Journal of Intelligent & Fuzzy Systems - Volume 44, issue 4

Authors: Kabilesh, S.K. | Mohanapriya, D. | Suseendhar, P. | Indra, J. | Gunasekar, T. | Senthilvel, N.

Article Type: Research Article

Abstract: Monitoring fruit quality, volume, and development on the plantation are critical to ensuring that the fruits are harvested at the optimal time. Fruits are more susceptible to the disease while they are actively growing. It is possible to safeguard and enhance agricultural productivity by early detection of fruit diseases. A huge farm makes it tough to inspect each tree to learn about its fruit personally. There are several applications for image processing with the Internet of Things (IoT) in various fields. To safeguard the fruit trees from illness and weather conditions, it is difficult for the farmers and their workers …to regularly examine these large areas. With the advent of Precision Farming, a new way of thinking about agriculture has emerged, incorporating cutting-edge technological innovations. One of the modern farmers’ biggest challenges is detecting fruit diseases in their early stages. If infections aren’t identified in time, farmers might see a drop in income. Hence this paper is about an Artificial Intelligence Based Fruit Disease Identification System (AI-FDIS) with a drone system featuring a high-accuracy camera, substantial computing capability, and connectivity for precision farming. As a result, it is possible to monitor large agricultural areas precisely, identify diseased plants, and decide on the chemical to spray and the precise dosage to use. It is connected to a cloud server that receives images and generates information from these images, including crop production projections. The farm base can interface with the system with a user-friendly Human-Robot Interface (HRI). It is possible to handle a vast area of farmland daily using this method. The agricultural drone is used to reduce environmental impact and boost crop productivity. Show more

Keywords: Fruit quality, Internet of Things (IoT), fruit disease, artificial intelligence, drone system

DOI: 10.3233/JIFS-222017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6593-6608, 2023

Authors: Long, Le Ngoc Bao | Kim, Hwan-Seong | Cuong, Truong Ngoc | You, Sam-Sang

Article Type: Research Article

Abstract: Pricing and production policies play a key role in ensuring the added value of supply chain systems. For perishable inventory management, the pricing and production lines must be manipulated dynamically since several uncertainties are involved in the system’s behavior. This study discusses the impact of dynamic pricing and production policies on an uncertain stochastic inventory system with perishable products. The mathematical model of the inventory management system under external disturbance is formulated using a continuous differential equation in which the price and production rates are considered as control factors to optimize total profits, which is described as an objective …function. An analytical solution for the optimal pricing and production rate was obtained using the Hamilton-Jacobi-Bellman equation. The unknown disturbance was approximated using an intelligent approach called radial basis function neural network. Finally, extensive numerical simulations were presented to validate the theoretical results and optimization solutions (including the efficiency of the approximation of the unknown disturbance) for the dynamic pricing and production management strategy of an uncertain stochastic inventory system against volatile markets. The performance of the proposed method was analyzed under different stock level conditions, which highlighted the importance of keeping the inventory levels at an optimal range to ensure the profitability of business operations. This management strategy can assist a business with solutions for inventory policies while supporting decision-making processes to facilitate coping with production management disruptions. Show more

Keywords: Optimization, uncertain stochastic system, production-inventory system, perishable products, adaptive neural networks

DOI: 10.3233/JIFS-222804

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6609-6629, 2023

Authors: Faiza, | Khalil, K.

Article Type: Research Article

Abstract: This study envisages assessing the effects of the COVID-19 on the on-time performance of US-airlines industry in the disrupted situations. The deep learning techniques used are neural network regression, decision forest regression, boosted decision tree regression and multi class logistic regression. The best technique is identified. In the perspective data analytics, it is suggested what the airlines should do for the on-time performance in the disrupted situation. The performances of all the methods are satisfactory. The coefficient of determination for the neural network regression is 0.86 and for decision forest regression is 0.85, respectively. The coefficient of determination for the …boosted decision tree is 0.870984. Thus boosted decision tree regression is better. Multi class logistic regression gives an overall accuracy and precision of 98.4%. Recalling/remembering performance is 99%. Thus multi class logistic regression is the best model for prediction of flight delays in the COVID-19. The confusion matrix for the multi class logistic regression shows that 87.2% flights actually not delayed are predicted not delayed. The flights actually not delayed but wrongly predicted delayed are12.7%. The strength of relation with departure delay, carrier delay, late aircraft delay, weather delay and NAS delay, are 94%, 53%, 35%, 21%, and 14%, respectively. There is a weak negative relation (almost unrelated) with the air time and arrival delay. Security delay and arrival delay are also almost unrelated with strength of 1% relationship. Based on these diagnostic analytics, it is recommended as perspective to take due care reducing departure delay, carrier delay, Late aircraft delay, weather delay and Nas delay, respectively, considerably with effect of 94%, 53%, 35%, 21%, and 14% in disrupted situations. The proposed models have MAE of 2% for Neural Network Regression, Decision Forest Regression, Boosted Decision Tree Regression, respectively, and, RMSE approximately, 11%, 12%, 11%, respectively. Show more

Keywords: Air transport, airline flight delays, artificial intelligence, machine learning

DOI: 10.3233/JIFS-222827

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6631-6653, 2023

Authors: Stephen, M. | Felix, A.

