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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Anand, R.
Article Type: Research Article
Abstract: The COVID-19 outbreak has impacted huge number of individuals all around the world and has caused a great economic loss all over the world. Vaccination is most effective solution to prevent this disease. It helps in protecting the whole community. It improves the human immune system and fights against corona virus reducing the death rate. This paper deals with the different types of COVID-19 vaccine and their related distribution, it includes measures to ensure safe and secured distribution of the vaccine through block chain technology with the help of supply chain. Any malfunction in the chain is identified by the …trust value of the function point method and the value of the Markov Chain. Show more
Keywords: COVID-19, vaccination, corona, pandemic, blockchain, markov chain
DOI: 10.3233/JIFS-220614
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 467-475, 2023
Authors: Qiyas, Muhammad | Abdullah, Saleem | Naeem, Muhammad | Khan, Neelam
Article Type: Research Article
Abstract: In daily life, the decision making problem is a complicated work related to uncertainties and vagueness. To overcome this vagueness and uncertainties, many fuzzy sets and theories have been presented by different scholars and researchers. EDA𝒮 (Evaluation based on distance from average solution) method plays a major role in decision-making problems. Especially, when multi-attribute group decision-making (MAGDM) problems have more conflicting attribute. In this paper, a new approach known as Spherical fuzzy rough-EDA𝒮 (SFR-EDA𝒮) method is used to handle these uncertainties in the MAGDM problem. The aggregation operators have the ability to combine different sources of information, which plays an …essential role in decision making (DM) problem. Keeping in view the increasing complexity of the DM problem, it will be useful to combine the aggregation operators with the fuzzy sets in solving DM problem. Therefore, an aggregation operator known as SFR-EDA𝒮 method is utilized. For this propounded some new averaging and geometric aggregation is investigated. Moreover, the essential and desirable properties with some particular cases are deliberated and discussed detail. To evaluate the emergency program, a MAGDM approach is used based on the new introduced operators. Later on, the viability and applicability the proposed method is certified by a detailed analysis with the other existing approaches. Show more
Keywords: Spherical fuzz sets, rough sets, EDA𝒮 method, aggregation operators
DOI: 10.3233/JIFS-211056
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 477-498, 2023
Authors: Gopikarani, N. | Gayathri, B. | Praja, S.S. | Sridharan, Sneha
Article Type: Research Article
Abstract: Counterfeit drugs are without a doubt becoming a greater hazard to consumers and the pharmaceutical sector. As a result, real-time visibility of drug manufacturing and management is required. The proposed system uses Ethereum blockchain as the main technology. The primary advantage of blockchain technology is that the transactions are maintained in immutable digital ledger format and it may be read easily without jeopardizing the users’ security and privacy. In our proposed system, the admin validates and adds the manufacturers. The manufacturer after registering and logging in can perform tasks like adding the drug and seller list. The seller can place …order to the manufacturer which the manufacturer can accept or reject. The seller can update status of order of accepted orders to delivered. The customer can view the order details by entering the serial number on the drug package. Any transaction or exchange that occurs in the network is recorded in the chain. It functions similarly to other networks, but blockchain technology is distinguished by the fact that no data can be removed or altered by anyone in the network. No changes to the network can be made unless it has been validated by all of the network’s authorized users. All the information stored can be read by anybody so to incorporate more security, AES has been used to store data in the blockchain. The use of AES encryption technique distinguishes this system from all the existing implementations. Thus, this makes it easy to trace to the exact point in the supply chain and detect any counterfeit drugs in movement. As an extension to the drug counterfeit prevention system a Drug Recommendation System is also performed using the ensemble model with a combination of Random Forest and Logistic Regression for sentiment analysis training. Furthermore, when compared to the existing Linear SVM model, which has an accuracy of 90.39%, the suggested model has the best accuracy of 93.31%. Using the obtained sentiment for each drug, the drug is predicted accurately for the specified medical condition. Show more
Keywords: Blockchain (BC), Ethereum, smart contract, health- care, ensemble model, logistic regression, random forest
DOI: 10.3233/JIFS-220636
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 499-517, 2023
Authors: Lu, Shuya | Cao, Minglei
Article Type: Research Article
Abstract: Through scientific theoretical methods, we take the internal control optimization of the Financial Sharing Center of H company as the research object. Firstly, we introduce the Financial Sharing Center and the development background and research significance of internal control under this mode, sort out the existing international research and related concepts, analyze the problems existing in the internal control stage of the Financial Sharing Center, and analyze the problems one by one from the five elements of internal control. What is more innovative is that we use the quality function deployment theory in the field of system science, combined with …the intuitionistic fuzzy set theory, G1 method and entropy method in fuzzy mathematics to evaluate the five elements affecting the internal control optimization of the Financial Sharing Center of H company, and give the priority of optimization in theory. Finally, according to the implementation conditions of the Financial Sharing Center, this paper puts forward relevant countermeasures and suggestions to optimize the internal control of the financial sharing mode of H company, which can also provide experience for other enterprises that are building the Financial Sharing Center. Show more
Keywords: Financial sharing mode, internal control, Financial Sharing Center, quality function deployment theory, G1 method, entropy method
DOI: 10.3233/JIFS-221540
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 519-541, 2023
Authors: Zhou, Chunguo | Qiao, Ning | Mao, Jin | Zeng, Zhicheng | Zhou, Yongjun
Article Type: Research Article
Abstract: In order to improve the comprehensive performance of adaptive cruise control system in the car-following process and take the safety into account, an improved model predictive control algorithm considering multi-performance objective optimization is designed. In the prediction model part, the grey Verhulst model with saturation state is introduced to predict the acceleration disturbance of the preceding vehicle, and the particle swarm optimization algorithm is used to estimate the parameters, which is then applied to the car following model. The control problem is transformed into a quadratic programming problem with multiple constraints through multi-objective quadratic performance index, and the vector constraint …management method is introduced to solve the problem of no feasible solution caused by hard constraints. The emergency acceleration, deceleration and stable following are simulated. Finally, the Worldwide Harmonized Light Vehicles Test Cycle is co-simulated. The results show that the improved model predictive control algorithm can improve the tracking capability, fuel economy and comfort of adaptive cruise system. Show more
Keywords: Adaptive cruise, multi-performance objective optimization, model predictive control, grey prediction
DOI: 10.3233/JIFS-221690
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 543-553, 2023
Authors: Sun, Gang | Wang, Mingxin | Li, Xiaoping | Huang, Wei
Article Type: Research Article
Abstract: In real life, people often need to aggregate some multi criteria fuzzy information and then make reasonable and effective decisions. The distance measure in intuitionistic fuzzy set (IFS) space is an important tool to deal with multi criteria information fuzzy decision making problems. Motivated by these reasons, an intuitionistic fuzzy TOPSIS multi criteria decision-making method is proposed based on distance measure represented by centroid coordinates. Firstly, some existing distance measures in IFS space are summarized, and some of existing shortcomings are discussed. Secondly, the concept of hesitation factor is proposed by using the centroid coordinate representation of hesitation region, and …then a new distance measure between two intuitionistic fuzzy numbers is defined. It is proved that the distance measure satisfies the traditional distance axioms. Then, an intuitionistic fuzzy TOPSIS method based on the proposed distance measure is developed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method. Also, the superiority and advantages of the method are shown via comparative analysis and discussion. Show more
Keywords: Intuitionistic fuzzy set (IFS), centroid coordinate representation, hesitation factor, distance measure, TOPSIS method
DOI: 10.3233/JIFS-221732
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 555-571, 2023
Authors: Thilagavathi, S. | GeethaPriya, C.
Article Type: Research Article
Abstract: Improving the network lifetime is a major concern in wireless sensor networks (WSNs). Due to the limited energy capacity of the sensor nodes, wireless sensor network faces several challenges for improving the lifetime. Clustering is the influential technique used to minimize the energy consumption of the sensor nodes. Researchers have developed lot of clustering algorithms with unique features and challenges. First, this paper begins with the discussion of the clustering characteristics of the different approaches in detail and the radio model used. Further, the survey of the clustering algorithms which is classified into two categories: Traditional and computational based is …presented and performance comparison is given according to the requirements of the WSN like energy efficiency, scalability, delivery delay and link quality. Show more
Keywords: Wireless sensor networks, clustering, network lifetime, traditional, computational intelligence
DOI: 10.3233/JIFS-210858
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 573-593, 2023
Authors: Ramya, R. | Padmapriya, K.
