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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: Jebin Bose, S. | Kalaiselvi, R.
Article Type: Research Article
Abstract: The use of smartphones is increasing rapidly and the malicious intrusions associated with it have become a challenging task that needs to be resolved. A secure and effective technique is needed to prevent breaches and detect malicious applications. Through deep learning methods and neural networks, the earliest detection and classification of malware can be performed. Detection of Android malware is the process to identify malicious attackers and through the classification method of malware, the type is categorized as adware, ransomware, SMS malware, and scareware. Since there were several techniques employed so far for malware detection and classification, there were some …limitations like a reduced rate of accuracy and so on. To overcome these limitations, a deep learning-based automated process is employed to identify the malware. In this paper, initially, the datasets are collected, and through the preprocessing method, the duplicate and noisy data are removed to improve accuracy. Then the separated malware and benign dataset from the preprocessing phase is dealt with in feature selection. The reliable features are extracted in this process by Meta-Heuristic Artificial Jellyfish Search Optimizer (MH-AJSO). Further by the process of classification, the type of malware is categorized. The classification method is performed by the proposed Dense Dilated ResNet101 (DDResNet101) classifier. According to the type of malware the breach is prevented and secured on the android device. Although several methods of malware detection are found in the android platform the accuracy is effectively derived in our proposed system. Various performance analysis is performed to compare the robustness of detection. The results show that better accuracy of 98% is achieved in the proposed model with effectiveness for identifying the malware and thereby breaches and intrusion can be prevented. Show more
Keywords: Android, smartphones, datasets, malware, detection, classification, deep learning neural network, benign, preprocessing, feature selection, meta-heuristic artificial jellyfish search optimizer, dense dilated ResNet101
DOI: 10.3233/JIFS-230186
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9297-9310, 2023
Authors: Jiang, Lin | Chen, Biyun
Article Type: Research Article
Abstract: To study the bilateral matching problem of new R&D institution-talent teams based on uncertain linguistic assessment information and multiple indicators-multiple talents, a cloud model regret theory-based information gathering method is proposed, and a bi-objective bilateral matching model based on single-indicator utility maximization and overall indicator utility maximization is constructed.. The method firstly constructs the demand indicators of new R&D institutions for talent teams, uses cloud data to characterize uncertain group linguistic assessment information, and converts cloud data into cloud perceived utility based on power function; secondly, calculates the indicator weights of each expert based on entropy power method, and secondly …uses entropy power method to calculate comprehensive indicator weights, optimally solves objective expert weights based on the minimum variance of assessment information among experts, and integrates with subjective expert Again, based on regret theory, the cloud perceived utility of each talent under each index is converted into regret cloud perceived utility, and set with the index weights and expert weights into comprehensive cloud perceived utility; finally, a local-whole dual-objective bilateral matching model is constructed to obtain the matched talent team, and example analysis and method comparison are used to show that the method has feasibility and effectiveness. Show more
Keywords: New R&D institution, talent team, cloud model, regret theory, bilateral matching
DOI: 10.3233/JIFS-221944
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9311-9325, 2023
Authors: Muthuvinayagam, M. | Vengadachalam, N. | Subha Seethalakshmi, V. | Rajani, B.
