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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: Kaladevi, P. | Janakiraman, Sengathir | Ramalingam, Praveen | Muthusankar, D.
Article Type: Research Article
Abstract: The advent of machine learning in the recent decade has excelled in determining new potential features and non-linear relationships existing between the data derived from the Electronic Health Records (EHR). Machine learning also enhances the process of handling data with maximum predictor variables compared to observations during the data mining process of prediction. The EHR data is often confronted with quality issues that are related to misclassification, missingness and measurement errors. In this context, ensemble classification schemes are determined to be essential for preventing the quality issues of EHR data. Moreover, the data sources like EHR include sensitive information that …needs to be protected from disclosure before it is forwarded to the mining process. Further, the sensitive data of EHR must be hidden without modifying the dataset such that it does not influence the prediction accuracy of the incorporated ensemble classification mechanism. In this paper, the process of hiding EHR data is facilitated through Improved Sensitivity Drift based k-Anonymized Data Perturbation Scheme (ISD-k-ADP) that randomly perturbs the data in the dataset by including restricted amount of noise. This controlled amount of included noise is derived carefully from the Sensitivity Drift based depending on the expected privacy level before it is sent to the process of classification. This ISD-k-ADP scheme is reliable such that, it prevents the impact induced by the hidden data during the process of Two Stage Bagging Pruning based Ensemble Classification (TSBP-EC). Furthermore, the TSBP-EC uses the methods of distance and accuracy based pruning that aids in minimizing the size of the ensemble for ensuring effective and efficient classification using machine learning. The simulation results of the proposed ISD-k-ADP-TSBP-EC scheme is determined to be predominant based on Classification Accuracy, Precision, Recall and Kappa Statistic in contrast to the standard schemes. Show more
Keywords: Ensemble classification, two stage bagging pruning, sensitivity drift, heuristic-based data perturbation, electronic health records, machine learning
DOI: 10.3233/JIFS-221615
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 149-166, 2023
Authors: Uma Maheswari, K. | Valarmathi, A.
Article Type: Research Article
Abstract: A heart attack is a common cause of death globally. It can be treated successfully through a simple and accurate diagnosis. Getting the right diagnosis at the right time is very important for the treatment of heart failure. Currently, the conventional method of diagnosing heart disease is not reliable. Machine learning is a type of artificial intelligence that can be used to analyze the data collected by sensors. Data mining is another type of technology that can be utilized in the healthcare industry. These techniques help predict heart disease based on various factors. We developed a prediction and recommendation model …aimed at predicting heart disease using the Optimized Deep Belief Network. It does so by taking into account the various features of the heart disease UCI and Stalog database. Finally, the proposed method classifies healthy people and people with heart illness with an accuracy of 97.91%. Show more
Keywords: Heart disease, diagnosis, machine learning, deep learning
DOI: 10.3233/JIFS-221272
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 167-184, 2023
Authors: Zhenlin, Wei | Chuantao, Wang | Xuexin, Yang
Article Type: Research Article
Abstract: Sentiment classification aims to complete the automatic judgment task of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning models require a large number of manually annotated samples of sentiment tendency for supervised training. Faced with massive online review data, the feasibility of manual tagging is worrisome. In addition, the traditional deep learning model ignores the imbalanced distribution of the number of classification samples, which will lead to a decline in classification performance in the practical application of the model. Considering that the online review data contains weak tagging information such as scores and labels, …and the distribution is imbalanced, a weak tagging and imbalanced networks for online review sentiment classification is constructed. The experimental results show that the model significantly outperforms the traditional deep learning model in the sentiment classification task of hotel review data. Show more
Keywords: Sentiment classification, imbalanced classification, weak tagging, deep learning
DOI: 10.3233/JIFS-221565
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 185-194, 2023
Authors: Balasubramanian, Kishore | Prabu, A.V. | Shaik, Mahammad Firose | Naik, R. Anjaneyulu | Suguna, S. Kanimozhi
Article Type: Research Article
