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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: Van Pham, Hai | Thai, Kim Phung | Nguyen, Quoc Hung | Le, Duy Dong | Le, Thanh Trung | Nguyen, Thi Xuan Dao | Phan, Thi Thuy Kieu | Thao, Nguyen Xuan
Article Type: Research Article
Abstract: The picture fuzzy set is an extension of the fuzzy and intuitionistic fuzzy set for solving real-world problems. Entropy and distance measures play significant roles in measures for solving problems involving fuzzy environments. This paper has presented some new distance and entropy measures using picture fuzzy sets to solve problems of medical diagnosis and multi-criteria decision making problems. In addition, the entropy measure is induced from the distances of picture fuzzy sets in order to determine entropy measure of picture fuzzy sets. The proposed methods combined entropy and distance measures to construct the Technique for Order of Preference by Similarity …to Ideal Solution model to solve multi-criteria decision making problem. To validate the proposed methods, some numerical examples are given to demonstrate new measurements. The efficiency of the measure is proven by comparison to other measures when solving medical diagnosis in multi-criteria decision making for illustrations in numerical COVID-19 medicine selection. Show more
Keywords: Picture fuzzy set, distances, entropy, TOPSIS, COVID-19 medicine
DOI: 10.3233/JIFS-221556
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6775-6791, 2023
Authors: Wu, Yaoqiang
Article Type: Research Article
Abstract: In this paper, we introduce the notion of [φ , p ]-normed spaces, following the concept of ω -norms which was presented by Singh, and study the Aleksandrov problem in [φ , p ]-normed spaces (0 < p ≤ 1). On the other hand, we introduce the concept of Menger [φ , p ]-normed spaces, which includes the Menger φ -normed spaces defined by Golet as a special case, and present the topological properties of Menger [φ , p ]-normed spaces with some results of profile function.
Keywords: [φ, p]-normed spaces, isometry, distance one preserving property, profile function, Menger [φ, p]-normed spaces2010 MSC: 46B04, 46S50
DOI: 10.3233/JIFS-222947
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6793-6800, 2023
Authors: Cai, Jianyang | Yang, Haidong | Xu, Kangkang
Article Type: Research Article
Abstract: The energy consumption prediction of the chiller is an important means to reduce the energy consumption of buildings. Therefore, a novel energy consumption prediction model for chillers based on an improved support vector machine (ICA-DE-SVM) is proposed. The imperialist competitive algorithm (ICA) is used to optimize the penalty coefficient and kernel function width of SVM, greatly improving the generalization ability and prediction accuracy of the SVM model. The assimilation process is very important in ICA. Colonies of empires move randomly toward imperialists during the assimilation process in ICA, which decreases population diversity and can lead to premature convergence. Therefore, to …create more new locations for colonies and increase population diversity, the idea of differential mutation proposed by differential evolution (DE) was applied to ICA. The established model was experimentally verified in an actual multi-chiller system in a building, and the results showed that the ICA-DE-SVM model could obtain good prediction results. Finally, the proposed model was compared with SVM model, PSO-SVM model, GA-SVM model, WOA-SVM model, and ICA-SVM model. With an MAPE of 0.6%, an MSE of 2.3, and an R 2 of 0.9998, the findings demonstrate that the ICA-DE-SVM model has a greater prediction accuracy than the other models. Show more
Keywords: Energy consumption prediction, imperialist competitive algorithm, Chillers, support vector machine
DOI: 10.3233/JIFS-223994
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6801-6816, 2023
Authors: Li, Jianping | Guo, Chengzhou
Article Type: Research Article
Abstract: Granaries should have good airtightness to reduce grain loss in storage. Prediction of granary airtightness at the design stage is beneficial in improving granary design. This paper proposes a method for the prediction interval (PI) of granary airtightness by using small sample data, which can guide designers with granary design. PI that the probability of the true target falling in it is markedly close or larger compared with the confidence level can be the decision basis of the granary design scheme. This study adopts support vector machine as the regression model trained by the airtightness data set of built granaries, …and obtains the probability distribution of regression errors through information diffusion. The probability interval of errors is derived using a search algorithm, and PIs of granary airtightness can be acquired thereafter. Assessment indexes of PIs with confidence levels of 0.8 and 0.9 indicate that the proposed method can achieve confidence level and is superior to the comparative method using artificial neural network and bootstrap for PIs in cases of only a few samples. Thus, an innovative and feasible method is proposed for the computer-aided design of granary airtightness. Show more
Keywords: Support vector machine, information diffusion, prediction interval, granary airtightness
DOI: 10.3233/JIFS-210619
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6817-6827, 2023
Authors: Suresh, K.S. | Ravichandran, K.S. | Venugopal, S.
