Journal of Intelligent & Fuzzy Systems - Volume 45, issue 1

Authors: Xu, Wenxiang | Wang, Lei | Liu, Dezheng | Tang, Hongtao | Li, Yibing

Article Type: Research Article

Abstract: Multi-agent collaborative manufacturing, high energy consumption and pollution, and frequent operation outsourcing are the three main characteristics of large complex equipment manufacturing enterprises. Therefore, the production scheduling problem of large complex equipment to be studied is a distributed flexible job shop scheduling problem involving operation outsourcing (Oos-DFJSP). Besides, the influences of each machine on carbon emission and job scheduling at different processing speeds are also involved in this research. Thus the Oos-DFJSP of large complex equipment consists of the following four sub-problems: determining the sequence of operations, assigning jobs to manufactories, assigning operations to machines and determining the processing speed …of each machine. In the Oos-DFJSP, if a job is assigned to a manufactory of a group manufacturing enterprise, and the manufactory cannot complete some operations of the workpiece, then these operations will be assigned to other manufactories with related processing capabilities. Aiming at solving the problem, a multi-objective mathematical model including costs, makespan and carbon emission was established, in which energy consumption, power generation of waste heat and treatment capacity of pollutants were considered in the calculation of carbon emission. Then, a multi-objective improved hybrid genetic artificial bee colony algorithm was developed to address the above model. Finally, 45 groups of random comparison experiments were presented. Results indicate that the developed algorithm performs better than other multi-objective algorithms involved in the comparison experiments not only on quality of non-dominated solutions but also on Inverse Generational Distance and Error Ratio. That is, the proposed mathematical model and algorithm were proved to be an excellent method for solving the multi-objective Oos-DFJSP. Show more

Keywords: Large complex equipment manufacturing, operation outsourcing, distributed flexible job shop scheduling, carbon emission, multi-objective improved hybrid genetic artificial bee colony algorithm

DOI: 10.3233/JIFS-223435

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 147-175, 2023

Authors: Zhang, Shirong | Zhao, Lihong

Article Type: Research Article

Abstract: In view of the weak correlation between the signal features of the sports injury image extracted by the existing methods and the damage points, the accuracy is low and the recognition time is long. In order to improve the recognition accuracy of sports injury and reduce the loss of sports injury to human body, a method of college students’ long-distance running injury point recognition based on association rules is proposed. According to the contour of the injured part of college students’ long-distance running, the image is segmented, and the wavelet function is used to decompose the image signal into different …frequency bands. The strong correlation rules between the wavelet function and the image signal are analyzed, so that the total energy of the time domain waveform can replace the wavelet transform coefficient; Secondly, the laser harmonic imaging points which have strong correlation with the damage points are regarded as the damage points; Finally,construct the perspective image acquisition platform, collect the sports injury image data of College Students’ long-distance runners in sports school, and set up comparative experiments. The experimental results show that the design method improves the recognition accuracy of College Students’ long-distance sports injury points and reduces the recognition time. Show more

Keywords: Association rules, long-distance running injury, damage point, image preprocessing, contours of damaged sites

DOI: 10.3233/JIFS-224334

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 177-183, 2023

Authors: Li, Weidong | Yu, Yongbo | Meng, Fanqian | Duan, Jinlong | Zhang, Xuehai

Article Type: Research Article

Abstract: Some subtle features of planting structures in irrigation areas could only be visible on high-resolution panchromatic spectral images. However, low spatial resolution multispectral image makes it hard to recognize them. It is challenging to accurately obtain crop planting structure when using traditional methods. This paper proposes an extraction method of crop planting structure based on image fusion and U-Net depth semantic segmentation network, which can automatically and accurately extract multi-category crop planting structure information. This method takes Landsat8 commercial multispectral satellite data set as an example, chooses RGB pseudo-color synthetic image which highlights vegetation characteristics, and uses HLS(Hue, Luminance, Saturation), …NND(Nearest-Neighbor Diffusion) and G-S(Gram-Schmidt) methods to fuse panchromatic band to obtain 15m high-resolution fusion image to obtain training set and test set, six types of land features including cities and rivers were labeled by manual to obtain the verification set. The training and validation sets are cut and enhanced to train the U-Net semantic segmentation network. Taking the Xiaokaihe irrigation area in Binzhou City, Shandong Province, China, as an example, the planting structure was classified, and the overall accuracy was 87.7%, 91.2%, and 91.3%, respectively. The accuracy of crop planting structures (wheat, cotton, woodland) was 74.2%, 82.5%, 82.3%, and the Kappa coefficient was 0.832, 0.880, and 0.881, respectively. The results showed that the NND-UNet method was suitable for large-scale continuous crop types (wheat, cotton), and the GS-UNet method had a better classification effect in discrete areas of cash crops (Jujube and many kinds of fruit trees). Show more

Keywords: Multispectral remote sensing, U-Net network, crop planting structures, multi category, image fusion

DOI: 10.3233/JIFS-230041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 185-198, 2023

Authors: Hymlin Rose, S.G. | Jayasree, T.

Article Type: Research Article

Abstract: A jamming attack is a special case of a Denial of Service (DoS) attack that completely blocks the data transmission in Wireless Sensor Networks (WSNs). When sensor nodes are distributed in the field, numerous attacks, such as collision, black hole, selective forwarding, jamming, etc., caused by the presence of malicious nodes have the potential to cause network damage. Jamming is a highly risky attack that completely blocks data transmission within the wireless network. The existing technique for detecting jamming attacks are based on predetermined hopping-sequence, cryptographic, or random frequency hopping techniques. However, these mechanisms are more complex and frequently have …energy constraints and high overhead. A novel jamming detection method based on a statistical approach that provides high network performance measures is proposed. It is a technique that uses energy-based clustering with a Received Signal Strength Indicator (RSSI). The selection of thresholds used for the detection of jamming is analyzed. The proposed approach employs three detection performance metrics for investigating the jamming attack, namely, Packet to Delivery Ratio (PDR), ENERGY, and RSSI. The jamming node is identified using the Optimal Decision Rule (ODR), which is determined by the hypothesis rule. If the hypothesis is not satisfied, then jamming exists; otherwise, there is no jamming. The novel technique is implemented using a Network Simulator, and various performance metrics such as PDR, Energy consumption, Network throughput, Routing overhead, network, and node lifetime are evaluated to conclude that the statistical approach outperforms the timestamp and IEWMA approaches. Show more

Keywords: Statistical, hypothesis, WSN

DOI: 10.3233/JIFS-220443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 199-213, 2023

Authors: Karabacak, Mesut

Article Type: Research Article

Abstract: The correlation coefficient (CC) is a well-known functional information measures used to measure the interrelationship between uncertain, fuzzy sets. The use of neutrosophic sets (NS) in decision making has been increasing in recent times. Many studies have been considered to calculate the CC of NSs. These approaches assess only the strength of relationship between PNSs, and are described within the interval [0, 1]. However, the inclusion of the reliability level of the data in the process is very important for the final decision. Therefore, neutrosophic Z-Number sets (NZNS) has been defined for this purpose, which are not only provide an …assessment of the data but also take into account their confidence level. In this study, we define a correlation coefficient for NZNSs (CCNZNS) by employing the notions of mean, variance and covariance, and discuss some of its properties. This new approach defines correlation in the interval [–1, 1] similar to classical statistics, and indicates whether the NZNSs are either positively or negatively correlated. Then, two decision models are developed for the NZNS universe. In order to determine the partial known attribute weights, a maximizing optimization technique is derived which is taking into account both the objective and subjective aspects of assessments. To demonstrate the effectiveness of the proposed models, the first model is applied for solving a medical diagnostic problem. Then the performance evaluation process is chosen to demonstrate the application of the second model. Finally, the superior aspects of the developed models over other existing models are presented with a comparison and discussion analysis. The study is concluded with the conclusion part. Show more

Keywords: Neutrosophic Z-number set, correlation coefficient, variance, covariance, decision making

DOI: 10.3233/JIFS-222625

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 215-228, 2023

Authors: Liu, Wei | Liu, Qihan | Ye, Guoju | Zhao, Dafang | Guo, Yating | Shi, Fangfang