Article Type: Research Article
Abstract: The clustering approach can improve wireless sensor network parameters such as lifetime enhancement, load balancing, reliable communication, and fault tolerance. The Cluster head in the cluster is responsible for reliable data transmission between node and sink or base station. Selecting suitable cluster heads and establishing an optimal path for data transmission is the main objective of this research work. Fuzzy-based clustering based on cluster head selection, optimized routing using particle swarm optimization (PSO), adaptive whale optimization algorithm (AWOA) are presented in this research work. Fuzzy logic considers the parameters like the distance between base station to node, node centrality, node …degree, and residual energy for cluster head selection. The optimization model obtains an optimized node for routing from the selected cluster heads. In terms of network lifetime, delay, energy consumption, packet delivery ratio, and energy efficiency, simulation analysis of the proposed model is compared to conventional routing algorithms such as bacteria foraging optimization (BFO), Tree-based data gathering (TBDG) algorithm, Immune inspired routing (IIR), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy-Efficient Distributed (HEED) protocol. The results demonstrate that the proposed approach outperforms existing approaches in terms of network lifetime and energy efficiency. Show more
Keywords: Wireless sensor network, cluster head selection, Fuzzy logic, whale optimization, routing
DOI: 10.3233/JIFS-220963
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 595-610, 2023
Authors: Hu, Wujin | Li, Bo | Li, Changyue | Zhang, Tong
Article Type: Research Article
Abstract: Physical Health is an important part of health education and health promotion in our country. Strengthen the research on the comprehensive evaluation of college students’ physical health, establish a representative, scientific, practical and operable index system, provide simple evaluation methods, scientifically evaluate the physical and health status of college students, and promote the scientific development of college students. Effective physical exercise, the development of good physical exercise habits and the promotion of school physical education teaching reform are of great significance. The physical health evaluation of College students is frequently viewed as the multiple attribute decision making (MADM) issue. In …this paper, the generalized Heronian mean (GHM) operator and generalized weighted Heronian mean (GWHM) operator with fuzzy number intuitionistic fuzzy numbers (FNIFNs) is extended to build fuzzy number intuitionistic fuzzy GHM (FNIFGHM) operator and fuzzy number intuitionistic fuzzy GWHM (FNIFGWHM) operator. Then we depicted the FNIFWHM operator on the strength of this technique. In the rear, a case in point for Physical health evaluation of College students is described to prove the built methods. Show more
Keywords: Multiple attribute decision making (MADM), fuzzy number intuitionistic fuzzy numbers (FNIFNs), fuzzy number intuitionistic fuzzy GHM (FNIFGHM) operator, fuzzy number intuitionistic fuzzy GWHM (FNIFGWHM) operator, physical health evaluation
DOI: 10.3233/JIFS-221248
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 611-624, 2023
Authors: Karthika, J. | Rajkumar, M. | Vishnupriyan, J.
Article Type: Research Article
Abstract: Distributed generators (DG) with inverter based on renewable sources are generally utilized in microgrids. Most of these sources work in droop control mode to effectively share the load. Higher droop is chosen on these systems to recover dynamic power sharing. This paper proposes a Hybrid Control Technique for Small Signal Stability Analysis for Microgrids under Uncertainty. The proposed topology is to recover the capacity of power system is used to restore the normal operating condition. The proposed hybrid technique is the combination of chaotic Henry gas solubility optimization (CHGSO) and recalling-enhanced recurrent neural network (RENNN) and therefore called the CHGSO-RENNN …technique. The proposed technique is used to optimally predict the internal and external current loop control parameters in light and the variety of power and current parameters. The small stability is revealed through the working conditions of the whole machine. The overall stability of the small signal is investigated in a linear model so that both source and load are used to characterize the state matrix of the frame that is used for eigenvalue examination. The PI controller gain parameters are optimally tuned and the controller offers reliable frame operation. The proposed technique is performed on MATLAB/Simulink work platform. Show more
Keywords: Fuel cell, battery storage system, ultra capacitor, diesel generator, flywheel storage system, chaotic henry gas solubility optimization and recalling-enhanced recurrent neural network
DOI: 10.3233/JIFS-221425
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 625-645, 2023
Authors: Kang, Xinhui | Nagasawa, Shin’ya
Article Type: Research Article
Abstract: To show the unique charm of Jiangxi’s traditional culture, it is of great importance to apply Jiangxi’s unique red culture to products’ creative designs. This paper aims to apply Kansei Engineering (KE) and interactive genetic algorithm (IGA) to extract the apparent symbol elements of Jiangxi red culture and then transform them into the creative watch design with modern culture. First of all, KE is used to extract customers’ emotional resonance to red culture, and 16 pairs of Kansei image vocabulary pairs are preliminarily collected. The theory of semiotics is used to extract symbols such as shapes, colors, and patterns from …the perspective of Jiangxi’s red architecture. Secondly, through the designers’ subjective aesthetic thinking, these cultural symbols are broken up and reconstructed, thus forming the morphological deconstruction table combined with the case of the watch. Finally, IGA is implemented to code and decode the cultural symbols, thus building a product form’s evolutionary design system. Through biological genetic manipulation, cultural symbols of refinement, particularity, and regionality are retained. Then these superior cultural genes are integrated into the innovation of the watch to get creative products with the characteristics of Jiangxi red culture. The model proposed in this paper optimizes the decision-making process of cultural creative product design, and also explores a sustainable development path of culture. Show more
Keywords: Kansei engineering, interactive genetic algorithm, cultural and creative product design, jiangxi red culture
DOI: 10.3233/JIFS-221737
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 647-660, 2023
Authors: Liang, Meishe | Mi, Jusheng | Zhang, Shaopu | Jin, Chenxia
Article Type: Research Article
Abstract: Ranking intuitionistic fuzzy numbers is an important issue in the practical application of intuitionistic fuzzy sets. Many scholars rank intuitionistic fuzzy numbers by defining different measures. These measures do not comprehensively consider the fuzzy semantics expressed by membership degree, nonmembership degree, and hesitancy degree. As a result, the ranking results are often counterintuitive, such as the indifference problems, the non-robustness problems, etc. In this paper, according to geometrical representation, a novel measure for intuitionistic fuzzy numbers is defined, which is called the ideal measure. After that, a new ranking approach is proposed. It’s proved that the ideal measure satisfies the …properties of weak admissibility, membership degree robustness, nonmembership degree robustness, and determinism. A numerical example is applied to illustrate the effectiveness and feasibility of this method. Finally, using the presented approach, the optimal alternative can be acquired in multi-attribute decision-making problems. Comparison analysis shows that the ideal measure is more effective and simple than other existing methods. Show more
Keywords: Intuitionistic fuzzy number, intuitionistic fuzzy set, ideal measure, multi-attribute decision making
DOI: 10.3233/JIFS-221041
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 661-672, 2023
Authors: Yu, Yang | He, Kun | Yan, Gang | Cen, Shixin | Li, Yang | Yu, Ming
Article Type: Research Article
Abstract: Vehicle Re-Identification (Re-ID) aims to discover and match target vehicles in different cameras of road surveillance. The high similarity between vehicle appearances and the dramatic variations in viewpoints and illumination cause great challenges for vehicle Re-ID. Meanwhile, in safety supervision and intelligent traffic systems, one needs a quick efficient method of identifying target vehicles. In this paper, we propose a Multi-Attention Guided Feature Enhancement Network (MAFEN) to extract robust vehicle appearance features. Specifically, the Fusing Spatial-Channel information multi-receptive fields Feature Enhancement module (FSCFE) is first proposed to aggregate richer and more representative multi-receptive fields features at different receptive fields sizes. …It also learned the spatial structure information and channel dependencies of the multi-receptive fields features and embedded them to enhance the feature. Then, we construct the Spatial Attention-Guided Adaptive Feature Erasure (SAAFE) module, which uses spatial attention to erase the most distinguishing features. The network’s attention is shifted to potentially salient features to strengthen the ability of the network to extract salient features. In addition, a multi-loss knowledge distillation (MLKD) method using MAFEN as a teacher network is designed to improve computational efficiency. It uses multiple loss functions to jointly supervise the student network. Experimental results on three public datasets demonstrate the merits of the proposed method over the state-of-the-art methods. Show more
Keywords: Vehicle re-identification, deep learning, multi-receptive fields, feature erasure, knowledge distillation
DOI: 10.3233/JIFS-221468
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 673-690, 2023
Authors: Karthika, J. | Rajkumar, M. | Vishnupriyan, J.