Article Type: Research Article
Abstract: This paper proposes an electric vehicle charging station (EVCS) network design in urban communities using hybrid technique. The proposed approach consists of Reptile Search Algorithm (RSA) and Honey Badger Algorithm (HBA), which is jointly called as RSA-HBA technique.In this paper, a modeling method is embedded in the proposed technique to control the optimal design and sorts of electric vehicle distribution equipment for the community, considering the heterogeneity on demand and driver behaviors.Distance from home, drivers’ arrival patterns, willingness to walk, parking location and traffic on weekdays and weekends are certain important random data parameters deemed under this technique. To asymptotically …coverage an optimal solution, the hybrid algorithm is used. A hybrid technique is proposed to direct the computational challenges for huge-scale phenomena. A detailed computational experiment is conducted to quantify the performance of the proposed technique. The performance of the proposed hybrid system is executed in the MATLAB platform and related with various methods. Show more
Keywords: Electric vehicles (EVs), electric charging stations, willingness to walk, drivers’ arrival patterns, parking location
DOI: 10.3233/JIFS-221820
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9327-9345, 2023
Authors: Periakaruppan, Sudhakaran | Shanmugapriya, N. | Sivan, Rajeswari
Article Type: Research Article
Abstract: Self-Attention based Generative Adversarial Capsule Network optimized with Atomic orbital search algorithm based Sentiment Analysis is proposed in this manuscript for Online Product Recommendation (SFA-AGCN-AOSA-SA-OPR). Here, Collaborative filtering (CF) and product-product (P-P) similarity method is utilized for designing the new recommendation system. CF is employed for predicting the best shops and P-P similarity method is employed to predict the better product. Initially, the datas are gathered via Amazon Product recommendation dataset. After that, the datas are given to pre-processing. During pre-processing, Markov chain random field (MCRF) co-simulation is used to remove the unwanted content and filtering relevant text. The preprocessing …output is fed to feature extraction. The features, like manufacturing date, Manufacturing price, discounts, offers, quality ratings, and suggestions or reviews are extracted using Gray level co-occurrence matrix (GLCM) window adaptive algorithm based feature extraction method. Finally, Self-Attention based Generative Adversarial Capsule Network (SFA-AGCN) categorizes the product recommendation as excellent, good, very good, bad, very bad. Atomic orbital search algorithm optimizes the SFA-AGCN weight parameters. The performance metrics, like accuracy, precision, sensitivity, recall, F-measure, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) is examined. The efficiency of the proposed method provides higher mean absolute percentage error 98.23%, 88.34%, 90.35% and 78.96% and lower Mean squared error 92.15%, 90.25%, 89.64% and 92.48% compared to the existing methods, such as sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS (DLMNN-IANFIS-SA-OPR), intelligent sentiment analysis approach using edge computing based deep learning technique (DCNN-SA-OPR), sentiment analysis for online product reviews in Chinese depending on sentiment lexicon and deep learning (CNN-BiGRU-SA-OPR) and sentiment analysis on product reviews depending on weighted word embedding and deep neural networks (CNN-LSTM-SA-OPR) respectively. Show more
Keywords: Atomic orbital search algorithm, gray level co-occurrence matrix, markov chain random field, online product recommendation, self-attention generative adversarial capsule network, sentiment analysis
DOI: 10.3233/JIFS-222537
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9347-9362, 2023
Authors: Senthamil Selvi, M. | Ranjeeth Kumar, C. | Jansi Rani, S.
Article Type: Research Article
Abstract: A smart city is a phenomenon that combines information technology with physical and social infrastructure to regulate a city’s cooperative intelligence. Wireless sensor networks (WSN) are the fundamental technology that smart cities use to administer and sustain their service offerings. To decrease the network’s energy consumption, clustering and multihop routing algorithms have been suggested, verified, and put into practice in the literature. This inspiration led to the development of the “energy-aware clustered route approach” in the current study, which is suggested for WSNs in smart cities. The presented method focuses on choosing the right cluster heads (CHs) and the best …pathways in a WSN. The presented model includes a fitness value-based clustering scheme for efficient CH selection to achieve this. The Deep Neural Network (DNN) algorithm is then used to carry out the routing operation. The suggested approach technique calculates a fitness function (FF) that consists of three variables, including node degree, base station distance, and residual energy. This fitness function aids in the WSN’s best route selection. Simulations were run to verify the presented model’s superiority in terms of network lifespan and energy efficiency, and the results demonstrated the model’s outstanding performance. Show more
Keywords: Wireless sensor networks, cluster based routing, deep neural networks, genetic algorithm, and fitness function based route
DOI: 10.3233/JIFS-222615
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9363-9377, 2023
Authors: Gomes, Daiana | Serra, Ginalber
Article Type: Research Article
Abstract: In this paper, an interval type-2 evolving fuzzy Kalman filter is designed for processing of unobservable spectral components of uncertain experimental data. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi-Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral …Analysis of the experimental data. The computational results for tracking the Mackey-Glass chaotic time series illustrate the efficiency of proposed methodology as compared to relevant approaches from literature, and the experimental results for tracking a 2DoF helicopter demonstrate its applicability. Show more