Article Type: Research Article

Abstract: The interval rough number rough sets model is the generalization of the classical rough sets. Since the lower approximation condition of interval rough number rough sets model is a full inclusion relation which is too strict to tolerate noisy data, strict conditions increase the possibility of a sample classified into a wrong class. To overcome the above shortcomings, an interval rough number variable precision rough sets model is proposed in this paper, which is combined with interval rough number similarity and the concept of variable precision rough sets. The model introduces the error parameter and can improve the tolerance of …noise data. Then the related properties of the model are also proved. Moreover, we construct a maximal positive domain attribute reduction method based on the proposed model, which can process the data type of interval rough number without discretization. Finally, numerical examples are given to verify the rationality of the model. Show more

Keywords: Similarity, interval rough number, variable precision rough set, attribute reduction

DOI: 10.3233/JIFS-222781

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 229-238, 2023

Authors: Tran, Van Quan

Article Type: Research Article

Abstract: The unconfined compressive strength (Qu) is one of the most important criteria of stabilized soil to design in order to evaluate the effective of soft soil improvement. The unconfined compressive strength of stabilized soil is strongly affected by numerous factors such as the soil properties, the binder content, etc. Machine Learning (ML) approach can take into account these factors to predict the unconfined compressive strength (Qu) with high performance and reliability. The aim of this paper is to select a single ML model to design Qu of stabilized soil containing some chemical stabilizer agents such as lime, cement and bitumen. …In order to build the single ML model, a database is created based on the literature investigation. The database contains 200 data samples, 12 input variables (Liquid limit, Plastic limit, Plasticity index, Linear shrinkage, Clay content, Sand content, Gravel content, Optimum water content, Density of stabilized soil, Lime content, Cement content, Bitumen content) and the output variable Qu. The performance and reliability of ML model are evaluated by the popular validation technique Monte Carlo simulation with aided of three criteria metrics including coefficient of determination R2, Root Mean Square Error (RMSE) and Mean Square Error (MAE). ML model based on Gradient Boosting algorithm is selected as highest performance and highest reliability ML model for designing Qu of stabilized soil. Explanation of feature effects on the unconfined compressive strength Qu of stabilized soil is carried out by Permutation importance, Partial Dependence Plot (PDP 2D) in two dimensions and SHapley Additive exPlanations (SHAP) local value. The ML model proposed in this investigation is single and useful for professional engineers with using the mapping Maximal dry density-Linear shrinkage created by PDP 2D. Show more

Keywords: Machine learning, unconfined compressive strength, stabilized soil, gradient boosting, monte carlo simulation, local SHAP value

DOI: 10.3233/JIFS-222899

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 239-256, 2023

Authors: Hou, Yan-e | Wang, Chunxiao | Wang, Congran | Fan, Gaojuan

Article Type: Research Article

Abstract: Multi-compartment vehicle routing problem (MCVRP) is an extension of the classical capacitated vehicle routing problem where products with different characteristics are transported together in one vehicle with multiple compartments. This paper deals with this problem, whose objective is to minimize the total travel distance while satisfying the capacity and maximum route length constraints. We proposed a hybrid iterated local search metaheuristic (HILS) algorithm to solve it. In the framework of iterated local search, the current solution was improved iteratively by five neighborhood operators. For every obtained neighborhood solution after the local search procedure, a large neighborhood search-based perturbation method was …executed to explore larger solution space and get a better neighborhood solution to take part in the next iteration. In addition, the worse solutions found by the algorithm were accepted by the nondeterministic simulated annealing-based acceptance rule to keep the diversification of solutions. Computation experiments were conducted on 28 benchmark instances and the experimental results demonstrate that our presented algorithm finds 17 new best solutions, which significantly outperforms the existing state-of-the-art MCVRP methods. Show more

Keywords: Multi-compartment vehicle routing problem, hybrid metaheuristic, iterated local search, large neighborhood search, simulated annealing

DOI: 10.3233/JIFS-223404

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 257-268, 2023

Authors: Lei, Deming | Du, Haoyang | Tang, Hongtao

Article Type: Research Article

Abstract: Distributed assembly flow shop scheduling problem (DAFSP) has been extensively considered; however, DAFSP with Pm  → 1 layout, in which m parallel machines are at fabrication stage and one machine is at assembly stage, is seldom handled. In this study, DAFSP with the above layout and transportation time is studied and an imperialist competitive algorithm with cooperation and division (CDICA) is presented to minimize makespan. Feature of the problem is used and a heuristic is applied to produce initial solution. Adaptive assimilation and evolution are executed in the weakest empire and adaptive cooperation is implemented between the winning empire and …the weakest empire in imperialist competition process. Empire division is performed when a given condition is met. Many experiments are conducted. The computational results demonstrate that new strategies are effective and CDICA is a very competitive in solving the considered DAFSP. Show more

Keywords: Assembly scheduling problem, distributed scheduling, imperialist competitive algorithm, cooperation

DOI: 10.3233/JIFS-223929

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 269-284, 2023

Authors: Wang, Ning | Zhu, Ping

Article Type: Research Article

Abstract: The three-way decision model based on linguistic term sets has been extensively investigated since decision makers frequently utilize natural language to evaluate in an actual decision-making process. The existing models require decision makers to select appropriate linguistic terms from a given linguistic term set. However, making such a choice is not always simple, and decision makers occasionally choose words that are related to their own experience. In order to deal with this kind of decision problem, we appeal to the theory of computing with words pioneered by Zadeh and establish a three-way decision model based on computing with words in …this paper. The paper focuses on how to deal with more general linguistic information using the theory of computing with words. Initially, using the concept of computing with words, we translate more broad linguistic information into a linguistic distribution assessment on a balanced linguistic term set in order to better analyze linguistic information. The three-way decision based on computing with words is then discussed. Decision-theoretic rough fuzzy sets take into account the ambiguity of the decision target as a generalization of the classical decision-theoretic rough sets. This is what motivated us to develop a three-way decision based on decision-theoretic rough fuzzy sets using computing with words. Additionally, a fabricated example demonstrates that our three-way decision model is more adaptable in processing linguistic information and can handle more general linguistic information provided by decision makers. Show more

Keywords: Computing with words, Three-way decision, Linguistic distribution assessments, Decision-theoretic rough fuzzy sets, Linguistic term sets

DOI: 10.3233/JIFS-224215

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 285-304, 2023

Authors: Al-Sharqi, Faisal | Romdhini, Mamika Ujianita | Al-Quran, Ashraf

Article Type: Research Article

Abstract: A Q-neutrosophic soft environment is an innovative hybrid tool that combines features of both a Q-neutrosophic set (Q-NS) and a parametric tool “soft set” (SS) in order to manage imprecise and indeterminate situations in various mathematical problems. In this article, we introduce a new algebraic approach called Q-neutrosophic soft matrices (Q-NSMs) to address the issues of two-dimensional (two variables) in a universal set by representing the concept of Q-neutrosophic soft sets (Q-NSSs) in matrices. On Q-NSMs, we define the fundamental set operations and some algebraic operations, i.e., complement, union, intersection, addition, subtraction, multiplication, and scalar multiplication, and prove related properties …of these operations. Moreover, these operations are illustrated via several numerical examples. Then, two algorithms are proposed to tackle group decision making (GDM) problems: The first depends on the score function of Q-NSMs, and the second is based on the aggregation operator of Q-NSMs. Finally, this study is supported by a brief comparison with some relevant previous models. Show more

Keywords: Group decision-making, neutrosophic set, Q-Neutrosophic soft set, Q-Neutrosophic soft matrix, soft set

DOI: 10.3233/JIFS-224552

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 305-321, 2023

Authors: Zhao, Lei | Guo, Junmei | Sun, Kai

Article Type: Research Article

Abstract: Modern industrial processes often have nonlinearity, multivariate, time-delay, and measurement outliers, which make accurate data-driven modeling of key performance indicators difficult. To address these issues, a robust and regularized long short-term memory (LSTM) neural network for soft sensors in complex industrial processes was proposed. First, a conventional LSTM architecture was used as the basic model to deal with nonlinearity and time delay. Thereafter, a novel LSTM loss function that combines the excellent resistance to outliers of Huber M-Loss with the superior model reduction capability of ℓ1 regularization was designed. Subsequently, a backpropagation through time training algorithm for the proposed …LSTM was developed, including the chain derivative calculation and updating formulas. The adaptive moment estimation was applied to perform the gradient update, while the grid search and moving window cross-validation were used to find the optimal hyperparameters. Finally, nonlinear artificial datasets with time series and outliers, as well as an industrial dataset of a desulfurization process, were applied to investigate the performance of the proposed soft sensor. Simulation results show that the proposed algorithm outperforms other state-of-the-art soft sensors in terms of predictive accuracy and training time. The causal relationship of the data-driven soft sensor trained by the proposed algorithm is consistent with the field operation and chemical reactions of the desulfurization process. Show more

Keywords: Soft sensor, backpropagation through time, huber M-Loss, long short-term memory, ℓ1 regularization

DOI: 10.3233/JIFS-224557

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 323-343, 2023

Authors: Jeyalakshmi, P. | Karuppasamy, K.

