methods. Show more

Keywords: Posture transfer, self-attention mechanism, dual-tasking mechanism, character image generation, generating adversarial networks

DOI: 10.3233/JIFS-231289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4725-4735, 2023

Authors: Ponniah, Krishna Kumar | Retnaswamy, Bharathi

Article Type: Research Article

Abstract: The internet of things (IoT) has significantly influenced day-to-day life in large industrial systems. The Internet of Things (IoT) offers a platform for information systems to integrate effectively with network servers. In contrast, cyber threats are becoming critical, especially for IoT servers. A strong strategy must be in place to protect the network system from multiple attacks. In order to detect malicious behaviors that deteriorate network performance, an intrusion detection system (IDS) is crucial. An IDS use a detection method to monitor network activity to alert IoT users regularly. This paper proposes a novel IDS for IoT using log-sigmoid kernel …principal component analysis (LSK-PCA) and activation updated deep feed-forward neural network (AU-DFFNN) based dimensionality reduction (DR) and classification technique. Initially, the input data is taken from the NSLKDD dataset and undergoes pre-processing. Afterwards, attribute extraction is carried out, followed by Fisher’s Yates Adapted Golden Eagle Optimizer (FY-GEO) based feature selection. Then, DR of the feature selected data is done using the LSK-PCA model. Finally, the reduced dataset is given as an input to the classifier for classifying the data as attacked and normal data. As a final point, experimental analysis is performed using performance metrics like precision (PR), recall (RC), f-score (FS), accuracy (AC), false alarm rate (FAR) and computational time (CT). The results proved that the proposed work detects intrusion effectively compared to state-of-art techniques. Show more

Keywords: Intrusion Detection System (IDS), Internet of Things (IoT), Golden Eagle Optimizer (GEO), Feed Forward Neural Network (FFNN), Attribute extraction, Dimensionality reduction, Principal Component Analysis (PCA)

DOI: 10.3233/JIFS-223437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4737-4751, 2023

Authors: Ashok Kumar, M. | Saravanan, K.

Article Type: Research Article

Abstract: In multicasting packets of data from a node will be sent to a group of receiver nodes at the same time. Multicasting lowers transmission costs. Energy conservation is critical to a sensor network’s long-term viability. Sensor networks have limited and non-replenishable energy supplies, maximizing network lifetime is crucial in sensor nodes. As a result, clustering has become one of the popular methods for extending the lifetime of an entire system by integrating information at the cluster head. Cluster head (CH) selection is the important serving node in each cluster in the Wireless sensor networks (WSN). This paper introduces a High …Power Node (HPN) multicasting approach which embeds a cluster of sink node data in packet headers to allow receiver for utilizing a approach for transferring multicast packet data via the shortest paths. The proposed Energy efficient multicasting cluster based routing (EEMCR) protocol utilized high power nodes, which shall play a critical role in minimal energy usage. The implementation findings demonstrate that, when compared with the previous methodologies, the suggested algorithm has enhanced in terms of packet delivery ratio (PDR), End to end delivery rate, efficiency and achieves low energy consumption. The proposed EEMCR obtain 95% efficiency. The results are then compared to other existing algorithms to determine the superiority of the proposed methodology. Show more

Keywords: Routing, wireless sensor networks, multicasting, cluster head selection, clustering

DOI: 10.3233/JIFS-223536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4753-4766, 2023

Authors: Nathezhtha, T. | Vaidehi, V. | Sangeetha, D.

Article Type: Research Article

Abstract: In recent days, malicious users try to captivate the consumers using their fraudulent marketing URL post in social networking sites. Such malicious URL posted by fake users in Social Networking Services (SNS) is hard to identify. Therefore, there occurs a need to detect such fraudulent URLs in SNS. In order to detect such URLS, this paper proposes a SNS Fraudulent Detection (SFD) scheme. The proposed SFD scheme includes a Deterministic Finite Automata Tokenization (DFA-T) and Web Crawler (WC) based Neuro Fuzzy System (WC-NFS). DFA-T extracts the URL features and calculates a Penalty Score (PS) based on the malicious words in …the extracted URL. The DFA extracted URL features with PS are fed into WC-NFS. Subsequently, the WC fetches the numeric WC-Index (WCI) value from the URLs which are added to the WC-NFS. The existing URL data set is used to identify the malicious web links and suitable machine learning techniques are used to identify the malicious URLs. From the experimental results, it is found that the proposed SFD provides 92.6 % accuracy in classifying the benign from malicious URLs when compared with the existing methods. Show more

Keywords: Consumer electronics, fraudulent, web crawler, social networking service, malicious users

DOI: 10.3233/JIFS-223569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4767-4775, 2023

Authors: Bai, Zhiqiang | Yang, Zhiyong | Jiang, Yusheng | Gao, Hongji | Sun, Zhengyang | Sun, Wei

