Journal of Intelligent & Fuzzy Systems - Volume 44, issue 6

Authors: Madhavi, S. | Santhosh, N.C. | Rajkumar, S. | Praveen, R.

Article Type: Research Article

Abstract: In Wireless Sensor Networks (WSNs), resource depletion attacks that focusses on the compromization of routing protocol layer is identified to facilitate a major influence over the network. These resource depletion attacks drain the batter power of the sensor nodes drastically with persistent network disruption. Several protocols were established for handling the impact of Denial of Service (DoS) attack, but majority of them was not able to handle it perfectly. In specific, thwarting resource depletion attack, a specific class of DoS attack was a herculean task. At this juncture, Multicriteria Decision Making Model (MCDM) is identified as the ideal candidate for …evaluating the impact introduced by each energy depletion compromised sensor nodes towards the process of cooperation into the network. In this paper, A Pythagorean Fuzzy Sets-based VIKOR and TOPSIS-based multi-criteria decision-making model (PFSVT-MCDM) is proposed for counteracting with the impacts of resource depletion attacks to improve Quality of Service (QoS) in the network. This PFSVT-MCDM used the merits of Pythagorean Fuzzy Sets information for handling uncertainty and vagueness of information exchanged in the network during the process of data routing. It utilized VIKOR and TOPSIS for exploring the trust of each sensor nodes through the exploration of possible dimensions that aids in detecting resource depletion attacks. The experimental results of PFSVT-MCDM confirmed better throughput of 21.29%, enhanced packet delivery fraction of 22.38%, minimized energy consumptions 18.92%, and reduced end-to-end delay of 21.84%, compared to the comparative resource depletion attack thwarting strategies used for evaluation. Show more

Keywords: Wireless sensor networks, resource depletion attacks, pythagorean fuzzy sets, TOPSIS (Technique For Order Performance By Similarity To Ideal Solution), quality of service, VIKOR (VlseKriterijumska Optimizacija Kompromisno Resenje)

DOI: 10.3233/JIFS-224141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9441-9459, 2023

Authors: Cao, Maojun | Hu, Yingda | Yue, Lizhu

Article Type: Research Article

Abstract: The uncertainty of weight makes the weight density between samples not fixed. Aiming at the problem that the existing CLIQUE clustering algorithm does not consider the weight of object features, which leads to low accuracy, an improved weighted method combined with the thought of posets is proposed. In addition, this method does not need accurate weight assignment, only the weight order can run efficiently. First, the weight order of object features is obtained, and then the partial order weight is applied to the original data to obtain weighted data with weights. Then the traditional CLIQUE algorithm is used to cluster …according to weighted data, and finally the partial order weighted CLIQUE model is obtained. Through the experiment of six groups of data, the results show that: under the given weight sequence constraints, the clustering quality of the weighted CLIQUE model is significantly higher than that of the unweighted model, and the clustering accuracy and other aspects are significantly improved. In this method model, weight information is effectively integrated into the algorithm when only the feature weight order is obtained, and the function of feature weight is fully played to enhance the robustness of clustering results. At the same time, the idea of poset can effectively integrate expert information, and the representation of the nearest neighbor elements in Hasse graph can show the effect intuitively. It is an effective improvement method of CLIQUE clustering algorithm. Show more

Keywords: Clustering, proposed, CLIQUE algorithm, feature weight, Hasse graph

DOI: 10.3233/JIFS-224214

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9461-9473, 2023

Authors: Peng, Peng | Wu, Danping | Han, Fei-Chi | Huang, Li-Jun | Wei, Zhenlin | Wang, Jie | Jiang, Yizhang | Xia, Kaijian

Article Type: Research Article

Abstract: Currently, breast cancer is one of the most common cancers among women. To aid clinicians in diagnosis, lesion regions in mammography pictures can be segmented using an artificial intelligence system. This has significant clinical implications. Clustering algorithms, as unsupervised models, are widely used in medical image segmentation. However, due to the different sizes and shapes of lesions in mammography images and the low contrast between lesion areas and the surrounding pixels, it is difficult to use traditional unsupervised clustering methods for image segmentation. In this study, we try to apply the semisupervised fuzzy clustering algorithm to lesion segmentation in mammography …molybdenum target images and propose semisupervised fuzzy clustering based on the cluster centres of labelled samples (called SFCM_V, where V stands for cluster centre). The algorithm refers to the cluster centre of the labelled sample dataset during the clustering process and uses the information of the labelled samples to guide the unlabelled samples during clustering to improve the clustering performance. We compare the SFCM_V algorithm with the current popular semisupervised clustering algorithm and an unsupervised clustering algorithm and perform experiments on real patient mammogram images using DICE and IoU as evaluation metrics; SFCM_V has the highest evaluation metric coefficient. Experiments demonstrate that SFCM_V has higher segmentation accuracy not only for larger lesion regions, such as tumours, but also for smaller lesion regions, such as calcified spots, compared with existing clustering algorithms. Show more

Keywords: Medical image segmentation, semisupervised, fuzzy clustering algorithm, mammogram

DOI: 10.3233/JIFS-224458

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9475-9493, 2023

Authors: Wang, Zeyuan | Cai, Qiang | Lu, Jianping | Wei, Guiwu

Article Type: Research Article

Abstract: With the development of globalization, companies from all over the world are now more closely connected, and they all play different roles in the industry in which they are located. There are more and more companies in a complete supply chain, which can greatly influence the stability of the supply chain, presents certain challenges. Therefore, choosing suppliers with sustainable development capabilities, especially in the event of interruption, can ensure the stability of the entire supply chain, thereby enhancing the company’s image and competitive advantage in a large-scale competition. The sustainable supplier selection is a classical multiple attribute group decision making …(MAGDM) issues. In this study, the dual probabilistic linguistic EDAS (DPL-EDAS) method is built based on the traditional EDAS method and dual probabilistic linguistic term sets (DPLTSs). Firstly, the DPLTSs is introduced. Then, combine the traditional EDAS method with DPLTSs information, the DPL-EDAS method is established and the computing steps for MAGDM are built. Finally, there are a numerical case involving sustainable supplier selection and some comparisons in this paper. The comparisons are used to illustrate advantages of DPL-EDAS method. Show more

Keywords: Multiple attribute group decision making (MAGDM), dual probabilistic linguistic term sets (DPLTSs), EDAS method, ITARA method, sustainable supplier selection

DOI: 10.3233/JIFS-230117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9495-9512, 2023

Authors: Wang, Encheng | Liu, Xiufeng | Wan, Jiyin

Article Type: Research Article

Abstract: Among the indoor localization algorithms, the algorithm based on traditional Back Propagation Neural Network (BPNN) has the problems of slow convergence and easy to fall into local optimum. It is difficult to apply the algorithm in noisy environments. Therefore, in this paper, we propose a novel indoor localization algorithm where the whole localization process is divided into two parts: data preprocessing and localization output. Data preprocessing means using filtering algorithm to process the Received Signal Strength Indication (RSSI) sequence. It is considered that the initial value of the received sequence has a significant impact on the performance of Kalman Filter …(KF). An improved Kalman Filtering algorithm (DBSCAN-KF) is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. First, the RSSI values that are seriously disturbed by noise in the sequence are removed using the DBSCAN algorithm, and then the RSSI sequences are processed using KF so that the RSSI values can be closer to the theoretical values. The localization output part is to reduce the localization error caused by the BPNN. In this paper, the Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm are combined, and the Differential Evolution Particle Swarm Optimization (DE-PSO) algorithm is proposed. The BPNN weights and thresholds are optimized in parallel, which improves the speed and ability of global optimization search and further avoids the shortcomings of traditional BPNNs that are prone to fall into local optimization in the training process. Experimental results show that the BPNN localization algorithm based on DBSCAN-KF improves the average localization accuracy by 0.26m compared with the BPNN localization algorithm without filtering. After filtering, the localization algorithm based on DE-PSO improved BPNN (DE-PSO-BP) improves the average localization accuracy by about 24% compared with the localization algorithm based on DE-PSO-BP. The localization algorithm based on DE-PSO-BP improves the average localization accuracy by about 61% compared with the traditional BPNN. Show more

Keywords: Indoor localization, RSSI, Kalman filtering, DBSCAN-KF, DE-PSO-BP

DOI: 10.3233/JIFS-230178

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9513-9525, 2023

Authors: Zhang, Xianyong | Wang, Qian | Fan, Yunrui

Article Type: Research Article

Abstract: Feature selection facilitates classification learning and can resort to uncertainty measurement of rough set theory. By fuzzy neighborhood rough sets, the fuzzy-neighborhood relative decision entropy (FNRDE) motivates a recent algorithm of feature selection, called AFNRDE. However, FNRDE has fusion defects for interaction priority and hierarchy deepening, and such fusion limitations can be resolved by operational commutativity; furthermore, subsequent AFNRDE has advancement space for effective recognition. For the measurement reinforcement, an improved measure (called IFNRDE) is proposed to pursue class-level priority fusion; for the algorithm promotion, the corresponding selection algorithm (called AIFNRDE) is designed to improve AFNRDE. Concretely, multiplication fusion of …algebraic and informational measures is preferentially implemented at the class level, and the hierarchical summation generates classification-level IFNRDE. IFNRDE improves FNRDE, and its construction algorithm and granulation monotonicity are acquired. Then, IFNRDE motivates a heuristic algorithm of feature selection, i.e., AIFNRDE. Finally, relevant measures and algorithms are validated by table examples and data experiments, and new AIFNRDE outperforms current AFNRDE and relevant algorithms FSMRDE, FNRS, FNGRS for classification performances. Show more

Keywords: Feature selection, fuzzy neighborhood rough set, uncertainty measure, relative decision entropy, hierarchical fusion

DOI: 10.3233/JIFS-223384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9527-9544, 2023

Authors: Shen, Hanhan | Pan, Xiaodong | Peng, Xiaoyu | Dan, Yexing | Qiao, Junsheng

Article Type: Research Article

Abstract: This paper focuses on simplifying the structure of fuzzy systems and improving the precision. By regarding the fuzzy rule base as a mapping from the vague partition on the input universe to the vague partition on the output universe, we first design a new type of fuzzy system using the complete and continuous fuzzy rule base in terms of vague partitions. We then exploit Weierstrass’s approximation theorem to show that this new type of fuzzy system can approximate any real continuous function on a closed interval to arbitrary accuracy and provide the corresponding approximation accuracy with respect to infinite norms. …We also provide two numerical examples to illustrate the effectiveness of this new type of fuzzy system. Both theoretical and numerical results show that this new type of fuzzy system achieves the quite approximation effect with a few fuzzy rules. Show more

Keywords: Vague partition, Fuzzy system, Fuzzy rule base, Approximation

DOI: 10.3233/JIFS-223542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9545-9563, 2023

Authors: Sathya, V. | Mahendra Babu, G.R. | Ashok, J. | Lakkshmanan, Ajanthaa

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-224586

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9565-9579, 2023

Authors: Yu, Bin | Zhu, Qing | Fu, Yu | Cai, Mingjie

Article Type: Research Article

Abstract: Forecasting is making predictions about what will happen or how things will change. This can help people avoid blindness and losses and play a significant role in their lives. In multi-attribute prediction problems, the correlation between attributes is often ignored, which affects prediction accuracy. Based on fuzzy rough sets and logistic regression, this paper proposes a new logistic regression method that fully considers attribute correlation, namely a twin logistic regression method based on attribute-oriented fuzzy rough sets. Firstly, attribute-oriented fuzzy rough sets are studied and analyzed. Then, the optimistic and pessimistic predictions are achieved by fuzzy rough sets and logistic …regression, and the final result is obtained by fusing the optimistic and pessimistic predictions. Finally, the effectiveness of the twin logistic regression method is verified. Show more

Keywords: Attribute-oriented fuzzy rough set, logistic regression, twin logistic regression based on attribute-oriented fuzzy rough set, multi-attribute prediction

DOI: 10.3233/JIFS-222986

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9581-9597, 2023

Authors: Li, Bing | Cao, Yuwei | Li, Yongkun

Article Type: Research Article

Abstract: In this paper, the existence, uniqueness and global exponential stability of pseudo almost periodic solutions for a class of octonion-valued neutral type high-order Hopfield neural network models with D operator are established by using the Banach fixed point theorem and differential inequality techniques. Compared with most existing models, in this class of networks, all connection weights and activation functions are assumed to be octonion-valued functions except for time delays. And unlike most of the existing methods of studying octonion-valued neural networks, our method is a non-decomposition method, that is, the method of directly studying octonion-valued systems. The results and …methods in this paper are new. In addition, an example and its numerical simulation are given to illustrate the feasibility of our results. Show more

Keywords: Octonion, neutral type neural network, D operator, pseudo almost periodic solution, global exponential stability

DOI: 10.3233/JIFS-223766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9599-9613, 2023

Authors: Zhu, Yakun | Gong, Weiqiang | Lu, Xuesong | Wang, Haixian

Article Type: Research Article

Abstract: Using functional neuroimaging, electrophysiological techniques and neural data processing techniques, neuroscientists have found that mathematically gifted adolescents exhibit unusual neurocognitive features in the activation of task-related brain regions. Hemispheric information interaction, functional reorganization of networks, and utilization of task-related brain regions are beneficial to rapid and efficient task processing. Based on Granger causality channel selection, the transfer entropy (TE) value between effective channels was computed, and the information flow patterns in the directed functional brain networks derived from electroencephalography (EEG) data during deductive reasoning tasks were explored. We evaluated the workspace configuration patterns of the brain network and the global …integration characteristics of separated brain regions using node strength, motif, directed clustering coefficient and characteristic path length in the brain networks of mathematically gifted adolescents with effective connectivity. The empirical results demonstrated that a more integrated functional network at the global level and a more efficient clique at the local level support a pattern of workspace configuration in the mathematically gifted brain that is more conducive to task-related information processing. Show more