Abstract: Today’s healthcare sectors are driven and work to rescue patients as soon as possible by giving them the right care and treatment. A healthcare monitoring system works in two ways: by keeping track of the patient’s activities and overall health. For prompt treatment, such as giving the right and suitable medication, administering an injection, and providing additional medical help, nursing supervision is required. Wearable sensors are fixed or connected to the patient’s body and can follow their health. These IoT medical gadgets let clinicians diagnose patients and comprehend the processes from remote. However, the amount of data produced by IoT …devices is so large that it cannot be handled manually. A model for automated analysis is required. Convolution Neural Network with Long-Short Term Memory (CNN-LSTM) was therefore suggested in this study as a Hybrid Deep Learning Framework (HDLF) for a Patient Activity Monitoring System (PAMS) that brings all healthcare activities with its classes. To incorporate medical specialists from all over the world and enhance treatment outcomes, the framework offers an advanced model where patient activities, health conditions, medications, and other activities are distributed in the cloud. An effective architecture for Wearable Sensor Network-based Human Action Recognition that combines neural network Simple Recurrent Units (SRUs) and Gated Recurrent Units (GRUs). For assessing the multimodal data input sequence, deep SRUs and a variety of internal memory states is utilized in this research. Furthermore, for addressing the concerns about accuracy oscillations or instability with decreasing gradients, a deep GRUs to store and learn the knowledge is conveyed to the future state. The analysis suggests that CNN-LSTM is then contrasted with some of the currently used algorithms, and it is found that the new system has a 99.53% accuracy rate. The difference between this accuracy result and the current value is at least 4.73%. Show more
Keywords: Sensor network, Body Wearable Sensors, surveillance monitoring, Healthcare Monitoring System (HMS), Physiological Parameter Analyzation
DOI: 10.3233/JIFS-212958
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 195-211, 2023
Authors: Zhao, Shuping | Wang, Dong | Lei, Ting | Wang, Yifan
Article Type: Research Article
Abstract: The selection of a waste-to-energy (WTE) plant site is the core issue that determines whether the WTE project can effectively treat municipal solid waste, reduce environmental pollution, and promote the development of a circular economy, and is often determined through group decision-making. The complexity of this group decision problem makes the opinions of decision makers often with uncertainty. The single-valued neutrosophic set (SVNS) can reduce the loss of information that contains uncertainty by quantitatively describing the information through three functions. In addition, existing studies on group decision-making for WTE plant siting suffer from the problem that decision maker weights do …not change in concert with those decision makers’ decision information. Therefore, this study proposes a group decision-making method based on SVNSs. First, a group consensus strategy is proposed to improve group consensus by removing the evaluation value of the corresponding solution for decision makers who do not reach consensus and are unwilling to modify their preferences. Second, a decision maker weight determination and adjustment method is proposed to readjust the decision maker weights from the solution level according to their respective consensus degree when the decision makers’ preference information changes. This method enables the decision makers’ preferences and weights to be changed jointly. An illustrative example and a comparative analysis of WTE plant siting decisions demonstrate the feasibility and superiority of the method. The experimental results show that the method is effective in helping decision makers to select the optimal WTE plant site more accurately. Show more
Keywords: Waste-to-energy, site selection, single-valued neutrosophic sets, group consensus
DOI: 10.3233/JIFS-220124
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 213-224, 2023
Authors: Abu Talha, Muhammad | Zafar, Adeel
Article Type: Research Article
Abstract: False information is becoming more frequent in distributing disinformation by distorting people’s awareness and decision-making by altering their views or knowledge. The propagation of disinformation has been aided by the proliferation of social media and online forums. Allowing it to readily blend in with true information. Parody news and rumors are the most common types of misleading and unverified information, and they should be caught as soon as possible to avoid their disastrous consequences. As a result, in recent years, there has been a surge in interest in effective detection approaches. For this study, a customized dataset was built that …included both real and parody tweets from Pakistan and India. This study proposes a two-step strategy for detecting parody tweets. In the first stage of the approach the unstructured data is converted into structured data set. In the second step, multiple supervised artificial intelligence algorithms were employed. An experimental assessment of the different classification methods inside a customized dataset was undertaken in this study, and these classification models were compared using evaluation metrics. Our results showed accuracy of 92%. Show more
Keywords: Social media, parody tweets, binary classification, machine learning, deep learning, word embedding
DOI: 10.3233/JIFS-221200
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 225-236, 2023
Authors: Liu, Xuning | Zhang, Zixian | Zhang, Guoying
Article Type: Research Article