Article Type: Research Article

Abstract: The earth pressure balance (EPB) shield tunneling efficiency is greatly affected by the choice of soil transport mode. In this study, the influence of two soil transport modes, such as the continuous belt conveyor and rail train, on the efficiency of shield excavation was analyzed using the Markov chain model. A method was proposed to define the ideal and non-ideal excavation states and quantitatively evaluate the excavation efficiency of the two soil transportation modes of the EPB shield. Based on this model framework, a profitable Markov chain model was established to predict the expected profits of the two soil transportation …modes. The Beijing Metro New Airport Line first-phase project was used as a case study to verify the model established. The results show that under the same conditions, the continuous belt conveyor soil transport mode can have a higher excavation efficiency and expected profit. This advantage gradually increases over time. Show more

Keywords: Markov chain, soil transport, excavation efficiency, expect profit, shield construction

DOI: 10.3233/JIFS-223833

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4777-4790, 2023

Authors: Jegajothi, B. | Kathir, I. | Shukla, Neeraj Kumar | Prakash, R.B.R.

Article Type: Research Article

Abstract: Because of environmental issues and energy crises, significant attention has been received in the domain of renewable and clean energy systems. Solar energy is the most effective source of renewable energy technologies. Recently, photovoltaic (PV) system have become common in grid-linked applications and plays a vital part in power production. MPPT algorithms enable PV systems to capture the maximum available power from the solar panels, regardless of variations in solar irradiance, temperature, and other environmental factors. By continuously tracking the MPP, MPPT techniques ensure that the PV system operates at its highest efficiency, resulting in increased energy harvesting and improved …overall performance. Meanwhile, the frequent modifications in irradiance and temperature pose a major challenging issue which can be resolved by the use of artificial intelligence MPPT methodologies like artificial neural networks (ANN), fuzzy logic (FL), and metaheuristics systems. In this aspect, this work presents a new quasi-oppositional artificial algae optimization (QOAAO) with an adaptive neuro-fuzzy inference system (ANFIS) technique, named QOAAO-ANFIS for maximum efficiency MPPT technique for minimizing the present ripple and power oscillations over the MPP. The presented QOAAO-ANFIS model mainly depends upon the integration of the ANFIS and QOHOA techniques. In addition, the presented QOAAO-ANFIS model involves optimal MF selection of the ANFIS model to estimate the irradiation level and compute PV voltage equivalent to maximal power point. The QOAAO model can be utilized for enhancing the optimization process of membership function variables under varying conditions and awareness of global optima. The simulation result analysis of the QOAAO-ANFIS model takes place in terms of different evaluation measures. Extensive comparative results reported the better performance of the QOAAO-ANFIS model with maximum tracking efficiency of 99.89% and a minimum convergence time of 13.51 ms. Show more

Keywords: Membership function, photovoltaic systems, maximum power point tracking, artificial intelligence, fuzzy logic controller

DOI: 10.3233/JIFS-223889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4791-4805, 2023

Authors: Zheng, Zhangqi | Liu, Yongshan | Zhang, Bing | Ren, Jiadong | Zong, Yongsheng | Wang, Qian | Yang, Xiaolei | Liu, Qian

Article Type: Research Article

Abstract: A software defect is a common cyberspace security problem, leading to information theft, system crashes, and other network hazards. Software security is a fundamental challenge for cyberspace security defense. However, when researching software defects, the defective code in the software is small compared with the overall code, leading to data imbalance problems in predicting software vulnerabilities. This study proposes a heterogeneous integration algorithm based on imbalance rate threshold drift for the data imbalance problem and for predicting software defects. First, the Decision Tree-based integration algorithm was designed following sample perturbation. Moreover, the Support Vector Machine (SVM)-based integration algorithm was designed …based on attribute perturbation. Following the heterogeneous integration algorithm, the primary classifier was trained by sample diversity and model structure diversity. Second, we combined the integration algorithms of two base classifiers to form a heterogeneous integration model. The imbalance rate was designed to achieve threshold transfer and obtain software defect prediction results. Finally, the NASA-MDP and Juliet datasets were used to verify the heterogeneous integration algorithm’s validity, correctness, and generalization based on the Decision Tree and SVM. Show more

Keywords: Software defect, imbalance rate, heterogeneous, integration, threshold shift

DOI: 10.3233/JIFS-224457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4807-4824, 2023

Authors: Jin, Xiu | Li, He | Hou, Yuting

Article Type: Research Article

Abstract: Emerging markets, such as the Chinese financial market, are occasionally subject to extreme risk events that result in investor losses during the investment process. To address the challenge of investment selection amidst market fluctuations, considering the fuzzy uncertainty and tail risk compensation based on the asymmetric perspective, we propose to use the lower VaR ratio and the upper VaR ratio as investment objectives to construct a multi-period credibilistic portfolio selection model. The study reveals that the cumulative returns and terminal wealth of the constructed model surpassed those of the benchmark models, delivering greater social and economic welfare to investors. During …extreme events, investors could promptly adjust their portfolio structure to achieve higher investment returns. Investors who prefer the lower VaR ratio tend to make conservative investment decisions and allocate a higher proportion to defensive assets, such as bonds and risk-free assets. Conversely, investors who favor the upper VaR ratio are inclined to adopt aggressive investment strategies and allocate a larger proportion to high-risk stocks. The findings demonstrate that the proposed model offers differentiated investment decisions, and the research conclusions serve as valuable references for investors engaged in multi-period asset allocation and risk management. Show more