Keywords: Effective connectivity, EEG, mathematically gifted adolescents, information flow, graph theory

DOI: 10.3233/JIFS-223819

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9615-9626, 2023

Authors: Varish, Naushad | Hasan, Mohammad Kamrul | Khan, Asif | Zamani, Abu Taha | Ayyasamy, Vadivel | Islam, Shayla | Alam, Rizwan

Article Type: Research Article

Abstract: This paper proposed a novel texture feature extraction technique for radar remote sensing image retrieval application using adaptive tetrolet transform and Gray level co-occurrence matrix. Tetrolets have provided fine texture information in the radar image. Tetrominoes have been employed on each decomposed radar image and best pattern of tetrominoes has been chosen which represents the better radar image geometry at each decomposition level. All three high pass components of the decomposed radar image at each level and low pass component at the last level are considered as input values for Gray level co-occurrence matrix (GLCM), where GLCM provides the spatial …relationship among the pixel values of decomposed components in different directions at certain distances. The GLCMs of decomposed components are computed in (1). (0, π/2, π, 3π/2), (2). (π/4, 3π/4, 5π/4, 7π/4) (3). (0, π/4, π/2, 3π/4, π, 3π/2, 5π/4, 7π/4) directions individually and subsequently a texture feature descriptor is constructed by computing statistical parameters from the corresponding GLCMs. The retrieval performance is validated on two standard radar remote sensing image databases: 20-class satellite remote sensing dataset and 21-class land-cover dataset. The average metrices i.e., precision, recall and F-score are 61.43%, 12.29% and 20.47% for 20-class satellite remote sensing dataset while 21-class land-cover dataset have achieved 67.75%, 9.03% and 15.94% average metrices. The retrieved results show the better accuracy as compared to the other related state of arts radar remote sensing image retrieval methods. Show more

Keywords: Radar remote sensing image retrieval, adaptive tetrolet transform, gray level Co-occurrence matrix, prediction learning, statistical parameters

DOI: 10.3233/JIFS-224083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9627-9650, 2023

Authors: Du, Baigang | Zha, Dahu | Guo, Jun | Yu, Xiaobing

Article Type: Research Article

Abstract: The water transmission and distribution process of the water supply pump station of the water purification plant plays a key role in the entire urban water supply system. When the requirements of water supply quantity and water pressure are satisfied, the reduction of operating energy consumption of the pump set and improvement of its service life are urgent problems. Therefore, to reduce the cost of water supply pump station, a mathematical model is established to minimize the energy consumption of pump group, the number of pump switches and the load balancing in this paper. In order to solve the pump …scheduling problem, a two-stage strategy based on genetic algorithm is proposed. In stage one, the frequency conversion ratio and the number of pumps needed to be turned on at the lowest energy consumption are calculated. In stage two, through the improved genetic algorithm and iterative way to reduce the number of pump switches and load balancing. Finally, a case study from a real waterworks in Suzhou, China is used to verify the validity of the proposed model. Numerical results reveal that the improved genetic algorithm outperforms the competing algorithms. In addition, a proper sensitivity analysis allows assessing the effects under different pump operating conditions. Show more

Keywords: Pump scheduling, load balancing, improved genetic algorithm

DOI: 10.3233/JIFS-224245

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9651-9669, 2023

Authors: Hashmi, Mohammad Farukh | Naik, Banoth Thulasya | Keskar, Avinash G.

Article Type: Research Article

Abstract: Computer vision algorithms based on deep learning have evolved to play a major role in sports analytics. Nevertheless, in sports like table tennis, detecting the ball is a challenge as the ball travels at a high velocity. However, the events in table tennis games can be detected and classified by obtaining the locations of the ball. Therefore, existing methodologies predict the trajectories of the ball but do not detect and classify the in-game events. This paper, therefore, proposes a ball detection and trajectory analysis (BDTA) approach to detect the location of the ball and predict the trajectory to classify events …in a table tennis game. The proposed methodology is composed of two parts: i) Scaled-YOLOv4 which can detect the precise position of the ball ii) Analysis of trajectory based on ball coordinates to detect and classify the events. The dataset was prepared and labeled as a ball after enhancing the frame resolution with a super-resolution technique to get the accurate position of the ball. The proposed approach demonstrates 97.8% precision and 98.1% f1-score in detecting the location of the ball and 97.47% precision and achieved 97.8% f-score in classifying in-game events. Show more

Keywords: Table tennis sport, ball detection, event classification, Scaled-YOLOv4, Trajectory analysis

DOI: 10.3233/JIFS-224300

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9671-9684, 2023

Authors: Huang, Juan | Zhang, Chaoren

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-230572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9685-9696, 2023

Authors: Shanmuga Priya, K. | Vasanthi, S.

Article Type: Research Article

Abstract: An emotion is a conscious logical response that varies for different situations in women’s life. These mental responses are caused by physiological, cognitive, and behavioral changes. Gender-based violence undermines the participation of women in decision-making, resulting in a decline in their quality of life. More accurate and automatic classification of women’s emotions can enhance human-computer interfaces and security in real time. There are some wearable technologies and mobile applications that claim to ensure the safety of women. However, they rely on limited social action and are ineffective at ensuring women’s safety when and where it is needed. In this work, …a novel CDB-LSTM network has been proposed to accurately classify the emotions of women in seven different classes. The electroencephalogram (EEG) offers non-radioactive methods of identifying emotions. Initially, the EEG signals are preprocessed and they are converted into images via Time-Frequency Representation (TPR). A smoothed pseudo-Wigner-Ville distribution (SPWVD) is employed to convert the EEG time-domain signals into input images. Consequently, these converted images are given as input to the Convolutional Deep Belief Network (CDBN) for extracting the most relevant features. Finally, Bi-directional LSTM is used for classifying the emotions of women into seven classes namely: happy, relax, sad, fear, anxiety, anger, and stress. The proposed CDB-LSTM network preserves the high accuracy range of 97.27% in the validation phase. The proposed CDB-LSTM network improves the overall accuracy by 6.20% 32.98% 6.85% and 3.30% better than CNN-LSTM, Multi-domain feature fusion model, GCNN-LSTM and CNN with SVM and DT respectively. Show more

Keywords: Women safety, EEG signals, emotion classification, smoothed pseudo-wigner-ville distribution, convolutional deep belief network, bi-directional LSTM

DOI: 10.3233/JIFS-221825

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9697-9707, 2023

Authors: Chitra, R. | Prabakaran, K.

Article Type: Research Article

Abstract: Accumulation of q ˜ -rung picture fuzzy information plays an essential part in decision-making situations. q ˜ -rung picture fuzzy sets can handle the uncertain information more precisely and flexibly because of the presence of parameter q ˜ . Also the Frank t-norm and t-conorm operations perform suitably for the data accumulation with the operational parameter. In this paper, we introduce q ˜ -Rung picture fuzzy Frank weighted averaging operator and q ˜ -rung picture …fuzzy Frank weighted geometric operator by extending q ˜ -rung orthopair fuzzy Frank arithmetic and geometric aggregation operators respectively. We establish an algorithm to address the tedious decision-making problems using these operators. Eventually, we discuss a multiple attribute decision-making problem to demonstrate the utility and efficacy of the proposed method. A comparison of existing methods is made to reveal the supremacy and benefits of our proposed method. Show more

Keywords: Accumulation, q-rung picture fuzzy, Frank weighted averaging, Frank weighted geometric, decision-making

DOI: 10.3233/JIFS-221889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9709-9721, 2023

Authors: Nguyen, Tuan Anh

Article Type: Research Article

Abstract: This article studies the instability of automobiles when steering at high speeds. In this article, the model of spatial dynamics is used to simulate vehicle oscillation. Besides, the model of nonlinear double-track dynamics is also combined to determine the effects of the wheel when steering. To limit the instability when steering, the hydraulic stabilizer bar is suggested. The performance of the system depends on the previously designed controller. The FLC algorithm with two inputs is used to control the operation of the system. The membership function and fuzzy rules are determined based on the designer’s experience. The simulation is performed …by MATLAB software with three specific steering cases. In each case, the speed of the vehicle will be increased gradually. The results of the article show that the value of the roll angle is greatest in the third case, corresponding to the speed of v3  = 100 (km/h). If the vehicle does not have a stabilizer bar, the vehicle can roll over at any time. In contrast, when the active stabilizer bar was combined with the proposed FLC algorithm, the vehicle’s stability was significantly improved. The vehicle’s roll angle and the difference in vertical force at the wheels were also significantly reduced when using this algorithm for the stabilizer bar model. This result should be further verified through the experimental process. Show more

Keywords: Rollover, active stabilizer bar, nonlinear dynamics model, fuzzy control

DOI: 10.3233/JIFS-222780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9723-9741, 2023

Authors: Huang, Shuangliu | Chen, Huazai

Article Type: Research Article

Abstract: As one of the teaching models that promote the orderly development of vocational education in China, the integration of industry and education has been recognized by all sectors of society in China’s many years of practice. In recent years, with the strong advocacy of the education sector in China, its development speed has been rapidly improved. Rural vocational education in China has also actively implemented and innovated the teaching mode of integration of industry and education, which has trained more excellent talents for agricultural development in various regions. The quality evaluation of industry-education integration for rural vocational education in the …perspective of rural revitalization is viewed as the multiple attribute group decision making (MAGDM). In this paper, the probabilistic linguistic Mixed Aggregation by Comprehensive Normalization Technique (PL-MACONT) method is built for MAGDM. At last, to verify the validity of the extended method, a numerical example to further account for quality evaluation of industry-education integration for rural vocational education in the perspective of rural revitalization is put into use. Show more

Keywords: Multiple attribute group decision making (MAGDM), Probabilistic linguistic term sets (PLTSs), Mixed Aggregation by Comprehensive Normalization Technique (MACONT), entropy weight method (EWM), quality evaluation of industry-education integration

DOI: 10.3233/JIFS-223856

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9743-9755, 2023

Authors: Wei, Chun | Shi, Haiyan | Liu, Baoliang | Zhang, Zhiqiang | Wen, Yanqing

Article Type: Research Article

Abstract: A system undergoes a failure process in which the internal degradation and external loads are independent and compete with each other. In the reliability model of competitive failure, the threshold of failure is often a dynamic process that changes with time. Based on this, this paper constructs a model in which the failure threshold is an uncertain Liu process and applies it to the competitive failure reliability process, where soft and hard failure thresholds are modeled by different Liu processes. In the absence of a large amount of failure data, uncertainty theory is used as a tool to analyze the …belief reliability and mean time to failure of the system. Taking gas-insulated substation (GIS) as an example, the effect of parameters on belief reliability is analyzed, and the reliability under Liu process threshold and constants threshold is compared, which shows the validity of the prosed model. Show more

Keywords: Belief reliability, uncertain failure threshold, uncertain liu process, uncertainty theory

DOI: 10.3233/JIFS-224057

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9757-9767, 2023

Authors: Kalaivani, K. | Kshirsagarr, Pravin R. | Sirisha Devi, J. | Bandela, Surekha Reddy | Colak, Ilhami | Nageswara Rao, J. | Rajaram, A.

Article Type: Research Article

Abstract: The electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) are all very useful diagnostic techniques. The widespread availability of mobile devices plus the declining cost of ECG, EEG, and EMG sensors provide a unique opportunity for making this kind of study widely available. The fundamental need for enhancing a country’s healthcare industry is the ability to foresee the plethora of ailments with which people are now being diagnosed. It’s no exaggeration to say that heart disease is one of the leading causes of mortality and disability in the world today. Diagnosing heart disease is a difficult process that calls for much …training and expertise. Electrocardiogram (ECG) signal is an electrical signal produced by the human heart and used to detect the human heartbeat. Emotions are not simple phenomena, yet they do have a major impact on the standard of living. All of these mental processes including drive, perception, cognition, creativity, focus, attention, learning, and decision making are greatly influenced by emotional states. Electroencephalogram (EEG) signals react instantly and are more responsive to changes in emotional states than peripheral neurophysiological signals. As a result, EEG readings may disclose crucial aspects of a person’s emotional states. The signals generated by electromyography (EMG) are gaining prominence in both clinical and biological settings. Differentiating between neuromuscular illnesses requires a reliable method of detection, processing, and classification of EMG data. This study investigates potential deep learning applications by constructing a framework to improve the prediction of cardiac-related diseases using electrocardiogram (ECG) data, furnishing an algorithmic model for sentiment classification utilizing EEG data, and forecasting neuromuscular disease classification utilizing EMG signals. Show more

Keywords: Electrocardiography (ECG), electroencephalography (EEG), electromyographic (EMG), deeplearning techniques, prediction, heart attack, emotion recognition, neuromuscular disease, R-CNN

