Journal of Intelligent & Fuzzy Systems - Volume 44, issue 6

Authors: Yu, Ming | Lin, Xiaoqing | Liu, Yi | Guo, Yingchun

Article Type: Research Article

Abstract: Existing saliency detection methods have achieved great progress in extracting multi-level features, however it is a challenging problem to catch accurate long-range dependencies that can enhance the accuracy of semantic information. To address this, a Transformer-based multi-scale attention and boundary enhancement with long-range dependency (MSBE) network is proposed in this paper. A multi-scale attention enhancement module (MSAEM) is designed to reduce the redundant or noisy features and generate a high-quality feature representation by integrating multiple attentional features with diverse perspectives. The high-quality features are then fed into the triple Transformer encoder embedding module (TEM) to enhance high-level semantic features by …learning long-range dependencies across layers. In the decoder part, a cross-layer feature fusion module (CLFFM) and boundary enhancement module (BEM) are designed to improve the effect of feature fusion and get accurate prediction results. Extensive experiments on six challenging public datasets demonstrate that the proposed method achieves competitive performance. Show more

Keywords: Salient object detection, long-range dependencies, transformer encoder, cross-layer feature fusion, boundary enhancement module

DOI: 10.3233/JIFS-223726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8957-8969, 2023

Authors: Belal, Mohamad Mulham | Sundaram, Divya Meena

Article Type: Research Article

Abstract: The security defenses that are not comparable to sophisticated adversary tools, let the cloud as an open environment for attacks and intrusions. In this paper, an intelligent protection framework for intrusion detection in a cloud computing environment based on a covariance matrix self-adaptation evolution strategy (CMSA-ES) and multi-criteria decision-making (MCDM) is proposed. The proposed framework constructs an optimal intrusion detector by using CMSA-ES algorithm which adjusts the best parameter set for the attack detector. Moreover, the proposed framework uses a MEREC-VIKOR, a hybrid standardized evaluation technique. MEREC-VIKOR generates the own performance metrics (S, R, and Q) of the proposed framework …which is a combination of multi-conflicting criteria. The proposed framework is evaluated for attack detection by using CICIDS 2017 dataset. The experiments show that the proposed framework can detect cloud attacks accurately with low S (utility), R (regret), and Q (integration between S and R). The proposed framework is analyzed with respect to several evolutionary algorithms such as GA, IGASAA, and CMA-ES. The performance analysis demonstrates that the proposed framework that depends on CMSA-ES converges faster than the other evolutionary algorithms such as GA, IGASAA, and CMA-ES. The outcomes also demonstrate that the proposed model is comparable to the state-of-the-art techniques. Show more

Keywords: Multi-criteria decision-making, MEREC, VIKOR, CMSA-ES, intrusion detection system, security

DOI: 10.3233/JIFS-224135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8971-9001, 2023

Authors: Zhang, Haibo

Article Type: Research Article

Abstract: For a long time, the level of endurance quality of our male basketball athletes is not high, and there is a gap with the strongest countries in Europe and America. The former head coach of Chinese men’s basketball team diagnosed the biggest problem of Chinese men’s basketball team and Chinese youth men’s basketball team is the poor quality of endurance. It is especially important to strengthen the endurance training of our basketball players and improve their endurance level. However, from the current situation, the teams in the training due to the lack of standards for endurance quality training has led …to a great blindness in endurance quality training. The endurance quality level evaluation of young male basketball players is a classic multiple attribute group decision making (MAGDM) issue with vague, inconsistent, and indeterminate information. The 2-tuple linguistic neutrosophic sets (2TLNSs) is an appropriate form to express the indeterminate decision-making information in the endurance quality level evaluation of young male basketball players. Therefore, in this paper, the 2-tuple linguistic neutrosophic numbers CLVA (2TLNN-CLVA) is built based on traditional close value (CLVA) method and applies it to evaluate the endurance quality level of young male basketball players. Finally, a numerical example for evaluating the endurance quality level of young male basketball players has been given and some decision comparisons are also conducted to further illustrate the advantages of the 2TLNN-CLVA method. Show more

Keywords: Multiple attribute group decision making (MAGDM) problems, 2-tuple linguistic neutrosophic sets (2TLNSs), CLVA method, endurance quality level, basketball players