Keywords: Lower VaR ratio, upper VaR ratio, multi-period portfolio selection, generalized fuzzy numbers, credibility measure

DOI: 10.3233/JIFS-224517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4825-4845, 2023

Authors: Onat Bulak, Fatma | Bozkurt, Hacer

Article Type: Research Article

Abstract: In this study, we define soft quasilinear functionals on soft normed quasilinear spaces and we examine some of its qualities. By using the soft quasilinear operator defined in [6 ] we specify and prove some theorem related to the continuity and boundedness of soft quasilinear operators and functionals. Furthermore, we give some examples in favor of the soft quasilinear functionals.

Keywords: Soft set, soft quasilinear space, soft normed quasilinear space, soft quasilinear operator, soft quasilinear functional

DOI: 10.3233/JIFS-230035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4847-4856, 2023

Authors: Zhang, Hengshan | Wang, Yun | Chen, Tianhua

Article Type: Research Article

Abstract: Methods on the basis of the consensus reaching process are prevalent in Group Decision Making (GDM), which typically forces some evaluators to revise initial opinions in order to reach group consensus without being able to precisely reflect original viewpoints. Furthermore, in case the correct opinion is embedded in the hand of the minority, existing methods may not reach the correct consensus. With the aim to tackle these observations, a novel approach of the Positive and Negative Prediction Selection Rate (PNPSR) is proposed on the basis of the Pythagorean Fuzzy Preference Relation (PFPR) which enables to present individuals’ opinions in a …pairwise manner using the linguistic preference relation. The PFPR expressed opinions then serve as input for the computation of the proposed PNPSR, the minimum of which is subsequently selected as the correct one. Finally, the full ranking of the alternatives can be calculated through the proposed iterative algorithm. In the process, the evaluators’ original opinions are not required to modify, and the correct result can be achieved when the minority evaluators provide the correct opinions. Experimental results demonstrate the efficacy of the proposed approach in comparison with two state-of-the-art methods. Show more

Keywords: Group decision making, Pythagorean fuzzy preference relation, positive and negative prediction selection rate, consensus measure, consensus reaching process

DOI: 10.3233/JIFS-230395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4857-4870, 2023

Authors: Hu, Hongqiang | Zhai, Ce | Chu, Yunxia | Feng, Jiu | Shi, Jianfeng | Liu, Xuning | Zhang, Genshan

Article Type: Research Article

Abstract: The prediction of coal and gas outburst is very necessary for the prevention of gas disaster, so an outburst prediction model coupled with feature extraction and feature weighting using optimized classifier is proposed. First, Pearson correlation coefficient(PCC) and symmetric uncertainty(SU) are employed to measure the effective information in outburst sample data. Second, Kernel principal component analysis(KPCA) and linear discriminant analysis(LDA) methods are used to extract the exiting discriminate information, and the extracted linear and nonlinear feature information can effectively reflect significant information of outburst influencing factors. Third, the combination of gradient boost decision tree(GBDT) and grey relation analysis(GRA) is used …to weight and fuse the extracted linear and nonlinear feature components, then form a new feature set as important discriminant information. Forth, the weighted and fused features of the coal and gas outburst influencing factors are used as the input of support vector machine(SVM) classifier with optimized parameters, it can classify outburst states, and the achieved classification accuracy can obtain 95%. Finally, the proposed model and the existing outburst classification models in literatures are used to predict outburst, then the experiment results verify the effectiveness of the proposed model and conclude that the performance of the proposed predication model are significant than present outburst prediction models. Show more

Keywords: Coal and gas outburst, KPCA, LDA, GBDT, GRA, SVM

DOI: 10.3233/JIFS-222979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4871-4884, 2023

Authors: Liu, Weiling | Xu, Jinliang | Ren, Guoqing | Xiao, Yanjun

Article Type: Research Article

Abstract: Due to the dynamic nature of work conditions in the manufacturing plant, it is difficult to obtain accurate information on process processing time and energy consumption, affecting the implementation of scheduling solutions. The fuzzy flexible job shop scheduling problem with uncertain production parameters has not yet been well studied. In this paper, a scheduling optimization model with the objectives of maximum completion time, production cost and delivery satisfaction loss is developed using fuzzy triangular numbers to characterize the time parameters, and an improved quantum particle swarm algorithm is proposed to solve it. The innovations of this paper lie in designing …a neighborhood search strategy based on machine code variation for deep search; using cross-maintaining the diversity of elite individuals, and combining it with a simulated annealing strategy for local search. Based on giving full play to the global search capability of the quantum particle swarm algorithm, the comprehensive search capability of the algorithm is enhanced by improving the average optimal position of particles. In addition, a gray target decision model is introduced to make the optimal decision on the scheduling scheme by comprehensively considering the fuzzy production cost. Finally, simulation experiments are conducted for test and engineering cases and compared with various advanced algorithms. The experimental results show that the proposed algorithm significantly outperforms the compared ones regarding convergence speed and precision in optimal-searching. The method provides a more reliable solution to the problem and has some application value. Show more