Article Type: Research Article
Abstract: Due to the problem’s high level of complexity, the optimization strategies used for the mobile robot path planning problem are quite expensive. The Mobile Robot Path Search based on a Multi-objective Genetic Algorithm (MRPS-MOGA) is suggested as a solution to the complexity. The MRPS-MOGA resolves path planning issues while taking into account a number of different factors, including safety, distance, smoothness, trip duration, and a collision-free path. In order to find the best approach, the suggested MRPS-MOGA takes into account five main objectives. The MOGA is used to pick the best path from a variety of viable options. Paths produced …at random are used to initialise the population with viable paths. By using objective functions for various objectives, the fitness value is assessed for the quantity of potential candidate paths. In order to achieve diversity in the population, another GA operator mutation is carried out at random on the sequence. Once more, the individual fitness criterion is supported in order to derive the best path from the population. With various situations, an experimental research of the suggested MRPS-MOGA is conducted. The outcome shows that the suggested MRPS-MOGA performs better when choosing the best path with the least amount of time complexity. MRPS-MOGA is more effective than the currently used approaches, according to the experimental analysis. Show more
Keywords: Mobile robot path planning, Multiple objectives, meta-heuristic search, Fitness, tournament selection, ring crossover, adaptive bit string mutation
DOI: 10.3233/JIFS-220886
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6829-6842, 2023
Authors: Zhigang, Zhang | Chunmeng, Lu | Bei, Lu
Article Type: Research Article
Abstract: One of the biggest challenges for Internet of Things (IoT) systems is traffic congestion in large networks. For this reason, the bandwidth should be increased in such systems. In addition, the issue of routing is raised in sending packets from the origin to the destination. Therefore, if there are many IoT devices in the network, it will increase the traffic, which makes faultless routing important in these networks. In this paper, a novel routing method based on Routing Protocol for Low-Power (RPL) is presented to minimize the energy consumption of the Internet of Things. Using the backward method based on …the A* method to reduce energy consumption in a large graph, promising nodes are selected. A coordinate node is used to manage packets and transfer them. The selection of the coordinator node helps to receive packets with less energy and less delay from its neighbors, and the head node selects the best coordinator node with the shortest distance and the highest residual energy. The proposed method improves the energy consumption criteria, the delay between nodes, and the network overhead criterion by considering the estimated energy to the destination with the A* method. Show more
Keywords: Routing algorithm, energy consumption, delay, internet of things
DOI: 10.3233/JIFS-222536
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6843-6853, 2023
Authors: Jun-Fang, Song | Yan, Chen
Article Type: Research Article
Abstract: In order to alleviate the increasingly serious traffic congestion problem in China, realize intelligent traffic control, and provide accurate and real-time traffic flow prediction data for traffic flow guidance and traffic travel, this paper designs a GPS-based vehicle trajectory fusion optimization deep model BN-LSTM-CNN which makes full use of the temporal and spatial correlation characteristics of dynamic traffic flow to improve the accuracy of short-term traffic flow prediction. The parameters of the historical GPS dynamic trajectory of the traffic network link are converted into a two-dimensional matrix image of time and space relationship. First, the spatial features are input to …the CNN network, and the spatial dependence relationship between the links is mined, then the traffic flow time series modeling is performed with a four-layer ConvLSTM network, and the BN normalization layer is added to normalize the activation value of the previous layer on each batch, so that the model can obtain higher training accuracy and quickly complete the prediction of the traffic flow state in a certain period of time in the future. The experimental results show that the prediction model is fast to optimize, the prediction error is the smallest compared with other methods, and it can meet the real-time requirements of urban traffic control. Show more
Keywords: Traffic flow state prediction, vehicle trajectory, GPS, matrix image, CNN, ConvLSTM
DOI: 10.3233/JIFS-212998