Abstract: Accurate and rapid prediction of the coal and gas outburst is very significant for preventing accident and protecting environment, the paper presents a novel feature selection and outburst classifier framework which can identify effective candidate features and improve the classification accuracy. First, Apriori is applied for preliminarily extracting the association rules from sample data and attribute features in coal and outburst, and it can present the effective sample data and features for outburst prediction. Second, in order to reduce the redundancy of the strong association rules obtained from Apriori, Boruta is applied for selecting all highly relevant optimal features based …on the obtained strong association rules. Third, Random Forest(RF) is used to assign different weights to different features in optimal candidate features considering the importance of different features to outburst, based on the above obtained high-quality sample data and optimal features, the parameters of KNN model optimized by Bayesian Optimization(BO) is used to predict the coal and gas outburst. The experimental results show that the proposed feature selection model Apriori-Boruta can obtain significant sample data, and the proposed RF- KNN optimized classifier model can achieve higher performance in terms of the number of optimal features and prediction accuracy compared with traditional prediction models. Show more
Keywords: Coal and gas outburst, Apriori, Boruta, RF, KNN
DOI: 10.3233/JIFS-213457
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 237-250, 2023
Authors: Arslan, Serdar | Yazici, Adnan
Article Type: Research Article
Abstract: The semantic query problem is commonly called the semantic gap and is one of the significant problems in multimedia data retrieval. In this study, we focus on multimedia data retrieval by combining semantic information with data content to solve the semantic gap problem effectively. The main idea behind the combination of low-level content descriptors and the concept of multimedia data is to represent the content information with the semantic information by adding a low-level content descriptor as a new dimension to the index structure. This new dimension is represented by constructing an array index structure that uses a fuzzy clustering …algorithm. Thus, a new high-dimensional index structure, named MM-FOOD, supporting querying of multimedia data, including fuzzy querying, is presented in this paper. This proposed index structure’s construction and query algorithms are explained throughout this paper. Our experiments show that our indexing mechanism is considerably efficient compared to the basic indexing approach, which stores low-level content and semantic concept descriptors in separate structures when the data size is large. Show more
Keywords: High-dimensional indexing, multimedia data retrieval, fuzzy querying, multidimensional scaling
DOI: 10.3233/JIFS-220673
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 251-282, 2023
Authors: Wang, Jinyan | Wu, Fangjing
Article Type: Research Article
Abstract: Most of the published literature on concrete containing fly ash was limited to predicting the hardened properties of concrete. It is understood that exist so restricted studies focusing on forecasting both hardened and fresh features of self-compacting concrete (SCC). Hence, it is goaled for developing models for predicting the fresh and hardened properties of SCC by the support vector regression method (SVR). This study aims to specify SVR method key parameters using Ant lion optimization (ALO) and Biogeography-based optimization (BBO) algorithms. The considered properties of SCC in the fresh phase are the L-box test, V-funnel test, slump flow, and in …the hardened phase is CS. Results demonstrate powerful potential in the learning section for all considered properties as well as approximating in the testing phase. It can be seen that the proposed models have R2 incredible value in the learning and testing phase. It means that the correlation between observed and predicted properties of SCC from hybrid models is acceptable so that it represents high accuracy in the training and approximating process. All in all, in most of the cases, the SVR model developed by ALO outperforms BBO-SVR, which depicts the capability of the ALO algorithm for determining the optimal parameters of the considered method. Show more
Keywords: Fly ash, self-compacting concrete, rheological properties, support vector regression, ALO, BBO, compressive strength
DOI: 10.3233/JIFS-220744
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 283-297, 2023
Authors: Merbah, Amal | Makrizi, Abdelilah | Essoufi, El Hassan
Article Type: Research Article
Abstract: One of the pertinent concerns in traffic management is to optimize the waiting time at the traffic light junctions. We have has already developed an integrated nonlinear model which heavily relies on the genetic algorithm (GA). Indeed, GA proves efficient in terms of the computational time given the environmental constraints and the various variables inherent to the types of users and the degree of priority allotted to each of them. However, it was revealed that some issues having to do with instability require further adjustments. In the present article the aforementioned model is revisited with the aim of addressing …the high standard deviations attributed to the objective function. More specifically, the present work considers the side effects of GA in sweeping the entire space of eligible solutions. In this respect, fuzzy Logic (FL) is integrated as a major component in order to orient the GA research. At the computational level, GA places the solution found by FL at the center of the solution space around which the initial population can be built. The implementation of this hybrid method reduces both the waiting time at traffic lights and the standard deviation of the results, showing a significant improvement in the management system. Show more