Keywords: Fuzzy flexible job shop scheduling, PSO, QPSO, simulated annealing, local search

DOI: 10.3233/JIFS-231640

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4885-4905, 2023

Authors: Yang, Juan

Article Type: Research Article

Abstract: In order to improve the accuracy of English online course teaching effect evaluation results, a paper proposed an English online course teaching effect evaluation method based on ResNet algorithm. The effect of College English online teaching was evaluated from five aspects: pre-class preparation, teaching content, basic skills, ability training, and teaching methods. Each evaluation item was set with seven levels of scoring standards. An evaluation model of the classroom teaching effect was constructed based on convolutional neural network according to the internal relationship between facial expression recognition and classroom teaching effect evaluation. The problem of network depth deepening affecting the …accuracy of evaluation in convolutional neural network models was innovatively solved by utilizing the ResNet algorithm. The evaluation of the effectiveness of English online course teaching was achieved. The experimental results showed that this method could effectively improve the effect of English online course teaching evaluation and improve the teaching quality of English online courses. Show more

Keywords: ResNet algorithm, English online teaching, teaching evaluation, face recognition, convolutional neural network

DOI: 10.3233/JIFS-230048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4907-4916, 2023

Authors: Xu, Wan | Zhang, Yuhao | Yu, Leitao | Zhang, Tingting | Cheng, Zhao

Article Type: Research Article

Abstract: In order to solve the problem that the traditional DWA algorithm cannot have both safety and speed because of the fixed parameters in the complex environment with many obstacles, a parameter adaptive DWA algorithm (PA-DWA) is proposed to improve the robot running speed on the premise of ensuring safety. Firstly, the velocity sampling space is optimized by the current pose of the mobile robot, and a criterion of environment complexity is proposed. Secondly, a parameter-adaptive method is presented to optimize the trajectory evaluation function. When the environment complexity is greater than a certain threshold, the minimum distance between the mobile …robot and the obstacle is taken as the input, and the weight of the velocity parameter is adjusted according to the real-time obstacle information dynamically. The current velocity of the mobile robot is used as input to dynamically adjust the weight of the direction angle parameter. In the Matlab simulation, the total time consumption of PA-DWA is reduced by 47.08% in the static obstacle environment and 39.09% in the dynamic obstacle environment. In Gazebo physical simulation experiment, the total time of PA-DWA was reduced by 26.63% in the case of dynamic obstacles. The experimental results show that PA-DWA can significantly reduce the total time of the robot under the premise of ensuring safety. Show more

Keywords: Speed sampling space, parameter adaptation, DWA, local path planning

DOI: 10.3233/JIFS-221837

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4917-4933, 2023

Authors: Huang, Haojian | Liu, Zhe | Han, Xue | Yang, Xiangli | Liu, Lusi

Article Type: Research Article

Abstract: Dempster-Shafer theory (DST) has attracted widespread attention in many domains owing to its powerful advantages in managing uncertain and imprecise information. Nevertheless, counterintuitive results may be generated once Dempster’s rule faces highly conflicting pieces of evidence. In order to handle this flaw, a new belief logarithmic similarity measure ( BLSM ) based on DST is proposed in this paper. Moreover, we further present an enhanced belief logarithmic similarity measure ( EBLSM ) to consider the internal discrepancy of subsets. In parallel, we prove that EBLSM satisfies several desirable properties, …like bounded, symmetry and non-degeneracy. Finally, a new multi-source data fusion method based on EBLSM is well devised. Through its best performance in two application cases, specifically those pertaining to fault diagnosis and target recognition respectively, the rationality and effectiveness of the proposed method is sufficiently displayed. Show more

Keywords: Dempster-Shafer theory, basic belief assignment, logarithmic similarity measure, belief entropy, data fusion

DOI: 10.3233/JIFS-230207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4935-4947, 2023

Authors: Park, Choonkil | Rehman, Noor | Ali, Abbas | Alahmadi, Reham A. | Khalaf, Mohammed M. | Hila, Kostaq

