Journal of Intelligent & Fuzzy Systems - Volume 47, issue 1-2

Authors: Qiu, Laixiang | Zhou, Wang | Tian, Ying | Ul Haq, Amin | Ahmad, Sultan

Article Type: Research Article

Abstract: Recommender systems aim to filter information effectively and recommend useful sources to match users’ requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect …trust relationship. Additionally, it’s certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. Show more

Keywords: Recommender system, LDA model, matrix factorization, social circle

DOI: 10.3233/JIFS-231264

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 1-13, 2024

Authors: Li, Dengao | Feng, Ran | Wu, Fanming | Zhao, Jinhua | Zhao, Jumin

Article Type: Research Article

Abstract: In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only …ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations. Show more

Keywords: Visual odometry, SLAM, LSTM, FLANN_PSC-RANSAC

DOI: 10.3233/JIFS-232279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 15-28, 2024

Authors: Yu, Jing | Lin, Chin-Tsai

Article Type: Research Article

Abstract: Corporate social responsibility (CSR) is a popular research topic, but there have not been comprehensive discussions on CSR evaluation in the banking sector. The purpose of this study is to propose a two-stage analysis combining the modified Delphi method (MDM) and Analytic Network Process (ANP) in order to construct a model for evaluating banks’ CSR. First, we use MDM to select and determine the interdependence of the criteria and then employ ANP to obtain their weights and to rank the alternatives. The results show that 5 criteria and 18 sub-criteria need to be considered in CSR evaluation. The most important …criterion and sub-criterion are bank governance and regulatory compliance, respectively. The evaluation model constructed herein can be taken as a decision-making guide for evaluating banking organizations’ CSR and to help promote CSR development in China’s financial industry. Show more

Keywords: Banking industry, Corporate social responsibility, MCDM, MDM, ANP

DOI: 10.3233/JIFS-233098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 29-44, 2024

Authors: Yu, Ping | Li, Rong-bin | Cao, Jie | Qin, Jun-hua

Article Type: Research Article

Abstract: In the application of deep learning-based fault diagnosis, more often than not, the network model could perform better with a balanced dataset input, whereby the number of fault data is equivalent to that of normal data. However, in the context of real-world applications, the number of fault data is generally insufficient compared to the normal data. In this study, a new approach for fault diagnosis in unbalanced data sets is proposed using the Gramian angular field (GAF) method. Firstly, the GAF method is employed to convert one-dimensional data into two-dimensional data, which enhances the feature extraction process. Secondly, to balance …the sample distribution, fault data is generated using Generative Adversarial Networks (GANs). Finally, the Residual neural network (ResNet) with an attention mechanism is utilized to improve the accuracy of fault diagnosis. The proposed method is experimentally validated using open-source bearing datasets that are published by Case Western Reserve University and the University of Ottawa. The experimental results show that the proposed method has greatly improved fault diagnosis performance in cases of data distribution imbalance, surpassing that of the compared methods. Show more

Keywords: Fault diagnosis, deep learning, unbalanced data set, Gramian angular field, generative adversarial networks

DOI: 10.3233/JIFS-233797

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 45-54, 2024

Authors: Zhang, Shiwei | Xiao, Jinzhuang | Liu, Yingying | Dong, Mingxuan | Zhou, Zhen

Article Type: Research Article

Abstract: To address the problems of weak search ability, easily falling into local optimal solutions and poor path quality of sparrow search algorithm in AGV path planning, a multi-strategy improved sparrow search algorithm (MISSA) is proposed in this paper. MISSA improves the global search ability by improving the discoverer position update operator and introducing the sine cosine algorithm; adopts the adaptive number of vigilantes and adaptive adjustment step size to improve the convergence speed; introduces the Levy flight variation strategy to reduce the probability of falling into any local optimal solution; optimizes the boundary handling mechanism to prevent the loss of …population diversity at a later stage; finally, uses the large-scale neighborhood search strategy and path smoothing mechanism for path optimization to further improve the path quality. The superiority-seeking ability of MISSA was verified by 12 standard test functions, and then 30 simulation experiments were conducted in grid maps with two specifications of 20×20 and 30×30. The experimental results showed that, by using MISSA, the path length was reduced by 44.1% and 63.1%, the number of turns was reduced by 68.4% and 78.4%, and the risk degree was reduced by 61.3% and 77.2%, which verifies the superiority of MISSA in path planning. Finally, MISSA was ported to the QBot2e mobile robot for physical verification to prove its feasibility in practical applications. Show more