DOI: 10.3233/JIFS-224327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9003-9014, 2023

Authors: Yao, Zhigang | Ran, Hui

Article Type: Research Article

Abstract: At present, the basic pension insurance system for urban and rural residents in China has played a positive role in guaranteeing the basic life of the elderly in urban and rural areas. At present, the basic pension insurance system for urban and rural residents is not perfect, and there is still a great lag in the formulation of cross-system and cross-regional policies. There are differences in treatment between groups, between regions and between urban and rural areas. The coverage is not comprehensive enough and there are still some people who are not included in the basic protection system, etc. People …urgently need a social pension insurance system that can provide reliable and sustainable protection in their old age. The operational efficiency evaluation of urban and rural residents’ basic pension insurance systems is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers grey relational analysis (TFNN-GRA) method is built based on the traditional grey relational analysis (GRA) and triangular fuzzy neutrosophic sets (TFNSs). Finally, a numerical example for operational efficiency evaluation of urban and rural residents’ basic pension insurance systems has been given and some comparisons are used to illustrate advantages of 2TLNN-GRA method. Show more

Keywords: Multiple attribute decision making (MAGDM) problems, triangular fuzzy neutrosophic sets (TFNSs), GRA method, operational efficiency evaluation

DOI: 10.3233/JIFS-221631

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9015-9026, 2023

Authors: Premananthan, G. | Nagaraj, B. | Jaya, J.

Article Type: Research Article

Abstract: In recent times, ML algorithms that plays a significant role right from drug discovery to clinical decision making. The recent advances in DL technologies contribute towards improved performance for carrying out computer aided medical image analysis and disease diagnosis. The key benefit of AI in processing of medical big data offers spectacular insights into the hierarchal relationships that exist among data which can be algorithmically explored thus replacing the tedious manual processes to extract and localize specific areas of interests in medical images thus considerably changing the way medicine has been practiced so far. In bio medical related clinical applications, …there is a constant demand pertaining the research and development with respect to deploying AI as a mainstream tool to perform several medical imaging activities like analysis, diagnosis, segmentation as well as classification. The increased usage of electronic health records and medical images being its integral component the need for appropriate and efficient AI assisted medical image analysis system that takes care of accurate and automated decision making could be of great help to radiologists and medical practitioners. Molecular image analysis is a dynamic field that makes use of ML and DL algorithms that utilizes labeled and structured information which also proves to be helpful to the patients as they serve as an initial interface before further diagnosis and treatments. Thus our research aims to offer a novel and efficient AI based medical analysis system that can assist clinical practitioners to focus on enhancing the disease diagnosis through DL based medical image analysis and decision making. In addition, we also address specific challenges related to disease diagnosis and propose novel GAN model for improved diagnosis and implementation. Our proposed technique can also be generalized to generate synthetic data for further issues related to molecular image analysis in the field of medicine and help towards building a better disease diagnosis model. Show more

Keywords: Artificial Intelligence (AI), Deep learning (DL), electronic health records (EHR), Generative Adversarial networks (GAN), medical image analysis, Machine learning (ML).

DOI: 10.3233/JIFS-223354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9027-9037, 2023

Authors: He, Qiang | Wang, Guanqun | Huo, Lianzhi | Wang, Hengyou | Zhang, Changlun

Article Type: Research Article

Abstract: Multivariate time series anomaly detection has made significant progress and has been studied in many fields. One of the difficulties in time-series data analysis is the complex nonlinear dependencies between multiple time steps and multiple variables. Therefore, detecting anomalies in these data is challenging. Although many studies used classical attention mechanisms to model the temporal patterns of data, few have combined multiple attention mechanisms and analyzed the data’s temporal characteristics and feature correlations. Therefore, we propose an autocorrelation and attention mechanism-based anomaly detection (ACAM-AD) framework that combines an autocorrelation model based on the Autoformer model, which is superior to the …self-attention mechanism, a multi-head graph attention network, and a dot-product attention mechanism to model the complex dependencies of data considering temporal and feature dimensions. The autoregressive model is parallelized with the neural network, and a sparse autocorrelation mechanism and sparse graph attention network are used to reduce model complexity. Experiments on public datasets show that the model is effective and performs better than the baseline model. Show more

Keywords: Multivariate time series, anomaly detection, autocorrelation, multi-head graph attention network