Keywords: Systems identification, Kalman filter, interval type-2 fuzzy model, singular spectral analysis, evolving fuzzy systems
DOI: 10.3233/JIFS-222919
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9379-9394, 2023
Authors: Liu, Jinpei | Bao, Anxing | Jin, Feifei | Zhou, Ligang | Shao, Longlong
Article Type: Research Article
Abstract: Multiplicative probabilistic linguistic preference relation (MPLPR) has been widely used by decision-makers (DMs) to tackle group decision-making (GDM) problems. However, due to the complexity of the decision-making circumstance and individual subjectivity of DMs, they often provide inconsistent MPLPRs which often lead to unreasonable decision results. To solve this problem, this paper investigates a novel approach to GDM with MPLPRs based on consistency improvement and upgraded multiplicative data envelopment analysis (DEA) cross-efficiency. First, the concept of sequential consistency of MPLPR is defined. Then, a consistency improvement algorithm is proposed, which can convert any unacceptable consistent MPLPR into an acceptable one. Furthermore, …we use geometric averages to transform MPLPR into multiplicative preference relation (MPR). Meanwhile, considering the conservative psychology of DMs, an upgraded multiplicative DEA cross-efficiency model based on the pessimistic criterion is constructed, which can derive the priority vector of MPLPR. Therefore, we can obtain the rational ranking results for all alternatives. Finally, a case analysis of emergency logistics under COVID-19 is provided to illustrate the validity and applicability of the proposed approach. Show more
Keywords: Group decision-making approach, multiplicative probabilistic linguistic preference relations, consistency adjustment, pessimistic criterion, DEA cross-efficiency
DOI: 10.3233/JIFS-223117
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9395-9410, 2023
Authors: Jin, LeSheng | Chen, Zhen-Song | Yager, Ronald R. | Langari, Reza
Article Type: Research Article
Abstract: This letter reports a new type of uncertain information that is different from some well known existing uncertain information, such as probability information, fuzzy information, interval information and basic uncertain information. This type of uncertain information allows some specified compromise in interacting decision environments and gives some acceptance area when facing with uncertainties. We firstly introduce the cognitive interval information and then naturally propose the cognitive uncertain information as an extension. The featured acceptance area provides more flexibility in uncertain information handling and it can be regarded as some specified uncertain range (versus the certainty degree in basic uncertain information). …The new proposals have advantages in some uncertain decision making scenarios where intersubjectivity and interaction of decision makers play important roles. Besides, some basic structural properties are briefly discussed. Moreover, some motivational examples are presented to show its usage in group decision making to help automatically obtain consistency or consensus in aggregating the different individual evaluations. Show more
Keywords: Cognitive interval information, cognitive uncertain information, decision making, group decision making, information fusion, uncertain information
DOI: 10.3233/JIFS-223119
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9411-9418, 2023
Authors: Sangeetha, M. | Thiagarajan, Meera Devi
Article Type: Research Article
Abstract: A recommendation System (RS) is an emerging technology to figure out the user’s interests and intentions. As the amount of data increases exponentially, it is hard to analyze the user intentions and trigger the recommendation accordingly. In this research work, a novel recommendation system called the Deep Knowledge Graph based Attribute Preserving Recommendation (DKG-APR) is presented to analyze massive data and provide personalized recommendations to users. The Deep Knowledge Graph for Recommendation System (DKG-RS) uses Deep Convolutional Neural Network (DCNN) and attention mechanism to explicitly model high-order connections in knowledge graphs. According to empirical findings, Knowledge Graph Attention Network (KGAT) …performs better than other state-of-the-art recommendation techniques like RippleNet and Neural FM. Additional research demonstrates the effectiveness of embedding propagation for high-order relation modeling and the advantages of the attention mechanism for interpretability.The results also show that user information is crucial in the recommendation system, as seen from the optimal node-drop-out ratio of 0.2, which led to the best recall value of 0.2 for all datasets. Show more
Keywords: Knowledge graph, DCNN, DKG, recommendation system
DOI: 10.3233/JIFS-223775
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9419-9430, 2023
Authors: Osman, H. Saber | El-Sheikh, S.A. | Radwan, Abdelaziz E. | El-Atik, Abdelfattah A.
Article Type: Research Article
Abstract: In this paper, the generalization of pre-topological spaces called bipretopological spaces (briefly, π-pre-topology) depending on two pre-topologies on an arbitrary universal set has been introduced. New kinds of separations axioms on π-pre-topological spaces are established and some of their properties are investigated. A comparison between four separation axioms on π-pre-topological spaces and pre-topological spaces with different sorts of counterexamples are presented. The topological property for some π-pre-separation axioms are satisfied and its relation with disubgraphs are discussed. A human heart will be studied through it is generated digraph. It is noted that all separation axioms for human heart are not …all satisfied. Show more
Keywords: Pre-topology, π-pre-topology, separation axioms, human heart, regularity, normality, hereditary, pre-topological property
DOI: 10.3233/JIFS-223891
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9431-9439, 2023
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