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

Authors: Zhang, Yang | Zhou, Wentao | Ma, Lina

Article Type: Research Article

Abstract: The success of technological innovation is related to the future and destiny of enterprises, but because of its uncertainty and high risk, the risk of failure of technological innovation exists objectively. This paper uses grounded theory to code the typical cases of technological innovation failure at home and abroad and explores the causes of technological innovation failure. It is found that policies and regulations, institutional environment, and market environment are the important external factors that cause the failure of enterprise technological innovation, while the defects of enterprise technological innovation products, enterprise system, internal management, technological resources, and managers are the …important internal factors that cause the failure of enterprise technological innovation. By constructing the evolution model of enterprise technological innovation failure, it is found that the failure of enterprise technological innovation is the result of the joint action of enterprise management operation mechanism, technology, capital, and other restraint mechanisms, as well as market and policy system guidance mechanism. Show more

Keywords: Failure of technological innovation, influence factors, formation mechanism, grounded theory, multicase

DOI: 10.3233/JIFS-221756

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6277-6291, 2023

Authors: Cui, Chunsheng | Che, Libin | Wei, Meng

Article Type: Research Article

Abstract: The steady development of commercial banks plays a key role in maintaining the healthy development of the economy. The ability to judge financial risks is a reflection of the comprehensive risk management level of a commercial bank, and it is also an important criterion for measuring its competitiveness and operational stability. Based on the analysis of economic development laws and reference to relevant literature, this paper screened out the eight most representative risk evaluation measurement indicators of commercial banks, ranked these indicators in preference according to expert opinions, established a group decision-making model, and then obtained the consensus ranking by …using the least divergence method. The PCbHA method was used to check the consistency of the results, modify the expert opinions, iteratively calculate, and finally construct the importance ranking of commercial bank risk indicators. This paper discusses the construction of an evaluation system based on the perspective of risk management to enrich and improve the risk management content of commercial banks, enhance the risk prevention and control ability, and provide suggestions for the prevention and management of risks in commercial banks. Show more

Keywords: Iteration, group decision-making, PCbHA, commercial bank, risk monitoring

DOI: 10.3233/JIFS-222508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6293-6302, 2023

Authors: Shukla, Poorva | Patel, Ravindra | Varma, Sunita

Article Type: Research Article

Abstract: Recently, Vehicular Ad-hoc Network (VANET) has been one of the emerging fields of research. Many researchers are doing their research on various challenges of VANET. Congestion or blockage has become a critical issue in intelligent transportation systems, and this problem may arise daily due to the usage of smart technology in VANET. So we need some mechanism which controlscongestion. This paper present the trustworthy, long-lasting and consistent block chain congestion control mechanism using the heterogeneity of Dullening Nural Network (DNN), Q-Learning, and Software Define Network (SDN) model for an accurate result, fixed infrastructure, together with a correct prediction of congestion …when it occurs at the edge of the network and give the fast and correct decision of congestion w.r.t VANET trust, Quality of service (QOS) and other vehicles current request. The focus of our research is on distributed SDN Technology and block chain technology for the development of smart cities and linked vehicles. So we proposed an inexpensive mechanism with low latency and a low bandwidth block chain system. Based on the Simulation result, our proposed architecture gives 82% and 98% reliability and efficiency gain in a congestion environment compared to traditional approaches. This paper aims to increase throughput, Packet Delivery Ratio (PDR), energy consumption time, and less end-to-end delay and routing overhead during communication. Show more

Keywords: Edge computing, blockchain system, DNN, Q-learning, SDN

DOI: 10.3233/JIFS-223073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6303-6326, 2023

Authors: Yang, Hong | Wang, Fan | Wang, Lina

Article Type: Research Article

Abstract: In this paper, the second-order fuzzy homogeneous differential equation is transformed into a more special simplest form under the condition that the solution of the boundary value problem of the equation exists and is unique. Then the eigenvalues of the boundary value problem of the second-order simplest fuzzy homogeneous differential equation are studied and the theorems that make the eigenvalues exist are proposed and then illustrated with examples. Finally, it is proved that when the second-order fuzzy coefficient p ˜ ( t ) in the second-order fuzzy homogeneous differential equation is a fuzzy number, …the solution set of its corresponding second-order granular homogeneous differential equation becomes larger, that is, the solution set of fuzzy differential equations with real numbers is a subset of the solution set with fuzzy coefficients as fuzzy numbers. Show more