Keywords: Traffic control, nonlinear model, fuzzy logic, genetic algorithms
DOI: 10.3233/JIFS-221535
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 299-307, 2023
Authors: Akram, Muhammad | Umer Shah, Syed Muhammad | Allahviranloo, Tofigh
Article Type: Research Article
Abstract: Transportation Problems (TP) have multiple applications in supply chain management to reduce costs. Efficient methods have been developed to address TP when all factors, including supply, demand, and unit transportation costs, are precisely known. However, due to uncertainty in practical applications, it is necessary to study TP in an uncertain environment. In this paper, we define the Trapezoidal Fermatean Fuzzy Number (TrFFN) and its arithmetic operations. Then we introduce a new approach to solve TP, where transportation cost, supply, and demand are treated as TrFFN, and we call it Fermatean Fuzzy TP (FFTP). We illustrate the feasibility and superiority of …this method with two application examples, and compare the performance of this method with existing methods. Furthermore, the advantages of the proposed method over existing methods are described to address TP in uncertain environments. Show more
Keywords: Trapezoidal Fermatean fuzzy sets, linear programming problem, transportation problem, supply and demand
DOI: 10.3233/JIFS-221959
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 309-328, 2023
Authors: Rakesh, V. | Balamurugan, R.
Article Type: Research Article
Abstract: Recently, Induction motor (IM) become the most prevalent machine type and finds an applications in many fields such as industries, electric cars etc., A typical IMD system includes IM, power controller, converter and measurement sensors. The effective performance of the IM indirectly depends upon the sensors connected with IMD. Recently, sensor fault diagnosis plays a vital role in IMD control. Thus, this work formulated a unique methodology using current vector determined from the stator currents of IM to identify sensor failures. ANN topology is incorporated to detect the Sensor failure. MATLAB software is utilized to verify the efficacy of the …suggested topology. To demonstrate the practicality of this technology, experimental verification is carried out. The efficiency of the proposed approach for IM drives is demonstrated by both simulation and experimental findings. From the obtained results, it is proven that this technique detects the failure of the sensors within less time duration (about 0.25 ms). Hence, it can be effectively utilized in automobile industry. Show more
Keywords: Induction motor, ANN, fault detection, current sensor, speed sensor, sensor failure
DOI: 10.3233/JIFS-221998
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 329-339, 2023
Authors: Yan, Zhang | Miyuan, Zhang | Yajun, Wang | Xibiao, Cai | Yanjun, Li
Article Type: Research Article
Abstract: Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is …the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm. Show more
Keywords: Adaptive particle swarm optimization algorithm (APSO), BP neural network, fuzzy control, PV power generation, MPPT
DOI: 10.3233/JIFS-213387
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 341-351, 2023
Authors: Vajravelu, Ashok | Selvan, K.S. Tamil | Jamil, Muhammad Mahadi Bin Abdul | Jude, Anitha | Diez, Isabel de la Torre
Article Type: Research Article
Abstract: Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleeding frames from normal ones and locate more bleeding areas. We have a dual-system suggestion. We use entire color information on the WCE pictures and the pixel-represented clustering approach to get the clustered centers that characterize WCE pictures as words. Then we evaluate the status of a WCE framework using the nearby SVM and …K methods (KNN). The classification performance is 95.75% accurate for the AUC 0.9771% and validates the exciting performance for bleeding classification provided by the suggested approach. Second, we present a two-step approach for extracting saliency maps to emphasize bleeding locations with a distinct color channel mixer to build a first-stage salience map. The second stage salience map was taken with optical contrast.We locate bleeding spots following a suitable fusion approach and threshold. Quantitative and qualitative studies demonstrate that our approaches can correctly distinguish bleeding sites from neighborhoods. Show more
Keywords: Bleeding classification and region detection, words-based color histograms, wireless capsule endoscopy
DOI: 10.3233/JIFS-213099
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 353-364, 2023
Authors: Zhao, Xiaohan | Zhu, Liangkuan | Wu, Bowen
Article Type: Research Article
Abstract: Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of …IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images. Show more