DOI: 10.3233/JIFS-230399

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9769-9782, 2023

Authors: Kianifar, Mohammad Ali | Motallebi, Hassan | Bardsiri, Vahid Khatibi

Article Type: Research Article

Abstract: Dynamic Classifier Selection (DCS) techniques aim to select the most competent classifiers from an ensemble per test sample. For each test sample, only a subset of the most competent classifiers is used to estimate its target value. The performance of the DCS highly depends on how we define the local region of competence, which is a local region in the feature space around the test sample. In this paper, we propose a new definition of region of competence based on a new proximity measure. We exploit the observed similarities between traffic profiles at different links, days and hours to obtain …similarities between different values. Furthermore, long-term traffic pattern prediction is a complex problem and most of the traffic prediction literature are based on time-series and regression approaches and their prediction time is limited to next few hours or days. We tackle the long-term traffic pattern prediction as a classification of discretized traffic indicators to improve the accuracy of urban traffic pattern forecasting of next weeks by using DCS. We also employ two different link clustering methods, for grouping traffic links. For each cluster, we train a dynamic classifier system for predicting the traffic variables (flow, speed and journey time). Our results on strategic road network data shows that the proposed method outperforms the existing ensemble and baseline models in long-term traffic prediction. Show more

Keywords: Long-term traffic prediction, monthly SRN data set, traffic link clustering, dynamic classifier selection, region of competence

DOI: 10.3233/JIFS-220759

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9783-9797, 2023

Authors: Ramesh, Pinapilli | Yadaiah, Narri

Article Type: Research Article

Abstract: This paper presents the design and development of Brain Emotional Learning based adaptive Type-2 Fuzzy Systems for control of dynamical systems. The BEL controller belongs to the class of bio inspired controllers, as its architecture is based on limbic system of human brain and is capable of providing solutions for complex real time problems. In this work, dynamics of Brain Emotional Learning are used for the adaptation of membership functions in the design of Type-2 Fuzzy Logic Controllers. The stability of the overall system is analysed through Lyapunov Yakubovich’s criteria. The proposed approach is validated on the benchmark system such …as inverted pendulum, CSTR and Ship heading control through simulation and in real-time environment using OPAL RT OP5600. Show more

Keywords: Type-2 fuzzy logic controller, brain emotional learning, adaptive memberships functions

DOI: 10.3233/JIFS-222143

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9799-9820, 2023

Authors: Wang, Yuan | Yu, Xiaobing | Wang, Xuming

Article Type: Research Article

Abstract: Multi-verse optimizer (MVO) is a novel nature-inspired algorithm that has been applied to solve many practical optimization problems. Nevertheless, the original MVO has problems of low convergence speed and accuracy of final solutions. Besides, the failure to strike a balance between exploration and exploitation and the easiness of falling into local optimum in the early stages makes MVO hard to converge. In this paper, we propose a novel hybrid algorithm called Hybrid Queuing Search algorithm with MVO (HQS-MVO) by introducing Queuing Search Algorithm (QSA) and Metropolis rule to overcome these shortcomings. The introduction of QSA is to improve the accuracy …of final solutions. At the same time, the Metropolis rule is employed to prevent the algorithm from falling into the local optimum, thus improving the convergence speed of the original MVO. Then, we compare the performance of HQS-MVO on 30 benchmark functions of CEC2014 and 10 benchmark functions of CEC2019 with the other four related algorithms and three latest algorithms. The results show that HQS-MVO has the most accurate solutions in most cases compared with other seven algorithms in most cases, and gains the lowest standard deviations. Moreover, we make convergence curve of the eight algorithms. Compared with other algorithms, HQS-MVO shows outstanding performances and converge faster in general. Finally, we apply the proposed algorithm in a real engineering optimization problem and compare its performance with other algorithms, the results show that HQS-MVO is still the best one in problem of designing of gear train. Show more

Keywords: Multi-verse optimizer, queuing searching algorithm, metropolis rule

DOI: 10.3233/JIFS-223369

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9821-9845, 2023

Authors: Zhu, Xingchen | Wu, Xiaohong | Wu, Bin | Zhou, Haoxiang

Article Type: Research Article

Abstract: The fuzzy c-mean (FCM) clustering algorithm is a typical algorithm using Euclidean distance for data clustering and it is also one of the most popular fuzzy clustering algorithms. However, FCM does not perform well in noisy environments due to its possible constraints. To improve the clustering accuracy of item varieties, an improved fuzzy c-mean (IFCM) clustering algorithm is proposed in this paper. IFCM uses the Euclidean distance function as a new distance measure which can give small weights to noisy data and large weights to compact data. FCM, possibilistic C-means (PCM) clustering, possibilistic fuzzy C-means (PFCM) clustering and IFCM are …run to compare their clustering effects on several data samples. The clustering accuracies of IFCM in five datasets IRIS, IRIS3D, IRIS2D, Wine, Meat and Apple achieve 92.7%, 92.0%, 90.7%, 81.5%, 94.2% and 88.0% respectively, which are the highest among the four algorithms. The final simulation results show that IFCM has better robustness, higher clustering accuracy and better clustering centers, and it can successfully cluster item varieties. Show more

Keywords: Fuzzy clustering, FCM, PCM, Euclidean distance, distance function

DOI: 10.3233/JIFS-223576

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9847-9862, 2023

Authors: Bolourchi, Pouya | Ghasemzadeh, Aman

Article Type: Research Article

Abstract: In bioinformatics studies, many modeling tasks are characterized by high dimensionality, leading to the widespread use of feature selection techniques to reduce dimensionality. There are a multitude of feature selection techniques that have been proposed in the literature, each relying on a single measurement method to select candidate features. This has an impact on the classification performance. To address this issue, we propose a majority voting method that uses five different feature ranking techniques: entropy score, Pearson’s correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and t -test. By using a majority voting approach, only the features that appear in …all five ranking methods are selected. This selection process has three key advantages over traditional techniques. Firstly, it is independent of any particular feature ranking method. Secondly, the feature space dimension is significantly reduced compared to other ranking methods. Finally, the performance is improved as the most discriminatory and informative features are selected via the majority voting process. The performance of the proposed method was evaluated using an SVM, and the results were assessed using accuracy, sensitivity, specificity, and AUC on various biomedical datasets. The results demonstrate the superior effectiveness of the proposed method compared to state-of-the-art methods in the literature. Show more

Keywords: Classification, correlation coefficient, feature selection, feature ranking, gene data, majority

DOI: 10.3233/JIFS-224029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9863-9877, 2023

Authors: Mecheri, Karima | Klai, Sihem | Souici-Meslati, Labiba

Article Type: Research Article

Abstract: Web service recommender systems have a fundamental role in the selection, composition and substitution of services. Indeed, they are used in several application areas such as Web APIs and Cloud Computing. Likewise, Deep Learning techniques have brought undeniable advantages and solutions to the challenges faced by recommendations in all areas. Unfortunately, the field of Web services has not yet benefited well from these deep methods, moreover, the works using these methods for Web services domain are very recent compared to the works of other fields. Thus, the objective of this paper is to study and analyze state-of-the-art work on Web …services recommender systems based on Deep Learning techniques. This analysis will help readers wishing to work in this field, and allows us to direct our future work concerning the Web services recommendation by exploiting the advantages of Deep Learning techniques. Show more

Keywords: Deep learning, recommendation systems, web services, mashup, quality of service, performance evaluation metrics

DOI: 10.3233/JIFS-224565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9879-9899, 2023

Authors: Qu, Ying | Chen, Hong

Article Type: Research Article

Abstract: During an emergency, the negative Internet public opinion in colleges and universities, especially the negative endogenous public opinion, will have a serious impact on the reputation of colleges and universities. It is of great significance to find out the negative influencing factors of endogenous public opinion and explore the mechanism of public opinion dissemination for resolving the crisis of public opinion in universities. The existing research does not distinguish the endogenous Internet public opinion in colleges and universities from the general Internet public opinion in colleges and universities, and the SIR model adopted fails to fully reflect the difference between …students and other dissemination subjects of endogenous public opinion in campus. In addition, various research methods and models currently used focus on the static expression of dissemination results, and the explanation of results is insufficient. The reason is that they do not well express the dynamic interaction mechanism between influencing factors and the dynamic conversion rate between roles. In this study, based on the improved infectious disease model and system dynamics theory, AnyLogic software is used to simulate the improved SNIDR model of infectious disease, to analyze the sensitivity of school supervision, school intervention, school response time and information transparency and to study the dynamic conversion rate between different roles. The SNIDR model effectively simulates the process of endogenous public opinion dissemination in colleges and universities after emergencies. The results show that, what has the greatest impact on the dissemination of public opinion is the school’s supervision and intervention efforts, which can suppress the dissemination from the source. Information transparency is an auxiliary variable and cannot function independently. During the dissemination period, the timelier the school responds, the faster the spreaders will drop to zero, and the better it will be to control the secondary dissemination of public opinion. Show more

Keywords: SNIDR model, governance strategies, internet public opinion, dissemination mechanism, emergency

DOI: 10.3233/JIFS-230002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9901-9917, 2023

Authors: Liu, Biyu | Chen, Ting | Yang, Haidong | Segerstedt, Anders

Article Type: Research Article

Abstract: Suppliers significantly affect the effectiveness of sustainable supply chain management. Hence, it is extremely important to evaluate and select suppliers scientifically and objectively. Based on the theory of triple bottom line (economic, social, and environmental dimension) and a balanced scorecard, a measureable supplier evaluation framework in a sustainable supply chain is first formulated. Second, to reduce the defects of the single weight method, the subjective and objective weights of evaluation indicators are determined by combining the fuzzy best-worst method (BWM) and the entropy method, and then the combination weights are obtained through linear weighting. Third, the grey relational technique for …order performance by similarity to ideal solution (TOPSIS) method is further adopted to evaluate and rank the suppliers. Finally, a case study illustrates and demonstrates the availability of the proposed supplier evaluation index system and evaluation method. Subsequently, some suggestions are proposed according to the results. Show more

Keywords: Sustainable supply chain management, supplier evaluation, the fuzzy BWM, grey relational, TOPSIS

DOI: 10.3233/JIFS-212996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9919-9932, 2023

Authors: Gao, Wang | Ni, Mingyuan | Deng, Hongtao | Zhu, Xun | Zeng, Peng | Hu, Xi

Article Type: Research Article

Abstract:  As people increasingly use social media to read news, fake news has become a major problem for the public and government. One of the main challenges in fake news detection is how to identify them in the early stage of propagation. Another challenge is that detection model training requires large amounts of labeled data, which are often unavailable or expensive to acquire. To address these challenges, we propose a novel Fake News Detection model based on Prompt Tuning (FNDPT). FNDPT first designs a prompt-based template for early fake news detection. This mechanism incorporates contextual information into textual content and extracts …relevant knowledge from pre-trained language models. Furthermore, our model utilizes prompt-based tuning to enhance the performance in a few-shot setting. Experimental results on two real-world datasets verify the effectiveness of FNDPT. Show more

Keywords: Fake news detection, few-shot, prompt-based tuning, pre-trained language model

DOI: 10.3233/JIFS-221647

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9933-9942, 2023

Authors: Geo Jenefer, G. | Deepa, A.J.

Article Type: Research Article

Abstract: Globally, diabetes directly causes 1.5 million fatalities each year. It is necessary to predict such diseases at an earlier stage and cure them. Since modern healthcare data comprises huge amounts of information, it is tough to process such data in conventional databases. Previously, various machine learning (ML) algorithms were used to predict diabetics, and their performance was evaluated. But still, those existing algorithms result in poor accuracy and performance.This work proposes a FOCB (Firefly Optimization-based CatBoost) classifier for predicting diabetes. The PIMA Indian diabetic dataset has been taken as the input dataset. The proposed FOCB algorithm has been compared with …various machine learning algorithms. From the results, we can see that the FOCB classifier gives the best accuracy of 96% with improved performance. The proposed system has been compared with other FO-based machine learning algorithms like NB, KNN, RF, AB, GB, XGB, CNN, DBN, and CB, and it has been proven that CB based on FO produces better accuracy with less hamming loss. Show more

Keywords: CatBoost(CB), feature scaling, machine learning

DOI: 10.3233/JIFS-223105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9943-9954, 2023

Authors: Dhiyanesh, B. | Rameshkumar, M. | Karthick, K. | Radha, R.

Article Type: Research Article

Abstract: Healthcare data is the most sensitive information for processing through machine learning and cloud computing in the various healthcare organizations. Electronic Health Record (EHR) manipulation are now on the rise, and we need to focus on using the data generated by the healthcare applications. Many sensitive data are associated with various health care domains, particularly neurology and cardiology. Previous approaches, such as manual data records, had significant disadvantages, and hence disease prediction based on the above records was found ineffective resulting with improper diagnosis on the patients. These data records require special attention, and current frameworks focused on these areas …must implement sophisticated technologies to predict specific patterns. To address the above concerns, the proposed work incorporates the integration of Neuro Fuzzy Logistic Regression (NFLR) machine learning algorithm and cloud computing storage management to solve these problems. The usage of cloud storage reduces data duplication while handling the storage of EHRs where the proposed ML algorithm accurately predict the disease. In the proposed research, the features are extracted using a specific algorithm –Self-organizing Clustering (SOC) which forms a clustered data with highest weight. To select the maximum number of features, and to predict the disease risk factors, the S2 NO algorithm and NFLR algorithms are used in this work. Further, the database storage estimation with fuzzy rules, logistic analysis, and other benefits such as experimental learning of different ML tools, data privacy constraints related to healthcare are considered in this paper. Show more

Keywords: Neuro-Fuzzy Logistic Regression (NFLR), Social Spider Neural Optimization (S2NO), Self-organizing Clustering (SOC), Electronic Health Record (EHR), Healthcare Medical database

DOI: 10.3233/JIFS-223280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9955-9964, 2023

Authors: Zhang, Yunqi | Sheng, Yuhong

Article Type: Research Article

Abstract:  Risk measurement and insurance pricing have always been issues of concern in actuarial science. Under the framework of uncertainty theory, this paper puts forward a new premium principle: uncertain standard deviation premium principle, proposes some of its properties about risk and compares the premiums of different risks. Based on the utility function of risk aversion, the additional premium coefficient is derived and two specific numerical examples are used to calculate the maximum premium. Furthermore, the unknown parameters of the policy with deductible are estimated by uncertain moment estimation and uncertain maximum likelihood estimation.