Keywords: Fuzzy numbers, fuzzy differential equations(FDEs), granular differentiability, the horizontal membership function, fuzzy eigenvalues

DOI: 10.3233/JIFS-223003

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6327-6340, 2023

Authors: Cao, Yunrui | Ma, Jinlin | Hao, Chaohua | Yan, Qi

Article Type: Research Article

Abstract: Tangut characters were created by the Tangut of the Western Xia (Xi Xia) Dynasty in ancient China and are over 1000 years old. In deep-learning-based recognition studies on Tangut characters, the lack of category-complete datasets has been problematic. Data augmentation cannot augment the character categories of unknown styles, whereas the use of image generation can effectively solve the problem. In this study, we consider the generation of antique book calligraphy styles of Tangut characters as a problem of learning to map from existing printed styles to personalized antique book calligraphy styles. We present M-ResNet, a multi-scale feature extraction residual unit, …and Tangut-CycleGAN, a model for generation Tangut characters that combine M-ResNet and a cycle-consistent adversarial network (CycleGAN). This method uses unpaired data to generate Tangut character images in the calligraphy style of ancient books. To enhance the response of the model to significant channels, a squeezing-and-excitation (SE) module is introduced based on Tangut-CycleGAN to design the Tangut-CycleGAN+SE method for generating images of Tangut characters. This method is not only suitable for Tangut character image generation, but also can effectively generate calligraphy with aesthetic value. In addition, we propose an overall quality discrepancy evaluation metric, FA (Fréchet inception distance + Accuracy), to evaluate the quality of character image generation, which combines style discrepancy and content accuracy metrics. Show more

Keywords: Tangut character, CycleGAN, unpaired data, image generation, evaluation metric

DOI: 10.3233/JIFS-221892

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6341-6358, 2023

Authors: Zhao, Dongfang | Chen, Yesheng | Liu, Shulin | Shen, Jiayi | Miao, Zhonghua

Article Type: Research Article

Abstract: Fault diagnosis is of great significance for industrial equipment maintenance, and feature extraction is a key step of the entire diagnosis scheme. The symbolic aggregate approximation (SAX) is a popular feature extraction approach with great potential recently. In spite of the achievements the SAX has made, the adverse information aliasing still exists in its calculation procedure, and it may make the SAX fail to guarantee the information correctness. This work focuses on analyzing the information aliasing phenomenon of the SAX, followed by developing a novel alternative method, i.e. parallel symbolic aggregate approximation (PSAX). In the proposed PSAX, the information aliasing …is suppressed by designing anti-aliasing procedure, and the average of the symbolic results of several intermediate sequence is adopted to replace the final symbolic result. The Case Western Reserve University (CWRU) rolling bearing data together with the gas valve data of an actual reciprocating compressor assist in verifying the superiority exhibited by the suggested method. The experimental results show that, compared with other methods, the accuracy advantage of the PSAX on the 2 datasets can reach 1% –5%, indicating it is capable of providing high-quality feature vector for intelligent fault diagnosis. Show more

Keywords: Fault diagnosis, feature extraction, symbolic aggregate approximation, parallel symbolic aggregate approximation

DOI: 10.3233/JIFS-223575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6359-6374, 2023

Authors: Pichaimani, Venkateswari | Kalava, Manjula Ramakrishama

Article Type: Research Article

Abstract: Wireless localization or positioning is essential for delivering location-based services for designing location tracking systems. Traditional indoor floor planning system employs wireless signals for accurate position estimation. But these positioning schemes failed to perform position estimation effectively and accurately through many obstacles or objects. The novel technique called Linear Features Projective Geometric Damped Convolutional Deep Belief Network (LFPGDCDBN) is introduced to improve the position estimation accuracy with minimum error. The proposed LFPGDCDBN technique includes two major processes namely dimensionality reduction and position estimation. First, the dimensionality reduction process is performed by projecting the principle features using Linear Helmert–Wolf blocked Sammon …projection. After the feature selection, Geometric Levenberg–Marquardt Convolutional deep belief network is employed to estimate the position of the devices with higher accuracy and minimum error. The Convolutional deep belief network uses the triangulation geometric method to identify the position of the device in an indoor positioning system. Then the Levenberg–Marquardt function is a Damped least square method to minimize the squares of the deviations between the expected and observed results at the output unit. As a result, the LFPGDCDBN increases the positioning accuracy and minimizes the error rate. Experimental MATLAB assessment is carried out with various factors such as computational time, Computational space, positioning accuracy, and positing error. The experimental results and discussion indicate that the proposed LFPGDCDBN provides improved performance in terms of achieving higher positioning accuracy and minimum error as well as computational time when compared to the existing methods. The experimental results and discussion indicate that the proposed LFPGDCDBN increases the positioning accuracy by 47% and computational time, computational complexity, and reduces the positioning error by 45%, 29%, and 74% as compared to state-of-the-art works. Show more