Article Type: Research Article
Abstract: The increased use of DC microgrid for complex application leads to the need for advanced control design for stable operation of the system. Loads connected to a DC microgrid are controlled by power electronic devices and exhibit constant power load (CPL) behavior, which is a serious challenge for stability as it enhances nonlinearity and reduces effective damping. This manuscript proposes an effective hybrid approach based on DC micro grid (MG) connected constant power loads. The proposed control approach is the consolidation of Marine Predators Algorithm (MPA) and mayfly optimization algorithm (MOA), hence it is named as hybrid MPA-MOA approach. The …DC microgrid system contains the sources, like two photovoltaic (PV), two wind turbine (WT), grid, battery. The major objective of the proposed approach is “to find the problems while interfacing the sources of the microgrid and increase the security of the system”. The proposed approach contains two controllers, they are primary and secondary. The primary controller is based on droop controller that shares the current and limits the oscillations because of the constant power loads (CPL). The secondary controller is used to regulate the voltage of the system from a single area. The secondary control is executed using the proposed MPA-MOA method. The proposed method is executed on MATLAB/Simulink platform; its performance is analyzed with the existing methods. The THD (%), efficiency (%) and Eigen value of the proposed technique achieves 1.4%, 92% and -9.3541±j2.4209. Show more
Keywords: Microgrid, primary controller, secondary controller, stability, marine predators algorithm, mayfly optimization algorithm
DOI: 10.3233/JIFS-221632
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 691-712, 2023
Authors: Xu, Zhiyun | Hu, Zhaoyan | Zheng, Xiaoyao | Zhang, Haiyan | Luo, Yonglong
Article Type: Research Article
Abstract: Adding noise to user history data helps to protect user privacy in recommendation systems but affects the recommendation performance. To solve this problem, a matrix factorization tourism point of interest recommendation model based on interest offset and differential privacy is proposed in this paper. The recommendation performance of the model is improved by analyzing user interest preferences. Specifically, user interest offsets are extracted from user tags and user ratings under time-series factors to calculate user interest drift. Then, similar neighbors are found to train user feature preferences which are integrated into the matrix model in the form of regular terms. …Meanwhile, based on the differential privacy theory, a privacy neighbor selection algorithm combining the K-Medoides clustering algorithm and index mechanism is designed to effectively protect the identity of neighbors and prevent KNN attacks. Besides, the Laplace mechanism is used to implement differential privacy protection for the model’s gradient descent process. Finally, the feasibility of the proposed recommendation model is verified through experiments, and the experimental results indicate that this model has advantages in recommendation accuracy and privacy protection. Show more
Keywords: Matrix factorization, recommendation system, differential privacy, interest shift, clustering
DOI: 10.3233/JIFS-211542
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 713-727, 2023
Authors: Hussain, Zahid | Abbas, Sahar | Rahman, Shams ur | Hussain, Rashid | Sharif, Razia
Article Type: Research Article
Abstract: Fuzzy sets (FSs) with belief and plausibility measures in Dempster-Shafer theory (DST) are recognized as different methodology to model imperfect, uncertain, and vague information more accurately than probability. Various generalizations of DST to FSs are suggested in the numerous literatures but the generalization of DST to Pythagorean fuzzy sets (PFSs) has not yet been considered so far. In this paper, we first suggest an intuitive and simple way to develop a generalization of DST to PFSs with the characterization of belief function in terms of membership function and plausibility function in terms of 1-nonmemberhip function respectively. We give the interpretation …of belief and plausibility on PFSs and then construct belief-plausibility intervals (BPIs) of PFSs. On the basis of suggested BPIs, we use Hausdorff distance to describe the distance between two BPIs and then construct several similarity measures in the generalized context of DST to PFSs. By utilizing the method of VIekriterijumsko KOmpromisno Rangiranje (VIKOR), the suggested belief and plausibility measures on PFSs in the framework of DST enable us to develop a belief-plausibility VIKOR (BP-VIKOR) to manage multicriteria decision-making (MCDM) problems related to daily life settings. Numerical analysis with examples are given to show the suggested method is reasonable, and suitable in the environment of PFSs in the context of generalization of DST. Show more
Keywords: Fuzzy sets, pythagorean fuzzy sets, belief and plausibility measure, hausdorff distance, multicriteria decision making, BP-VIKOR
DOI: 10.3233/JIFS-212098
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 729-743, 2023
Authors: Bansal, Kanishk | Singh, Amar
Article Type: Research Article
Abstract: Computer Vision (CV) is constantly inundated with massive volumes of data. One of the most challenging types of data for an Artificial Intelligence (AI) system is imagery data. Convolutional neural networks (CNNs) are utilized to cope with Big Data of such type, but progress is gradual. The 3 Parent Genetic Algorithm (3PGA), an evolutionary computation method, is employed to evolve a default CNN in this study. 3PGA is an extension of GA which has been developed further for better optimization. We observed from the literature that 3PGA is giving excellent results on standard benchmark functions as compared to other recent …soft-computing-based approaches. The accuracy of the evolved CNN increased from 53% to 75%, resulting in a net improvement of more than 40%. Furthermore, it was noted that the hyperparametric combinations or features of a CNN, which are very distinct from those commonly utilized, appear to perform better. A geographical landmarks dataset from Google was used for testing purposes. Landmark recognition is one of the most time-consuming jobs for an AI system, and the optimization of a network on a landmarks dataset shows that evolutionary computation can be substantially used in the future for the evolution of Artificial Neural Networks (ANNs). Show more
Keywords: Convolutional neural network, 3 parent genetic algorithm, optimization, geographical landmark recognition, hyperparametric features
DOI: 10.3233/JIFS-221473
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 745-756, 2023
Authors: Souidi, Mohammed El Habib | Haouassi, Hichem | Ledmi, Makhlouf | Maarouk, Toufik Messaoud | Ledmi, Abdeldjalil
Article Type: Research Article
Abstract: Multi-Pursuers Multi-Evader Game (MPMEG) is considered as a multi-agent complex problem in which the pursuers must perform the capture of the detected evaders according to the temporal constraints. In this paper, we propose a metaheuristic approach based on a Discrete Particle Swarm Optimization in order to allow a dynamic coalition formation of the pursuers during the pursuit game. A pursuit coalition can be considered as the role definition of each pursuer during the game. In this work, each possible coalition is represented by a feasible particle’s position, which changes the concerned coalition according to its velocity during the pursuit game. …With the aim of showcasing the performance of the new approach, we propose a comparison study in relation to recent approaches processing the MPMEG in term of capturing time and payoff acquisition. Moreover, we have studied the pursuit capturing time according to the number of used particles as well as the dynamism of the pursuit coalitions formed during the game. The obtained results note that the proposed approach outperforms the compared approaches in relation to the capturing time by only using eight particles. Moreover, this approach improves the pursuers’ payoff acquisition, which represents the pursuers’ learning rate during the task execution. Show more
Keywords: Multi-agent systems, coalition formation algorithm, discrete particle swarm optimization, pursuit-evasion game
DOI: 10.3233/JIFS-221767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 757-773, 2023
Authors: Peng, Weishi | Fang, Yangwang
Article Type: Research Article
Abstract: In performance evaluation, the widely used root-mean-square error is easily affected by large error terms and is also an incomprehensive measure. Therefore, the error spectrum as a comprehensive measure was proposed for parameter estimation. However, error spectrum (ES) is a three-dimension plot (among ES, r axis and time t axis) in the whole time horizon in dynamic evaluation system, which is not intuitive and easy to be analyzed. To smooth this, a new dynamic error spectrum (NDES) is proposed in dynamic evaluation system in this paper. Firstly, the NDES is defined for EPE in dynamic systems. Secondly, the …computation method is proposed to calculate the NDES. Thirdly, several nice properties of NDES are presented for dynamic system performance evaluation. Finally, the effectiveness of the proposed new dynamic error spectrum is verified by a numerical example. Show more
Keywords: Performance evaluation, decision support systems, parameter estimation, new dynamic error spectrum
DOI: 10.3233/JIFS-221958
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 775-782, 2023
Authors: Luo, Wei | Feng, Tao | Liang, Hong
Article Type: Research Article
Abstract: Change detection in synthetic aperture radar (SAR) images is an important part of remote sensing (RS) image analysis. Contemporary researchers have concentrated on the spatial and deep-layer semantic information while giving little attention to the extraction of multidimensional and shallow-layer feature representations. Furthermore, change detection relies on patch-wise training and pixel-to-pixel prediction while the accuracy of change detection is sensitive to the introduction of edge noise and the availability of original position information. To address these challenges, we propose a new neural network structure that enables spatial-frequency-temporal feature extraction through end-to-end training for change detection between SAR images from two …different points in time. Our method uses image patches fed into three parallel network structures: a densely connected convolutional neural network (CNN), a frequency domain processing network based on a discrete cosine transform (DCT), and a recurrent neural network (RNN). Multi-dimensional feature representations alleviate speckle noise and provide comprehensive consideration of semantic information. We also propose an ensemble multi-region-channel module (MRCM) to emphasize the central region of each feature map, with the most critical information in each channel employed for binary classification. We validate our proposed method on four benchmark SAR datasets. Experimental results demonstrate the competitive performance of our method. Show more
Keywords: Change detection, SFTNet, feature extraction, synthetic aperture radar (SAR) images, deep learning, neural network
DOI: 10.3233/JIFS-220689
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 783-800, 2023
Authors: Kuppulakshmi, V. | Sugapriya, C. | Nagarajan, D.