Keywords: Uncertainty theory, standard deviation premium principle, additional premium coefficient, utility function, parameter estimation

DOI: 10.3233/JIFS-223297

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9965-9975, 2023

Authors: Yu, Yan | Qiu, Dong | Yan, Ruiteng

Article Type: Research Article

Abstract: To mine more semantic information between words, it is important to utilize the different semantic correlations between words. Focusing on the different degrees of modifying relations between words, this article provides a quantum-like text representation based on syntax tree for fuzzy semantic analysis. Firstly, a quantum-like text representation based on density matrix of individual words is generalized to represent the relationship of modification between words. Secondly, a fuzzy semantic membership function is constructed to discuss the different degrees of modifying relationships between words based on syntax tree. Thirdly, the tensor dot product is defined as the sentence semantic similarity by …combining the operation rules of the tensor to effectively exploit the semantic information of all elements in the quantum-like sentence representation. Finally, extensive experiments on STS’12, STS’14, STS’15, STS’16 and SICK show that the provided model outperforms the baselines, especially for the data set containing multiple long-sentence pairs, which confirms there are fuzzy semantic associations between words. Show more

Keywords: Quantum-like text representation, fuzzy semantic analysis, fuzzy semantic membership function, neural networks, syntax tree

DOI: 10.3233/JIFS-223499

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9977-9991, 2023

Authors: Dai, Songsong | Zheng, Jianwei

Article Type: Research Article

Abstract: In a recent work (Wang et al. 2020), a partial order ⪯, a join operation ⊔ and a meet operation ⊓ of probabilistic linguistic term sets (PLTSs) were introduced and it was proved that L 1  ⊓ L 2  ⪯ L 1  ⪯ L 1  ⊔ L 2 and L 1  ⊓ L 2  ⪯ L 2  ⪯ L 1  ⊔ L 2 . In this paper, we demonstrate that its join and meet operations are not satisfy the above requirement. To satisfy this requirement, we modify its join and meet operations. Moreover, we define a negation operation of PLTSs based on the partial order ⪯. The …combinations of the proposed negation, the modified join and meet operations yield a bounded, distributive lattice over PLTSs. Meanwhile, we also define a new join operation and a new meet operation which, together with the negation operation, yield a bounded De Morgan over PLTSs. Show more

Keywords: Probabilistic linguistic term sets, operations, orders, lattices

DOI: 10.3233/JIFS-223747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9993-10003, 2023

Authors: Fu, Chao | Qin, Keyun | Yang, Lei | Hu, Qian

Article Type: Research Article

Abstract: Covering rough sets have been successfully applied to decision analysis because of the strong representing capability for uncertain information. As a research hotspot in decision analysis, hesitant fuzzy multi-attribute decision-making (HFMADM) has received increasing attention. However, the existing covering rough sets cannot handle hesitant fuzzy information, which limits its application. To tackle this problem, we set forth hesitant fuzzy β-covering rough set models and discuss their application to HFMADM. Specifically, we first construct four types of hesitant fuzzy β-covering ( T , I ) rough set models via hesitant fuzzy logic operators and hesitant fuzzy …β-neighborhoods, which can handle hesitant fuzzy information without requiring any prior knowledge other than the data sets. Then, some intriguing properties of these models and their relationships are also discussed. In addition, we design a new method to deal with HFMADM problems by combining the merits of the proposed models and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. In this method, we not only consider the risk preferences of decision-makers, but also present a new hesitant fuzzy similarity measure expressed by hesitant fuzzy elements to measure the degree of closeness between two alternatives. Finally, an enterprise project investment problem is applied to illustrate the feasibility of our proposed method. Meanwhile, the stability and effectiveness of our proposed method are also verified by sensitivity and comparative analyses. Show more

Keywords: Hesitant fuzzy sets, covering rough sets, hesitant fuzzy logic operators, hesitant fuzzy β-covering, multi-attribute decision-making

DOI: 10.3233/JIFS-223842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10005-10025, 2023

Authors: Wali, Aamir | Ahmad, Muzammil | Naseer, Asma | Tamoor, Maria | Gilani, S.A.M.

Article Type: Research Article

Abstract: Deep networks require a considerable amount of training data otherwise these networks generalize poorly. Data Augmentation techniques help the network generalize better by providing more variety in the training data. Standard data augmentation techniques such as flipping, and scaling, produce new data that is a modified version of the original data. Generative Adversarial networks (GANs) have been designed to generate new data that can be exploited. In this paper, we propose a new GAN model, named StynMedGAN for synthetically generating medical images to improve the performance of classification models.  StynMedGAN builds upon the state-of-the-art styleGANv2 that has produced remarkable results …generating all kinds of natural images. We introduce a regularization term that is a normalized loss factor in the existing discriminator loss of styleGANv2. It is used to force the generator to produce normalized images and penalize it if it fails. Medical imaging modalities, such as X-Rays, CT-Scans, and MRIs are different in nature, we show that the proposed GAN extends the capacity of styleGANv2 to handle medical images in a better way. This new GAN model (StynMedGAN) is applied to three types of medical imaging: X-Rays, CT scans, and MRI to produce more data for the classification tasks. To validate the effectiveness of the proposed model for the classification, 3 classifiers (CNN, DenseNet121, and VGG-16) are used. Results show that the classifiers trained with StynMedGAN-augmented data outperform other methods that only used the original data. The proposed model achieved 100%, 99.6%, and 100% for chest X-Ray, Chest CT-Scans, and Brain MRI respectively. The results are promising and favor a potentially important resource that can be used by practitioners and radiologists to diagnose different diseases. Show more

Keywords: GANs, deep learning, synthetic data, data augmentation, CNN, styleGANv2, brain tumor, MRI, CT-scan, CXRs

DOI: 10.3233/JIFS-223996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10027-10044, 2023

Authors: Zhu, Sheng | Tan, Min Keng | Lim, Kit Guan | Chin, Renee Ka Yin | Chua, Bih Lii | Teo, Kenneth Tze Kin

Article Type: Research Article

Abstract: Misfire fault is a common engine failure which is caused by incomplete combustion in the engine cylinders. Conventionally, the misfire fault is diagnosed manually by mechanics, but the diagnosis process is time-consuming. Therefore, this study aims to explore the feasibility of using Subtractive Clustering based Adaptive Neuro-Fuzzy Inference System (SC-ANFIS) algorithm to assist in diagnosing misfire faults. The Subtractive Clustering (SC) approach initializes the parameters of Adaptive Neuro-Fuzzy Inference System (ANFIS), whereas Back Propagation (BP) and Least Square Estimation (LSE) approaches are implemented to optimize the ANFIS parameters. The proposed algorithm will pre-diagnose the cause of misfire faults based on …the engine exhaust gas. In this work, exhaust gases for different causes of misfire faults are collected from Volkswagen 1.8TSI 4-cylinder petrol engine. These collected data are used to train the proposed algorithm. The performances of the proposed algorithm are compared to two commonly used algorithms, namely Fuzzy C-Mean Clustering based ANFIS (FCM-ANFIS) and BP algorithms. The simulation results show the proposed algorithm has improved 2.4% to 5.5% averagely in terms of accuracy, efficiency and stability. Show more

Keywords: Engine misfire, fault diagnosis, SC-ANFIS, FCM-ANFIS

DOI: 10.3233/JIFS-224059

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10045-10066, 2023

Authors: Wu, Meiqin | Song, Jiawen | Fan, Jianping

Article Type: Research Article

Abstract: As COVID-19 swept through, production in various industries was affected. Epidemic control leads to logistical disruptions from time to time and suppliers have to make production and shipping decisions after analyzing the customer’s situation. Therefore, the majority of manufacturers need to establish effective methods for the selection of distribution customers. The method presented in this paper can classify customers into three regions and rank their status to help suppliers effectively make decisions. The three-way decision (3WD) is a well-known fast sorting method in multi-attribute decision-making (MADM). In this paper, we proposed the 3WD model based on Indifference Threshold based Attribute …Ratio Analysis (ITARA), ELimination Et Choix Traduisant la REalite III (ELLECTRE III) in the spherical fuzzy environment. Then, we used the SF-ITARA-ELECTRE III-3WD method to select the suitable customers for dispensing. In addition, comparison with the conventional SF-PROMETHEE-3WD, SF-EVAMIX-3WD, SF-TOPSIS-3WD and SF-VIKOR-3WD are created to verify the effectiveness of the proposed method. An effective risk-averse solution to the MADM problem for spherical fuzzy environment is provided. Show more

Keywords: 3WD, ITARA, ELECTRE III, spherical fuzzy number (SFN), customers selection

DOI: 10.3233/JIFS-224062

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10067-10084, 2023

Authors: Liu, Xinyu | Liu, Lu | Jiang, Tianhua

Article Type: Research Article

Abstract: Energy-aware scheduling has been viewed as a feasible way to reduce energy consumption during the production process. Recently, energy-aware job shop scheduling problems (EAJSPs) have received wide attention in the manufacturing area. However, the majority of previous literature about EAJSPs supposed that all jobs are fabricated in the in-house workshop, while the outsourcing of jobs to some available subcontractors is neglected. To get close to practical production, the outsourcing and scheduling are simultaneously determined in an energy-aware job shop problem with outsourcing option (EAJSP-OO). To formulate the considered problem, a novel mathematical model is constructed to minimize the sum of …completion time cost, outsourcing cost and energy consumption cost. Considering the strong complexity, a self-learning interior search algorithm (SLISA) is developed based on reinforcement learning. In the SLISA, a new Q-learning algorithm is embedded to dynamically select search strategies to prevent blind search in the iteration process. Extensive experiments are carried out to evaluate the performance of the proposed algorithm. Simulation results indicate that the SLISA is superior to the compared existing algorithms in more than 50% of the instances of the considered EAFJSP-OO problem. Show more

Keywords: Job shop, outsourcing option, energy-aware scheduling, interior search algorithm

DOI: 10.3233/JIFS-224624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10085-10100, 2023

Authors: Zhang, Zhandong | Wang, Xiaoyan

Article Type: Research Article

Abstract: Traditional Chinese medicine is a complex discipline that needs to combine theory with practice under the background of the magnificent Chinese history and civilization. It is a subject that needs “lifelong” learning. Teachers should gradually change the dull and rigid teaching mode in the past and explore a scientific and effective teaching mode that conforms to the background of the current era. Applying the advantages of the Internet to organically integrate teaching modes such as flipped classroom, which can stimulate students’ learning interest, cultivate students’ thinking mode of traditional Chinese medicine and clinical problem-solving ability, and realize the common development …of students’ ability and quality of traditional Chinese medicine. While improving the teaching effect of internal medicine of traditional Chinese medicine, this diversified teaching method will provide new ideas and methods for deepening the reform of traditional Chinese medicine teaching and lead the teaching of traditional Chinese medicine to a new level. The teaching quality evaluation of Chinese medicine specialty in higher vocational colleges is classical multiple-attribute group decision-making (MAGDM) issues. Recently, the TODIM and VIKOR method has been used to solve MAGDM issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information during the teaching quality evaluation of Chinese medicine specialty in higher vocational colleges. In this manuscript, we design the TODIM-VIKOR model to solve the MAGDM under PULTSs. In the end, a numerical case study for teaching quality evaluation of Chinese medicine specialty in higher vocational colleges is given to validate the proposed method. Show more

Keywords: Multiple-attribute group decision-making (MAGDM), probabilistic uncertain linguistic term sets (PULTSs), TODIM, VIKOR, teaching quality evaluation

DOI: 10.3233/JIFS-230760

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10101-10112, 2023

Authors: Arul King, J. | Helen Sulochana, C.