Article Type: Research Article

Abstract: A signed graph Σ = (G , σ) is a graph with a sign attached to each arc. A subset S of V  (Σ) is called a dominating set of Σ if |N +  (v )  ∩ S | > |N -  (v )  ∩ S | for all v  ∈ V  - S  . A dominating set S  ⊆ V is a connected dominating set of Σ if <S > is connected. The minimum cardinality of a connected dominating set of Σ denoted by γsc , is called the connected domination number of Σ . In this paper, we introduce the connected domination number in a signed graph …Σ and study different bounds and characterization of the connected domination number in a signed graph Σ . Furthermore, we find the best possible upper and lower bounds for γ sc ( Σ ) + γ sc ( Σ α c ) where Σ is connected. Show more

Keywords: Signed graph, dominating set, connected dominating set, connected domination number

DOI: 10.3233/JIFS-223857

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 345-356, 2023

Authors: Reeba Jennifer, R. | Albert Raj, A.

Article Type: Research Article

Abstract: An Intracranial cyst is an abnormal growth of mass in the brain that affects functioning of the nervous system and so an early detection of the lesion enables to avoid adverse effects. The processing unit in the Magnetic Resonance Imaging (MRI) system performs reading the images followed by primary image enhancement to suppress distortions thereby enhancing the feature quality in terms of its intensity, augmenting the resolution by image segmentation, post-processing by thresholding based on grayscale values and performing several morphological operations. With the existing methodologies, extracting the Region Of Interest (ROI) with the overlapping intensity values lead to inaccurate …results. A novel method in which the input image that is anisotropically diffused and blurred is converted into a sharp image. Further, fuzzy partitioning of pixels deployed on Global Thresholding –Clustering Methodology (GT-CM) based segmentation takes 4 clusters into account hence forth seperating the exterior portion of the skull, the border region of the skull, the ventricles which may include the lesion and the noise. Statistical results based on several metrics such as sensitivity, specificity, F measure, Jaccord Index, Dice Coefficient and precision show that the proposed method is far more effective. An accuracy of 99.26% is obtained in exactly locating and extracting the lesion along with its attributes. Show more

Keywords: MRI, image segmentation, ROI, fuzzy, GT-CM

DOI: 10.3233/JIFS-221947

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 357-368, 2023

Authors: Bekhouche, Maamar | Haouassi, Hichem | Bakhouche, Abdelaali | Rahab, Hichem | Mahdaoui, Rafik

Article Type: Research Article

Abstract: Feature Selection (FS) for Sentiment Analysis (SA) becomes a complex problem because of the large-sized learning datasets. However, to reduce the data dimensionality, researchers have focused on FS using swarm intelligence approaches that reflect the best classification performance. Crocodiles Hunting Strategy (CHS), a novel swarm-based meta-heuristic that simulates the crocodiles’ hunting behaviour, has demonstrated excellent optimization results. Hence, in this work, two FS algorithms, i.e., Binary CHS (BCHS) and Improved BCHS (IBCHS) based on original CHS were applied for FS in the SA field. In IBCHS, the opposition-based learning technique is applied in the initialization and displacement phases to enhance …the search space exploration ability of the IBCHS. The two proposed approaches were evaluated using six well-known corpora in the SA area (Semeval-2016, Semeval-2017, Sanders, Stanford, PMD, and MRD). The obtained result showed that IBCHS outperformed BCHS regarding search capability and convergence speed. The comparison results of IBCHS to several recent state-of-the-art approaches show that IBCHS surpassed other approaches in almost all used corpora. The comprehensive results reveal that the use of OBL in BCHS greatly impacts the performance of BCHS by enhancing the diversity of the population and the exploitation ability, which improves the convergence of the IBCHS. Show more

Keywords: Sentiment analysis, Opinion mining, feature selection, swarm-based intelligence, crocodiles hunting strategy optimization algorithm, Opposition-based learning

DOI: 10.3233/JIFS-222192

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 369-389, 2023

Authors: Hatiboglu, Melek | Dayioglu, Habip | İssever, Halim | Ayvaz, Berk

Article Type: Research Article

Abstract: It is difficult to evaluate ergonomic risk factors in occupations with unpredictable tasks, random demands, and variable settings such as emergency medical services (EMS). This study deals with the problem of selecting an ergonomic risk-evaluation method with Pythagorean Fuzzy Sets (PFSs) based Pythagorean Fuzzy AHP (PF-AHP) and Pythagorean Fuzzy WASPAS (PF-WASPAS) methodology. The method selection criteria were obtained by consulting five different anonymous experts on the candidate criteria obtained from the literature review. The final four main criteria and ten sub-criteria were then decided. After the determination of the decision criteria, five experts were asked to evaluate the criteria and …to express their opinions on criteria-alternative scoring by means of a questionnaire for method selection. A two-step method is suggested for the selection of the ergonomic risk-evaluation method. In the first step, PF-AHP is utilized in order to identify the weight of criteria used in the method selection. In the second step, the PF-WASPAS method is proposed in order to OWAS, RULA, and REBA methods. The accuracy and validity of the suggested hybrid model is tested with real data in İstanbul Ambulance Service stations. A sensitivity analysis is carried out to test the reliability of the model. Moreover a comparative analysis is carried out with AHP and Fuzzy AHP methods to identify criteria weights. Study results show that REBA is the most appropriate ergonomic risk-evaluation method in EMS. Show more

Keywords: Ergonomic risk assessment method, Pythagorean fuzzy sets, AHP, WASPAS, emergency medical service

DOI: 10.3233/JIFS-222974

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 391-405, 2023

Authors: Meenakshi, A. | Mythreyi, O.

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-223484

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 407-420, 2023

Authors: Meenakshi, A. | Mythreyi, O. | Bramila, M. | Kannan, A. | Senbagamalar, J.

Article Type: Research Article

Abstract: Neutrosophic graphs deals with more complex, uncertain problems in real-life applications which provides more flexibility and compatibility than Intuitionistic fuzzy graphs. The aim of this paper is to enrich the efficiency of the network in accordance with productivity and quality. Here we develop two Neutrosophic graphs into a fully connected Neutrosophic network using the product of graphs. Such a type of network is formed from individuals with unique aspects in every field of work among them. This study proposes extending the other graph products and forming a single valued Neutrosophic graph to find the efficient productivity in the flow of …information on a single source network of a single valued Neutrosophic network. An Optimal algorithm is proposed and illustrated with an application. Show more

Keywords: Neutrosophic graph, graph operation, domination number, optimal network, score function

DOI: 10.3233/JIFS-223718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 421-433, 2023

Authors: Dong, Qi | Gao, Yongli | Zhang, Wanhong | Chen, Zhipeng | Liu, Qian

Article Type: Research Article

Abstract: Radial distribution system is an important link connecting power supply and users, and its power supply reliability is directly related to users. Radial distribution network reconfiguration can transform the network structure by changing the switching state of the distribution network lines, and achieve the goals of reducing network operational losses, improving power quality, and power supply reliability while meeting various constraints such as radial operation, power supply and demand balance, capacity, and voltage. Radial distribution systems have the characteristics of multiple components and complex structures. How to quickly and accurately evaluate the health performance of radial distribution systems and find …an optimal solution for network reconfiguration are important issues in distribution network analysis. The network health performance evaluation of radial distribution system is classical multiple attributes group decision making (MAGDM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the network health performance evaluation of radial distribution system. In this paper, we extend the classical grey relational analysis (GRA) method to the probabilistic hesitancy fuzzy MAGDM with unknown weight information. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are briefly introduced. Then, the definition of the score values is employed to compute the attribute weights based on the information entropy method. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MAGDM under PHFSs. Finally, a practical case study for network health performance evaluation of radial distribution system is designed to validate the proposed method and some comparative studies are also designed to verify the applicability. Show more

Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets, grey relational analysis method (GRA), information entropy, network health performance evaluation

DOI: 10.3233/JIFS-230028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 435-443, 2023

Authors: Yang, Lan | Wang, Xiaofeng | Ding, Hongsheng | Yang, Yi | Zhao, Xingyu | Pang, Lichao

Article Type: Research Article

Abstract: Constraint satisfaction problems have a wide range of applications in areas such as basic computer theory research and artificial intelligence, and many major studies in industry are not solved directly, but converted into instances of satisfiability problems for solution. Therefore, the solution of the satisfiability problem is a central problem in many important areas in the future. A large number of solution algorithms for this problem are mainly based on completeness algorithms and heuristic algorithms. Intelligent optimization algorithms with heuristic policies run significantly more efficiently on large-scale instances compared to completeness algorithms. This paper compares the principles, implementation steps, and …applications of several major intelligent optimization algorithms in satisfiability problems, analyzes the characteristics of these algorithms, and focuses on the performance in solving satisfiability problems under different constraints. In terms of algorithms, evolutionary algorithms and swarm intelligence algorithms are introduced; in terms of applications, the solution to the satisfiability problem is studied. At the same time, the performance of the listed intelligent optimization algorithms in applications is analyzed in detail in terms of the direction of improvement of the algorithms, advantages and disadvantages and comparison algorithms, respectively, and the future application of intelligent optimization algorithms in satisfiability problems is prospected. Show more

Keywords: Constraint satisfaction problem, satisfiability problem, completeness algorithm, heuristic algorithm, intelligent optimization algorithms

DOI: 10.3233/JIFS-230073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 445-461, 2023

Authors: Sun, Hanjie

Article Type: Research Article

Abstract: With the development of information technology, online learning has become an important way of teaching in colleges and universities. The importance of online learning is particularly prominent, especially during the COVID-19 pandemic. How to improve online learning quality is a common problem faced by educators. Online learning quality is closely related to information presentation form, so it is necessary to study the influence of information presentation form on online learning. Based on the dynamics theory of visual perception form and its operating principle, this study compares the differences in post-test scores, cognitive load and satisfaction between the information dynamics presentation …form and the traditional information presentation form through a two-factor random experiment. The data analysis shows that information presentation form plays a significant role in improving students’ academic performance and reducing cognitive load. To a certain extent, there search proves the effectiveness of the information presentation form based on dynamics theory of visual perception form in promoting online learning. Relevant improvement suggestions are proposed to provide a reference and basis for the in-depth development of online learning and the improvement of online learning quality. Show more

Keywords: Dynamics theory of visual perception form, information presentation form, online learning, associative cues, CLC Number: G434 Document Code: A

DOI: 10.3233/JIFS-230083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 463-475, 2023

Authors: Deva, K. | Mohanaselvi, S.

Article Type: Research Article

Abstract: Picture fuzzy aggregation operators are the standard mathematical tools for the combination of several inputs with respect to attributes into one unique output. The Choquet integral operator has been proven more ideal than traditional aggregation operators in the modelling of interaction phenomena among the attributes in decision-making problems. Firstly, we propose the Choquet integral picture fuzzy Einstein geometric aggregation operator and Choquet integral picture fuzzy Einstein ordered geometric aggregation operator with certain properties of these operators being established. We validate the functioning of the operators with illustrative examples. The proposed operators clearly capture the comprehensive correlative relationships of attributes in …a simpler manner. Furthermore, the algorithm for a multi attribute decision-making problem based on proposed operators is given. The application of the proposed operators was explored to deal with the selection of the best mobile apps for online education. Finally, comparisons are conducted to illustrate the discussion and advantages of the proposed operators. Show more

Keywords: Multi attribute decision-making, picture fuzzy set, choquet integral, aggregation opertaors

DOI: 10.3233/JIFS-230472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 477-490, 2023

Authors: Savitha, S. | Rajiv Kannan, A.

Article Type: Research Article

Abstract: Chronic Kidney Disease (CKD) is a crucial life-threatening condition due to impaired kidney functionality and renal disease. In recent studies, Kidney disorder is considered one of the essential and deadliest issues that threaten patients’ survival with the lack of earlier prediction and classification. The earlier prediction process and the proper diagnosis help delay or stop the chronic disease progression into its final stage, where renal transplantation or dialysis is a known way of saving the patient’s life. Global studies reveal that nearly 10% of the population is affected by Chronic Kidney Disease (CKD), and millions die because of non-affordable treatment. …Early detection of CKD from the biological parameters would save people from this crisis. Machine Learning algorithms are playing a predominant role in disease diagnosis and prognosis. This work generates compound features from CKD indicators by two novel algorithms: Correlation-based Weighted Compound Feature (CWCF) and Feature Significance based Weighted Compound Feature (FSWCF). Any learning algorithm is as good as its features. Hence, the features generated by these algorithms are validated on different machine learning algorithms as a test for generality. The simulation is done in MATLAB 2020a environment where various metrics like prediction accuracy gives superior results compared to multiple other approaches. The accuracy of CWCF over different methods like LR is 97.23%, Gaussian NB is 99%, SVM is 99.18%, and RF is 99.89%, which is substantially higher than the approaches without proper methods feature analysis. The results suggest that generated compound features improve the predictive power of the algorithms. Show more

Keywords: Feature selection, correlation, feature significance, chronic kidney disease, feature projection, mutual information

DOI: 10.3233/JIFS-222401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 491-504, 2023

Authors: Karuppuchamy, V. | Palanivelrajan, S.

Article Type: Research Article

Abstract: Chronic diseases like diabetes, Heart Failure (HF), malignancy, and severe respiratory sickness are the leading cause of mortality around the globe. Dissimilar indications or traits are extremely difficult to identify in HF patients. IoT solutions are becoming increasingly commonplace as smart wearable gadgets become more popular. Sudden heart attacks have a short life expectancy, which is terrible. As a result, a patient monitoring of heart patients based on IoT-centered Machine Learning (ML) is presented to help with HF prediction, and treatment is administered as necessary. Verification, Encryption, and Categorization are the three phases that make up this developed model. Initially, …the datasets from the IoT sensor gadget are gathered by authenticating with a specific hospital through encryption. The patient’s integrated IoT sensor module then transfers sensing information to the cloud. The Improved Blowfish Encryption (IBE) approach is used to protect the sensor data transfer to the cloud. Then the encrypted data is decrypted, and the classification is performed using the Adaptive Fuzzy-Based Long Short-Term Memory with Recurrent Neural Network (AF-LSTM-RNN) algorithm. The results are classed as malignant or benign. It assesses the patient’s cardiac state and sends an alert text to the doctor for treatment. The AF-LSTM-RNN-based HF prediction outperforms the existing techniques. Accuracy, sensitivity, specificity, precision, F-measure and Matthews Correlation Coefficient (MCC) are compared to existing procedures to ensure the planned research is genuine. Using the Origin tool, these metrics are shown as research findings. Show more

Keywords: Heart failure (HF), IoT, machine learning, improved blowfish encryption (IBE), adaptive fuzzy-based long short-term memory with recurrent neural network (AF-LSTM-RNN), origin tool

DOI: 10.3233/JIFS-224298

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 505-520, 2023

Authors: Dhivya, S. | Mohanavalli, S. | Kavitha, S.