DOI: 10.3233/JIFS-224416

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9039-9051, 2023

Authors: Yang, Guangfen | Zhang, Hui

Article Type: Research Article

Abstract: Owing to the lack of information, it is more realistic that the sum of probabilities is less than or equal to one in the probabilistic hesitant fuzzy elements (P-HFEs). Probabilistic-normalization method and cardinal-normalization method are common processing methods for the P-HFEs with incomplete information. However, the existed probabilistic-normalization method of sharing the remaining probabilities will lose information and change the information integrity of the P-HFEs. The first existed cardinal-normalization method of adding maximum or minimum membership degree with probability zero are influenced by the subjectivity of the decision makers. And the second existed cardinal-normalization method named as reconciliation method only …applicable to the P-HFEs with complete information. Aiming at solving those shortcomings, we propose a possibility degree method based on a novel cardinal-normalization method for the sake of comparing the P-HFEs in pairs. In the process of comparison, the information integrity remains unchanged. Then, we propose a multi-criteria decision making (MCDM) problem, where the attribute weight is determined by entropy measures of the integration results. Finally, an application case in green logistics area is given for the sake of illustrating the efficiency of the proposed method, where the evaluation values are given in the P-HFEs form with incomplete information. Numerical and theoretical results show that a MCDM problem based on the proposed cardinal-normalization method and possibility degree method have a wide range of application. Show more

Keywords: Probabilistic hesitant fuzzy element, possibility degree method, entropy measures, reconciliation method, the identical membership method

DOI: 10.3233/JIFS-222733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9053-9072, 2023

Authors: Ding, Ji-Feng | Weng, Ju-Hui | Chou, Chien-Chang

Article Type: Research Article

Abstract: Evaluating the factors affecting customer value in department stores will shed light on the motivations of customers when choosing department stores, which will help department stores to improve their business performance and competitiveness. This paper applies the fuzzy Analytic Hierarchy Process (AHP) method to empirically analyze the determinants of customer value at department stores in Taiwan. This study first found the major factors influencing customer value at department stores in Taiwan through a review of the literature and expert interviews, and these factors consisted of four evaluation dimensions and 20 evaluation criteria. An empirical investigation was then conducted through an …AHP expert questionnaire survey. The main findings of this paper were as follows: (1) “Physical environment” was the most important evaluation dimension for customer value at department stores in Taiwan. (2) The four leading factors influencing customer value in department stores were “roomy and comfortable space,” “responsive customer service,” “planning of lines of movement at counters,” and “parking area and facilities.” This study also performed further discussion of the four evaluation criteria as a reference for department stores that wish to raise their competitiveness. Show more

Keywords: Customer value, determinant, department store, fuzzy, analytic hierarchy process (AHP)

DOI: 10.3233/JIFS-222175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9073-9089, 2023

Authors: Lian, Lian

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-222395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9091-9107, 2023

Authors: Karthigha, M. | Latha, L.

Article Type: Research Article

Abstract: Industrial Control Systems (ICS) are susceptible to threats or attacks, and even minor changes or manipulation could cause major damage to industrial operations. Industrial control system cybersecurity is vital owing to the severe negative effects it could have on the economy, the environment, people, and politics. Therefore, it’s also crucial to design intrusion detection systems for industrial control systems. In this paper, an efficient intrusion detection system with clustered ensemble feature selection and a Multi-Level Modified Gated Recurrent Unit (M-GRU) classification model is proposed. This intrusion detection system with a general framework for clustered ensemble feature ranking approach is proposed …to effectively find the best feature subset in network packet traffic data. The features designated are fed into a multi class classification algorithm Multi-Level Modified Gated Recurrent Unit (M-GRU) to efficiently detect the cyberattacks. Evaluation criteria including precision, accuracy, recall and F1 score are assessed and compared to other cutting-edge algorithms to assess the performance of the proposed model. The proposed model attained an average accuracy of 98.21 %. Results show that the suggested model increased the attack detection accuracy by an average of 5.935% and 0.116% when compared to the Gated Recurrent Unit, Long Short Term Memory, random forest and naïve bayes models. Show more

Keywords: Industrial control system, intrusion detection, ensemble feature selection, classification, gated recurrent unit

DOI: 10.3233/JIFS-222643

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9109-9127, 2023