Article Type: Research Article

Abstract: Lung cancer is a severe disease that may lead to death if left undiagnosed and untreated. Lung cancer recognition and segmentation is a difficult task in medical image processing. The study of Computed Tomography (CT) is an important phase for detecting abnormal tissues in the lung. The size of a nodule as well as the fine details of nodule can be varied for various images. Radiologists face a difficult task in diagnosing nodules from multiple images. Deep learning approaches outperform traditional learning algorithms when the data amount is large. One of the most common deep learning architectures is convolutional neural …networks. Convolutional Neural Networks use pre-trained models like LeNet, AlexNet, GoogleNet, VGG16, VGG19, Resnet50, and others for learning features. This study proposes an optimized HDCCARUNet (Hybrid Dilated Convolutional Channel Attention Res-UNet) architecture, which combines an improved U-Net with a modified channel attention (MCA) block, and a HDAC (hybrid dilated attention convolutional) layer to accurately and effectively do medical image segmentation for various tasks. The attention mechanism aids in focusing on the desired outcome. The ability to dynamically allot input weights to neurons allows it to focus only on the most important information. In order to gather key details about different object features and infer a finer channel-wise attention, the proposed system uses a modified channel attention (MCA) block. The experiment is conducted on LIDC-IDRI dataset. The noises present in the dataset images are denoised by enhanced DWT filter and the performance is analysed at various noise levels. The proposed method achieves an accuracy rate of 99.58 % . Performance measures like accuracy, sensitivity, specificity, and ROC curves are evaluated and the system significantly outperforms other state-of-the-art systems. Show more

Keywords: Lung, segmentation, CNN, hybrid, UNet

DOI: 10.3233/JIFS-222215

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10113-10129, 2023

Authors: Zhou, Wei | Wang, Degang | Li, Hongxing | Bao, Menghong

Article Type: Research Article

Abstract: The aim of this study is to improve randomized methods for designing a Takagi-Sugeno-Kang (TSK) fuzzy system. A novel adaptive incremental TSK fuzzy system based on stochastic configuration, named stochastic configuration fuzzy system (SCFS), is proposed in this paper. The proposed SCFS determines the appropriate number of fuzzy rules in TSK fuzzy system by incremental learning approach. From the initial system, new fuzzy rules are added incrementally to improve the system performance until the specified performance is achieved. In the process of generation of fuzzy rules, the stochastic configuration supervision mechanism is applied to ensure that the addition of fuzzy …rules can continuously improve the performance. The premise parameters of new adding fuzzy rules are randomly assigned adaptively under the supervisory mechanism, and the consequent parameters are evaluated by Moore-Penrose generalized inverse. It has been proved theoretically that the supervisory mechanism can help to ensure the universal approximation of SCFS. The proposed SCFS can reach any predetermined tolerance level when there are enough fuzzy rules, and the training process is finite. A series of synthetic data and benchmark datasets are used to verify SCFS’s performance. According to the experimental results, SCFS achieves satisfactory prediction accuracy compared to other models. Show more

Keywords: Stochastic configuration, fuzzy system, universal approximation, incremental learning

DOI: 10.3233/JIFS-222930

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10131-10143, 2023

Authors: Wu, Haowen | Rao, Fengshuo

Article Type: Research Article

Abstract: Teaching quality is the foundation and lifeline of colleges and universities. To establish a distinctive university, we must adhere to the scientific concept of development, deepen teaching reform and improve teaching quality. The classroom teaching quality (CTQ) evaluation of college physical education (PE) is an essential part of the teaching process. Building a scientific, comprehensive, reasonable and effective evaluation system is crucial to improve the quality of college PE classroom teaching. This process is not easy and needs long-term efforts and persistence. The CTQ evaluation of college volleyball training is viewed as the multi-attribute decision-making (MADM). In this paper, we …connect the geometric Heronian mean (GHM) operator and power geometric (PG) operator with 2-tuple linguistic neutrosophic sets (2TLNSs) to build the generalized 2-tuple linguistic neutrosophic numbers weighted power GHM (G2TLNWPGHM) operator. Then, the G2TLNWPGHM operator is used to tackle MADM with 2TLNSs. Finally, an example for CTQ evaluation of college volleyball training is used to show the proposed methods. Show more

Keywords: Multiple attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic sets (2TLNSs), G2TLNWPGHM operator, CTQ evaluation

DOI: 10.3233/JIFS-223830

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10145-10158, 2023

Authors: Razzaq, Ayesha | Riaz, Muhammad

Article Type: Research Article

Abstract: Picture fuzzy sets (PFSs), the generalization of intuitionistic fuzzy sets (IFSs), are more capable of dealing with vague data in real-life problems. Models based on PFSs may be suitable particularly in those circumstances where human perceptions become challenging as well as various kinds of reasoning, like yes, no, abstention, or denial. The aggregation operators (AOs) are essential components in information aggregation as they have the ability to aggregate a group of fuzzy numbers into a single fuzzy number of the same kind. A lot of aggregation operations for PFSs have been developed. Nevertheless, the existing aggregation operators for picture fuzzy …information are inaccurate as they fail to aggregate a group of picture fuzzy numbers into a single picture fuzzy number (PFN). To cover the drawbacks of existing AOs, we developed some modified picture fuzzy aggregation operators (PFAOs) named as picture fuzzy modified weighted averaging (PFMWA), picture fuzzy modified ordered weighted averaging (PFMOWA) and picture fuzzy modified hybrid averaging (PFMHA) aggregation operator along with their distinctive features. These operators are essential in developing new multi-criteria decision-making (MCDM) techniques. This paper defines a number of stakeholder roles (or tactics), with an objective of overcoming the challenges to executing Education 4.0 (EDUC4) that have recently been highlighted in the literature. A MCDM problem provides the basis for the evaluation of the responsibilities of the stakeholders with respect to these constraints. Several management concerns are provided as stepping stones for the development of EDUC4 implementation. The purpose of this study is to identify the qualities that influence the degree of optimism for the adoption and implementation of the EDUC4 in Pakistan’s education system while taking government policies into account. Show more

Keywords: Picture fuzzy information, accuracy function, score function, PFMWA operator, PFMOWA operator, PFMHA operator, MCDM

DOI: 10.3233/JIFS-224600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10159-10181, 2023

Authors: Nguyen-Trong, Khanh | Trinh, Thinh

Article Type: Research Article

Abstract: Visually rich documents, such as forms, invoices, receipts, and ID cards, are ubiquitous in daily business and life. Various methods have been used to convey such diverse information, including text, layout, font size, or text position. Combining these elements in information extraction can improve the result performance. However, previous works have not effectively utilized the cooperation between these rich information sources. Text detection and recognition have been performed without semantic supervision (e.g., entity name annotation), and text information extraction has been performed using only serialized plain text, ignoring rich visual information. This paper presents a method for extracting information from …such documents, which integrates textual, non-spatial, and spatial visual features. The method consists of two main steps and uses three deep neural networks. The first step, Text Reading, employs two CNN models (Lightweight DB and C-PREN) for OCR tasks, based on the state-of-the-art models DB and PREN, with two improvements. These improvements include reducing noise by removing the SE block of DB and integrating both context and position features in PREN. The second step, Text Information Extraction, uses a graph convolutional network, RGCN, for name entity recognition. Experiments on self-collected and two public datasets have demonstrated that our method improves the performance of the original models and outperforms other state-of-the-art methods. Show more

Keywords: Graph Convolutional Network, OCR, Text detection, text recognition, NER

DOI: 10.3233/JIFS-230204

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10183-10195, 2023

Authors: Teimoury, Ebrahim | Rashid, Reza

Article Type: Research Article

Abstract: In recent years, e-commerce has become increasingly popular, and consumers expect quick and affordable delivery, placing additional pressure on city logistics activities. An innovative approach is proposed to coordinate ground vehicles and drones for delivery services, which has gained tremendous attention from academia and logistic service providers. This paper introduces a variant of this problem: the two-echelon truck and drone routing problem, characterized by stochastic demand existence and soft time windows. A Markov chain is used to model the problem, and a linear mathematical model is presented. This work employs a hybrid large-neighborhood search approach. Numerous computational experiments are conducted …to evaluate the performance of the proposed solution method, and the results demonstrate its efficacy. Show more

Keywords: Last-mile delivery, truck and drone routing, stochastic optimization, Markov chain, large-neighborhood search

DOI: 10.3233/JIFS-224307

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10197-10211, 2023

Authors: Cao, Guo | Shen, Lixiang

Article Type: Research Article

Abstract: As an extension of picture fuzzy sets (PFSs), interval-valued picture fuzzy sets (IVPFSs) can better model and handle incomplete, indeterminate and inconsistent information in some practical applications. One of the important topics in IVPFSs is the similarity measure of IVPFSs, for which few studies have been proposed within the literature. Moreover, some existing similarity measures cannot adequately meet the conditions of similarity measure with some counterintuitive cases. In this work, we devise a novel similarity measure between IVPFSs based on the effect of the margin of the degree of refusal membership. First, the interval-valued picture fuzzy numbers will be transformed …into two right-angled triangular-based pyramids in a spatial rectangular coordinate system. Then, a new parameter distance measure for IVPFSs is defined to assess the similarity between IVPFNs according to the centers of gravity of their corresponding right-angled triangular-based pyramids. Meanwhile, a comparison between different similarity measures is performed to illustrate that the proposed similarity measure can overcome the deficiencies of other extant measures. Finally, we apply it to handle pattern recognition problems. The comparison results indicate that the proposed algorithm can adequately meet the conditions of similarity measure, produce more reasonable and creditable results and perform well in complex contexts. Show more

Keywords: Interval-valued picture fuzzy sets (IVPFSs), distance measure, similarity measure, pattern recognition

DOI: 10.3233/JIFS-224314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10213-10239, 2023

Authors: Khan, Madad | Anis, Saima | Ahmad, Sarfraz | Zeeshan, Muhammad

Article Type: Research Article

Abstract: A fuzzy soft matrix is a type of mathematical matrix that combines the principles of fuzzy set theory and soft set theory. It is used to handle uncertainty and vagueness in decision-making problems. Fuzzy soft matrix theory cannot handle negative information. To overcome this difficulty, we define the notion of bipolar fuzzy soft (BFS) matrices and study their fundamental properties. We define products of BFS matrices and investigate some useful properties and results. We also give an application of bipolar fuzzy soft matrices to decision-making problems. We propose a decision-making algorithm based on computer programs under the environment of the …bipolar fuzzy soft sets. Show more

Keywords: Soft sets, fuzzy soft matrices, bipolar fuzzy soft matrices, BFS decision-makings

DOI: 10.3233/JIFS-221569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10241-10253, 2023

Authors: Fernandes, Anita Maria da Rocha | Cassaniga, Mateus Junior | Passos, Bianka Tallita | Comunello, Eros | Stefenon, Stefano Frizzo | Leithardt, Valderi Reis Quietinho

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-223218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10255-10274, 2023

Authors: Mudgil, Pooja | Gupta, Pooja | Mathur, Iti | Joshi, Nisheeth

Article Type: Research Article

Abstract: Social media platforms, namely Instagram, Facebook, Twitter, YouTube, etc. have gained a lot of attention as users used to share their views, and post videos, audio, and pictures for social networking. In near future, understanding the meaning and analyzing this enormously rising volume and size of online data will become a necessity in order to extract valuable information from them. In a similar context, the paper proposes an analysis model in two phases namely the training and the sentiment classification using the reward-based grasshopper optimization algorithm. The training architecture and context analysis of the tweet are presented for the sentiment …analysis along with the ground truth processing of emotions. The proposed algorithm is divided into two phases namely the exploitation and the exploration part and creates a reward mechanism that utilizes both phases. The proposed algorithm also uses cosine similarity, dice coefficient, and euclidean distance as the input set and further processes using the grasshopper algorithm. Finally, it presents a combination of swarm intelligence and machine learning for attribute selection in which the reward mechanism is further validated using machine learning techniques. The comparative performance in terms of precision, recall, and F-measure has been measured for the proposed model in comparison to existing swarm-based sentiment analysis works. Overall, simulation analysis showed that the proposed work based on grasshopper optimization outperformed the existing approaches for Sentiment 140 by 5.93% to 10.05% SemEval 2013 by 6.15% to 12.61% and COVID-19 tweets by 2.72% to 9.13%. Thus, demonstrating the efficiency of the context-aware sentiment analysis using the grasshopper optimization approach. Show more

Keywords: Grasshopper optimization, sentiment, social media, swarm intelligence, Twitter

DOI: 10.3233/JIFS-221879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10275-10295, 2023

Authors: Zhi, Zhaodan | Tao, Juan

Article Type: Research Article

Abstract: In this study, the constrained interval arithmetic (CIA) is used as an effective mathematical tool for solving the stability analysis for interval two-dimensional semi-linear differential equations. Under certain assumptions, the origin is a focus of the interval semi-linear differential equations if it is a focus of the interval linear ones. Meanwhile, the origin can be a center, a center-focus or a focus of interval semi-linear differential equations if it is a center of the interval linear ones. On the other word, the types of equilibrium point are still determined by the linear part when a nonlinear disturbance is added to …the interval linear differential equations. Based on CIA, the stability results of interval differential equations are the same as those of the real differential equations. At last, three illustrative examples validate the stability results of the origin for interval two-dimensional semi-linear differential equations. Show more

Keywords: Constrained interval arithmetic (CIA), interval differential equations, semi-linear, stability

DOI: 10.3233/JIFS-222020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10297-10310, 2023

Authors: Korkoman, Malak Jalwi | Abdullah, Monir

Article Type: Research Article

Abstract: Online services have advanced to the point where they have made our lives much easier, but many problems should be solved to make these services safer for consumers. Numerous transactions are conducted daily, and much personal information is published and shared on e-commerce and social media platforms. This makes security, privacy, and problematic reliability barriers to overcome. One of these problems is detecting credit card fraud because thieves aim to make all transactions legitimate by stealing credit card information. Imbalanced data is a potential problem in machine learning that impairs the performance of the classifiers used in real-world systems. For …example, anomaly detection and fraudulent transactions. The term “data imbalance” refers to the problem in which the sample distribution is skewed or skewed towards a particular class. Due to its inherent nature, the software failure prediction dataset falls into the same category as non-defective software modules. The main objective of this paper is to solve the problem of the imbalanced fraud credit card dataset for enhancing the detection accuracy of using machine learning algorithms. This paper provides a unique fraud detection model using the Particle Swarm Optimization (PSO) based on oversampling technique of the minority class to solve the imbalanced dataset problem compared with the Genetic Algorithm (GA) technique. Random Forest (RF) algorithm shows up with sensitivity, specificity, and accuracy. The experimental results achieved 99.3% and 99.4% for GA and PSO within seconds, respectively. Experiments show that the proposed methods outperform other methods, evidenced by the higher classification accuracy obtained. Show more

Keywords: Fraud detection, genetic algorithm, particle swarm optimization, oversampling, random forest