Article Type: Research Article

Abstract: In clasical logic, it is possible to combine the uniary negation operator ¬ with any other binary operator in order to generate the other binary operators. In this paper, we introduce the concept of (N ∗ , O , N , G )-implication derived from non associative structures, overlap function O , grouping function G and two different fuzzy negations N ∗ and N are used for the generalization of the implication p  → q  ≡ ¬ [p  ∧ ¬ (¬ p  ∨ q )] . We show that (N ∗ , O , N , G )-implication are fuzzy implication without any restricted …conditions. Further, we also study that some properties of (N ∗ , O , N , G )-implication that are necessary for the development of this paper. The key contribution of this paper is to introduced the concept of circledcircG ,N -compositions on (N ∗ , O , N , G )-implications. If ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) - or ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) -implications constructed from the tuples ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) or ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) satisfy a certain property P , we now investigate whether circledcircG ,N -composition of ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) - and ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) -implications satisfies the same property or not. If not, then we attempt to characterise those implications ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) -, ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) -implications satisfying the property P such that circledcircG ,N -composition of ( M 1 ∗ , O ( 1 ) , M 1 , G ( 1 ) ) - and ( M 2 ∗ , O ( 2 ) , M 2 , G ( 2 ) ) -implications also satisfies the same property. Further, we introduced sup-circledcircO -composition of (N ∗ , O , N , G )-implications constructed from tuples (N ∗ , O , N , G ) . Subsequently, we show that under which condition sup-circledcircO -composition of (N ∗ , O , N , G )-implications are fuzzy implication. We also study the intersections between families of fuzzy implications, including R O -implications (residual implication), (G , N )-implications, QL -implications, D -implications and (N ∗ , O , N , G )-implications. Show more

Keywords: Overlape function, grouping function, fuzzy implication, fuzzy negation

DOI: 10.3233/JIFS-222878

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4949-4977, 2023

Authors: Wang, Jing

Article Type: Research Article

Abstract: The traditional text-image confrontation model utilizes a convolutional network in the discriminator to extract image features, yet this fails to involve the spatial relationship between underlying objects, resulting in a poor-quality generated image. To remedy this, a capsule network is proposed to improve the model. The convolutional network in the discriminator is replaced with a capsule network, thereby improving the robustness of the images. Through experiments on the Oxford-102 and CUB datasets, it has been found that the new model can effectively improve the quality of generated text-image. The FID value of the generated flower image decreased by 14.49%, and …the FID value of the generated bird image decreased by 9.64%. Additionally, the Inception Score of images generated on the Oxford-102 and CUB datasets increased by 22.60% and 26.28%, respectively, indicating that the improved model generated richer and more meaningful image features. Show more

Keywords: Generating images, capsule network, generation adversarial network, convolutional network, robustness

DOI: 10.3233/JIFS-223741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4979-4989, 2023

Authors: Li, Yuqi

Article Type: Research Article

Abstract: The method based on entropy was used for the Bayesian optimization. Based on compelling information theory, Entropy Search (ES) and Predictive Entropy Search (PES) maximized information about the unknown function when the loss function reaches the maximum value. However, both methods were plagued by complicated calculations for estimating entropy. The most important motivation of this article is to improve and modularize the entropy search itself, making this method more flexible and effective for model adaptation. After the initial optimization and pruning module processing, a reasonable initial configuration for the complex model was successfully established, further reducing the space required for …secondary optimization hyper-parameter search. The advantage of this method is that, on the one hand, the basic method of Bayesian optimization is used to get the best result of the iteration, while ensuring that the algorithm has theoretical boundedness. On the other hand, through the maximum entropy, the information features of the original space and data set are retained as much as possible to reduce the loss of information due to the initialization process, so as to improve the precision of the secondary optimization of the model. Further, a new algorithm framework is proposed, integrating MES and Sequential Model-Based Optimization (SMBO). With MES as the final module of the whole optimization process, a more accurate and reasonable algorithmic model was built, which lays a solid mathematical basis for the final empirical analysis. Show more

Keywords: Bayesian optimization, SMBO, hyperparameter optimization, entropy search

DOI: 10.3233/JIFS-230470

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4991-5006, 2023

Authors: Chen, Chuen-Jyh | Huang, Chieh-Ni | Yang, Shih-Ming

Article Type: Research Article

Abstract: Weather forecasts are essential to aviation safety. Unreliable forecasts not only cause problems to pilots and air traffic controllers, but also lead to aviation accidents and incidents. To enhance the forecast accuracy, an integrated model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) network is developed to achieve improved weather visibility forecasting. In this model, the CNN acts as the precursor of the LSTM network and classifies weather images to increase the visibility forecasting accuracy achieved with the LSTM network. For a dataset with 1500 weather images, the training, validation, and testing accuracy achieved with the integrated …model is 100.00%, 97.33%, and 97.67%, respectively. On a numerical dataset of 10 weather features over 10 years, the RMSE and MAPE of an LSTM forecast can be reduced by multiple linear regression from RMSE 12.02 to 11.91 and 44.46% to 39.02%, respectively, and further by the Pearson’s correlation coefficients to 10.12 and 36.77%, respectively. By using CNN result as precursor to LSTM, the visibility forecast by integrating both can decrease the RMSE and MAPE to 2.68 and 13.41%, respectively. The integration by deep learning is shown an effective, accurate aviation weather forecast. Show more

Keywords: Aviation weather, convolutional neural network, long short-term memory network, weather forecasting

DOI: 10.3233/JIFS-230483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5007-5020, 2023