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6855-6863, 2023
Authors: Shao, Sijie | Li, Zhiyong
Article Type: Research Article
Abstract: The new power system information network has the security problem of computer virus attack, and the study of its transmission mechanism is helpful to discover the law and influence of virus transmission. In this paper, the research method of epidemic theory is introduced, and a new Susceptible-Exposed-Infectious-Recovered-Susceptible(SEIR-S) virus model is proposed. The immune time-delay parameter is introduced to simulate the evolution and mutation of the virus so that nodes immune to the virus can still be re-infected after a certain time interval. At the same time, the immune time of different nodes is different, and the distributed immune time delay …is used to enhance the authenticity of the simulated virus transmission; and considering the influence of the scale-free characteristics of the information network, this paper establishes a continuous Markov chain based on time. The transmission process of the virus, and then deduce the theoretical analysis results of the virus infection rate threshold. Based on theoretical analysis, the propagation process of the SEIR-S virus model with distributed immune time delay was simulated by using the Monte Carlo method, and the accuracy of the threshold formula of virus infection rate was verified. The influence rule of the hysteresis parameter, that is, increasing the average immune time of nodes to viruses can reduce the infection density of the network in a steady, and at the same time, making the immune time of network nodes obey a normal distribution can effectively reduce the oscillation effect of viruses on the network. Show more
Keywords: New power system, information network, computer virus, SEIR-S model, distributed immune time-delay
DOI: 10.3233/JIFS-220575
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6865-6876, 2023
Authors: Lekkoksung, Somsak | Iampan, Aiyared | Julatha, Pongpun | Lekkoksung, Nareupanat
Article Type: Research Article
Abstract: It is known that any ordered semigroup embeds into the structure consisting of the set of all fuzzy sets together with an associative binary operation and a partial order with compatibility. In this study, we provide two classes of ordered semigroups in which any model in these classes is a representation of any ordered semigroup. Moreover, we give an interconnection of a class we constructed.
Keywords: ordered semigroup, fuzzy ordered semigroup, representation
DOI: 10.3233/JIFS-223356
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6877-6884, 2023
Authors: Sahaya Elsi, S. | Michael Raj, F. | Prince Mary, S.
Article Type: Research Article
Abstract: Grey wolf-optimized artificial neural networks used in DC–AC hybrid distribution networks, to regulate the energy consumption, is presented in this study. Energy management system that takes into consideration, the distributed generation, load demand, and battery state of charge are being considered. The artificial neural network have been trained, utilising the profile data, based on the energy storage system’s charging and discharging characteristics, under various distribution network power conditions. Moreover, the error rate was kept, well under 10%. The suggested energy management system, that employs an artificial neural network, has been trained to function in the optimal mode, utilising grey wolf …optimization for each grid-connected power converter. Small-scale hybrid DC/AC microgrids have been developed and tested, in order to simulate and verify the proposed energy management system. The grey wolf optimized neural network energy management system has been proven to provide 99.48 % efficiency, which is superior when compared to other methods existing in the literatures. Show more
DOI: 10.3233/JIFS-222112
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6885-6899, 2023
Authors: Li, Jingyi | Chao, Shiwei
Article Type: Research Article
Abstract: Most existing classifiers are better at identifying majority classes instead of ignoring minority classes, which leads to classifier degradation. Therefore, it is a challenge for binary classification to imbalanced data, to address this, this paper proposes a novel twin-support vector machine method. The thought is that majority classes and minority classes are found by two support vector machines, respectively. The new kernel is derived to promote the learning ability of the two support vector machines. Results show that the proposed method wins over competing methods in classification performance and the ability to find minority classes. Those classifiers based-twin architectures have …more advantages than those classifiers based-single architecture in classification ability. We demonstrate that the complexity of imbalanced data distribution has negative effects on classification results, whereas, the advanced classification results and the desired boundaries can be gained by optimizing the kernel. Show more