Article Type: Research Article
Abstract: Inventory plays an important role in the production process. One of the primary reasons why inventory management modeling is essential for the industry is because it will suffer immensely if there are insufficient food products to stock during the shutdown period. By determining the combined optimal cost of the retailers and wholesalers, this research significantly improves the service of the supply chain from wholesaler to retailer. The stochastic number for the imperfect perishable items is provided in this inventory study. By altering the parameter values, the uniform distribution is used to calculate these damaged items. This approach identifies the backordering …quantity for both regular and uncertain fish band circumstances. The cost of maintaining the inventory will rise significantly of increased wastage due to a rise in deteriorating, which will result in the loss of perishable food items. The primary goal of this research paper is to transport them without being destroyed until they reach their desired consumers. By determining the back ordering quantity during a shutdown, one can decrease the overall expenses incurred by the retailers. These computational complexity measures are proven in a fuzzy uncertain environment. The main goal of this paper is to analyze the variation of demand during the unanticipated period and find the optimum total cost of the perishable products. The growth of production in a particular area at a particular time, interconnect with another large number of products in the same area and is calculated by Verhulst’s demand with time depended on proficiency rate. Concerning the existing Verhulst’s demand pattern for the production process, this paper introduced that for perishable items in a fuzzy unanticipated situation. A bountiful system analysis is performed to find the cost function under fuzzy environment and the sensitivity analysis is carried out to perform the key representation constant. Show more
Keywords: Perishable items, unanticipated period, verhulst’s demand, hexagonal fuzzy number, backorders, geographical market, fish ban period
DOI: 10.3233/JIFS-220832
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 801-814, 2023
Authors: Yang, Yulin | Sheng, Yuhong
Article Type: Research Article
Abstract: The contradiction between logistics and distribution capacity and people’s increasing demand has attracted more and more attention from all walks of life. At present, there are many problems and deficiencies in logistics in China. This paper establishes the location model of the national supply chain logistics center, taking the vegetable logistics distribution as an example. Based on the principle of maximum satisfaction and satisfying demand, a vegetable material flow location model with the ability to predict the annual yield of vegetables is proposed by using the advantages of Holt’s linear trend method and immune algorithm in solving multi-objective optimization problems. …Finally, the algorithm is applied to select the optimal logistics center in 31 provinces in China to maximize customer satisfaction. Thus, Chengdu, Guangzhou, Nanchang, Nanjing, Shijiazhuang, and Changchun re used as vegetable logistics centers. Show more
Keywords: Uncertainty theory, location model, immune algorithm, uncertain programming, expected value model
DOI: 10.3233/JIFS-220885
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 815-825, 2023
Authors: Wu, Mengmeng
Article Type: Research Article
Abstract: Using Ultra-High Performance Concrete (UHPC) as the highly resistant material is widely advised in constructing sensitive structures to enhance safety. The utilization of eco-friendly contents such as fly-ash and silica-fume replacing cement can decrease the pollution rate in the production process of concrete and improve the compressive strength (CS) factor. There are many ways to appraise the CS of concretes as empirically and mathematically Artificial Neural Networks (ANN) as the high-accurate model is used in the present study. In this regard, Radial Basis Function (RBF) coupling with Biogeography-Based Optimization (BBO) and Flow Direction Algorithm (FDA) created the two high-accurate frameworks: …BBO-RBF and FDA-RBF. Enhancing the accuracy of RBF to predict the CS and decreasing the ANN net complexity leads to having better results evaluated by various metrics. Therefore, using the proposed frameworks, the correlation index of R2 to model the CS in the training phase for FDA-RBF was calculated at 0.9, although BBO-RBF could get 0.85, with a 0.5% difference. However, the RMSE of FDA-RBF was 9 MPa, and for BBO-RBF, this index was calculated at 10 MPa the former model has about three percent more accuracy than the latter. Show more
Keywords: Ultra-high-performance concrete, radial basis function, flow direction algorithm, biogeography-based optimization, compressive strength
DOI: 10.3233/JIFS-221092
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 827-837, 2023
Authors: Xiong, Qiang | Lian, Shuai | Zeng, Zhangying | He, Runxin | Zhu, Binxin | Yang, Xinqi
Article Type: Research Article
Abstract: The vulnerability patch R&D has become an important part of information security governance. An effective collaboration with software vendors in patch R&D is of great significance to reduce the existence time of information security risks. This works aims to explore the relationship between vulnerability information disclosure and patch R&D of software vendors. The data regarding the vulnerability and software vendors is gathered from third-party vulnerability sharing platforms, including (China’s national information security vulnerability database, CNNVD) and Tianyacha.com. Based on the theory of organizational information processing, linear regression model and Cox proportional risk regression model are built for appropriately addressing the …research questions. The results show that the vulnerability disclosure of the third-party sharing platform can improve the patch R&D probability of software vendors. The information processing requirements, such as vulnerability information attention, vulnerability score and whether vulnerabilities are disclosed in advance accelerate the vulnerability patch R&D. The enterprise information processing capability indicators, including the industry dependence of software product customers and the staff size of software vendors accelerate the patch R&D. The number of products affected by the vulnerabilities and the number of software copyrights of software vendors have no significant impact on patch R&D. Show more
Keywords: Patch R&D, vulnerability information disclosure, information processing theory, third-party vulnerability sharing platforms
DOI: 10.3233/JIFS-221316
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 839-853, 2023
Authors: Ibrahim, Hariwan Z.