Article Type: Research Article

Abstract: Breast cancer can be successfully treated if diagnosed at its earliest, though it is considered as a fatal disease among women. The histopathology slide turned images are the gold standard for tumor diagnosis. However, the manual diagnosis is still tedious due to its structural complexity. With the advent of computer-aided diagnosis, time and computation intensive manual procedure can be managed with the development of an automated classification system. The feature extraction and classification are quite challenging as these images involve complex structures and overlapping nuclei. A novel nuclei-based patch extraction method is proposed for the extraction of non-overlapping nuclei patches …obtained from the breast tumor dataset. An ensemble of pre-trained models is used to extract the discriminating features from the identified and augmented non-overlapping nuclei patches. The discriminative features are further fused using p-norm pooling technique and are classified using a LightGBM classifier with 10-fold cross-validation. The obtained results showed an increase in the overall performance in terms of accuracy, sensitivity, specificity, and precision. The proposed framework yielded an accuracy of 98.3% for binary class classification and 95.1% for multi-class classification on ICIAR 2018 dataset. Show more

Keywords: Breast cancer, histopathology, nuclei-based patches, nuclei feature fusion, LightGBM

DOI: 10.3233/JIFS-222136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 521-535, 2023

Authors: Yang, Biqin | Deng, Yu

Article Type: Research Article

Abstract: Due to the increasingly strengthened role of finance in modern economic development, theoretical research on regional financial competitiveness in the study of regional economic competitiveness becomes very important. For China at this stage, finance is in a period of rapid development, and its role has penetrated into all aspects of social and economic life. Especially after China’s entry into the WTO, the pace of opening up the financial market has been further accelerated, and comprehensive evaluation and analysis of financial competitiveness is of great significance for comprehensively understanding and accurately grasping China’s national conditions, national strength, and international competitiveness, promoting …the long-term growth of China’s financial competitiveness, and the sustainable development of the financial industry. The competitiveness evaluation of regional financial centers is looked as the multiple attribute decision-making (MADM) problem. This paper intends to propose a MADM methodology based on CoCoSo (Combined Compromise Solution) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for sustainable competitiveness evaluation of regional financial centers. At the end of this study, we noticed to a comparison between the proposed IVIF-CoCoSo approach with other existing methods to verify the effectiveness of the algorithm. Show more

Keywords: Multi-attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), IVIF-CoCoSo method, CRITIC method, competitiveness evaluation

DOI: 10.3233/JIFS-222607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 537-547, 2023

Authors: Kadeeja Mole, K.P. | Sameena, Kalathodi

Article Type: Research Article

Abstract: In this work, several operations on fuzzy graphs are introduced: u -product, strong edge product, and k th power. The relationship between the fuzzy chromatic number of resultant fuzzy graphs of operations union, join, and newly developed operations and the fuzzy chromatic number of associated fuzzy graphs is also investigated using fuzzy colouring techniques. The number of captures in a chess puzzle move is calculated using the fuzzy colouring approach.

Keywords: Fuzzy graph, fuzzy chromatic number, operations of fuzzy graphs, strong edge, fuzzy colouring

DOI: 10.3233/JIFS-223263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 549-561, 2023

Authors: Sun, Ke | Zhao, Xiaojie | Huang, He | Yan, Yunyang | Zhang, Haofeng

Article Type: Research Article

Abstract: Zero-Shot Learning (ZSL) has made significant progress driven by deep learning and is being promoted further with the advent of generative models. Despite the success of these methods, the type and number of unseen categories are nailed in the generative models, which makes it challenging to recognize unseen categories in an incremental manner, and the profits of some superior performance algorithms largely arise from their advanced capability of feature extraction, such as Transformers. This paper rigidly follows the assumptions introduced in conventional ZSL and proposes a visual feature filtering method based on a semantic mapping model, namely, filtering visual features …through class-specific filters to effectively remove class-agnostic information. Extensive experiments are conducted on four benchmark datasets and have achieved very competitive performance. Show more

Keywords: Generalized zero-shot learning, class-specific filter, matching score calculation

DOI: 10.3233/JIFS-224297

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 563-576, 2023

Authors: Li, Wenqiao | Wang, Ruijie | Ai, Qisheng | Liu, Qian | Lu, Shu Xian

Article Type: Research Article

Abstract: The compressive strength and slump of concrete have highly nonlinear functions relative to given components. The importance of predicting these properties for researchers is greatly diagnosed in developing constructional technologies. Such capacities should be progressed to decrease the cost of expensive experiments and enhance the measurements’ accuracy. This study aims to develop a Radial Basis Function Neural Network (RBFNN) to model the hardness features of High-Performance Concrete (HPC) mixtures. In this function, optimizing the predicting process via RBFNN will be aimed to be accurate, as the aim of this research, conducted with metaheuristic approaches of Henry gas solubility optimization (HGSO) …and Multiverse Optimizer (MVO). The training phase of models RBHG and RBMV was performed by the dataset of 181 HPC mixtures having fly ash and superplasticizer. Regarding the results of hybrid models, the MVO had more correlation between the predicted and observed compressive strength and slump values than HGSO in the R2 index. The RMSE of RBMV (3.7 mm) was obtained 43.2 percent lower than that of RBHG (5.3 mm) in the appraising slump of HPC samples, while, for compressive strength, RMSE was 3.66 MPa and 5 MPa for RBMV and RBHG respectively. Moreover, to appraise slump flow rates, the R2 correlation rate for RBHG was computed at 96.86 % while 98.25 % for RBMV in the training phase, with a 33.30% difference. Generally, both hybrid models prospered in doing assigned tasks of modeling the hardness properties of HPC samples. Show more

Keywords: Compressive strength, slump flow, multiverse optimization algorithm, concrete hardness, neural network

DOI: 10.3233/JIFS-230005

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 577-591, 2023

Authors: Liu, Lin

Article Type: Research Article

Abstract: With the rapid development of the construction industry, people’s requirements for the construction quality continue to improve, and the supervision and management of the construction project quality has been paid more and more attention. The perfect quality supervision and management system is not only an important guarantee for the whole construction project implementation process, but also provides support for the smooth implementation of the construction project. With the increasing number of high-rise buildings in cities and the increasing difficulty of construction, it has posed great challenges to the construction industry, which also means that the quality supervision and management of …construction projects are facing new challenges. Therefore, the project quality supervision and management department should review the situation, optimize the quality supervision and management work according to the current situation and needs of the construction project development, effectively improve the system guarantee and content optimization, maximize the role of quality supervision and management, and provide assistance for the high-quality and sustainable development of the construction industry. The quality evaluation of construction project is a classical multiple attribute group decision making (MAGDM). In this paper, we extended multi-attributive border approximation area comparison (MABAC) method for MAGDM with Pythagorean 2-tuple linguistic sets (P2TLSs). Firstly, a brief review of the definition of P2TLSs is given. Next, two aggregation operators of P2TLSs are used to fuse overall evaluation information. Moreover, combining traditional MABAC model with P2TLSs, Pythagorean 2-tuple linguistic number MABAC (P2TLN-MABAC) is built with all computing steps depicted in detail. Furthermore, a numerical example related to quality evaluation of construction project is conducted to demonstrate the effectiveness of the proposed method. Finally, some comparisons with P2TLWA and P2TLWG operators are also carried out. Show more

Keywords: Multiple attribute group decision making (MAGDM), Pythagorean 2-tuple linguistic sets (P2TLSs), MABAC method, quality evaluation, construction project

DOI: 10.3233/JIFS-230963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 593-602, 2023

Authors: Wang, Xiaomin | Zhang, Xueyuan | Zhou, Rui

Article Type: Research Article

Abstract: In this paper, we introduce a new hybrid model called probabilistic hesitant N-soft sets by a suitable combination of probability with hesitant N-soft sets, a model that extends hesitant N-soft sets. Our novel concept extends the ability of hesitant N-soft set by considering the occurrence probability of hesitant grades, which could effectively avoid the loss of decision-making information. Moreover, we investigate some basic properties of probabilistic hesitant N-soft sets and construct fundamental operations on them. Then we describe group decision-making methods including TOPSIS, VIKOR, choice value and weighted choice value based on probabilistic hesitant N-soft sets. The corresponding algorithms are …put forward and their validity is proved by examples. Show more

Keywords: N-soft set, hesitant N-soft set, probabilistic hesitant N-soft set, probabilistic hesitant fuzzy set, decision-making

DOI: 10.3233/JIFS-222563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 603-617, 2023

Authors: Haj Seyed Javadi, Mohammadreza | Haj Seyyed Javadi, Hamid | Rahmani, Parisa