DOI: 10.3233/JIFS-222344

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10311-10323, 2023

Authors: Wang, Chunying | Zhang, Jiahui | Yang, Qi

Article Type: Research Article

Abstract: The traditional fuzzy C-means clustering technology only considers one performance Angle of image segmentation process when processing data, resulting in low accuracy of image segmentation. In this paper, the traditional FCM algorithm is analyzed, and the low clustering accuracy, noise interference and lack of flexibility and other problems are fully considered from the relationship between parameter components, non-local spatial information elements and noise sensitivity. Firstly, a distance calculation method based on robust statistics theory is proposed, which can deal with abnormal noise stably. Secondly, based on the extreme learning machine theory, the non-local spatial information coefficient is introduced to improve …the identification ability of the influence factors. This method not only guarantees the anti-noise performance of the algorithm, but also preserves the image data, improving the iteration efficiency and segmentation accuracy of the algorithm. The test results show that the accuracy of the improved C-means clustering algorithm for image segmentation is 95.5%, which is compared with the traditional C-means clustering technique and other optimization algorithms. Show more

Keywords: C-means, noise, clustering, image processing, fuzzy

DOI: 10.3233/JIFS-222912

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10325-10335, 2023

Authors: Wang, Weize | Feng, Yurui

Article Type: Research Article

Abstract:  There are various uncertainties in the multi-criteria group decision making (MCGDM) process, including the definition of the importance of decision information and the assignment of criterion assessment values, etc., which cause decision makers to be unconfident in their decisions. In this paper, an MCGDM approach based on the reliability of decision information is proposed in Fermatean fuzzy (FF) environment, allowing a decision to be made with confidence that the alternative chosen is the best performing alternative under the range of probable circumstances. First, we prove that the FF Yager weighted averaging operator is monotone with respect to the total order …and note the inconsistency between the monotonicity of some FF aggregation operators and their application in MCGDM. Second, we extend the divergence measure of FFS to order σ for calculating the variance of decision information and accordingly develop an exponential FF entropy measure to measure the uncertainty of decision information. Then, the reliability of decision information is defined, which accounts for the degree of variance of decision information across criteria from the criterion dimension and the uncertainty of the decision information from the alternative dimension. Following that, an integrated MCGDM framework is completed. Finally, the applications to a numerical example and comparisons with previous approaches are conducted to illustrate the validity of the established approach. Show more

Keywords: Multi-criteria group decision making, Fermatean fuzzy set, Divergence measure, Entropy measure, Supplier selection

DOI: 10.3233/JIFS-223014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10337-10356, 2023

Authors: Upendra Raju, K. | Amutha Prabha, N.

Article Type: Research Article

Abstract: Reversible data hiding (RDH) based on Steganography is considered as one of the future related aspects in the field of security for the information hiding paradigm. Existing research work has been carried out based on secure data transmission as well as reducing the dataloss from one user to other users. But due to encryption data expansion over non-linear transformation, complexity in attacking caused due to keyspace, ineffective image compression, poor embedding ratio, poor quality, overflow/underflow problems, data loss etc., leads to inefficient data transmission causing a security risk. This paper proposes a novel method named Triple Secured Data Hiding Steganography …Model which provides solutions to the above challenges. This work is initiated with Hyper Chaos 2D Compressive Sensing that performs image compression and encryption simultaneously. It provides control over low dimension chaos system bearing secure risks with suffering from data encrypted expansion while adopt non-linear transformation. In addition to reduce the error rate and providing signal synchronization as well as system reliability over the transmission channel, Manchester Encoder/Decoder is initiated. To cope up with data embedding and extraction our work has proposed Circular Queue Exploiting Modification Direction(CQEMD). Thus, overall proposed model enhances effective secure data transmission under RDH by inhabiting a triple secured system. Show more

Keywords: Circular Queue Exploiting Modification Direction (CQEMD), Hyper Chaos 2D Compressive Sensing (CS), ManchesterEncoder/Decoder, Reversible Data Hiding (RDH), steganography

DOI: 10.3233/JIFS-223131

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10357-10367, 2023

Authors: Cabrera-Ponce, Aldrich A. | Martin-Ortiz, Manuel | Martinez-Carranza, Jose

Article Type: Research Article

Abstract:  Geo-localisation from a single aerial image for Uncrewed Aerial Vehicles (UAVs) is an alternative to other vision-based methods, such as visual Simultaneous Localisation and Mapping (SLAM), seeking robustness under GPS failure. Due to the success of deep learning and the fact that UAVs can carry a low-cost camera, we can train a Convolutional Neural Network (CNN) to predict position from a single aerial image. However, conventional CNN-based methods adapted to this problem require off-board training that involves high computational processing time and where the model can not be used in the same flight mission. In this work, we explore the …use of continual learning via latent replay to achieve online training with a CNN model that learns during the flight mission GPS coordinates associated with single aerial images. Thus, the learning process repeats the old data with the new ones using fewer images. Furthermore, inspired by the sub-mapping concept in visual SLAM, we propose a multi-model approach to assess the advantages of using compact models learned continuously with promising results. On average, our method achieved a processing speed of 150 fps with an accuracy of 0.71 to 0.85, demonstrating the effectiveness of our methodology for geo-localisation applications. Show more

Keywords: Continual learning, geo-localisation, aerial image, GPS

DOI: 10.3233/JIFS-223627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10369-10381, 2023

Authors: Westarb, Gustavo | Stefenon, Stefano Frizzo | Hoppe, Aurélio Faustino | Sartori, Andreza | Klaar, Anne Carolina Rodrigues | Leithardt, Valderi Reis Quietinho

Article Type: Research Article

Abstract:  This paper presents the development and application of graph neural networks to verify drug interactions, consisting of drug-protein networks. For this, the DrugBank databases were used, creating four complex networks of interactions: target proteins, transport proteins, carrier proteins, and enzymes. The Louvain and Girvan-Newman community detection algorithms were used to establish communities and validate the interactions between them. Positive results were obtained when checking the interactions of two sets of drugs for disease treatments: diabetes and anxiety; diabetes and antibiotics. There were found 371 interactions by the Girvan-Newman algorithm and 58 interactions via Louvain.

Keywords: Drug interaction, graph neural network, communities detection

DOI: 10.3233/JIFS-223656

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10383-10395, 2023

Authors: Li, Dan | Gao, Fei

Article Type: Research Article

Abstract: Slime mould algorithm (SMA) is a novel meta-heuristic algorithm with fast convergence speed and high convergence accuracy. However, it still has some drawbacks to be improved. The exploration and exploitation of SMA is difficult to balance, and it easy to fall into local optimum in the late iteration. Aiming at the problems existing in SMA, a multistrategy slime mould algorithm named GCSMA is proposed for global optimization in this paper. First, the Logistic-Tent double chaotic map approach is introduced to improve the quality of the initial population. Second, a dynamic probability threshold based on Gompertz curve is designed to balance …exploration and exploitation. Finally, the Cauchy mutation operator based on elite individuals is employed to enhance the global search ability, and avoid it falling into the local optimum. 12 benchmark function experiments show that GCSMA has superior performance in continuous optimization. Compared with the original SMA and other novel algorithms, the proposed GCSMA has better convergence accuracy and faster convergence speed. Then, a special encoding and decoding method is used to apply GCSMA to discrete flexible job-shop scheduling problem (FJSP). The simulation experiment is verified that GCSMA can be effectively applied to FJSP, and the optimization results are satisfactory. Show more

Keywords: Slime mould algorithm, double chaotic map, Gompertz dynamic probability, Cauchy mutation, flexible job shop scheduling problem

DOI: 10.3233/JIFS-223827

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10397-10415, 2023

Authors: Shan, Chuanhui | Ou, Jun | Chen, Xiumei

Article Type: Research Article

Abstract: As one of the main methods of information fusion, artificial intelligence class fusion algorithm not only inherits the powerful skills of artificial intelligence, but also inherits many advantages of information fusion. Similarly, as an important sub-field of artificial intelligence class fusion algorithm, deep learning class fusion algorithm also inherits advantages of deep learning and information fusion. Hence, deep learning fusion algorithm has become one of the research hotspots of many scholars. To solve the problem that the existing neural networks are input into multiple channels as a whole and cannot fully learn information of multichannel images, Shan et al. proposed …multichannel concat-fusional convolutional neural networks. To mine more multichannel images’ information and further explore the performance of different fusion types, the paper proposes new fusional neural networks called multichannel cross-fusion convolutional neural networks (McCfCNNs) with fusion types of “R+G+B/R+G+B/R+G+B” and “R+G/G+B/B+R” based on the tremendous strengths of information fusion. Experiments show that McCfCNNs obtain 0.07-6.09% relative performance improvement in comparison with their corresponding non-fusion convolutional neural networks (CNNs) on diverse datasets (such as CIFAR100, SVHN, CALTECH256, and IMAGENET) under a certain computational complexity. Hence, McCfCNNs with fusion types of “R+G+B/R+G+B/R+G+B” and “R+G/G+B/B+R” can learn more fully multichannel images’ information, which provide a method and idea for processing multichannel information fusion, for example, remote sensing satellite images. Show more

Keywords: Information fusion, fusion type “R+G+B/R+G+B/R+G+B”, fusion type “R+G/G+B/B+R”, CNN, McCfCNN

DOI: 10.3233/JIFS-224076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10417-10436, 2023

Authors: Zhang, Dongping | Lan, Hao | Ma, Zhennan | Yang, Zhixiong | Wu, Xin | Huang, Xiaoling

Article Type: Research Article

Abstract: The key to solving traffic congestion is the accurate traffic speed forecasting. However, this is difficult owing to the intricate spatial-temporal correlation of traffic networks. Most existing studies either ignore the correlations among distant sensors, or ignore the time-varying spatial features, resulting in the inability to extract accurate and reliable spatial-temporal features. To overcome these shortcomings, this study proposes a new deep learning framework named spatial-temporal gated graph convolutional network for long-term traffic speed forecasting. Firstly, a new spatial graph generation method is proposed, which uses the adjacency matrix to generate a global spatial graph with more comprehensive spatial features. …Then, a new spatial-temporal gated recurrent unit is proposed to extract the comprehensive spatial-temporal features from traffic data by embedding a new graph convolution operation into gated recurrent unit. Finally, a new self-attention block is proposed to extract global features from the traffic data. The evaluation on two real-world traffic speed datasets demonstrates the proposed model can accurately forecast the long-term traffic speed, and outperforms the baseline models in most evaluation metrics. Show more

Keywords: Traffic speed forecasting, graph convolution operation, gated recurrent unit, self-attention block

DOI: 10.3233/JIFS-224285

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10437-10450, 2023

Authors: Han, Nana | Qiao, Junsheng

Article Type: Research Article

Abstract: Lately, Jiang and Hu (H.B. Jiang, B.Q. Hu, On ( O , G ) -fuzzy rough sets based on overlap and grouping functions over complete lattices, Int. J. Approx. Reason. 144 (2022) 18-50.) put forward ( O , G ) -fuzzy rough sets via overlap and grouping functions over complete lattices. Meanwhile, they showed the characterizations of O -upper and G -lower L -fuzzy rough approximation operators in ( O , G ) -fuzzy rough set …model based on some of specific L -fuzzy relations and studied the topological properties of the proposed model. Nevertheless, we discover that the partial results given by Jiang and Hu could be further optimized. So, as a replenish of the above article, in this paper, based on G -lower L -fuzzy rough approximation operator in ( O , G ) -fuzzy rough set model, we further explore several new conclusions on the relationship between G -lower L -fuzzy rough approximation operator and different L -fuzzy relations. In particular, the equivalent descriptions of relationship between G -lower L -fuzzy rough approximation operator and O -transitive ( O -Euclidean) L -fuzzy relations are investigated, which are not involved in above literature and can make the theoretical results of this newly fuzzy rough set model more perfect. Show more

Keywords: (𝔒, 𝔊)-fuzzy rough set, 𝔏-fuzzy relation, overlap function, grouping function, complete lattice

DOI: 10.3233/JIFS-224286

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10451-10457, 2023

Authors: Liu, Lin | Yang, Lijun

Article Type: Research Article

Abstract: The level of education in colleges is career and development-focused compared to that from high schools. Quality education relies on the teachers’ qualifications, knowledge, and experience over the years. However, the demand for technical and knowledge-based education is increasing with the world’s demands. Therefore, assessing the knowledge of teaching professionals to meet external demand becomes mandatory. This article introduces an Acceded Data Evaluation Method (ADEM) using Fuzzy Logic (FL) for teaching quality assessment. The proposed method inputs the teachers’ skills and students’ productivity for evaluation. The teachers’ knowledge and updated skills through training and self-learning are the key features for …evaluating the independents’ performance. The impact of the above features on the student qualifying ratio and understandability (through examination) are analyzed periodically. Depending on the qualifications and performance, the teachers’ knowledge update is recommended with the new training programs. In this evaluation process, fuzzy logic is implied for balancing and identifying the maximum validation criteria that satisfy the quality requirements. The recommendations using partial and fulfilled quality constraints are identified using the logical truth over the varying assessments. The proposed method is analyzed using the metrics evaluation rate, quality detection, recommendations, evaluation time, and data balancing. Show more

Keywords: Data balancing, decision recommendations, fuzzy logic, teaching quality

DOI: 10.3233/JIFS-224290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10459-10475, 2023

Authors: Al-Andoli, Mohammed Nasser | Tan, Shing Chiang | Sim, Kok Swee | Goh, Pey Yun | Lim, Chee Peng

Article Type: Research Article

Abstract: Malicious software, or malware, has posed serious and evolving security threats to Internet users. Many anti-malware software packages and tools have been developed to protect legitimate users from these threats. However, legacy anti-malware methods are confronted with millions of potential malicious programs. To combat these threats, intelligent anti-malware systems utilizing machine learning (ML) models are useful. However, most ML models have limitations in performance since the training depth is usually limited. The emergence of Deep Learning (DL) models allow more training possibilities and improvement in performance. DL models often use gradient descent optimization, i.e., the Back-Propagation (BP) algorithm; therefore, their …training and optimization procedures suffer from local sub-optimal solutions. In addition, DL-based malware detection methods often entail single classifiers. Ensemble learning overcomes the shortcomings of individual techniques by consolidating their strengths to improve the performance. In this paper, we propose an ensemble DL classifier stacked with the Fuzzy ARTMAP (FAM) model for malware detection. The stacked ensemble method uses several heterogeneous deep neural networks as the base learners. During the training and optimization process, these base learners adopt a hybrid BP and Particle Swarm Optimization algorithm to combine both local and global optimization capabilities for identifying optimal features and improving the classification performance. FAM is selected as a meta-learner to effectively train and combine the outputs of the base learners and achieve robust and accurate classification. A series of empirical studies with different benchmark data sets is conducted. The results ascertain that the proposed ensemble method is effective and efficient, outperforming many other compared methods. Show more

Keywords: Ensemble learning, fuzzy ARTMAP, deep learning, malware detection, particle swarm optimization, backpropagation algorithm

DOI: 10.3233/JIFS-230009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10477-10493, 2023

Authors: Rose, Biji | Aruna Devi, B.