Keywords: Indoor floor planning, linear Helmert–Wolf blocked, Sammon projection based feature selection, Geometric Levenberg–Marquardt Convolutional deep belief network, damped least square method

DOI: 10.3233/JIFS-223618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6375-6386, 2023

Authors: Sandhu, Muhammad Abdullah | Amin, Asjad

Article Type: Research Article

Abstract:  During the last decade, dengue fever has emerged as a life-threatening disease. Dengue fever is caused by the bite of the dengue mosquito, and it spreads rapidly especially in the rainy season due to the availability of water carriers inside and outside the living vicinity. In this work, we propose an automated model for dengue larvae detection and tracking using Convolutional Neural Network (CNN) and Kalman filters. Despite substantial literature available on object tracking, no model has been proposed for dengue larvae. We started our work by collecting water areas and dengue larvae datasets as no public datasets were available. …Our water areas dataset has 30 videos of different containers and environments. The dengue larvae dataset has 50 short videos of dengue larvae having different locations, backgrounds, and textures. In the first step, we used CNN to detect water areas, and the detected water area is then processed for the detection and tracking of larvae. Next, we propose a Kalman filter-based workflow for dengue larvae detection and tracking. A Gaussian Mixer Model with background subtraction is applied for foreground and object detection. Then we used Kalman filters to track the moving larvae in the experimental videos. The proposed model shows excellent results considering the small size of larvae and the challenging dataset. Subjective and objective experimental results clearly show the superior performance of the proposed model. The feedback received from the health authorities has been encouraging and the work is expected to facilitate the health department in eliminating the dengue. Show more

Keywords: Dengue larvae, Detection, Tracking, CNN, Kalman Filtering

DOI: 10.3233/JIFS-223660

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6387-6401, 2023

Authors: Li, Ze | Liu, Xiaoze | Ji, Lin | He, Guanglong | Sun, Liang

Article Type: Research Article

Abstract: The diversity of attribute categories brings certain difficulties to data feature detection. In order to improve the accuracy and efficiency of feature detection, a hybrid attribute feature detection method for power system intelligent operation and maintenance big data based on improved random forest algorithm is proposed. Clustering processing power system intelligent operation and maintenance big data, based on data clustering results to reduce the characteristics of data mixed attributes, reduce the scale of data processing, and discretize the data mixed attributes; BP neural network is used to preprocess the results. Make corrections to improve the accuracy of feature detection, use …the improved random forest algorithm to classify the data, and improve the convergence speed of the method. Finally, the support vector machine method is used to realize the feature detection of data mixed attributes. The experimental results show that the feature detection accuracy and efficiency of the method designed in this paper are high, and more features can be detected, which verifies its effectiveness. The method designed in this paper has the minimum RMSE value and the maximum value is only 0.12, which is far lower than the RMSE value of the improved spectral clustering algorithm and multi-domain feature extraction method, and has high detection accuracy. Show more

Keywords: Improved random forest algorithm, power system, operation and maintenance big data, mixed attributes, BP neural network, support vector machine

DOI: 10.3233/JIFS-223852

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6403-6412, 2023

Authors: Gobinath, C. | Gopinath, M.P.

Article Type: Research Article

Abstract: Recent reports indicate a rise in retinal issues, and automatic artery vein categorization offers data that is particularly instructive for the medical evaluation of serious retinal disorders including glaucoma and diabetic retinopathy. This work presents a competent and precise deep-learning model designed for vessel segmentation in retinal fundus imaging. This article aims to segment the retinal images using an attention-based dense fully convolutional neural network (A-DFCNN) after removing uncertainty. The artery extraction layers encompass vessel-specific convolutional blocks to focus the tiny blood vessels and dense layers with skip connections for feature propagation. Segmentation is associated with artery extraction layers via …individual loss function. Blood vessel maps produced from individual loss functions are authenticated for performance. The proposed technique attains improved outcomes in terms of Accuracy (0.9834), Sensitivity (0.8553), and Specificity (0.9835) from DRIVE, STARE, and CHASE-DB1 datasets. The result demonstrates that the proposed A-DFCNN is capable of segmenting minute vessel bifurcation breakdowns during the training and testing phases. Show more

Keywords: Deep learning, fundus image, fully-convolutional neural networks, blood vessel segmentation, artery vein classification

DOI: 10.3233/JIFS-224229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6413-6423, 2023

Authors: Gomathi, S.V. | Jayalakshmi, M.