Article Type: Research Article
Abstract: An n,m-rung orthopair fuzzy set is the one of the most useful expansions of fuzzy sets for coping with information uncertainties. In such circumstances, in this article, we define an n,m-rung orthopair fuzzy topology and investigate the basic aspects of this topology. We introduce their relationship with Fermatean fuzzy topology, Pythagorean fuzzy topology and intuitionistic fuzzy topology, and provide some examples. In addition, we introduce separated n,m-rung orthopair fuzzy sets and then we present the concept of diconnected n,m-rung orthopair fuzzy sets. Moreover, we study and characterize n,m-rung orthopair fuzzy continuous maps in great depth. Furthermore, we establish T 0 …and T 1 in n,m-rung orthopair fuzzy topology and discover the links between them. Finally, we create a new relation extension on n,m-rung orthopair fuzzy sets and provide a method for classifying children with learning disabilities. Show more
Keywords: n,m-ROFSs, n,m-ROFT, separated n,m-ROFSs, diconnected n,m-ROFS, n,m-ROFS continuous maps, T0 , T1 and relation on n,m-ROFSs
DOI: 10.3233/JIFS-221528
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 855-869, 2023
Authors: Hu, Shan | Han, Jialin | Rong, Lingda | Zong, Qiwei | Dai, Mingxiao
Article Type: Research Article
Abstract: After COVID-19, some initiatives such as Healthy China, and Smart Living have been widely mentioned. This study explored the factors influencing user satisfaction in sports and healthcare integration services to help system builders and interaction designers better seek opportunities and directions for systems construction. Based on grounded theory method, conducted semi-structured interviews with people who have home exercise needs, and then summarised the influencing factors after coding the raw information level by level. It applied the user experience honeycomb to classify potential variables, used principal component analysis (PCA) to extract representative evaluation indicators as observed variables, and followed the construction …of a theoretical model of the satisfaction factors. The structural equation model (SEM) was validated and analyzed to prove its scientific validity and reasonableness. Research showed that the core factors affecting the user experience of sports and healthcare integration system include usefulness, interactivity, usability, credibility, and findability, all of which have a positive and significant impact on user satisfaction. According to the results of empirical analysis, A multidimensional design strategy for sports and healthcare integration system is proposed to provide a reference for improving user satisfaction. Show more
Keywords: Post-Pandemic Era, user satisfaction, grounded theory, Principal Component Analysis (PCA), Structural Equation Model (SEM)
DOI: 10.3233/JIFS-221533
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 871-887, 2023
Authors: Wang, Jiangrui | Zhu, Jiwei | Zhao, Xin | Li, Liang | Wang, Bing
Article Type: Research Article
Abstract: Expert group decision-making in the process of engineering consulting is an important part of the smooth development of engineering projects. Whether the conceptual design scheme of the project is reasonable or not will directly affect the construction quality of the project. After the preliminary selection of a river ecological governance project, four conceptual design schemes were obtained. The owner invited 20 experts in relevant fields to make decisions on the four schemes collected in the early stage. The experts gave preference information for each scheme after reading the relevant materials of the project and clarifying the actual needs of the …project. Based on this background, this paper uses a combination of quantitative and qualitative methods to construct a model for group decision-making and conflict resolution in the engineering consulting process. We use the preference relationship to reflect the degree of experts’ preference for the scheme, cluster them through similarity calculation, calculate the conflict degree of group preference and personal preference respectively, and comprehensively use the sequence difference method and personal preference correction method to resolve the conflict, so that their opinions can be quickly agreed within the specified time. The results calculated by model are consistent with the actual situation of the project, which verifies the effectiveness of the model proposed in this paper and can provide a reference for similar project decision-making and conflict resolution process. Show more
Keywords: Group decisions and negotiations, engineering consulting, conflict resolution, preference correction
DOI: 10.3233/JIFS-222099
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 889-904, 2023
Authors: Sureshkumar, T. | Sivaraj, R. | Vijayakumar, M.
Article Type: Research Article
Abstract: The Internet of Things (IoT) has altered the world in the last few years due to its capacity to impact almost every part of life. However, IoT raises concerns about data security and privacy because it collects data from devices via the cloud, increasing its vulnerability to hacking. IoT security is a serious issue that has delayed its widespread adoption. Several security and privacy solutions have been proposed for IoT contexts that meet prevalent security criteria such as authentication, integrity, and secrecy. However, due to resource restrictions and heterogeneous IoT devices, present solutions are unable to address the security requirements …of the approaching large-scale IoT paradigm. Blockchain, well known for bitcoin and Ethereum, provides an intriguing approach for IoT security. The IoT and blockchain technologies may be combined and significant improvements in distributed systems have been made as a result of the widespread use of IoT technology. A novel framework with a unique design was proposed to improve security in bitcoin transaction by combining blockchain and SHA-256 hash algorithm. Additionally, the performance of proposed framework is compared with the state-of-the-art algorithms like MD5 and SHA1 in term of encryption time, power consumption, latency, speed and security. It is observed that the proposed framework takes 12 ms lesser latency than MD5 and consumes 2.7Wh lesser power consumption than SHA1 and provides better security than both the techniques. Show more
Keywords: Blockchain, IoT, security, bitcoin, privacy
DOI: 10.3233/JIFS-220366
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 905-918, 2023
Authors: Hu, Chunjiao | Huang, Hengjie
Article Type: Research Article
Abstract: Feature selection is one basic technology for data mining. This paper investigates feature selection for interval-valued data via fuzzy rough iterative computation model (FRIC -model). To depict the similarity between samples in an interval-valued decision information system (IVDIS), the fuzzy symmetry relation in an IVDIS is first introduced from the perspective of “The similarity between information values is fed back to the feature set”. After that, several attribute evaluation functions, such as fuzzy positive regions, dependency functions and attribute importance functions are defined. Subsequently, FRIC -model for interval-valued data is established by using the iterations of these functions. Next, An …feature selection algorithm in an IVDIS based on this model is presented. Lastly, numerical experiments and statistics tests are carried out to estimate the performance of the presented algorithm. The experimental results illustrate that the presented algorithm maintains high classification accuracy, and does not occupy too much memory. These findings will provide new perspective for feature selection in an IVDIS. Show more
Keywords: IVDIS, Feature selection, FRS, Attribute evaluation function, FRIC-model
DOI: 10.3233/JIFS-221621
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 919-938, 2023
Authors: Rajesh, D. | Rajanna, G.S.
Article Type: Research Article
Abstract: Smart Dust environment face additional challenges as a result of the use of movable Smart Dust basestation(BS), despite its benefits. The main point of contention is the BS positioning updates to the smart dust nodes. Each smart object ought to be aware of the BS location so that it can send its data to the BS. According to the prevailing Flooding approach, the moveable BS must continuously distribute its location throughout the network in order to inform smart dust nodes about the BS location. In every case, visit positioning upgrades from the BS can result in maximal power usage as …well as enhanced network breakdowns. Different sorts of routing architectures can be used to reduce BS position updating. A routing strategy based on the movable BS is successful if it preserves the network network’s power consumption and latencies to a minimum. The study’s main goal is to develop an energy-efficient routing mechanism focused on adaptive movable BS modification. In the Smart Dust Head (SDH) establishing the inferred surroundings, the most latest movable BS location will be preserved. As a result, rather than soliciting SDH in the environment, the location of the BS is propagated to the smart dust nodes located at the sectors in integrated networking. By transmitting request information to the nearest sector, the remaining SDH can find the most current BS location. The message’s recipient is determined based on the information gathered. The best fuzzy related clustering algorithm will be used to accomplish this. The Enhanced Oppositional grey wolf optimization (EOGWO) methodology can be used to perform the improvement. Optimum network throughput, low latency, and other metrics are used to assess performance. To enhance productivity, the findings will be analyzed and compared to previous routing methodologies. Show more
Keywords: Data collection, smart dust, lifetime, energy utilization, and movable BS
DOI: 10.3233/JIFS-221719
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 939-949, 2023
Authors: Sridhar, M. | Pankajavalli, P.B.
Article Type: Research Article
Abstract: In the resource-constrained wireless sensor network (WSN) geographic routing has been considered as an attractive method where it exploits the location data instead of global topology to transmit the data. The geographic routing protocol faces the routing issues when it is used by a heterogeneous device and utilizes high energy during the propagation of data. The lifespan of the sensor network depends on the efficiency of energy and capacity of the battery. Hence, successful data transmission, enrichment of battery capacity and energy utilization is necessary for WSN. To attain this requirements an effective change is made in the data transmission …environment and network topology. In this paper proposed a dynamic cluster based duty cycle scheduling is initiated for the data transmission. The cluster-based scheduling and routing in geographic routing protocol (CSRGR) utilize the clustering mechanism which in turn reduces the consumption of energy and maximizes the throughput. The objective function of the proposed approach provides a scheduling and routing strategy. The demonstration of simulation results shows the effective cluster size balancing with data transmission range dynamically. The proposed algorithm is compared with the existing approach and from the results, the energy consumption is minimum for diverse scenarios. Show more
Keywords: Duty cycle schedule, throughput, energy efficiency, routing, scheduling cluster, and geographic routing
DOI: 10.3233/JIFS-220966
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 951-961, 2023
Authors: Saravana Kumar, K. | Ramasubramanian, S.