Keywords: Non-linear attraction coefficients, Tent chaotic mapping, Lévy flight, color image segmentation, mayfly algorithm
DOI: 10.3233/JIFS-221161
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 365-380, 2023
Authors: Hemam, Sofiane Mounine | Hioual, Ouided | Hioual, Ouassila
Article Type: Research Article
Abstract: In the last decade, the considerable increase of the cloud services use has led to the need to have search and selection techniques that match both the requirements of end users and those of the system. Indeed, to select a cloud service that meet the needs of both system and user is a challenge, due to the several conflicting criteria problem for the user on one hand, and for the system, i.e., the load balancing between Virtual Machines (VMs), on the second hand. Therefore, the main challenge, in this context, is how to ensure the user requirements by maintaining the …system performance constraint. To deal with this challenge, we present in this paper an approach based on the cloud service replication on one or more VMs when the number of the user requests will be important at a given moment. This allows better load balancing between VMs by distrusting the users’ requests over them. In addition, it allows to select the best cloud service according to the users need. However, the cloud services replication introduces the problem of the storage space saturation. Thus, our second contribution is to select and delete the cloud service replicas without degradation of the load balancing. The two proposed contributions are based on the MCDM techniques in order to select the VMs that can receive the replica of the cloud service and to select those, which their storage space is overloaded in order to delete the replica cloud service. The experimental results, based on Cloudsim simulator, show that our proposal can effectively achieve good performance (load balancing) and improve the response time. Show more
Keywords: Load balancing, dynamic, replication, deletion, Markov chain, TOPSIS method
DOI: 10.3233/JIFS-221989
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 381-393, 2023
Authors: Wang, Xindi | Xu, Zeshui | Qin, Yong
Article Type: Research Article
Abstract: In this paper, we establish a chance constrained model for the priority of hesitant fuzzy preference relation based on the idea of statistical distribution for preference information as stochastic variables with unknown distribution. Inspired by the idea of conditional value-at-risk (CVaR) robust optimization, a deterministic convex reformulation is proposed for tackling the chance constrained problem. The existing state-of-the-art methods usually assume that the probability density function of preference information is known a priori, such as Gaussian distribution. However, it is generally over-conservatism. On the contrary, our proposed method provides a tractable second-order cone (SOC) reformulation for the chance constrained problem …with the first and second moments, which is easy to handle and calculate. We also analyze the weight acquisition problem of hesitant fuzzy preference relation with unknown distribution preference using the SOC programming method, and obtain the priority weight with its approximately equivalent computationally tractable conic optimization model. A case study is conducted which shows that the proposed method achieves a good general conclusion by comparing it with the optimization method under Gaussian distribution. In addition, this method can also get better decision support for incomplete preference information. Show more
Keywords: Hesitant fuzzy preference relation, unknown distribution, CVaR, SOC, incomplete preference information
DOI: 10.3233/JIFS-220472
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 395-408, 2023
Authors: Shally, | Kumar, Sunil | Gupta, Punit
Article Type: Research Article
Abstract: The proliferation of cloud computing infrastructure has increased the energy demand remarkably. Energy-efficient resource management is essential for running a cost effective and environment friendly data center. Virtual Machine (VM) consolidation is a well-accepted method for reducing the energy consumption of the cloud data center. Quality of service is an equally important aspect of cloud services. VM migrations caused by consolidation often cause degradation in QoS. These two parameters have been dealt with individually in most research and very few addressed both energy efficiency and QoS simultaneously. We have proposed a new E nergy and Q oS E fficient (EQSE) …VM selection and placement method for improving the energy efficiency along with quality of service (QoS). VM selection and placement are two critical steps of VM consolidation. EQSE uses Resource Gap Minimization (RGM) algorithm for VM selection and Utilization-Aware Best-Fit Decreasing (UABFD) algorithm for placement of these VMs. EQSE along with dynamic thresholds reduces energy consumption and improves the quality of service by reducing the number of VM migrations. CloudSim simulation performed on PlanetLab data establishes the superiority of the proposed method compared to the existing state of the art methods of VM consolidation. Show more
Keywords: Energy efficient method, resource gap minimization, EQSE, energy efficient cloud data center, SLA aware resource management
DOI: 10.3233/JIFS-220535
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 409-419, 2023
Authors: Rahman, K. | Iqbal, Q.