Keywords: Binary classification, imbalanced data, support vector machine
DOI: 10.3233/JIFS-222501
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6901-6910, 2023
Authors: Yagoub, Imam | Lou, Zhengzheng | Qiu, Baozhi | Abdul Wahid, Junaid | Saad, Tahir
Article Type: Research Article
Abstract: In a real-world, networked system, the ability to detect communities or clusters has piqued the concern of researchers in a wide range of fields. Many existing methods are simply meant to detect the membership of communities, not the structures of those groups, which is a limitation. We contend that community structures at the local level can also provide valuable insight into their detection. In this study, we developed a simple yet prosperous way of uncovering communities and their cores at the same time while keeping things simple. Essentially, the concept is founded on the theory that the structure of a …community may be thought of as a high-density node surrounded by neighbors of minor densities and that community centers are located at a significant distance from one another. We propose a concept termed “community centrality” based on finding motifs to measure the probability of a node becoming the community center in a setting like this and then disseminate multiple, substantial center probabilities all over the network through a node closeness score mechanism. The experimental results show that the proposed method is more efficient than many other already used methods. Show more
Keywords: Community detection, node density, node closeness, motifs, community center
DOI: 10.3233/JIFS-220224
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6911-6924, 2023
Authors: Geepthi, D. | Columbus, C. Christopher | Jeyanthi, C.
Article Type: Research Article
Abstract: P2P networks are particularly vulnerable to Sybil and Eclipse attacks, especially those based on Distributed Hash Tables (DHT). However, detecting Sybil and Eclipse attacks is a challenging task, and existing methods are ineffective due to unequal sample distribution, incomplete definitions of discriminating features, and weak feature perception. This paper proposes a Fuzzy Secure Kademlia (FSK) that detects and mitigates the Sybil and Eclipse attack. At first, a node requests authentication by providing its MAC address, location, Node Angle (NA), and Node Residual Energy (NRE) to an infrastructure server. As long as the packet’s ID, location, NA, and NRE match the …packet’s received ID, it can be recognized as normal. The incoming packet, however, is detected as Sybil or Eclipse attack packets if copies are made in locations other than those specified. When the Sybil or Eclipse attack has been detected, locate the multiplied nodes. By using the FSK, the malicious node can be removed, preventing it from causing any harm to the network. The suggested framework is compared with existing methods in terms of detection time, and energy consumption. Experimental results indicate that the suggested FSK technique achieves a better detection time of 29.4%, 25.5%, 22.6%, and 18.1% than CSI, DHT, CMA, and EDA methods. Show more
Keywords: To-peer, sybil attack, eclipse attack, fuzzy secure kademlia, distributed hash table, detection, mitigate
DOI: 10.3233/JIFS-222802
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6925-6937, 2023
Authors: Zhang, Zhenyu | Guo, Jian | Zhang, Huirong | Qin, Yong
Article Type: Research Article
Abstract: Preference relations have been extended to q-rung orthopair fuzzy environment, and the q-rung orthopair fuzzy preference relations (q-ROFPRs) with additive consistency are defined. Then, the concept of normalized q-rung orthopair fuzzy weight vector (q-ROFWV) is proposed, and the transformation method of constructing q-ROFPR with additive consistency is given. To obtain the weight vector of any q-ROFPRs, a goal programming model to minimize the deviation of the q-ROFPRs from the constructed additive consistent q-ROFPRs is established. The q-rung orthopair fuzzy weighted quadratic (q-ROFWQ) operator is selected to aggregate multiple q-ROFPRs, efficiently handling extreme values and satisfying monotonicity about the order relation. …Further, a group decision-making (GDM) method is developed by combining the q-ROFWQ operator and the goal programming model. Finally, the practicality and feasibility of the developed GDM method are demonstrated by an example of rail bogie crucial component identification. Show more