Article Type: Research Article

Abstract: The World health organization (WHO) reported that cardiovascular disease is the leading cause of death worldwide, particularly in developing countries. But while diagnosing cardiovascular disease, medical practitioners might have differences of opinions and faced challenging when there is inadequate information and uncertainty of the problem. Therefore, to resolve ambiguity and vagueness in diagnosing disease, a perfect decision-making model is required to assist medical practitioners in detecting the disease at an early stage. Thus, this study designs a fuzzy analytic hierarchy process (FAHP) point-factored inference system to detect cardiovascular disease. The attributes are selected and classified into sub-attributes and point factor …scale using the clinical data, medical practitioners, and literature review. Fuzzy AHP is used in calculating the attribute weights, the strings are generated using the Mamdani fuzzy inference system, and the strength of each set of fuzzy rules is calculated by multiplying the attribute weights with the point factor scale. The string weights determine the output ranges of cardiovascular disease. Moreover, the results are validated using sensitivity analysis, and comparative analysis is performed with AHP techniques. The results show that the proposed method outperforms other methods, which are elucidated by the case study. Show more

Keywords: Linguistic variable, fuzzy rules, FAHP, point factor scale, inference system, cardiovascular disease

DOI: 10.3233/JIFS-223048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6655-6684, 2023

Authors: Jeba Emilyn, J. | Ashokkumar, M.

Article Type: Research Article

Abstract: In Wireless Sensor Networks (WSNs), Clustering aids in maximizing the lifetime of the network with sustained energy stability in the sensor nodes during data dissemination. In this clustering process, the sensor nodes are organized into clusters with the potential fitness node designated as Cluster Heads (CHs) for collecting and forwarding the data to the sink. In specific, the energy consumption of sensor nodes during their role as CH is maximized with great impact over the network lifespan. In this paper, a Weight-imposed Elite Hybrid Binary Cuckoo Search (EHBCS)-based Clustering Mechanism is proposed for facilitating potent data transmission with minimized energy …consumption and improved network lifetime. This EHBCS is proposed as a novel energy-sensitive CH selection framework based on the process of hierarchical routing through the inclusion of hybrid optimization algorithm. It selected CH depending on the parameters of Quality of Service (QoS), delay, distance, and energy into account. It integrated the merits of Binary Cuckoo Search and Elite Mechanism for selecting CHs and performing effective processes by preventing sinkhole issues in WSNs. The results of EHBCS confirmed better throughout by 11.32%, minimized energy consumption by 13.84%, and minimized delay by 16.12% with an increasing number of sensor nodes, compared to the baseline CH selection approaches used for exploration. Show more

Keywords: Binary cuckoo search, clustering, cluster head selection, elite solution, crossover, mutation

DOI: 10.3233/JIFS-222137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6685-6698, 2023

Authors: Liu, Hongqi | Liu, Yulei

Article Type: Research Article

Abstract: A new three-dimensional college performance assessment index system is established. The grey relational method is used to evaluate college performance in a university and the CRITIC method of variation coefficient is used to weight the assessment indices. The performance of 15 colleges in NH University are assessed by using the index system and the grey method, and the results can supply some important information for management optimization and resource distribution of NH University. It also shows that the index system and grey assessment model proposed in this paper have good potential to solve the similar problem.

Keywords: Performance assessment, university, Grey relational analysis

DOI: 10.3233/JIFS-223286

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6699-6708, 2023

Authors: Fan, Ruguo | Chen, Fangze | Wang, Yitong | Wang, Yuanyuan | Chen, Rongkai