Authors: Wang, Yuxian | Li, Zhaowen | Zhang, Jie | Yu, Guangji

Article Type: Research Article

Abstract: Gene selection is an important research topic in data mining. A gene decision space means a real-valued decision information system (RVDIS) where objects, conditional attributes and information values are cells, genes and gene expression values, respectively. This paper explores gene selection in a gene decision space based on information entropy and considers its application for gene expression data classification. In the first place, the distance between two cells in a given decision subspace is constructed. In the next place, the binary relations induced by this decision subspace are defined. After that, some information entropy for a gene decision space are …investigated. Lastly, several gene selection algorithms in a gene decision space are presented by using the presented information entropy. The presented algorithms are applied to gene expression data classifications. Multiple publicly available gene expression datasets are employed to evaluate the gene selection performances of the proposed algorithms, while two commonly-used classifiers, KNN and CART, are utilized to obtain 10 fold cross validation accuracy of classification (ACC ). The classification results demonstrated that the proposed algorithms can lower significantly the number genes selected, achieve the higher ACC , and outperform the other competing methods, such as raw data, Fisher, tSNE, PCA, FMIFRFS and DNEAR, with respect to gene number and ACC . Show more

Keywords: Gene expression data, Gene decision space, Gene selection, Uncertainty measurement

DOI: 10.3233/JIFS-231569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5021-5044, 2023

Authors: Zhang, Yihao | Chen, Ruizhen | Hu, Jiahao | Zhang, Guangjian | Zhu, Junlin | Liao, Weiwen

Article Type: Research Article

Abstract: The key to sequential recommendation modeling is to capture dynamic users’ interests. Existing sequential recommendation methods (e.g., self-attention mechanism) have achieved extraordinary success in modeling users’ interests. However, these models ignore that users have different levels of preferences for different aspects of items, failing to capture users’ most concerning aspects. In addition, they are highly dependent on the quality of training data, which may lead to overfitting of the model when the training data is insufficient. To address the above issues, we propose a novel sequence-aware model (Multi-Aspect Features of Items for Time-Ordered Sequential Recommendation, MFITSRec), which combines the features …of items with user behavior sequences to learn more complex item-item and item-attribute relationships. Moreover, the model uses a self-attention network based on an absolute time relationship, which can better represent the changes in users’ interests and capture users’ preferences for particular aspects of items. Extensive experiments on five datasets demonstrate that our model outperforms various baseline models. In particular, the model’s prediction accuracy has been significantly improved on sparse datasets. Show more

Keywords: Sequential recommendation, multi-aspect preferences of users, data sparsity, absolute time relationship

DOI: 10.3233/JIFS-230274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5045-5061, 2023

Authors: Wen, Haolan | Chen, Yu | Wang, Weizhong | Ding, Ling

Article Type: Research Article

Abstract: Sustainable food consumption and production (SFCP) has become increasingly significant for creating new value, reducing costs, and reducing greenhouse gas emissions. However, there are some challenges and barriers to implementing SFCP in practice. Moreover, current methods for prioritizing barriers to SFCP seldom consider the behavioral preference of experts and interactions among factors, especially with q-Rung orthopair fuzzy set (q-ROFS)-based information. Thus, this study aims to construct a hybrid q-ROFS-based framework for ranking these barriers. First, the q-ROFS is introduced to express the experts’ uncertain information. Then, the q-ROF- CRITIC (CRiteria importance through intercriteria correlation) method is utilized to determine criteria …weights considering the interrelations among barriers. Next, the q-ROF generalized TODIM method is built to rank the barriers to SFCP by considering the impact of experts’ behavioral preferences. Finally, a numerical case of barriers analysis for SFCP is organized to display the application procedures of the constructed ranking method. The result indicates that the top-priority set is education and culture (a 4 ), with the most significant overall dominance value (0.839). Further, a comparison exploration is given to demonstrate the preponderances of the present barriers ranking method. The outcomes demonstrate that the proposed ranking method can provide a synthetic and reliable framework to handle the prioritizing issue for the barriers to SFCP within a complex and uncertain context. Show more

Keywords: Sustainable food consumption and production, q-Rung orthopair fuzzy set, generalized TODIM method, CRITIC approach

DOI: 10.3233/JIFS-230526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5063-5074, 2023

Authors: IssanRaj, R. | Visalakshi, S.

Article Type: Research Article

Abstract: Triple Diode Solar Cell Module (TDSCM) circuit with nine parameters for various environmental circumstances represents the behavior and practical performance of solar cell.The precise extraction of photovoltaic (PV) module parameters is essential for optimising the energy conversion efficiency of PV systems. Usually the equations describing solar panels are implicit in nature, and parameter extraction has been very complicated. The solar cell is mathematically modelled with nonlinear I-V (Current – Voltage) characteristics behavior, and it cannot be directly determined from the PV’s datasheet due to the lack of data offered by the PV manufacturers. On the basis of the technical datasheet …of the photovoltaic module (PV), only four equations can be obtained in single diode, double diode, and triple diode parameters. To be implemented with fifth equation, many researchers have been done with multiple approximations and it becomes with low accuracy, complexity of computation, convergence problem. To resolve these issues, a new multi-objective optimization (GA) genetic algorithm method is prescribed to frame the fifth equation using the Boole rules implemented with the curved area concept. The proposed Boole’s rule based model offers superior non-linearity performance and high precision modelling, and the error shows a significant reduction when compared to the single and double diode approaches used in the existing approach. The effectiveness of the proposed I-V curve characteristics efficiency was improved by the implementation of the proposed Boole’s rule with RMSE error 0.000034. Show more