Article Type: Research Article

Abstract: The assignment problem is one of the core combinatorial optimization problems in the optimization branch, and the theory and applications of fractional programming have made great strides in recent years. Usually, the possible coefficient values of linear fractional programming and real-world problems are frequently only known to the decision in vague or uncertain terms. Hence, it would be more acceptable to interpret the coefficients for as fuzzy numerical information. In this paper, a fuzzy bi-objective fractional assignment (FBOFAP) has been formulated. A problem has been defined. Here, triangular shapes are used to indicate the parameters. The fuzzy problem is turned …into a typical crisp problem through α-cut using a fuzzy number and then the compromise solution is generated by fuzzy programming. Show more

Keywords: Assignment problem, fractional programming problem, hyperbolic membership function, fuzzy numbers, fuzzy programming

DOI: 10.3233/JIFS-223312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6425-6433, 2023

Authors: Yu, Hongliang | Peng, Zhen | He, Zirui | Huang, Chun

Article Type: Research Article

Abstract: In China, steel structure building has become an important direction of national support and development. As a link in steel structure construction, welding plays a vital role. With the continuous development of domestic steel structures in the direction of “big, special, and new”, manual welding becomes increasingly difficult. The application of robotic technology can effectively reduce the problems of unstable quality and low efficiency caused by manual welding. However, how to evaluate the overall application effect of this technology from the perspective of steel structure construction projects is an urgent problem to be solved at present. Through an in-depth analysis …of the technology of building steel structure welding robots, combined with the idea of the capability maturity model and WSR methodology, the evaluation index system of application maturity of building steel structure welding robotic technology is designed. In addition, according to the gray, fuzzy, and difficult-to-quantify characteristics of the evaluation index system, the evaluation model of the application maturity of the building steel structure welding robot technology is established by using the subjective and objective combination weighting method and the gray system theory. In this model, the mixed weighting method of AHP and entropy weight method is used to determine the index weight, and then the multi-level gray theory is used to comprehensively evaluate the maturity of the technology application. Lastly, a project is used as an example to check the science and feasibility of the model. Show more

Keywords: Welding robot, steel structure, WSR, maturity model, multi-level grey theory

DOI: 10.3233/JIFS-223563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6435-6451, 2023

Authors: Li, Wanping | Zhang, Guidong | Sheng, Yuhong

Article Type: Research Article

Abstract: Solving uncertain differential equations is a critical subject in the field of uncertainty theory, where uncertain differential equations are a sort of differential equations that involve Liu processes. Currently, considerable effort has been put into addressing this issue. Regrettably, analytic solutions to uncertain differential equations are not always accessible. As a result, several numerical methods have been investigated. However, numerical methods have certain limitations in terms of providing a continuous representation of the solution as well as more information about the solution. This paper will propose a novel algorithm based on the variational iteration method (VIM) for solving uncertain differential …equations analytically or approximately analytically. The associated numerical experiments show that the proposed method is an efficient tool for solving uncertain differential equations. Show more

Keywords: Uncertain differential equation, analytic solution, variational iteration method, uncertainty theory

DOI: 10.3233/JIFS-223593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6453-6461, 2023

Authors: Gong, Yu | Liu, Mingzhou | Wang, Xiaoqiao | Liu, Conghu | Hu, Jing

Article Type: Research Article

Abstract: Multi-scale defect features, blurred edges and inability to locate geometric features have been the three key factors limiting the detection of surface defects on quality control system in the industrial manufacturing process. In this study, a method based on the fusion of multi-scale features and pixel-level semantic segmentation is proposed for the detection of surface defects. The proposed method firstly fuses multi-level feature maps to balance the expressiveness of multi-scale features, then adds a boundary refinement module to enhance the accurate inference of edge fine-grained, and finally adopts an en-decoder architecture to locate geometric features at the pixel-level for each …type of defects, realizing intelligent detection of geometric features of end-to-end multi-scale defects on the surface of parts. We conduct experiments using the collected parts datasets to evaluate the effectiveness of our framework. The experimental results show that the proposed model achieves MIoU of 80.1%, the recognition accuracy reaches more than 95 %, and a detection rate of up to 29.64 FPS, demonstrating the advancement and effectiveness of the proposed method with less misclassification and superior generalization performance and has progress and effectiveness in detecting surface defects of multi-scale features. It provides a research idea for the subsequent realization of surface quality inspection in the manufacturing process system. Show more

Keywords: Intelligent manufacturing, quality control, surface defects detection, multi-scale features, semantic segmentation

DOI: 10.3233/JIFS-223041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6463-6481, 2023

Authors: Ruhin Kouser, R. | Manikandan, T.