Article Type: Research Article

Abstract: From the signal received on a particular frequency band, spectrum sensing (SS) is used in cognitive radio (CR) to assess whether the primary user (PU) is using the spectrum and, consequently, whether the secondary user (SU) can utilize the spectrum. The main issue with SS is determining the presence of the primary signal in a low signal-to-noise ratio (SNR). Compared to conventional technologies, machine learning techniques are more effective and accurate at identifying the qualities of input data. This paper proposes a machine learning (ML) based SS model for CR with effective feature extraction and reduction techniques. The proposed work …comprises five phases: noise removal, wavelet transform, feature extraction, dimensionality reduction, and classification. Firstly, noise filtering is done on the received signal to remove the noise present in the input signal using the filters such as moving median filter (MMF), Gaussian filter (GF), and Gabor filter (GBF). After that, the filtered signal is transformed into a wavelet domain using Discrete Wavelet Transform (DWT) algorithm. Then the statistical features such as average absolute value, wavelet energy, variance, standard deviation, and peak value features are extracted from the DWT. Next, the dimensionality reduction (DR) is performed using Linear Discriminant Analysis (LDA). Finally, the classification is performed using the ensemble ML classifiers such as Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbour (KNN), which classify whether the PU signal is active or not. Simulations are carried out to analyze the efficiency of the presented models for SS. The results proved that SVM obtains the best performance for SS with higher accuracy and lower SNR. Show more

Keywords: Cognitive radio, spectrum sensing, discrete wavelet transform, machine learning, signal-to-noise ratio

DOI: 10.3233/JIFS-230438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10495-10509, 2023

Authors: Li, Huiru | Hu, Yanrong | Liu, Hongjiu

Article Type: Research Article

Abstract: Stock price volatility is influenced by many factors, including unstructured data that is not easy to quantify, such as investor sentiment. Therefore, given the difficulty of quantifying investor sentiment and the complexity of stock price, the paper proposes a novel LASSO-ATT-LSTM intelligent stock price prediction system based on multi-source data. Firstly, establish a sentiment dictionary in the financial field, conduct sentiment analysis on news information and comments according to the dictionary, calculate sentiment scores, and then obtain daily investor sentiment. Secondly, the LASSO (Least absolute shrinkage and selection operator) is used to reduce the dimension of basic trading indicators, valuation …indicators, and technical indicators. The processed indicators and investor sentiment are used as the input of the prediction model. Finally, the LSTM (Long short-term memory) model that introduces the attention mechanism is used for intelligent prediction. The results show that the prediction of the proposed model is close to the real stock price, MAPE, RMSE, MAE and R2 are 0.0118, 0.0685, 0.0515 and 0.8460, respectively. Compared with the existing models, LASSO-ATT-LSTM has higher accuracy and is an effective method for stock price prediction. Show more

Keywords: Stock price forecast, sentiment analysis, LSTM, attention, multi-source data

DOI: 10.3233/JIFS-221919

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10511-10521, 2023

Authors: Yanhu, Han | Huimin, Xin

Article Type: Research Article

Abstract: The location and capacity of precast concrete component factories (PC component factories) are not only the key factors for manufacturers to gain competitive advantage, but also the important factors affecting the operational efficiency of the prefabricated construction supply chain. This paper takes the capacitated location problem of PC component factories as the research object. Drawing on the model of traditional capacitated plant location problem, the model of capacitated location problem of PC component factories is constructed by setting the optional production scale by stages. According to the characteristics of this model, the optimal strategy of location is determined by using …the Tabu search algorithm. Taking the location problem of PC component factory in the Beijing-Tianjin-Hebei region as the object, the calculation example is designed, in which the influence of the distance parameters on the results of location problem is analyzed. The results can make the configuration of regional PC component factories more reasonable and balanced. Show more

Keywords: Prefabricated construction, location, PC component factories, capacity limitation

DOI: 10.3233/JIFS-222923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10523-10535, 2023

Authors: Chiranjeevi, Phaneendra | Rajaram, A.

Article Type: Research Article

Abstract: Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important features like hashtags, unigrams, multigrams, etc. from each piece of data. Here, we have learned …about four groups of features, including semantic features, syntactic features, symbolic features, and tweet-based features. Finally, the data is classified into positive, negative, and neutral categories according to the learned features. On the basis of classified sentiment, the review is generated to the users. Finally, the model is evaluated in terms of accuracy, precision, recall, f-measure, and error rate through extensive experiments in Matlab. The proposed LightDL model outperforms in all performance measures; specifically, it achieves 95% accuracy for the Twitter dataset. Show more

Keywords: Lightweight Dl, sentiment analysis, recommender system, twitter data

DOI: 10.3233/JIFS-223871

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10537-10550, 2023

Authors: Weng, Zhi | He, Dongchang | Zheng, Yan | Zheng, Zhiqiang | Zhang, Yong | Gong, Caili

Article Type: Research Article

Abstract: As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction …method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features. Show more

Keywords: 3D Reconstruction, approximate rigidity, multi-perspective, surface reconstruction

DOI: 10.3233/JIFS-224260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10551-10563, 2023

Authors: Lin, Tiantai | Yang, Bin

Article Type: Research Article

Abstract: In social life, conflict situations occur frequently all the time. To analyse a conflict situation, not only the intrinsic reason of the conflict but also the resolution of the conflict should be given. In this paper, we propose a combine conflict analysis model under q -rung fuzzy orthopair information system that contain conflict resolution, which is called discern function-based three-way group conflict analysis. Firstly, we propose three novel form conflict distances which are induced by discern functions, and examine their properties, then the comprehensive conflict distances are given based on the normality and symmetry they share. Thus, the conflict analysis …and resolution method in our model can be directly gained based on these novel form conflict distances. Secondly, from the view of group decision, the comprehensive q -rung fuzzy loss function is attained by aggregating a group of q -rung fuzzy loss functions through the q -rung orthopair fuzzy weighted averaging operator in the procedure of conflict resolution. Finally, we employ an example of the governance of a local government to demonstrate the process of finding an optimal feasible strategy in our model. Show more

Keywords: Conflict analysis, resolution of conflict analysis, q-rung orthopair fuzzy set, three-way decisions

DOI: 10.3233/JIFS-224589

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10565-10580, 2023

Authors: Rao, Juan | Peng, Ling | Rao, Jingjing | Cao, Xiaofen

Article Type: Research Article

Abstract: The evaluation of college physical education (PE) teaching quality is an indispensable part of the teaching process. Building a scientific, comprehensive, reasonable and effective evaluation system is crucial to improving the quality of college PE classroom teaching. This process is not easy, and requires long-term efforts and persistence. The PE teaching quality evaluation in Colleges and Universities is frequently viewed as the multiple attribute decision making (MADM) issue. In such paper, Taxonomy method is designed for MADM under double-valued neutrosophic sets (DVNSs). First, the score function of DVNSs and Criteria Importance Through Intercriteria Correlation (CRITIC) method is used to derive …the attribute weights. Second, then, the optimal choice is obtained through calculating the smallest double-valued neutrosophic number (DVNN) development attribute values from the DVNN positive ideal solution (DVNNPIS). Finally, a numerical example for PE teaching quality evaluation is given to illustrate the built method. Show more

Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), taxonomy method, CRITIC method, PE teaching quality

DOI: 10.3233/JIFS-230118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10581-10590, 2023

Authors: Guo, Tianlong | Shen, Derong | Kou, Yue | Nie, Tiezheng

Article Type: Research Article

Abstract:  Multi-view clustering that integrates the complementary information from different views for better clustering is a fundamental topic in data engineering. Most existing methods learn latent representations first, and then obtain the final result via post-processing. These two-step strategies may lead to sub-optimal clustering. The existing one-step methods are based on spectral clustering, which is inefficient. To address these problems, we propose a Multi-view fusion guided Matrix factorization based One-step subspace Clustering (MMOC) to perform clustering on multi-view data efficiently and effectively in one step. Specifically, we first propose a matrix factorization based multi-view fusion representation method, which adopts efficient matrix …factorization instead of time-consuming spectral representation to reduce the computational complexity. Then we propose a self-supervised weight learning strategy to distinguish the importance of different views, which considers both the gradient and the learning rate to make the learned weights closer to the real situation. Finally, we propose a one-step framework of MMOC, which effectively reduces the information loss by integrating data representation, multi-view data fusion, and clustering into one step. We conduct experiments on 5 real-world datasets. The experimental results show the effectiveness and the efficiency of our MMOC method in comparison with state-of-the-art methods. Show more

Keywords: multi-view clustering, matrix factorization, weight learning, subspace clustering

DOI: 10.3233/JIFS-224578

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10591-10604, 2023

Authors: Sudha, V. | Shanmugam, Sathiya Priya | Anitha, D. | Raja, R.

Article Type: Research Article

Abstract: An intelligent segmentation and identification of edemas diseases constitutes a most important crucial ophthalmological issues since they provide important information for the diagnosis process in accordance to the disease severity. But diagnosing the different edema diseases using the OCT-images are considered to be daunting challenge among the researchers. The implementation of computational intelligence techniques such as machine learning, deep learning, bio inspired algorithms and image processing techniques may help the doctors for some extent in improving the automatic extraction and diagnosis process consequently improving patients’ life quality. But, these are liable to more errors and less performance, which requires further …improvisation in designing the intelligent systems for an effective classification of edema diseases. In this context, this paper proposes the hybrid intelligent framework for the identification, segmentation and classification of three types of edemas such as using the retinal optical coherence tomography (OCT) Images. In this process, Single Feed Forward Training networks (SLFTN) are integrated with Convolutional Layers whose hyperparameters are tuned by using Lion Optimization algorithm. An intensive experimentation is carried out using the Kaggle Retinal OCT Image datasets-2020 with Tensor flow and the proposed framework is trained with the different set of 84,494 images in which performance metrics such as accuracy, sensitivity, specificity, recall and f1score are calculated. Results shows the proposed system has provided satisfactory performance, reaching the average highest accuracy of 99.9% in identifying and classifying the respectively. Show more

Keywords: Machine learning, deep learning, retinal optical coherance tomography images, convolutional layers, lion optimization algorithm

DOI: 10.3233/JIFS-230128

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10605-10620, 2023

Authors: Ye, Tingqing | Zheng, Haoran

Article Type: Research Article

Abstract:  Uncertain statistics is a set of mathematical techniques to collect, analyze and interpret data based on uncertainty theory. In addition, probability statistics is another set of mathematical techniques based on probability theory. In practice, when to use uncertain statistics and when to use probability statistics to model some quality depends on whether the distribution function of the quality is close enough to the actual frequency. If it is close enough, then probability statistics may be used. Otherwise, uncertain statistics is recommended. In order to illustrate it, this paper employs uncertain statistics, including uncertain time series analysis, uncertain regression analysis and …uncertain differential equation, to model the birth rate in China, and explains the reason why uncertain statistics is used instead of probability statistics by analyzing the characteristics of the residual plot. In addition, uncertain hypothesis test is used to determine whether the estimated uncertain statistical models are appropriate. Show more

Keywords: Uncertainty theory, uncertain time series analysis, uncertain regression analysis, uncertain differential equation, birth rate

DOI: 10.3233/JIFS-230179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10621-10632, 2023