Article Type: Research Article
Abstract: Cardiovascular disease (CVD) is a severe public health concern globally. Early and accurate CVD diagnosis is a difficult task but a necessary endeavour required to prevent further damage and protect patients’ lives. Machine Learning (ML)-based Clinical Decision Support Systems (CDSS) have the potential to assist healthcare providers in making accurate CVD diagnoses and treatments. Clinical data usually contains missing values (MVs); hence, the incorporated imputation techniques for ML have become a critical consideration when working with real-world medical datasets. Furthermore, removing instances with MVs will lead to essential data loss and produce incorrect results. To overcome these issues, this paper …proposes an efficient and reliable CDSS with Ensemble Two-Fold Classification (ETC) framework for classifying heart diseases. The effectiveness of the proposed ETC framework using different supervised ML algorithms is evaluated with four distinct imputation methods for handling MVs over the standard benchmark dataset, viz., the University of California, Irwin (UCI). Experimental results show that our proposed ETC framework with the k-Nearest Neighbors(k-NN) imputation method achieves better classification accuracy of 0.9999 and a lesser error rate of 0.0989 compared to other imputation methods and classifiers with similar execution times. Show more
Keywords: Clinical dataset, classification, data pre-processing, decision support system, heart disease prediction, imputation, machine learning algorithms, missing values
DOI: 10.3233/JIFS-221165
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 963-980, 2023
Authors: Sahoo, Santosh Kumar
Article Type: Research Article
Abstract: Social distance is considered one of the most effective prevention techniques to prevent the spread of Covid19 disease. To date, there is no proper system available to monitor whether social distancing protocol is being followed by individuals or not in public places. This research has proposed a hybrid deep learning-based model for predicting whether individuals maintain social distancing in public places through video object detection. This research has implemented a customized deep learning model using Detectron2 and IOU for monitoring the process. The base model adapted is RCNN and the optimization algorithm used is Stochastic Gradient Descent algorithm. The model …has been tested on real time images of people gathered in textile shops to demonstrate the real time application of the developed model. The performance evaluation of the proposed model reveals that the precision is 97.9% and the mAP value is 84.46, which makes it clear that the model developed is good in monitoring the adherence of social distancing by individuals. Show more
Keywords: Covid19, social-distancing, deep learning, Detectron 2, Intersection over Union, video object detection
DOI: 10.3233/JIFS-221174
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 981-999, 2023
Authors: Song, Hui-Hui | Wang, Ying-Ming | Jia, Xiang | Meng, Meng-Jun
Article Type: Research Article
Abstract: In order to avoid the hesitation of choosing between aggressive and benevolent strategies, we propose two cross-efficiency models to get interval cross-efficiency (ICE) from the relatively neutral angle in fuzzy environment, and then propose a novel aggregation method for ICE to solve the full ranking of Decision-Making Units (DMUs). Firstly, regard the expected value of fuzzy data as the input and output of Data Envelopment Analysis (DEA) method based on fuzzy set theory. Secondly, construct the cross-efficiency models based on the fuzzy expected values from the relatively neutral angle, and generate the lower and upper bounds of ICE for all …DMUs, which determines the interval cross-efficiency matrix (ICEM). Thirdly, project all ICE onto the plane as points, then seek the optimal rally point for each DMU based on ICEM as the comprehensive ICE. Fourthly, rank the comprehensive ICE to obtain the complete ranking of DMUs by using the optimal number sorting method. Finally, the proposed model is applied to the evaluation of manufacturing enterprises, and the results are compared with different models to prove its effectiveness. Show more
Keywords: Interval cross-efficiency DEA, fuzzy sets, fuzzy numbers, the optimal rally point, aggregation method
DOI: 10.3233/JIFS-221482
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1001-1015, 2023
Authors: Alqahtani, Yahya | Jamil, Muhammad Kamran | Alshehri, Hamdan | Ahmad, Ali | Azeem, Muhammad
Article Type: Research Article
Abstract: In November of 2019 year, there was the first case of COVID-19 (Coronavirus) recorded, and up to 3rd of April of 2020, 1,116,643 confirmed positive cases, and around 59,158 dying were recorded. Novel antiviral structures of the 2019 pandemic disease Coronavirus are discussed in terms of the metric basis of their molecular graph. These structures are named arbidol, chloroquine, hydroxy-chloroquine, thalidomide, and theaflavin. Metric dimension or metric basis is a concept in which the whole vertex set of a structure is uniquely identified with a chosen subset named as resolving set. Moreover, the fault-tolerant concept of those structures …is also included in this study. By this concept of vertex-metric resolvability of COVID antiviral drug structures are uniquely identified and help to study the structural properties of the structure. Show more
Keywords: COVID antiviral drug structures, vertex metric dimension, vertex fault-tolerant metric dimension, locating number, locating set
DOI: 10.3233/JIFS-220964
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1017-1028, 2023
Authors: Shobana Nageswari, C. | Vimal Kumar, M.N. | Vini Antony Grace, N. | Thiyagarajan, J.
Article Type: Research Article
Abstract: Ultrasound image quality management and assessment are an important stage in clinical diagnosis. This operation is often carried out manually, which has several issues, including reliance on the operator’s experience, lengthy labor, and considerable intra-observer variance. As a result, automatic quality evaluation of Ultrasound images is particularly desirable in medical applications. This research work plans to perform the fetal heart chamber segmentation and classification using the novel intelligent technology named as hybrid optimization algorithm Tunicate Swarm-based Grey Wolf Algorithm (TS-GWA). Initially, the US fetal images data is collected and data undergoes the preprocessing using the total variation technique. From the …preprocessed images, the optimal features are extracted using the TF-IDF approach. Then, Segmentation is processed on optimally selected features using Spatially Regularized Discriminative Correlation Filters (SRDCF) method. In the final step, the classification of fetal images is done using the Modified Long Short-Term Memory (MLSTM) Network. The fitness function behind the optimal feature selection as well as the hidden neuron optimization of MLSTM is the maximization of PSNR and minimization of MSE. The PSNR value is improved from 3.1 to 9.8 in the proposed method and accuracy of the proposed classification algorithm is improved from 1.9 to 12.13 compared to other existing techniques. The generalization ability and the adaptability of proposed TS-GWA method are described by conducting the various performance analysis. Extensive performance result shows that proposed intelligent techniques performs better than the existing segmentation methods. Show more
Keywords: Fetal heart chamber segmentation, optimal feature selection, modified long short term memory tunicate swarm-based grey wolf algorithm, fetal heart chamber classification
DOI: 10.3233/JIFS-221654
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1029-1041, 2023
Authors: Zhao, Guiping | Wang, Hongmei | Li, Zhanfa
Article Type: Research Article
Abstract: The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA ). Although meticulous experimental studies are needed to determine a rock’s CWA , predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR ) models were created: a traditional SVR , two …ensembled models, and a hybrid SVR model using the whale optimization technique (WOA - SVR ). Results show that all models have acceptable performance in predicting the CWA with R 2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA - SVR model compared to other methods. Show more
Keywords: Capillary water absorption, building stones, prediction, support vector regression, ensembled SVR, hybrid SVR
DOI: 10.3233/JIFS-221207
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1043-1055, 2023
Authors: Nithinsha, S. | Anusuya, S.
Article Type: Research Article
Abstract: The objective of the research work is to propose an intrusion detection system in a cloud environment using K-Means clustering-based outlier detection. In the open access and dispersed cloud architecture, the main problem is security and confidentiality because these are easily susceptible to intruders. Intrusion Detection System (IDS) is a commonly used method to identify the various attacks on the cloud which is easy to access from a remote area. The existing process can’t provide the data to transmit securely. This work describes and notifies the modernly established IDS and alarm management methods by giving probable responses to notice and …inhibit the intrusions in the cloud computing environment and to overcome the security and privacy issue. Proposed K-means Clustering based Outlier Detection (KmCOD) is used to detect the intruders and efficiently secure the data from malicious activity, where it is formulated respectively to increase the trustworthiness of the system by using applying intrusion detection techniques to virtual machines thus keeping the system safe and free from intrusion also provides system reliability. The parametric measures such as the detection rate, trace preprocessing, and correctly identified and incorrectly identified malicious activity are chosen. The performance analysis shows the accuracy of outlier detection as 81%, detection rate achieves 76%, packet arrival rate reaches 79%, pre-processing trace achieves 74%, and malicious activity rate of 21%. Show more
Keywords: Cloud, intrusion detection, data security, clustering algorithm, outlier detection, data privacy, anomaly
DOI: 10.3233/JIFS-220574
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1057-1068, 2023
Authors: Senthilkumar, D. | Reshmy, A.K. | Paulraj, S.