Keywords: Path planning, AGV, sparrow search algorithm, path smoothing

DOI: 10.3233/JIFS-234357

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 55-69, 2024

Authors: Hanief Wani, Mohd | Faridi, Arman Rasool

Article Type: Research Article

Abstract: The need for reliable video surveillance systems to detect and prevent suspicious activities has become more important with the increase in crime and security threats. This paper proposes a real-time video surveillance system based on the Long-term Recurrent Convolutional Network (LRCN) model, which can automatically detect and alert the authority about suspicious activities, such as fighting, accidents, and robbery. Our system comprises two main components: LRCN-based activity recognition and real-time alert generation. We evaluated the performance of the proposed system on a custom dataset compiled from two publicly available datasets and achieved state-of-the-art results in terms of accuracy, precision, and …recall. Our results demonstrate the effectiveness and scalability of the LRCN-based video surveillance system for real-time suspicious activity detection. We believe that our proposed system can be deployed in various public places, such as airports, train stations, and shopping malls, to enhance the security and safety of the public. Show more

Keywords: LRCN, video surveillance, suspicious activity, alert generation

DOI: 10.3233/JIFS-234365

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 71-82, 2024

Authors: Hu, Junhua | Zhou, Yingling | Li, Huiyu | Liang, Pei

Article Type: Research Article

Abstract: To enhance infection diseases interval prediction, an improved model is proposed by integrating neighborhood fuzzy information granulation (NNIG) and spatial-temporal graph neural network (STGNN). Additionally, the NNIG model can efficiently extract the most representative features from the time series data and identifies the support upper and lower bounds. NNIG model transfers time series data from numerical level to granular level, and processes data feed it into STGNN for interval prediction. Finally, experiments are conducted for evaluation based on the COVID-19 data. The results demonstrate that the NNIG outperforms baseline models. Further, it proves beneficial in offering a valuable approach for …policy-making. Show more

Keywords: Time series, fuzzy information granulation, interval prediction, spatial-temporal graph neural network

DOI: 10.3233/JIFS-236766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 83-97, 2024

Authors: Yang, Fan | Zhou, Qing | Su, Renbin | Xiong, Weihong

Article Type: Research Article

Abstract: Molecular graph representation learning has been widely applied in various domains such as drug design. It leverages deep learning techniques to transform molecular graphs into numerical vectors. Graph Transformer architecture is commonly used for molecular graph representation learning. Nevertheless, existing methods based on the Graph Transformer fail to fully exploit the topological structural information of the molecular graphs, leading to information loss for molecular representation. To solve this problem, we propose a novel molecular graph representation learning method called MTS-Net (Molecular Topological Structure-Network), which combines both global and local topological structure of a molecule. In global topological representation, the molecule …graph is first transformed into a tree structure and then encoded by employing a hash algorithm for tree. In local topological representation, paths between atom pairs are transcoded and incorporated into the calculation of the Transformer attention coefficients. Moreover, MTS-Net has intuitive interpretability for identifying key structures within molecules. Experiments on eight molecular property prediction datasets show that MTS-Net achieves optimal results in three out of five classification tasks, the average accuracy is 0.85, and all three regression tasks. Show more

Keywords: Molecular representation, graph structure, graph transformer, property prediction