Authors: Khan, Asghar | Aslam, Muhammad | Iqbal, Quaid

Article Type: Research Article

Abstract: Many unknowable elements make it difficult to measure cyclone disasters, traditional methods are insufficient to measure these factors. Fuzzy set theory and its expansions are effective ways to measure these uncertainties for these kinds of uncertainty. An evaluation of the cyclone disaster’s spatial vulnerability is necessary in order to build disaster damage reduction methods. In real life, we may come into a hesitant environment when making decisions. To explore such environments, we introduce hesitant fuzzy set (HFS) into Fermatean fuzzy set (FFS) and extend the existing research effort on FFSs in light of the effective tool of HFSs for expressing …the hesitant condition. In this study, we develop a comprehensive tropical cyclone disaster assessment by applying Fermatean hesitant fuzzy (FHF) information. In this paper, various unique aggregation strategies for the analysis of decision-making problems are introduced. As a result, Fermatean hesitant fuzzy average (FHFWA), Fermatean hesitant fuzzy ordered weighted average (FHFOWA), Fermatean hesitant fuzzy weighted geometric (FHFWG), and Fermatean hesitant fuzzy ordered weighted geometric (FHFOWG) operators have been developed. We also go over some of the most important features of these operators. Furthermore, we establish an algorithm for addressing a multiple attribute decision-making issue employing Fermatean hesitant fuzzy data by using these operators. and attribute prioritizing. A real-world problem of cyclone disaster damages in several parts of Pakistan is explored to test the applicability of these operators. In the final section, we expand the TOPSIS approach to a Fermatean hesitant fuzzy environment and compare the outcomes of the extended TOPSIS method with operators established in the FHF-environment. Show more

Keywords: Cyclone disaster, FHFSs, Aggregation operatos, TOPSIS method, MADM

DOI: 10.3233/JIFS-222144

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10633-10660, 2023

Authors: Zou, Yuan

Article Type: Research Article

Abstract: Bayesian decision models use probability theory as a commonly technique to handling uncertainty and arise in a variety of important practical applications for estimation and prediction as well as offering decision support. But the deficiencies mainly manifest in the two aspects: First, it is often difficult to avoid subjective judgment in the process of quantization of priori probabilities. Second, applying point-valued probabilities in Bayesian decision making is insufficient to capture non-stochastically stable information. Soft set theory as an emerging mathematical tool for dealing with uncertainty has yielded fruitful results. One of the key concepts involved in the theory named soft …probability which is as an immediate measurement over a statistical base can be capable of dealing with various types of stochastic phenomena including not stochastically stable phenomena, has been recently introduced to represent statistical characteristics of a given sample in a more natural and direct manner. Motivated by the work, this paper proposes a hybrid methodology that integrates soft probability and Bayesian decision theory to provide decision support when stochastically stable samples and exact values of probabilities are not available. According to the fact that soft probability is as a special case of interval probability which is mathematically proved in the paper, thus the proposed methodology is thereby consistent with Bayesian decision model with interval probability. In order to demonstrate the proof of concept, the proposed methodology has been applied to a numerical case study regarding medical diagnosis. Show more

Keywords: Soft probability, interval probability, Bayes rule, interval numbers, possibility degree

DOI: 10.3233/JIFS-223020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10661-10673, 2023

Authors: Qureshi, Saima Siraj | He, Jingsha | Qureshi, Sirajuddin | Zhu, Nafei | Zardari, Zulfiqar Ali | Mahmood, Tariq | Wajahat, Ahsan

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-220932

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10675-10687, 2023

Authors: Guo, Xiaoyong | Zhang, Kai | Peng, Jiahan | Chen, Xiaoyan | Guo, Guangjie

Article Type: Research Article

Abstract: This paper proposes that the task of single-image low-light enhancement can be accomplished by a straightforward method named Opt2Ada. It contains a series of pixel-level operations, including an opt imized illuminance channel decomposition, an ada ptive illumination enhancement, and an ada ptive global scaling. Opt2Ada is traditional and it does not rely on architecture engineering, super-parameter tuning, or specific training dataset. Its parameters are generic and it has better generalization capability than existing data-driven methods. For evaluation, both the full-reference, non-reference, and semantic metrics are calculated. Extensive experiments on real-world low-light images demonstrate the superiority of Opt2Ada over recent traditional …and deep learning algorithms. Due to its flexibility and effectiveness, Opt2Ada can be deployed as a pre-processing subroutine for high-level computer vision applications. Show more

Keywords: Low-light image enhancement, Image processing, Traditional method

DOI: 10.3233/JIFS-222644

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10689-10702, 2023

Authors: Akbaba, Ümmügülsün | Hikmet Değer, Ali

Article Type: Research Article

Abstract: In this study, new matrices which produce the Pell and Pell-Lucas numbers are given. By using these matrices, new identities and relations related to the Pell and Pell-Lucas numbers are obtained.

Keywords: Pell numbers, Pell-Lucas numbers, matrices

DOI: 10.3233/JIFS-222957

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10703-10707, 2023

Authors: He, Zihang | Zhao, Kaiyan | Li, Bohan | Li, Yong

Article Type: Research Article

Abstract: This paper proposes an approach that regulates the confidence of predicted boxes for corner-based detection methods. Corner-based methods have achieved state-of-the-art performance on MS-COCO by predicting corners and grouping them to generate boxes. However, the box confidence is simply defined to be the average score of grouped corners, ignoring the score and tag discrepancy between them. The discrepancy may lead to the generation of more false positives (FPs) since a larger discrepancy often means that the grouped corners less likely belong to the same object. Observing this, this paper proposes introducing the discrepancy of corners (DoC) to decrease the box …confidence. Also, the score and location of center (SLoC) of a detection box is utilized to further finely regulate the confidence. DoC and SLoC can effectively reduce FPs and missings and hence improve the detection performance without changing any model parameter. Experimental results on MS-COCO also show improvements. Show more

Keywords: Object detection, anchor-free, corner-based

DOI: 10.3233/JIFS-212804

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10709-10720, 2023

Authors: Li, Feng

Article Type: Research Article

Abstract: With the advent of the information age, the development direction of automobiles has gradually changed, both from the domestic and foreign policy support attitude, or from the actual actions of the automotive industry and scientific research institutes’ continuous efforts, it is not difficult to see that driverless vehicle. At this time, the testing and evaluation of the intelligent behavior of driverless vehicles is particularly important. It is particularly important not only to regulate the intelligent behavior of unmanned vehicles, but also to promote the key It can not only regulate the intelligent behavior of unmanned vehicles, but also promote the …improvement of key technologies of unmanned vehicles and the research and development of driver assistance systems. The evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is often considered as a multi-attribute group decision making (MAGDM) problem. In this paper, the EDAS method is extended to the interval neutrosophic sets (INSs) setting to deal with MAGDM and the computational steps for all designs are listed. Then, the criteria importance through intercriteria correlation (CRITIC) is defined to obtain the attribute’s weight. Finally, the evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is given to demonstrate the interval neutrosophic number EDAS (INN-EDAS) model and some good comparative analysis is done to demonstrate the advantages of INN-EDAS. Show more

Keywords: Multi-attribute group decision making (MAGDM), interval neutrosophic sets (INSs), EDAS method, comprehensive obstacle-avoiding behavior, unmanned vehicles

DOI: 10.3233/JIFS-223370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10721-10732, 2023

Authors: Sridevi, A. | Preethi, M.

Article Type: Research Article

Abstract: The technologically adapted agricultural procedures convert conventional farming practices and introduce smart farming or smart agriculture. Manual interventions in farming are unavoidable, however, it was reduced due to the Internet of Things (IoT). Sensors are used to monitor the farms which reduce the manpower requirements as well the cost. In this research work, a smart monitoring and prediction system was developed using IoT along with Fog computing. The physical data from farms are collected through IoT sensors and processed using a novel correlation-based ensemble classifier. Fog computing is adopted in the proposed work to reduce the data transmission delay and …computation complexities. Simulation analysis using benchmark datasets demonstrates the proposed model performance in terms of precision, recall, F1-score, and accuracy. Comparative analysis with conventional techniques like neural networks, extreme learning machine, and hybrid particle swarm optimization algorithm, validates the superior performance of the proposed model. With maximum accuracy of 96.67% proposed model outperforms conventional approaches. Show more

Keywords: Internet of Things (IoT), fog computing, latency, monitoring, feature extraction, prediction, correlation-based approach, ensemble classifier

DOI: 10.3233/JIFS-224225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10733-10746, 2023

Authors: Diao, Xiu-Li | Zheng, Cheng-Hao | Zeng, Qing-Tian | Duan, Hua | Song, Zheng-guo | Zhao, Hua

Article Type: Research Article

Abstract: With the increase in needs for personalized learning of online students, knowledge tracing (KT), a technique aimed at tracing the state of a student’s knowledge mastery and predicting performance in future exercises, has become a hot topic in personalized learning research. The behavioral features exhibited during students’ learning process bear information that impacts the state of a student’s knowledge mastery. To study the influence of learning behaviors on students’ knowledge mastery state in the learning process, we propose a Precise Modeling of Learning Process based on M ultiple B ehavioral F eatures for K nowledge T racing model (MBFKT), which …models a student’s learning process by making use of these behavioral features. MBFKT initially processes these features through multi-head attention networks, memory networks, and recurrent neural networks to model students’ learning process into three memory links: memory decline link, memory enhancement link, and memory update link. Various update strategies are designed for each memory link, and the performance of numerous possible combinations of behavioral features in the memory links is compared, for the rules of learning and forgetting to be explained. Furthermore, we also study the contribution and degree of influence of different behavioral features on a student’s knowledge mastery state, by which MBFKT is improved, thus enhancing the accuracy of prediction. Through experiments on real online education datasets and comparison with existing benchmark methods, it is observed that MBFKT has evident advantages in predicting performance with good interpretability. Show more

Keywords: personalized learning, knowledge tracing, multiple behavioral features, memory links, educational data mining

DOI: 10.3233/JIFS-224351

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10747-10764, 2023

Authors: Sivaranjani, S. | Vivek, C.

Article Type: Research Article

Abstract: Spectrum sensing will be an essential component in developing cognitive radio networks, which will be an essential component of the subsequent generation of wireless communication systems. Over the course of several decades, a great deal of different strategies, including cyclo-stationary, energy detectors, and matching filters, have been put up as potential solutions. Obviously, each of these methods comes with a few of negatives that you have to take into consideration. When the Signal-to-Noise Ratio (SNR) changes, energy detectors work poorly; cyclo-stationary detectors are technically sophisticated; and employing matching filters needs experience with Primary User (PU) signals. Researchers have recently been …devoting a great deal of attention to Machine Learning (ML) and Deep Learning (DL) algorithms as a result of the potential uses that these algorithms may have in the development of exceptionally accurate spectrum sensing models. The capacity to learn from data in a way that traditional learning algorithms are unable to has led to the rise in prominence of these types of algorithms. The Hybrid Model of Improved Long Short Term Memory with Improved Extreme Learning Models (HILSTM-IELM), to be more specific, is what is being suggested since it reduces the amount of energy that is used during data transmission as well as the range and the duty cycle. Because of this, the disadvantage in existing methodology, proposed technique reduced to a certain level in energy consumption. In the last step of this analysis, the performance of the HILSTM-IELM-based spectrum sensing is compared to that of a variety of different methods that are currently in use. According to the findings of recent studies, the spectrum sensing method that was created provides superior performance to that of technologies in terms of the accuracy, sensitivity, and specificity of data transmission systems. Show more

Keywords: Improved long short-term memory, improved extreme learning machines, energy detectors, cyclo-stationary features, machine learning, deep learning algorithms

DOI: 10.3233/JIFS-224376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10765-10779, 2023

Authors: Wang, Zhiyong

Article Type: Research Article

Abstract: Assessment of energy needed for crack growth in concrete structures has been an interesting topic since the use of fracture mechanics to concrete. However, experimental procedures need time, cost and efforts. Based on historical data, regression approaches were created using mechanical characteristics and mixed design factors to quantify the concrete preliminary (Gf ) and whole (GF ) fracture energy. This work combined support vector regression (SVR) analysis with antlion optimization (ALO) and Harris Hawks optimization (HHO) approaches to build a hybridized SVR evaluation to fully comprehend Gf and GF . Evaluation metrics demonstrate that both optimized ALO-SVR and HHO-SVR …assessments could perform wonderfully throughout the estimation mechanism. Whenever the superior SVR investigation was contrasted to the literature, it was observed that the uniquely developed ALO-SVR regression also provides a reasonable boost in effectiveness, with benefits across the board. Finally, although the HHO-SVR technique has its particular capabilities in the simulating procedure, the ALO-SVR analysis seems to be highly reliable for determining Gf and GF . Show more

Keywords: Preliminary and entire fracture energy, concrete, SVR analysis, metaheuristic optimization algorithms

DOI: 10.3233/JIFS-224464

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10781-10798, 2023

Authors: Kasture, Neha | Jain, Pooja

Article Type: Research Article

Abstract:  Speech Recognition and its potential applications in terms of “talking devices” have become indispensable in today’s world. Technological advances like mobiles, smart home assistants or tablets extensively use the techniques of automatic speech recognition that works good for adults but cannot always follow and understand children’s speech. The primary goal of this paper is to bridge the gap of communication between voice assistants and Indian children speaking English as secondary language. The issue of lack of children’s speech corpora with English as non-native language, is addressed by creating a dataset of children in the age group of 5-15 years, speaking …Hindi or Marathi as their mother tongue and English as their second language. The analysis and implementation of the proposed work shows the accuracy of approximately 96% and potential for further scope by increasing the size of dataset in lower age group. The key contributions of our work are (i) creating speech dataset of Indian children whose mother-tongue is Hindi or Marathi, (ii) employing and evaluating hybrid Convolutional Neural Network (CNN) as an age classifier, (iii) language modeling to customize children vocabulary, (iv) checking accuracy and performance of the system. Show more

Keywords: Analysis of Children’s Speech, Automatic Speech Recognition, Child-Machine Interaction, Children’s Speech Recognition, Convolutional Neural Network

DOI: 10.3233/JIFS-224472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10799-10813, 2023