Article Type: Research Article

Abstract: The Internet of Things (IoT) is a future-generation networking environment in which distributed smart objects can communicate directly and create a connection between different types of heterogeneous networks. Knowing the accurate localization of IoT-based devices is one of the most challenging issues in expanding the IoT network performance. This paper was done to propose a new fuzzy type2-based scheme to enhance the position accurateness of sensors deployed in the Internet of Things environments. Our proposed scheme is based on the weighted centralized localization strategy, in which the location of unknown nodes calculates using the fuzzy type-2 system. The flow measurement …via the wireless channel to calculate the separation distance between the sensor/anchor nodes is employed as the fuzzy system input. Also, the fuzzy membership functions to better adaptivity of our scheme with lossy IoT environments via learning automata algorithm are tuned. Then, in the proposed method, the fuzzy type-2 calculations are restricted by comparing the received signal strength with a predefined threshold value to extend the network lifetime. The effectiveness of the proposed scheme has been proven through extensive simulation. Based on the simulation results, our scheme, on average, reduced the localization error by 35.9% and 9.5% decreased the energy consumption by 13% and 7.2%, and reduced the convergence rate by 33.1% and 12.37 % compared to the HSPPSO and IMRL methods, respectively. Show more

Keywords: IoT, location, learning automata, fuzzy logic, signal strength

DOI: 10.3233/JIFS-223103

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 619-635, 2023

Authors: Zhao, Xue | Li, Qiaoyan | Xing, Zhiwei | Dai, Xuezhen

Article Type: Research Article

Abstract: Selecting appropriate features can better describe the characteristics and structure of data, which play an important role in further improving models and algorithms whether for supervised or unsupervised learning. In this paper, a new unsupervised feature selection regression model with nonnegative sparse constraints (URNS) is proposed. The algorithm combines nonnegative orthogonal constraint, L 2,1 -norm minimum optimization and spectral clustering. Firstly, the linear regression model between the features and the pseudo labels is given, and the indicator matrix, which describes feature weight, is subject to nonnegative and orthogonal constraints to select better features. Secondly, in order to reduce redundant and …even noisy features, L 2,1 -norm for indicator matrix is added to the regression model for exploring the correlation between pseudo labels and features by the row sparsity property of L 2,1 -norm. Finally, pseudo labels of all samples are established by spectral clustering. In order to solve the regression model efficiently and simply, the method of nonnegative matrix decomposition is used and the complexity of the given algorithm is analysed. Moreover, a large number of experiments and analyses have been carried out on several public datasets to verify the superiority of the given model. Show more

Keywords: Non-negative matrix factorization, L2,1-norm, feature selection, spectral clustering, unsupervised

DOI: 10.3233/JIFS-224132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 637-648, 2023

Authors: Duman, Ekrem

Article Type: Research Article

Abstract: The main function of the internal control department of a bank is to inspect the banking operations to see if they are performed in accordance with the regulations and bank policies. To accomplish this, they pick up a number of operations that are selected randomly or by some rule and, inspect those operations according to some predetermined check lists. If they find any discrepancies where the number of such discrepancies are in the magnitude of several hundreds, they inform the corresponding department (usually bank branches) and ask them for a correction (if it can be done) or an explanation. In …this study, we take up a real-life project carried out under our supervisory where the aim was to develop a set of predictive models that would highlight which operations of the credit department are more likely to bear some problems. This multi-classification problem was very challenging since the number of classes were enormous and some class values were observed only a few times. After providing a detailed description of the problem we attacked, we describe the detailed discussions which in the end made us to develop six different models. For the modeling, we used the logistic regression algorithm as it was preferred by our partner bank. We show that these models have Gini values of 51 per cent on the average which is quite satisfactory as compared to sector practices. We also show that the average lift of the models is 3.32 if the inspectors were to inspect as many credits as the number of actual problematic credits. Show more

Keywords: Predictive modeling, multi-classification, banking, internal control, data mining

DOI: 10.3233/JIFS-223679

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 649-658, 2023

Authors: Liu, Xuwang | Liu, Yanyang | Qi, Wei | Luo, Xinggang

Article Type: Research Article

Abstract: With the rapid development of O2O, offline experience and online purchase have become a method of purchase for more and more customers. Through offline experience, consumers can feel the quality of products directly. Such channel switching behavior of consumers will produce a “showroom” effect and affect the return rate of online channels. This study adopts the multinomial logit model to maximize profits by considering the difference in quality between online and offline products, quality defects, and offline service. Then, a pricing decision model is developed to analyze the influence of returning goods due to quality problems on the retailers’ optimal …pricing and profit. The result shows that retailers can obtain the optimal profit when the offline service is maintained at a certain level. As the return rate increases, the optimal pricing increases, but the maximum profit decreases. The optimal pricing decreases with the increase in online product quality, but the maximum profit increases accordingly. In the omni-channel environment, customers can freely switch between channels according to utility and preference when purchasing products. Based on customer returns, retailers can dynamically adjust their service, control product quality, and set optimal product pricing, thus achieving maximum profits. This study can provide a theoretical basis and decision support for omni-channel retailers in platform operation and revenue management. Show more

Keywords: Channel switching behavior, return behavior, omni-channel marketing, multinomial logit model, product pricing

DOI: 10.3233/JIFS-230078

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 659-673, 2023

Authors: Lin, Haofeng | Ullah, Inam | Abbas, Syed Zaheer | Shakeel, Muhammad | Ali, Asad

Article Type: Research Article

Abstract: To deal with the ambiguity in real-world problems, researchers strive to obtain extensions to classical set theory. They introduced ideas like fuzzy set theory, spherical, intuitionistic, and Pythagorean fuzzy sets. In comparison to fuzzy sets, spherical fuzzy sets are more realistic at handling uncertainty. Fundamentals are classified in Spherical Fuzzy Set according to an attribute, and each feature has a variety of criteria. In this study, we have created a new extended algebraic structure called Confidence Spherical Fuzzy Aggregation Operators by applying the idea of Confidence Levels to the already-existing Spherical Fuzzy Aggregation Operators. We have created a Confidence Spherical …Fuzzy Aggregation Operators-based end-product. We demonstrated various intriguing characteristics of Confidence Spherical Fuzzy Aggregation Operators, including operational laws. The study is validated by addressing the decision-making processes. Show more

Keywords: Spherical fuzzy numbers, confidence level, operational laws, aggregation operators, decision-making

DOI: 10.3233/JIFS-220102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 675-686, 2023

Authors: Richard, Amala S. | Jose Parvin Praveena, N. | Rajkumar, A.

Article Type: Research Article

Abstract: This research paper elucidates the significant role of Replacement problem in reliability optimization problems. Ambiguity and indeterminacy act as a plight in scheduling maintenance problems. When there is a need for replacement the devices of components work under the circumstances of the problem and the sustentation characteristics to reinstitute or restore the decrepit components of the systems. There is a vagueness associated with the elements performing intervals, erroneous, following assessment period create a new task in adjudicating optimal constituents’ distribution where it assessing future task effectively. In this paper, the group replacement model is solved using a special single valued …octagonal Neutrosophic number. The formula for the De-Neutrosophication of the Octagonal Neutrosophic number is deduced by using the area removal method. MATLAB code is used in De-Neutrosophication and also delineating this effective work. The MATLAB program is being used in the replacement problem to find the optimal year of replacement. A numerical illustration is used for validating the replacement model to determine its persuasiveness. This replacement problem using MATLAB has not been initiated by any researchers. Analytically, the time consumption for this method is less and very effective when compared with other methods. A comparative analysis has also been conducted using SVNN. Show more

Keywords: Neutrosophic number, replacement problem, Matlab, area removal method

DOI: 10.3233/JIFS-221567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 687-698, 2023

Authors: Bi, Shunjie | Wu, Zhiyong | Gao, Peng | Ding, Hangqi

Article Type: Research Article

Abstract: Evolutionary multitasking algorithms (EMT) study how to solve multiple optimization tasks simultaneously by evolutionary computation, and investigate how knowledge sharing can accelerate the convergence of individual tasks, meaning that useful knowledge gained in solving one task can be used to solve other tasks. However, as the evolutionary search continues, the learnability among tasks may decrease, leading to a decrease in the efficiency of knowledge transfer and affecting the population evolution. To solve this problem, a new multifactorial evolutionary algorithm (MFEA-VOM) is proposed in this paper, which applies to three strategies, namely, implicit conversion strategy, opposition matrix strategy, and regulatory gene …fusion strategy. The implicit conversion strategy is applied to minimize the threat of negative knowledge migration and reduce the impact caused by negative knowledge migration. The proposed opposition matrix strategy explores more unknown areas of the population and improves the exploration ability of the population by further exploring and utilizing the unified search space, transforming the parent individuals into an appropriate task through mapping relationships, and reducing the gap between tasks. The proposed regulatory gene fusion strategy is applied to the reproduction of individuals to produce better individuals applicable to the task, submitting the efficiency of knowledge transfer. Through a comprehensive experimental analysis of the EMT optimization problem, the experimental results demonstrate the better performance of MFEA-VOM compared to other EMT algorithms. Show more