Keywords: q-rung orthopair fuzzy preference relation (q-ROFPR), goal programming model, q-rung orthopair fuzzy weighted quadratic (q-ROFWQ) operator, group decision making
DOI: 10.3233/JIFS-221859
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6939-6955, 2023
Authors: Saravanabhavan, C. | Kirubakaran, S. | Premkumar, R. | Joyce, V. Jemmy
Article Type: Research Article
Abstract: One of the extremely deliberated data mining processes is HUIM (High Utility Itemset Mining). Its applications include text mining, e-learning bioinformatics, product recommendation, online click stream analysis, and market basket analysis. Likewise lot of potential applications availed in the HUIM. However, HUIM techniques could find erroneous patterns because they don’t look at the correlation of the retrieved patterns. Numerous approaches for mining related HUIs have been presented as an outcome. The computational expense of these methods continues to be problematic, both in terms of time and memory utilization. A technique for extracting weighted temporal designs is therefore suggested to rectify …the identified issue in HUIM. Preprocessing of time series-based information into fuzzy item sets is the first step of the suggested technique. These feed the Graph Based Ant Colony Optimization (GACO) and Fuzzy C Means (FCM) clustering methodologies used in the Improvised Adaptable FCM (IAFCM) method. The suggested IAFCM technique achieves two objectives: optimal item placement in clusters using GACO; and ii) IAFCM clustering and information decrease in FCM cluster. The proposed technique yields high-quality clusters by GACO. Weighted sequential pattern mining, which considers facts of patterns with the highest weight and low frequency in a repository that is updated over a period, is used to locate the sequential patterns in these clusters. The outcomes of this methodology make evident that the IAFCM with GACO improves execution time when compared to other conventional approaches. Additionally, it enhances information representation by enhancing accuracy while using a smaller amount of memory. Show more
Keywords: Service mining, reduction in dimensions, high-useful set-ups, recurring patterns, graphs, support and fuzzy both count
DOI: 10.3233/JIFS-221672
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6957-6971, 2023
Authors: Amutha, S.
Article Type: Research Article
Abstract: White blood cell (WBC) leukemia is caused by an excess of leukocytes in the bone marrow, and image-based identification of malignant WBCs is important for its detection. This research describes a new hybrid technique for accurate classification of WBC leukemia. To increase the image quality, the preprocessing is done using Contrast Limited Adaptive Histogram Equalization (CLAHE). The images are then segmented using Hidden Markov Random Fields (HMRF). To extract features from WBC images, Visual Geometry Group Network (VGGNet), a powerful Convolutional Neural Network (CNN) architecture, is used After that, an Efficient Salp Swarm Algorithm (ESSA) is used to optimize the …extracted features. The proposed method is tested on two Acute Lymphoblastic Leukemia Image Databases, yielding good accuracy of 98.1% for dataset 1 and 98.8% for dataset 2. While enhancing accuracy, the ESSA optimization picked just 1K out of 25K features retrieved with VGGNet. The combination of CNN feature extraction with ESSA feature optimization could be effective for a variety of additional image classification tasks. Show more
Keywords: WBC leukemia, VGGNet-CNN, ALLIDB, efficient scalp swarm algorithm
DOI: 10.3233/JIFS-221302
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6973-6989, 2023
Authors: Liu, Weiling | Liu, Ping | Han, Furong | Xiao, Yanjun
Article Type: Research Article
Abstract: The foul odor of foul gas has many harmful effects on the environment and human health. In order to accurately assess this impact, it is necessary to identify specific malodorous components and levels. In order to meet the qualitative and quantitative identification of the components of malodorous gas, an electronic nose system is developed in this paper. Both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality of the collected data. The reduced-dimensional data are combined with a support vector machine (SVM) and backpropagation (BP) neural network for classification and recognition to compare the …recognition results. Regarding qualitative recognition, this paper selects the method of LDA combined with the BP neural network after comparison. Experiments show that the qualitative recognition rate of this method in this study can reach 100%, and the amount of data after LDA dimensionality reduction is small, which speeds up the pattern speed of recognition. Regarding quantitative identification, this paper proposes a prediction experiment through Partial least squares (PLS) and BP neural networks. The experiment shows that the average relative error of the trained BP network is within 6%. Finally, the experiment of quantitative analysis of malodorous compound gas by this system shows that the maximum relative error of this method is only 4.238%. This system has higher accuracy and faster recognition speed than traditional methods. Show more