DOI: 10.3233/JIFS-236788

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 99-110, 2024

Authors: Liang, Hailin | Qu, Shaojian | Dai, Zhenhua

Article Type: Research Article

Abstract: In group decision-making (GDM), when decision-makers (DMs) feel it is unfair, they may take uncooperative measures to disrupt the consensus-reaching process (CRP). On the other hand, it is difficult for the moderator to objectively determine each DM’s unit consensus cost and weight in CRP. Hence, this paper proposes data-driven robust maximum fairness consensus models (RMFCMs) to address these. First, this paper uses the robust optimization method to construct multiple uncertainty sets to describe the uncertainty of the DMs’ unit adjustment cost and proposes the RMFCMs. Subsequently, based on the DMs’ historical data, the DMs’ weights in the CRP are determined …by a data-driven method based on the kernel density estimation (KDE) method. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between governments and enterprises, and the experimental results verify the rationality and robustness of the proposed consensus model. Show more

Keywords: Fairness, uncertain environment, consensus model, data-driven method

DOI: 10.3233/JIFS-237153

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 111-129, 2024

Authors: Du, Baigang | Rong, Yuying | Guo, Jun

Article Type: Research Article

Abstract: Quality Function Deployment (QFD) is a powerful approach for improving product quality that can transform customer requirements (CRs) into engineering characteristics (ECs) during product manufacturing. The limitations of traditional QFD methods lead to imprecise quantification of CRs and difficulty in accurately mapping customer needs. To address these issues, this paper introduces an innovative QFD approach that integrates extended hesitant fuzzy linguistic term sets (EHFLTSs), CRITIC, and cumulative prospect theory. The method expresses the subjectivity and hesitancy of decision makers when evaluating the relationship between ECs and CRs using EHFLTSs, considering the conflicts among CRs. The CRITIC is used to comprehensively …evaluate the comparison strength and conflict between indicators, and the cumulative prospect theory is utilized to derive the prioritization of ECs. A case study is presented to demonstrate the effectiveness of the proposed approach. Show more

Keywords: Extended hesitant fuzzy linguistic term set, cumulative prospect theory, quality function deployment, CRITIC

DOI: 10.3233/JIFS-237217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 131-142, 2024

Authors: Immanuel, Rajeswari Rajesh | Sangeetha, S.K.B.

Article Type: Research Article

Abstract: Human emotions are the mind’s responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study …includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. Show more

Keywords: EEG signal, emotion, CNN, LSTM, ensemble learning, feature extraction

DOI: 10.3233/JIFS-237884

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 143-154, 2024

Authors: Zheng, Z. | Gao, J.B. | Weng, Z.

Article Type: Research Article

Abstract: The body size parameter of cattle is an important index reflecting the growth and development and health condition of cattle. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal life habits of cattle. In this paper, we address this problem by proposing a contactless body size measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the body of the cattle are taken from three directions: top, left, and right, …using multiple cameras. Secondly, the image is segmented using a fuzzy clustering algorithm based on neighborhood adaptive local spatial information improvement, and the image is processed to extract the contour images of the top view and side view. The key points of body measurements were extracted using interval division and curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, which realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The correlation between body size and weight data obtained by contactless methods was investigated and the modeled using one-factor linear regression, one-factor nonlinear regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based support vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0%, 4.4%, 3.6%, and 5.5%, respectively. Correlation of BH, RH, BSL and AC with weight obtained by non-contact methods was > 0.75. The BH parameter can be selected in the single-factor growth monitoring. The multi-body scale can reflect the growth status of cattle more comprehensively, in which RH, BSL and AC are important detection parameter; the multi-factor nonlinear model can reflect the growth characteristics of cattle more comprehensively. The contactless measurement method proposed in the paper can effectively improve the work efficiency and reduce the stress reaction of cattle, which is a long-term and effective monitoring method, and is of great significance in promoting accurate and welfare cattle rearing. Show more

Keywords: Image processing, body size measurement, fuzzy clustering, non-contact measurement, cattle weight estimation

DOI: 10.3233/JIFS-238016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 155-167, 2024