Article Type: Research Article
Abstract: Multi-Target Regression (MTR) is used to study the relationship between the same set of input variables and multiple continuous target variables simultaneously. A dataset with many input and output variables is the prime issue to address in the MTR, which is computationally complex to build a prediction model. Also, dimensionality reduction from multiple target variables is a challenging and essential task that aims to reduce the size of the dataset to optimize the time complexity of analysis and remove the redundant and irrelevant variables. This paper proposes an efficient feature selection strategy, Multi-Target Feature Subset Selection (MTFSS), for MTR that …constructs a unique subset of features by considering multiple targets. On the other hand, two feature evaluators, correlation and ReliefF, support the MTR dataset without discretization. Furthermore, two new score functions, weighted mean aggregation strategy and threshold function, are introduced to identify the significant features. To evaluate the effectiveness of the proposed MTFSS, experiments were carried out on a benchmark dataset. The experimental results demonstrate that the proposed MTFSS can select fewer features and perform better than the original dataset results. Also, the correlation-based feature evaluator performs better than ReliefF with better performance. Show more
Keywords: Multi-target regression, feature selection, correlation, ReliefF
DOI: 10.3233/JIFS-220412
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1069-1083, 2023
Authors: George, Remya | Jose, Reshma | Meenakshy, K. | Jarin, T. | Senthil Kumar, S.
Article Type: Research Article
Abstract: Law enforcement teams across the globe experience the highest occupational stress and stress-related diseases. Physical exercise and an active lifestyle are recommended as part of their profession to equip them to fight stress and related health adversities. The research is carried out using objective measures of Heart Rate Variability (HRV), Electro Dermal Activity (EDA), Heart Rate Recovery (HRR), and subjective questionnaires. HRV was generated with an electrocardiogram (ECG) signal acquired using NI myRIO 1900 interfaced with the Vernier EKG sensor. HRR was acquired with the help of a Polar chest strap exercise heart rate monitor and EDA acquisition was carried …out with Mindfield E-Sense electrodes. Then statistical features are extracted from the collected data, and feed to the AQCNN (Aquila convolution neural network) classifier to predict the stress. Signal analyses were done in Kubios 4.0, Ledalab V3.x in a MATLAB environment. The results pointed out that exercise training is effective in increasing the vagal tone of the Autonomic Nervous System (ANS) and hence improves the recovery potential of the cardiovascular system from stress. The proposed AQCNN method improves the accuracy by 95.12% which is better than 93.13%, 85.36% and 80.13% from Statistical technique, CNN and ML-SVM respectively. The findings have the potential to influence decision-making in the selection and training of recruits in high-stress positions, hence optimizing the cost and time of training by identifying maladaptive recruits early. Show more
Keywords: Exercise training, ANS adaptation, machine learning, stress-recovery, heart rate variability, heart rate recovery, electrodermal activity
DOI: 10.3233/JIFS-221588
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1085-1097, 2023
Authors: Saeed, Maha Mohammed | Al-Ghour, Samer | Mehmood, Arif | Al-Shomrani, Mohammed M. | Park, Choonkil | Lee, Jung Rye
Article Type: Research Article
Abstract: This work investigates the new notion of operators, including the interior operator, exterior operator and closure operator in bipolar vague soft topological spaces. On the basis of these notions few results are addressed in bipolar vague soft topological spaces. Lastly, the intriguing concept is that of a sequence’s limit and on the basis of this concept few more results are addressed in bipolar vague soft topological spaces.
Keywords: Bipolar vague soft set, bipolar vague soft operations, bipolar vague soft topological space, bipolar vague soft α-open sets, bipolar vague soft α-close sets, bipolar vague soft operators, bipolar vague soft equence
DOI: 10.3233/JIFS-220498
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1099-1116, 2023
Authors: Miao, Yong | Liu, Zedong | Zhuang, Zijing | Yan, Xiaofeng
Article Type: Research Article
Abstract: The most significant parameter in groundwater movement in stones is capillary water absorption. Specifying the capillary water absorption (CWP ) of rocks needs hard and laborious experimental work, while prediction models can reduce the cost and required time. To this aim, different rock specimens were gathered from various rocks. For the prediction processes, the hybrid adaptive neuro-fuzzy inference system (ANFIS) models also were proposed to determine the optimal value of two constituent parameters of the ANFIS, which the particle swarm optimization (PSO) and whale optimization algorithm (WOA) algorithm applied to the ANFIS for this aim. Results present that ANFIS processes …have passable accomplishment in forecasting the CWA with R 2 larger than 0.832 and 0.917 for the training and testing data, respectively, a good connection among actual and anticipated values. Considering developed models, the ANFIS model optimized with WOA performs better than another model in training and testing datasets. In the training dataset, the value of R2 and RRSE is 0.917 and 29.29% for the WOA-ANFIS model, while the PSO-ANFIS model is 0.911 and 30.50%, respectively. Overall, it is clear that WOA-ANFIS can be recognized as the proposed model, which shows its capability to find the optimal value of two constituent parameters of the ANFIS. Show more
Keywords: Capillary water absorption, building stones, prediction, adaptive neuro-fuzzy inference system, hybrid ANFIS
DOI: 10.3233/JIFS-220640
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1117-1127, 2023
Authors: Mohammed, Awsan | Ghaithan, Ahmed | Al-Yami, Fahad
Article Type: Research Article
Abstract: The oil and gas industry is one of the harshest environments on reinforced concrete structures. Enhancing the reliability of these industries has been identified as a critical goal to meet anticipated production targets and maintain competitiveness. The purpose of this paper is to rank and prioritize risk factors on reinforced concrete structural systems in the oil and gas industry to reduce failures and improve system reliability. The risk factors influencing reinforced concrete systems are identified based on the experts interviewed who specialized in risk analysis. In this paper, a risk assessment approach based on a hybrid fuzzy failure mode and …effect analysis is developed in order to rank the factors and improve the process of reinforced concrete maintenance prioritization. The developed approach is also compared with the other two methods; namely, conventional failure mode and effect analysis (FMEA) and grey rational analysis (GRA) integrated with FMEA. The three developed approaches are designed to acquire the highest risk priority number (RPN) values; conventional RPN, GRA-FMEA RPN, and Fuzzy-FMEA RPN. These values will be utilized as the focus of improvements to reduce the possibility of some kind of failure occurring a second time and improve the deteriorated reinforced concrete structure to minimize the likelihood of failures. The results revealed that high-risk systems include the compression train, steam turbine, and combustion gas turbine generator, while the majority require maintenance of the supporting concrete foundation as soon as second-degree deterioration occurs. Furthermore, the results indicated that the Fuzzy FMEA approach was appropriate for assessing deteriorated reinforced concrete structures.. This work represents a step forward in the development of a tool that can be used to assess the risk of degraded concrete structures and improve their integrity through proper monitoring and maintenance. Show more
Keywords: Risk assessment, concrete structures, oil and gas industry, fuzzy FMEA, grey rational analysis
DOI: 10.3233/JIFS-221328
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1129-1151, 2023
Authors: Shabbir, Wasif | Aijun, Li | Taimoor, Muhammad | Yuwei, Cui
Article Type: Research Article
Abstract: Flight performance of unmanned aerial vehicles (UAVs) strongly depends on implemented attitude tracking control. For designing better controllers, nonlinear control design techniques are often opted instead of control design based on linearized models. Uncertainty in nonlinear dynamics estimation may arise due to inaccuracies in aerodynamic derivatives and simplifications/assumptions made during the derivation of nonlinear models. This paper considers attitude tracking control of fixed-wing UAVs having uncertain dynamics and corrupted gyro sensor outputs. An integral chain differentiator (ICD) is used to provide the analytical redundancy to the gyros used to measure the angular rates. Two control design schemes are proposed, a …neuro-fuzzy adaptive sliding mode control (NFASMC) and an ICD approximation-based fuzzy adaptive sliding mode control (ICD-FASMC). In NFASMC, the uncertain part of the dynamics is estimated using an adaptive radial basis function neural network. Gyro sensor output errors are estimated in real-time, using ICD based error estimation scheme and used in the control law along with the sensor’s corrupted outputs. In ICD-FASMC, the uncertain dynamics and angular rates of UAV are estimated using the ICD such that the requirement of the gyro sensor outputs for control design is bypassed. The switching gain of the designed controllers is made adaptive using fuzzy logic to mitigate the chattering effect. The stability of the proposed controllers is proved using the Lyapunov approach. The proposed schemes are implemented using a nonlinear simulation of a fixed-wing UAV. Simulation results are presented to show the effectiveness of the proposed techniques. Show more
