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

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