Article Type: Research Article

Abstract: In Vehicular cloud computing (VCC), at the time of congestion, to deal with traffic, the vehicles’ underutilized resources are shared; subsequently, these resources aren’t constrained to computing power, storage, along with internet connectivity. Nevertheless, owing to the vehicular network characteristics, attaining the QoS requirements search together with the allocation of resources in the Vehicular Cloud (VC) has turned into a complicated task. An intelligent Square Shaped (SS)-Adaptive Neuro-Fuzzy Interference (SS-ANFIS) methodology for Resource Scheduling (RS) in addition to Mean-centered Penguins Search Optimization Algorithm (M-PeSOA) for Optimal Path Selection (OPS) in the VC is proposed here for efficient resource allocation. (a) …Feature extraction, (b) Vehicles clustering, (c) OPS, (d) Resource information extraction, and (e) RS included in the proposed methodology. First, the vehicular network is initialized following that the vehicle features are extracted. Next, Cluster Heads (CHs) are generated regarding which vehicles are clustered; subsequently, the multi-paths are generated. After that, by employing the M-PeSOA, the OPS procedure is conducted; thus, the VC’s resource information is extracted aimed at scheduling the resources efficiently. Lastly, by employing the SS-ANFIS, vehicles are scheduled in the optimal paths. The proposed resource allocation system’s performance is assessed, and the experiential outcomes are analogized using the sumo tool and java platform. Show more

Keywords: Vehicular cloud computing (VCC), cluster formation, resource scheduling, adaptive neuro-fuzzy interference (ANFIS), penguins search optimization algorithm (PeSOA), cluster heads (CHs), cockroach swarm optimization (CSO), multipath creation

DOI: 10.3233/JIFS-223522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6483-6495, 2023

Authors: Xu, Chenguang | Zhang, Chao | Ma, Mingxi | Zhang, Jun

Article Type: Research Article

Abstract:  Blind image deconvolution has attracted growing attention in image processing and computer vision. The total variation (TV) regularization can effectively preserve image edges. However, due to lack of self-adaptability, it does not perform very well on restoring images with complex structures. In this paper, we propose a new blind image deconvolution model using an adaptive weighted TV regularization. This model can better handle local features of image. Numerically, we design an effective alternating direction method of multipliers (ADMM) to solve this non-smooth model. Experimental results illustrate the superiority of the proposed method compared with other related blind deconvolution methods.

Keywords: Blind deconvolution, Total variation regularization, Adaptive weighted matrix, ADMM

DOI: 10.3233/JIFS-223828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6497-6511, 2023

Authors: Duan, Jun | Ren, Qi | Zhang, Baoshuai

Article Type: Research Article

Abstract: In order to analyze the impact of policy effect on carbon efficiency in the Yangtze River Economic Belt, an experimental group, including 11 regions possessing the features of the Yangtze River Economic Belt, was constructed using the synthetic control method for project evaluation with the single-factor carbon efficiency value as the core study variable in this paper. Several regions were selected from the control group as synthetic objects through a data-driven approach to construct synthetic regions that meet basic requirements before the implementation of the policy by weight ratio and evaluate the policy’s impact on the implementation of carbon efficiency …in the Yangtze River Economic Belt. The study shows that the single-factor carbon efficiency is relatively robust. But since 2013, the tail of the Yangtze River Economic Belt has seen an improvement. Sichuan, Hunan, Chongqing and Hubei have exceeded other provinces and cities in the growth of carbon efficiency and Shanghai has also witnessed a significant increase. Upon the inception of the synthetic Yangtze River Economic Belt, there’s a discernible divergence between the curves of the real and the synthetic Yangtze River Economic Belt. Specifically, policy interventions in the Yangtze River Economic Belt didn’t work well in earlier years and even restrained the economic growth of the whole region. Show more

Keywords: Synthetic control method, Yangtze River Economic Belt, carbon efficiency