Keywords: Photovoltaic cell model, solar cell modelling, multi objective genetic algorithm, triple diode model, boole’s rule

DOI: 10.3233/JIFS-230663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5075-5092, 2023

Authors: Zhu, Cunxin | Huang, Xuhong | Chen, Yanyi | Tang, Shengping | Zhao, Nan | Xiao, Weihao

Article Type: Research Article

Abstract: Chinese couplet is one of the important forms of expression in Chinese and even world literature, with its own unique charm and beauty. In order to meet the needs of users who only need one image to obtain corresponding couplets, realize the function of computer automatically writing couplets with images, and improve the literary expression ability of couplets to images, this paper proposes an image based intelligent generative model of couplets. The model first outputs corresponding descriptions based on image extraction features, and then extracts keywords through an improved hybrid algorithm according to the descriptions. Then, based on the keywords, …the Chinese GPT-2 model automatically expands the first line of a couplet, and finally generates the second line of a couplet from the first line of a couplet through the encoding and decoding framework. Through experiments, it has been shown that the generated couplets of the model meet the requirements for image description, and the effectiveness of the model has been confirmed by manual evaluation results. Show more

Keywords: Chinese couplet, image description, keyword extraction, Encoding and decoding framework

DOI: 10.3233/JIFS-231155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5093-5105, 2023

Authors: Segura Dorado, Jhon | Anacona Mopan, Yesid Ediver | Solis Pino, Andrés Felipe | Paz Orozco, Helmer

Article Type: Research Article

Abstract: Colombia exhibits a considerable incidence rate of natural disasters because of its location within the intertropical zone, which exposes it to various meteorological and geological phenomena, including the Nevado del Huila volcano. The identification of suitable areas for the installation of temporary shelters is critical for managing these disasters. However, the task of identifying such locations is a complex problem that involves multiple criteria. This study uses a fuzzy systems approach to identify suitable sites for establishing temporary shelters in the Paez municipality during natural disasters, considering the essential criteria determined by experts through pairwise comparisons. The study results indicate …that responsiveness is the most significant criterion, followed by area profile. Using these criteria, it identified a specific locality in the Paez municipality as suitable for establishing temporary shelters during natural disasters caused by volcanic phenomena. The findings were compared with those obtained from existing scientific literature and validated by experts in natural disasters. The methodological process described in this study provides a valuable tool for public entities to make informed decisions concerning natural disasters in indigenous territories caused by volcanic phenomena. Show more

Keywords: Location temporary shelters, multiple criteria decisions making, analytic network process

DOI: 10.3233/JIFS-231453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5107-5121, 2023

Authors: Sakthi, K. | Nirmal Kumar, P.

Article Type: Research Article

Abstract: Rapid technological advances and network progress has occurred in recent decades, as has the global growth of services via the Internet. Consequently, piracy has become more prevalent, and many modern systems have been infiltrated, making it vital to build information security tools to identify new threats. An intrusion detection system (IDS) is a critical information security technology that detects network fluctuations with the help of machine learning (ML) and deep learning (DL) approaches. However, conventional techniques could be more effective in dealing with advanced attacks. So, this paper proposes an efficient DL approach for network intrusion detection (NID) using an …optimal weight-based deep neural network (OWDNN). The network traffic data was initially collected from three openly available datasets: NSL-KDD, CSE-CIC-IDS2018 and UNSW-NB15. Then preprocessing was carried out on the collected data based on missing values imputation, one-hot encoding, and normalization. After that, the data under-sampling process is performed using the butterfly-optimized k-means clustering (BOKMC) algorithm to balance the unbalanced dataset. The relevant features from the balanced dataset are selected using inception version 3 with multi-head attention (IV3MHA) mechanism to reduce the computation burden of the classifier. After that, the dimensionality of the selected feature is reduced based on principal component analysis (PCA). Finally, the classification is done using OWDNN, which classifies the network traffic as normal and anomalous. Experiments on NSL-KDD, CSE-CIC-IDS2018 and UNSW-NB15 datasets show that the OWDNN performs better than the other ID methods. Show more

Keywords: Intrusion detection system, deep learning, dimensionality reduction, butterfly optimization, k-means clustering, inception v3, multi head attention, deep neural network

DOI: 10.3233/JIFS-231758

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5123-5140, 2023

Authors: Wajahat, Ahsan | He, Jingsha | Zhu, Nafei | Mahmood, Tariq | Nazir, Ahsan | Pathan, Muhammad Salman | Qureshi, Sirajuddin | Ullah, Faheem