Keywords: Evolutionary multitasking, knowledge transfer, opposition matrix, implicit conversion, regulatory gene fusion

DOI: 10.3233/JIFS-222267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 699-718, 2023

Authors: Gu, Ming | Li, Dong | Gong, Lanlan | Liu, Jia | Liu, Shulin

Article Type: Research Article

Abstract: The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by …self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions. Show more

Keywords: Artificial immune algorithm, negative selection algorithm, anomaly detection, hyper-rectangle detectors, artificial intelligence

DOI: 10.3233/JIFS-222994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 719-730, 2023

Authors: Jain, Vipin | Kashyap, Kanchan Lata

Article Type: Research Article

Abstract: COVID-19 epidemic is one of the worst disaster which affected people worldwide. It has impacted whole civilization physically, monetarily, and also emotionally. Sentiment analysis is an important step to handle pandemic effectively. In this work, systematic literature review of sentiment analysis of Indian population towards COVID-19 and its vaccination is presented. Recent exiting works are considered from four primary databases including ACM, Web of Science, IEEE Explore, and Scopus. Total 40 publications from January 2020 to August 2022 are selected for systematic review after applying inclusion and exclusion algorithm. Existing works are analyzed in terms of various challenges encountered by …the existing authors with collected datasets. It is analyzed that mainly three techniques namely lexical, machine and deep learning are used by various authors for sentiment analysis. Performance of various applied techniques are comparative analyzed. Direction of future research works with recommendations are highlighted. Show more

Keywords: Sentiment analysis, COVID-19, opinion mining, neural networks, text classification

DOI: 10.3233/JIFS-224086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 731-742, 2023

Authors: Dey, Aniruddha | Ghosh, Manas | Chowdhury, Shiladitya | Kahali, Sayan

Article Type: Research Article

Abstract: This paper presents a novel decision-making method for face recognition where the features were extracted from the original image fused with its corresponding true and partial diagonal images. To extract features, we adopted the generalized two-dimensional FLD (G2DFLD) feature extraction technique. The feature vectors from a test image are given as input to neural network-based classifier. It is trained with the feature vectors of original image and diagonally fused images and thereby the merit weights with respect to different classes were generated. To address the factors that affect the face recognition accuracy and uncertainty related to raw biometric data, a …fuzzy score for each of the classes is generated by treating a type-2 fuzzy set. This type-2 fuzzy set is formed by the feature vectors of both the diagonally fused training samples and the test image of the respective classes. A concluding score for each of the classes under consideration is computed by fusing complemented merit weight with the complemented fuzzy score. These class-wise concluding scores are considered in the face recognition process. In this study, the well-known face databases (AT&T, UMIST and CMU-PIE) are used to evaluate the performance of the proposed method. The experimental results illustrate the fact that the proposed method has exhibited superior classification precision as compared with other state-of-art methods. Our T2FMFImg F method achieves highest face recognition accuracies of 99.41%, 98.36% and 89.80% in case of AT&T, UMIST and CMU-PIE (with expression), respectively while for CMU-PIE (with Light) the highest recognition accuracy is 97.957%. In addition to it, the presented method is quite successful in fusing and classifying textural information from the original and partial diagonal images by integrating them with type-2 fuzzy set-based treatment. Show more

Keywords: Image-level fusion, confidence factor, face recognition, fuzzy type-2

DOI: 10.3233/JIFS-224288

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 743-761, 2023

Authors: Badshah, Noor | Arif, Muhammad | Khan, Tufail Ahmad | Ullah, Asmat | Rabbani, Hena | Atta, Hadia | Begum, Nasra

Article Type: Research Article

Abstract: Segmenting outdoor images in the presence of haze, fog or smog (which fades the colors and diminishes the contrast of the observed objects) has been a challenging task in image processing with several important applications. In this paper, we propose a new fractional-order variational model that will be able to de-haze and segment a given image simultaneously. The proposed method incorporates the atmospheric veil estimation based on the dark channel prior (DCP). This transmission map can reduce significantly the edge artifacts and enhance estimation precision in the resulting image. The transmission map is then changed over to the high-quality depth …map, with which the new fractional-order variational model can be framed to look for the haze free segmenting image for both grey and color outdoor images. An explicit gradient descent scheme is employed to find efficiently the minimizer of the proposed energy functional. Experimental tests on real world scenes show that the proposed method can jointly de-haze and segment hazy or foggy images effectively and efficiently. Show more

Keywords: Foggy or hazy images, fractional-order total variation, image de-hazing, image segmentation, inhomogeneous intensity, object detection

DOI: 10.3233/JIFS-230385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 763-781, 2023

Authors: Li, Dongping | Shen, Shikai | Yang, Yingchun | He, Jun | Shen, Haoru

Article Type: Research Article

Abstract: In order to solve the problems of inaccurate trajectory time prediction and poor privacy protection of dataset publishing mechanism, the study adds deep learning models into the trajectory time prediction model and designs the SLDeep model. Its performance is compared with LRD, STTM and DeepTTE models for experiments, and the results show that the SLDeep model. The lowest mean absolute error value was 116.357, indicating that it outperformed the other models. The study designed the Travelet publishing mechanism by incorporating differential privacy methods into the publishing mechanism, and compared it with Li’s and Hua’s publishing mechanisms for experiments. The results …show that the mutual information index value of Travelet publishing mechanism is 0.06, which is better than Li’s and Hua’s publishing mechanisms. The experimental results show that the performance of the trajectory time prediction model incorporating deep learning and the dataset publishing mechanism incorporating differential privacy methods has been greatly improved, which can provide new ideas to obtain a more accurate and all-round trajectory big data management system. Show more

Keywords: Deep learning, differential privacy, trajectory time prediction, release mechanism

DOI: 10.3233/JIFS-231210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 783-795, 2023

Authors: Chola Raja, K. | Kannimuthu, S.

Article Type: Research Article

Abstract: Autism Spectrum Disorder (ASD) is a complicated neurodevelopment disorder that is becoming more common day by day around the world. The literature that uses machine learning (ML) and deep learning (DL) approaches gained interest due to their ability to increase the accuracy of diagnosing disorders and reduce the physician’s workload. These artificial intelligence-based applications can learn and detect patterns automatically through the collection of data. ML approaches are used in various applications where the traditional algorithms have failed to obtain better results. The major advantage of the ML algorithm is its ability to produce consistent and better performance predictions with …the help of non-linear and complex relationships among the features. In this paper, deep learning with a meta-heuristic (MH) approach is proposed to perform the feature extraction and feature selection processes. The proposed feature selection phase has two sub-phases, such as DL-based feature extraction and MH-based feature selection. The effective convolutional neural network (CNN) model is implemented to extract the core features that will learn the relevant data representation in a lower-dimensional space. The hybrid meta-heuristic algorithm called Seagull-Elephant Herding Optimization Algorithm (SEHOA) is used to select the most relevant and important features from the CNN extracted features. Autism disorder patients are identified using long-term short-term memory as a classifier. This will detect the ASD using the fMRI image dataset ABIDE (Autism Brain Imaging Data Exchange) and obtain promising results. There are five evaluation metrics such as accuracy, precision, recall, f1-score, and area under the curve (AUC) used. The validated results show that the proposed model performed better, with an accuracy of 98.6%. Show more

Keywords: Autism spectrum disorder, Meta-Heuristic, Deep learning, Convolution neural network, seagull and elephant herding optimization, LSTM, fMRI.