Keywords: Electronic nose, Malodorous gas detection, BP neural network
DOI: 10.3233/JIFS-222539
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6991-7008, 2023
Authors: Yu, Qingying | Xiao, Zhenxing | Yang, Feng | Gong, Shan | Shi, Gege | Chen, Chuanming
Article Type: Research Article
Abstract: With the continuous expansion of city scale and the advancement of transportation technology, route recommendations have become an increasingly common concern in academic and engineering circles. Research on route recommendation technology can significantly satisfy the travel demands of residents and city operations, thereby promoting the construction of smart cities and the development of intelligent transportation. However, most current route recommendation methods focus on generating a route satisfying a single objective attribute and fail to comprehensively consider other types of objective attributes or user preferences to generate personalized recommendation routes. This study proposes a multi-objective route recommendation method based on the …reinforcement learning algorithm Q-learning, that comprehensively considers multiple objective attributes, such as travel time, safety risk, and COVID-19 risk, and generates recommended routes that satisfy the requirements of different scenarios by combining user preferences. Simultaneously, to address the problem that the Q-learning algorithm has low iteration efficiency and easily falls into the local optimum, this study introduces the dynamic exploration factor σ and initializes the value function in the road network construction process. The experimental results show that, when compared to other traditional route recommendation algorithms, the recommended path generated by the proposed algorithm has a lower path cost, and based on its unique Q -value table search mechanism, the proposed algorithm can generate the recommended route almost in real time. Show more
Keywords: Route recommendation, multi-objective, user preferences, reinforcement learning, dynamic exploration factor
DOI: 10.3233/JIFS-222932
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 7009-7025, 2023
Authors: Karthikeyan, G. | Komarasamy, G. | Daniel Madan Raja, S.
Article Type: Research Article
Abstract: With the vast advancements in the medical domain, earlier prediction of disease plays a substantial role in enhancing healthcare quality and making better decisions during tough times. This research concentrates on modelling and automated disease prediction model to offer an earlier prediction model for heart disease and the risk factors. This work considers a standard UCI machine learning-based benchmark dataset for model validation and extracts the risk factors related to the disease. The outliers and imbalanced datasets are pre-processed using data normalization to enhance the classification performance. Here, feature selection is performed using non-linear Particle Swarm Optimization (NL - PSO ). …Finally, classification is done with the Improved Deep Evolutionary model with Feed Forward Neural Networks (IDEBDFN). The algorithm’s learning nature is used to evaluate the nature of the hidden layers to produce the optimal results. The outcomes demonstrate that the anticipated model provides superior prediction accuracy. The simulation is carried out in a MATLAB environment, and metrics like accuracy, F-measure, precision, recall, and so on are evaluated. The accuracy (without features) of the evolutionary model in the UCI ML dataset is 97.65%, accuracy (with features) is 98.56%, specificity is 95%, specificity is 2% higher than both the datasets, F1-score is 40%, execution time (min) is 0.04 min, and the AUROC is 96.85% which is substantially higher than other datasets. The proposed model works efficiently compared to various prevailing standards and individual approaches. Show more
Keywords: Heart disease prediction, pre-processing, feature selection, classification, evolutionary model, feed-forward neural network
DOI: 10.3233/JIFS-220912
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 7027-7042, 2023
Article Type: Retraction
DOI: 10.3233/JIFS-219326
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 7043-7043, 2023
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