Article Type: Research Article

Abstract: Positive developments in smartphone usage have led to an increase in malicious attacks, particularly targeting Android mobile devices. Android has been a primary target for malware exploiting security vulnerabilities due to the presence of critical applications, such as banking applications. Several machine learning-based models for mobile malware detection have been developed recently, but significant research is needed to achieve optimal efficiency and performance. The proliferation of Android devices and the increasing threat of mobile malware have made it imperative to develop effective methods for detecting malicious apps. This study proposes a robust hybrid deep learning-based approach for detecting and predicting …Android malware that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). It also presents a creative machine learning-based strategy for dealing with unbalanced datasets, which can mislead the training algorithm during classification. The proposed strategy helps to improve method performance and mitigate over- and under-fitting concerns. The proposed model effectively detects Android malware. It extracts both temporal and spatial features from the dataset. A well-known Drebin dataset was used to train and evaluate the efficacy of all creative frameworks regarding the accuracy, sensitivity, MAE, RMSE, and AUC. The empirical finding proclaims the projected hybrid ConvLSTM model achieved remarkable performance with an accuracy of 0.99, a sensitivity of 0.99, and an AUC of 0.99. The proposed model outperforms standard machine learning-based algorithms in detecting malicious apps and provides a promising framework for real-time Android malware detection. Show more

Keywords: Android malware detection, deep learning, CNN, LSTM, Drebin dataset

DOI: 10.3233/JIFS-231969

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5141-5157, 2023

Authors: Zhou, Qiaozhen | Wang, Fang | Zhao, Xuanyu | Hu, Kai | Zhang, Yujian | Shan, Xin | Lin, Xin | Zhang, Yupeng | Shan, Ke | Zhang, Kailiang

Article Type: Research Article

Abstract: Resistive random access memory (RRAM) has lots of advantages that make it a promising candidate for ultra-high-density memory applications and neuromorphic computing. However, challenges such as high forming voltage, low endurance, and poor uniformity have hampered the development and application of RRAM. To improve the uniformity of the resistive memory, this paper systematically investigates the HfOx -based RRAM by embedding nanoparticles. In this paper, the HfOx -Based RRAM with and without tungsten nanoparticles (W NPs) is fabricated by magnetron sputtering, UV lithography, and stripping. Comparing the various resistive switching behaviors of the two devices, it can be observed that the …W NPs device exhibits lower switching voltage (including a 69.87% reduction in Vforming and a reduction in Vset /Vreset from 1.4 V/-1.36 to 0.7 V/-1.0 V), more stable cycling endurance (>105 cycles), and higher uniformity. A potential switching mechanism is considered based on the XPS analysis and the research on the fitting of HRS and LRS: Embedding W NPs can improve the device performance by inducing and controlling the conductive filaments (CFs) size and paths. This thesis has implications for the performance enhancement and development of resistive memory. Show more

Keywords: Resistive random access memory (RRAM), HfOx, embedding W nanoparticles, uniformity, conduction mechanism

DOI: 10.3233/JIFS-232028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5159-5167, 2023

Authors: Li, Zhenjiang | Zhang, Qianxue

Article Type: Research Article

Abstract: The writer identification task infers the writer by analyzing the texture, structure, and other representative features of the handwriting. Inspired by the attention mechanism, an end-to-end writer identification model is proposed in this paper, which combines both global features and local features. The Vision Transformer is used as the backbone network, and the Convolutional block attention module (CBAM) is introduced to enhance the ability of global feature awareness of the model. The proposed method is evaluated on two public data sets, IAM and CVL respectively. In the task of word-level writer identification, the accuracy rates in two data sets were …90.1% and 92.3% respectively. In the task of page-level writer identification, the accuracy rates were 98.6% and 99.5%, as a state-of-the-art performance. Show more

Keywords: Biometrics, writer identification, computer vision, neural network, vision transformer

DOI: 10.3233/JIFS-232134

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5169-5179, 2023

Authors: Wang, Chia-Hung | Cai, Jiongbiao | Ye, Qing | Suo, Yifan | Lin, Shengming | Yuan, Jinchen

Article Type: Research Article

Abstract: In recent years, it has been shown that deep learning methods have excellent performance in establishing spatio-temporal correlations for traffic speed prediction. However, due to the complexity of deep learning models, most of them use only short-term historical data in the time dimension, which limits their effectiveness in handling long-term information. We propose a new model, the Multi-feature Two-stage Attention Convolution Network (MTA-CN), to address this issue. The MTA-CN intercepts longer single-feature historical data, converts them into shorter multi-feature data with multiple time period features, and uses the most recent past point as the main feature. Furthermore, two-stage attention mechanisms …are introduced to capture the importance of different time period features and time steps, and a Temporal Graph Convolutional Network (T-GCN) is used instead of traditional recurrent neural networks. Experimental results on both the Los Angeles Expressway (Los-loop) and Shen-zhen Luohu District Taxi (Sz-taxi) datasets demonstrate that the proposed model outperforms several baseline models in terms of prediction accuracy. Show more

Keywords: Traffic speed prediction, attentional mechanisms, temporal dependence, spatial dependence, graph convolutional network

DOI: 10.3233/JIFS-231133

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5181-5196, 2023

Article Type: Retraction

DOI: 10.3233/JIFS-219329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5197-5197, 2023