DOI: 10.3233/JIFS-223694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 797-807, 2023

Authors: Wu, Chong | Mao, Zengli | Zhan, Baoqiang | Wu, Yahui

Article Type: Research Article

Abstract: The ocean plays a crucial role in human society’s survival and development. While China’s marine economy has grown rapidly in recent years, it has also led to serious problems inhibiting ecosystem sustainability. This paper proposes high-quality development of the marine economy and combines the improved entropy value method, fuzzy hierarchical analysis method (FAHP), and data envelopment analysis (DEA) method to establish a quadratic relative evaluation model. A two-layer comprehensive index framework with 19 indicators is built to measure various aspects of the marine economy, including innovation, coordination, green, openness, and sharing. Empirical analysis conducted on 11 coastal provinces in China …using data mainly collected from the Chinese Statistical Yearbook reveals significant spatial patchiness in the high-quality development level of the marine economy. This discrepancy is largely due to differences in geographical locations, resources, and government policies. The study analyzes four benchmark provinces of high-quality development and summarizes their experiences. The paper concludes by providing suggestions and implications to support government decision-making. Show more

Keywords: Marine economy, high-quality, DEA, quadratic relative evaluation

DOI: 10.3233/JIFS-224173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 809-830, 2023

Authors: Sathish Kumar, P.J. | Ponnusamy, Muruganantham | Radhika, R. | Dhurgadevi, M.

Article Type: Research Article

Abstract: Underwater wireless sensor networks (UWSNs) are designed to perform cooperative monitoring and data collection tasks by combining several elements, such as automobiles and sensors located in a particular acoustic area. Several studies have been carried out to improve energy efficiency and routing reliability. However, UWSN faces several challenges, such as high ocean interference and noise, long transmission delays, limited bandwidth, and low sensor node battery energy. In this work, a novel underwater clustering-based hybrid routing protocol (UC-HRP) has been proposed to address these issues. The overall process is carried out in three phases. In the first phase, the fuzzy-ELM approach …is used to initialize the cluster based on parameters such as Doppler spread, path loss, noise, and multipath. In the second phase, the cluster head is selected using Cluster Centre Cluster Head Selection (C3HS) based on Link quality, distance, node degree, and residual energy. In the third phase, Hybrid Artificial Bee Colony (HABC) algorithm is used for selecting an optimal route based on the parameters such as reliability, bandwidth effectiveness, average path loss, and average transmission latency. The performance of the proposed UC-HRP method is evaluated using a variety of parameters, including the network lifetime, packet delivery ratio, alive nodes, and energy consumption. The proposed technique improves the network lifetime by 14.03%, 16.25%, and 18.34% better than ACUN, ANC-UWSNS, and MERP respectively. Show more

Keywords: Underwater wireless sensor networks, fuzzy extreme learning machine, cluster centre cluster head selection, hybrid artificial bee colony algorithm

DOI: 10.3233/JIFS-230172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 831-843, 2023

Authors: Chen, Guomin | Jin, Yingwei | Cheng, Shili | Jiao, Huihua

Article Type: Research Article

Abstract: Fuel Cells are novel devices that have been proposed as new power generation systems. The advantages of solid oxide fuel cells are higher efficiency, higher stability, fuel flexibility, lower emissions, and generally lower cost. In the present study, the fuzzy model is employed to build the model of the solid oxide fuel cell considering various sputtering power, thickness of electrolyte, and temperatures of cell. The maximum iterations for the adaptive neuro-fuzzy inference model was considered 50 iterations. About 3500 samples were applied for the training process, and almost 900 samples were utilized for the testing. After modeling process, the genetic …algorithm, particle swarm, simulated annealing, and hybrid firefly-particle swarm optimizers are applied to achieve the optimum value of current and power densities. The results showed that proposed fuzzy model could approximate the model the system with a good agreement with experimental data. Additionally, the obtained data confirm the accuracy, high convergence speed, and robustness of the proposed hybrid optimizer compared to three efficient optimization algorithms. Accordingly, the correlation factor for the proposed fuzzy model for the training and testing dataset was obtained to be 0.9298 and 0.9289, correspondingly. Show more

Keywords: Performance improvement of SOFC, adaptive neuro-fuzzy inference model, various optimization algorithms, experimental dataset accuracy, comparative study

DOI: 10.3233/JIFS-221125

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 845-862, 2023

Authors: Li, Lin

Article Type: Research Article

Abstract: In recent years, the use of Gas Turbines (GTs) to generate electricity has grown exponentially. Therefore, for the optimal performance of gas power plants, a lot of research has been done on modeling different parts of GTs, estimating model parameters, and controlling them. But most of the available methods are not accurate enough, like most linear methods, or are model-based, which require an accurate model of the system (like most nonlinear methods), or there is a constant need to adjust the controller parameters. To address these shortcomings, this study uses a new hybrid method including the brain emotional learning-based intelligent …controller, the nonlinear multivariate method in the form of feedback linearization, and an adaptive control method of mode predictive reference model used to quickly control the GT. The Rowen model is used to simulate the nonlinear model of the GT. Owing to the influence of exhaust temperature on the speed of GT and the multivariate system model, nonlinear multivariate controller design is considered. First, the adaptive control method of the state-predictive reference model for a multi-output multi-input system, in general, is presented, and then, the proposed method for a GT with real dynamic values is implemented. The simulation results show the ability of the proposed controller to control the GT. In order to prove the efficiency of the proposed method, the obtained results are compared with the PID industrial controller method and the classical reference model method. Show more

Keywords: GTs, speed control, brain emotional learning based intelligent controller, feedback linearization, dynamic simulation

DOI: 10.3233/JIFS-221408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 863-876, 2023

Authors: Ammasaikutti, Pradeep | Palanisamy, Kannan

Article Type: Research Article

Abstract: A single phase Soft Switching-Solid State Transformer (SS-SST) design is proposed with H-bridge topology as an alternative solution to fulfil the demand of low (or) medium grid power applications. A medium/low frequency transformers fed with H-bridge circuit are incorporate without DC-voltage link, and it’s provided sinusoidal output voltage into the grid. An optimization of Cuckoo Search Firefly (CSF) algorithm was proposed in this research to find optimum switching angle and duty cycle in bridge circuit unit. At present optimum grid power is achieved a maximum efficiency of medium/low power frequency with the help of proposed SS-SST (MS4T) model. For proposed …design is used to electric aircraft, ship power systems, battery energy storage systems (BESS) and fast charging electric vehicles (EV). Which are appealing the networks of medium-voltage DC (MVDC). Proposed MS4T design is based on soft-switching transformer with low conduction loss, low EMI and high efficiency via H-bridge converter circuit. The capacitor voltage balancing control between cascade module and design of the component including a medium level voltage frequency transformer that is implement a 1 kV to 0.25 kV MS4T described. Therefore, the efficacy of the present investigations are established with MATLAB platform. The medium voltage Micro Grid (MG) output is estimated under different operation load conditions. A simulation result of the grid power is measured minimum harmonics level by using optimum switching angle, switching frequency and duty cycle arrangements. Show more

Keywords: Soft switching-solid state transformer, cuckoo search firefly algorithm, H-bridge circuit, medium level voltage, grid

DOI: 10.3233/JIFS-224393

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 877-890, 2023

Authors: Yang, Wendong | Wang, Jingyi | Yang, Sibo | Zhang, Kai

Article Type: Research Article

Abstract: Short-term load prediction has always played an increasingly important part in power system administration, load dispatch, and energy transfer scheduling. However, how to build a novel model to improve the accuracy of load forecasts is not only an extremely challenging problem but also a concerning problem for the power market. Specifically, the individual model pays no attention to the significance of data selection, data preprocessing, and model optimization. So these models cannot always satisfy the time series forecasting’s requirements. With these above-mentioned ignored factors considered, to enhance prediction accuracy and reduce computation complexity, in this study, a novel and robust …method were proposed for multi-step forecasting, which combines the power of data selection, data preprocessing, artificial neural network, rolling mechanism, and artificial intelligence optimization algorithm. Case studies of electricity power data from New South Wales, Australia, are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed model has significantly increased the accuracy of load prediction in all quarters. As a result, the proposed method not only is simple, but also capable of achieving significant improvement as compared with the other forecasting models, and can be an effective tool for power load forecasting. Show more

Keywords: Short-term load prediction, data selection, data preprocessing, optimization, forecasting

DOI: 10.3233/JIFS-224567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 891-909, 2023