DOI: 10.3233/JIFS-223571

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6513-6523, 2023

Authors: Guo, Zhuen | Lin, Li

Article Type: Research Article

Abstract: Designers refer to existing product cases and innovate products to develop new products. However, when designers screen product cases, there is no user participation, which leads to the lack of user-side knowledge and emotional drive that is very important for design. Therefore, it is necessary to play the role of user emotional knowledge in promoting the whole design process. This paper proposes the concept of the positive perceptual sample, which applies the knowledge emotion integration of designers and users to the screening sample case stage at the beginning of the design process. This study is based on the lack of …user-side knowledge and emotional drive of reference cases and integrates user emotion into the reference case screening process. Then, in the emotion measurement process, users’ cognitive data in the screening process are obtained through the eye-brain fusion cognitive experiment. Finally, the XGBoost algorithm is used to process feature index data to realize the classification and recognition of cognitive data and applied to the positive perceptual classification of products. The results show that the classification accuracy of physiological cognitive data with user emotional representation by the XGBoost algorithm is 90.87%. The results of cognitive data classification are applied to the screening of positive perceptual samples, and the satisfaction rate is 98.35%. The results show that the method proposed in this paper provides a new source of ideas for obtaining positive perceptual samples and can be applied to new product development. Show more

Keywords: Positive perceptual sample, cognitive data, machine learning, Kansei Engineering, eye-brain fusion

DOI: 10.3233/JIFS-222656

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6525-6543, 2023

Authors: Mahmood, Tahir | Ali, Zeeshan | Pamucar, Dragan

Article Type: Research Article

Abstract: To manage ambiguity and uncertainty information in real life problems, certain intellectuals have initiated numerous modifications of fuzzy sets. But, in certain situations, the decision-makers have been neglected to take a feasible decision. For instance, when an intellectual provides information in the form of a two-dimension or type-1 fuzzy set, then the prevailing theories have been neglected. For this, in this study, we elaborated the new idea of type-2 picture fuzzy sets (T2-PFS) and their algebraic laws have been given. Moreover, by using the T2-PFS, Hamacher aggregation operators are proposed and discussed their important properties. The Minkowski sorts of measures …under the T2-PFS are also initiated. In the current study, we aim to utilize the matrix game in a type-2 picture fuzzy environment. By using the proposed operators and measures based on the Hausdorff metric. The elaborated idea has been defined with the biogas-plant implementation dilemma to verify the pertinence and authenticity. Lastly, to discuss the sensitivity analysis and geometrical shown of the initiated works, certain examples are illustrated to determine the supremacy and consistency of the proposed approaches. Show more

Keywords: Type-2 picture fuzzy sets, Hamacher/Einstein/Algebraic aggregation operators, Minkowski type measures, biogas-plant implementation problems

DOI: 10.3233/JIFS-223009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6545-6571, 2023

Authors: Geng, Ruijuan | Ji, Ying | Qu, Shaojian | Wang, Zheng

Article Type: Research Article

Abstract: The sudden COVID-19 epidemic has caused consumers to gradually switch to online shopping, the increasing number of online consumer reviews (OCR) on Web 2.0 sites has made it difficult for consumers and merchants to make decisions by analyzing OCR. Much of the current literature on ranking products based on OCR ignores neutral reviews in OCR, evaluates mostly given criteria and ignores consumers’ own purchasing preferences, or ranks based on star ratings alone. This study aims to propose a new decision support framework for the evaluation and selection of alternative products based on OCR. The decision support framework mainly includes three …parts: 1) Data preprocessing: using Python to capture online consumer comments for data cleaning and preprocessing, and extracting key features as evaluation criteria; 2) Sentiment analysis: using Naive Bayes to analyze the sentiment of OCR, and using intuitionistic fuzzy sets to describe the emotion score; 3) Benchmark analysis: a new IFMBWM-DEA model considering the preference of decision makers is proposed to calculate the efficiency score of alternative schemes and rank them according to the efficiency score. Then, the OCR of 15 laptops crawled from JD.com platform is used to prove the usefulness and applicability of the proposed decision support framework in two aspects: on the one hand, the comparison of whether the preference of decision makers is considered, and on the other hand, the comparison with the existing ranking methods. The comparison also proves that the proposed method is more realistic, the recommendations are more scientific and the complexity of the decision is reduced. Show more

Keywords: Intuitionistic fuzzy multiplicative best-worst method (IFMBWM), data envelopment analysis (DEA), online consumer reviews (OCR), product ranking, sentiment analysis

DOI: 10.3233/JIFS-223095

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6573-6592, 2023

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