Keywords: Neural network, tracking control, sliding mode control, fuzzy logic, UAV, sensors
DOI: 10.3233/JIFS-222630
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1153-1168, 2023
Authors: Subramanian, Kannimuthu | Kandhasamy, Premalatha
Article Type: Research Article
Abstract: Mining high utility itemsets (HUIs) from transaction databases is one of the current research areas in the data mining field. HUI mining finds itemsets whose utility meets a predefined threshold. It enables users to quantify the usefulness or preferences of products by utilizing different values. Since utility mining approaches do not satisfy the downward closure property, the cost of candidate generation for HUI mining in terms of time and memory space is excessive. This paper presents Genetic Algorithm based Particle Swarm Optimization (GA-PSO), which can efficiently prune down the number of candidates and optimally acquire the complete set of high …utility itemsets. The proposed algorithm’s performance is assessed using the synthetic dataset T20.I6.D100K and the real-time supermarket dataset, which comprises 38765 transactions and 167 unique products. It performs very effectively in terms of time and memory on large databases constituted of small transactions, which are challenging for existing high utility itemsets mining algorithms to manage. Experiments on real-world applications show the importance of high utility itemsets in business decisions, as well as the distinction between frequent and high utility itemsets. Show more
Keywords: Data mining, high utility itemset, genetic algorithm, particle swarm optimization, stagnation
DOI: 10.3233/JIFS-220871
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1169-1189, 2023
Authors: Riaz, Muhammad | Jamil, Nimra
Article Type: Research Article
Abstract: The idea of a cubic bipolar fuzzy set (CBFS ) is a new hybrid extension of the cubic set (CS) and the bipolar fuzzy set (BFS). A CBFS is a strong model to deal with bipolarity and fuzziness in terms of positive membership grades (PMGs) and negative membership grades (NMGs). A positive interval and a positive numbers represent a PMG to express the degree of belongingness of a specific property, and a negative interval and a negative number represent a NMG which defines the degree of non-belongingness of the specific property (or satisfaction level of its counter property). The …aim of this paper is to define the cubic bipolar fuzzy topology under P-order (CBFS P topology) as well as the cubic bipolar fuzzy topology under R-order (CBFS R topology). We investigate certain properties and results of CBFS P topology and CBFS R topology. Topological structures on CBFSs are helping in the development of new artificial intelligence (AI) techniques for healthcare domain strategies and investigating various critical diseases. Such techniques allow for the early detection and investigation of diseases, assisting clinicians in minimizing the possible risk factors. An extended linear assignment model (LAM) and superiority and inferiority ranking method (SIR method) are proposed for healthcare diagnosis based on newly developed structures. The proposed LAM and SIR method are successfully applied for investigation of critical diseases. Moreover, we discuss a comparison analysis of investigations made by suggested techniques with some existing approaches. Show more
Keywords: Cubic bipolar fuzzy set, cubic bipolar fuzzy topology, computational intelligence, linear assignment model, SIR method, healthcare
DOI: 10.3233/JIFS-222224
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1191-1212, 2023
Authors: Carmalatta, J. | Diwakaran, S. | Uma Maheswari, P. | Raja, S. | Robinson, Y. Harold | Julie, E. Golden | Kumar, Raghvendra | Son, Le Hoang | Le, Chung | Tung, Nguyen Thanh | Long, Hoang Viet
Article Type: Research Article
Abstract: In Passive Clustered Wireless Sensor Networks (WSNs), energy is lost in a sensor node during the data transmission. In order to avoid the energy loss due to data transmission, a data prediction technique is implemented. In this paper, we present a new multi-point data prediction technique, in which the prediction algorithm is initially implemented at both member nodes and cluster heads. The algorithm is updated to cluster head by member nodes by tracking temporal correlation of data. Neuro-Fuzzy model is used as a predictor in both member nodes and cluster heads. The simulation is performed using MATLAB and the overall …energy in nodes seems to increase. The mean square error (MSE) value is reduced to greater extend. Show more
Keywords: Neuro-fuzzy, wireless sensor networks, clustering, cluster head, mean square error value, energy consumption.
DOI: 10.3233/JIFS-212214
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1213-1228, 2023
Authors: Esmaeili, Mahin
Article Type: Research Article
Abstract: This paper presents a new combined algorithm for the fuzzy Travelling Salesman Problem (FTSP) based on a composition of the Intelligent Water Drops (IWD) and the Electromagnetism-like (EM) algorithms. In a FTSP, the time consumed distance between cities i and j can be described by vague knowledge, such as fuzzy quantity. The main goal of FTSP is to achieve the minimum distance of Hamilton circuit of G graph, where the Hamilton circuit is a closed route of cities (i.e., nodes) of G that have been visited only once. The proposed algorithm transfers the generated responses by …the IWD to the EM, where the best answer is selected. Importantly, the computed results from both algotithm are compared and the best is accumulated. In other words, in each iteration, the best result is collected by comparison between the current and previous hierarchies until the halt condition is fulfilled. Finally, the results of the genetic algorithm (GA), IWD and EM algorithms are compared, so that the efficiency of the proposed combined IWD-EM algorithm is determined. Show more
Keywords: Fuzzy travelling salesman problem (TSP), intelligent water drops (IWD) algorithm, electromagnetism-like (EM) algorithm, genetic algorithm (GA)
DOI: 10.3233/JIFS-213121
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1229-1240, 2023
Authors: Wu, Meiqin | Chen, Ruixin | Fan, Jianping
Article Type: Research Article
Abstract: Multi-criteria decision-making methods often include attributes with uncertain nature in practical applications, single-valued neutrosophic set is an important approach to solve above problem. QUALIFLEX method is a traditional decision method that makes decision by comparing different permutations of alternatives. In this paper, QUALIFLEX method is developed to solve the MCDM problem with the element of decision matrix is the single-valued neutrosophic number. Besides, since the defects of the original QUALIFLEX method about fusing information of different attributes, this paper uses Dempster-Shafer theory of evidence to integrate the information about weight and alternatives. Finally, by comparing the result with other MCDM …methods, we find that the new method can not only obtain reasonable results, but also explain the decision results by probability theory. This paper not only develops the traditional MCDM method, but also a meaningful attempt to apply AI algorithm in MCDM method. Show more
Keywords: Dempster-Shafer theory of evidence, QUALIFLEX, Single-valued neutrosophic set, multiple criteria decision making (MCDM), evidential reasoning
DOI: 10.3233/JIFS-220194
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1241-1256, 2023
Authors: Liu, Yitong | Mu, Xuewen
Article Type: Research Article
Abstract: A new neural network is proposed to solve the second-order cone constrained variational inequality (SOCCVI) problems. Instead of the smoothed Fishcer-Burmeister function, a smooth regularized Chen-Harker-Kanzow-Smale (CHKS) function is used to handle relevant complementarity conditions. By using a neural network approach based on the CHKS function, the KKT conditions corresponding to the SOCCVI are solved. Some stability properties of the neural network can be verified by the Lyapunov method. When the parameters of the neural network are different, the achieved convergence speed will also vary. Further by controlling the corresponding parameters, the neural network can achieve a faster convergence speed …than a classical model. Numerical simulations are applied to examine the computing capability of the neural network as well as the influence of parameters on it. Show more
Keywords: Neural network, Second-order cone, Variational inequality, CHKS function, Lyapunov method
DOI: 10.3233/JIFS-220972
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1257-1268, 2023
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