Article Type: Research Article
Abstract: The aim of the paper is to introduce some complex Einstein aggregation operators for aggregating the different complex Pythagorean fuzzy sets (CPFSs) by considering the dependency between the pairs of its membership degrees. In the existing studies of fuzzy and its extensions, the uncertainties present in the data are handled with the help of degrees of membership that are the subset of real numbers, which may also loss some valuable data and hence consequently affect the decision results. A modification to these, complex Pythagorean fuzzy set handles the uncertainties with the degree whose ranges are extended from real subset to …the complex subset with unit disc and hence handle the two dimensional information in a single set. Thus motivated by this and this paper we present some novel Einstein aggregation operators, namely complex Pythagorean fuzzy Einstein weighted averaging (CPFEWA) operator, complex Pythagorean fuzzy Einstein ordered weighted averaging (CPFEOWA) operator, complex Pythagorean fuzzy Einstein hybrid averaging (CPFEHA) operator, induced complex Pythagorean fuzzy Einstein ordered weighted averaging (I-CPFEOWA) operator, and induced complex Pythagorean fuzzy Einstein hybrid averaging (I-CPFEHA) operator. Also develop some of their desirable properties. Furthermore, based on these operators a multi-attribute group decision making problems developed. An illustrative example related to the selection of the best alternative is considered to show the effectiveness, of the novel developed methods. Show more
Keywords: Einstein operational laws, CPFEWA operator, CPFEOWA operator, CPFEHA operator, I-CPFEOWA operator, I-CPFEHA operator, decision-making problem
DOI: 10.3233/JIFS-221538
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 421-453, 2023
Authors: Yu, Song | Tan, Weimin | Zhang, Chengming | Tang, Chao | Cai, Lihong | Hu, Dong
Article Type: Research Article
Abstract: Considering the power transformers fault diagnosis model has unstable performance and prone to over-fitting, we propose a transformers fault diagnosis model based on a meta-learning approach to kernel extreme learning machine with opposition-based learning sparrow search algorithm optimization (Meta-OSSA-KELM) in this paper. Its learning proceeds in two steps. Firstly, the base-learner KELMs is trained on the disjoint training subset. Then, meta-learner KELM is trained with the hidden codes of training set in base-learner KELMs that have been trained. In this paper, chaotic mapping and opposition-based learning are integrated into Sparrow search algorithm(SSA) and used it to optimize each learner. We …simulate this model with measured dissolved gas analysis(DGA) data, the results show that compared with PSO and SSA, opposition-based learning sparrow search algorithm(OSSA) has better global search-ability on the optimization for the proposed model. In addition, compared with Adaboost.M1, BPNN, SVM and KELM, Meta-OSSA-KELM has a higher average accuracy (90.9% vs 78.5%, 74.0%, 76.9%, 76.9%) and a lower standard deviation (1.56×10–2 vs 4.21×10–2 , 10.5×10–2 , 3.7×10–2 , 2.18×10–2 ) in simulation tests for 30 times. It is shown that the proposed model is a stable and better performance transformers fault diagnosis method. Show more
Keywords: Power transformers fault diagnosis, KELM, SSA, meta-learning
DOI: 10.3233/JIFS-211862
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 455-466, 2023
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
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