Article Type: Research Article

Abstract: In the practice of COVID-19 prevention and control in China, the home quarantine policy directly connects and manages the residents, which plays a significant role in preventing the spread of the epi-demic in the community. We evaluate the effectiveness of current home quarantine policy in the actual execution process based on the evolutionary game relationship between the community and res-idents. This paper establishes a double-layer coupled complex network game model, and uses the multi-agent modeling method to study the game relationship between the community and residents in the context of home quarantine policies. The results show that initial strategy of …the community with strict supervision and reasonable government reward allocation will increase the proportion of the residents complying with the quarantine rule. When 80% of the communities chose to supervise strictly at the beginning, people are more likely to follow the rules. While when the residents can only get 20% of the government’s reward, the proportion of choosing to violate the quarantine rules is much higher than that when they can get 80% of the reward. Besides, the structure of small-world network and environmental noise will also affect the residents’ strategy. As the probability of reconnection of the small-world network rises from 0.2 to 0.8, the proportion of residents who choose to comply with the strategy becomes much higher. When the environmental noise reaches 0.5, the ratio of residents who choose to violate the strategy is higher than the ratio of complianc. The study is helpful to provide the basis for the government to formulate the quarantine policy and propose an optimization for making effective quarantine measures. In this way, the government can adjust the parameters to make residents achieve the possible level of compliance with quarantine policies as high as possible to contain the spread of the epidemic. Show more

Keywords: COVID-19, evolutionary game, double-layer net-work, agent-based modelling, small-world network

DOI: 10.3233/JIFS-221594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6709-6722, 2023

Authors: Zhang, Zhichao | Cheng, Xinghui | Zhao, Weifeng | Zhang, Qing

Article Type: Research Article

Abstract: With the development of complexity in complex equipment, the selection of suppliers referred to several groups. How to select the suppliers for the complex equipment under several groups becomes an important topic. To solve the problem, a two-level consensus reaching process is designed to select the suppliers of the complex equipment in uncertain environments. First, considering the fuzzy environment of selection, the cloud model, which could reflect the fuzziness and randomness, is used to present the uncertain preferences of the decision-makers. Then, considering the negotiation and interaction of two groups, the bi-level consensus reaching process is established to present the …master-slave features of complex equipment. Third, to solve the proposed bi-level model, the improved artificial bee colony is proposed, which adopts the gray wolf algorithm’ searching mechanism and levy flying method. The adopted strategies could enhance the searching power of artificial bee colony. Finally, a case study is used to verify the advantages of our study. Show more

Keywords: Decision making, mathematical modelling, fuzzy logic, supply chain management

DOI: 10.3233/JIFS-221903

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6723-6736, 2023

Authors: Gong, Zengtai | He, Lele

Article Type: Research Article

Abstract: Connectivity parameters play a crucial role in network analysis. The cyclic reachability is an important attribute that determines the connectivity of the network, the strength of the cycles in intuitionistic fuzzy graphs (IFGs) is not unique. This article first introduces several concepts of cycle connectivity of IFGs, and then discusses the related properties. On the basis of the cycle connectivity of IFGs, the concepts of cyclic connectivity index ( CCI ) and average cyclic connectivity index ( ACCI ) are proposed, which can be used to express the reachability of …cycle. Some results of CCI on IFGs are discussed, such as cutvertices, trees, and complete intuitionistic fuzzy graphs. The vertices of IFGs are divided into three categories according to ACCI . Two algorithms are introduced, one to find CCI and ACCI of a given IFGs and the other to identify the nature of vertices. Show more

Keywords: Cycle connectivity, intuitionistic fuzzy graphs, cyclic connectivity index

DOI: 10.3233/JIFS-222332

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6737-6748, 2023

Authors: Swethaa, S. | Felix, A.

Article Type: Research Article

Abstract: Land, marine and airborne are the three types of military robots used in the war-field. Land robots are the most crucially considered robots. Selecting a military land robot for a specific purpose is one of the challenging problems for a decision-maker to find the most preferred alternative when it involves fuzziness and uncertainty. Intangible factors are used while selecting the appropriate robotic system as it effectively deals with fuzziness. Intuitionistic dense fuzzy set, which is the combination of intuitionistic fuzzy set and dense fuzzy set, is capable of dealing with intangible factors. This study aims to design the integrated model …on intuitionistic dense fuzzy AHP-TOPSIS to choose the most preferable military land robots under various circumstances. Robots for different types of situations, namely bomb disposal, search and rescue, surveillance and reconnaissance and war-fighter are considered. Moreover, the intuitionistic dense fuzzy AHP is utilized to calculate the subjective weights of the criteria and intuitionistic dense fuzzy TOPSIS is used to rank the alternatives. Further, a sensitivity analysis is examined to demonstrate the quality of the outcome and the results are compared with the fuzzy set, intuitionistic fuzzy set, and dense fuzzy set to show the efficiency of the proposed methodology. Show more

Keywords: Robot selection, intuitionistic dense fuzzy set, intuitionistic trapezoidal dense fuzzy AHP, intuitionistic trapezoidal dense fuzzy TOPSIS

DOI: 10.3233/JIFS-223622

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6749-6774, 2023

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: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

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