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

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