Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Kumar, Ajay | Singh, Anuj Kumar | Garg, Ankit

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Abstract: This article has been retracted.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-233443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1259-1273, 2024

Authors: Jia, Xiaoying

Article Type: Research Article

Abstract: Under the premise of today’s socialist modernization construction, the development and progress of the country require more outstanding talents such as aspiring youth and intellectuals to participate, which puts forward higher requirements for education quality indicators and various aspects of operation. As the main institution and environment for implementing educational activities, the effectiveness of school management organization has a direct impact and even a decisive role on the quality of education. Therefore, how to improve the quality management of school education has become a hot topic in the education industry. The education quality management evaluation in higher education institutions is …viewed as the multiple-attribute decision-making (MADM) issue. In this paper, the interval-valued neutrosophic number cross-entropy (IVNN-CE) technique is built under interval-valued neutrosophic sets (IVNSs) based on the traditional cross-entropy technique. Then, combine traditional cross-entropy technique with IVNSs, the IVNN-CE technique is constructed for MADM under IVNSs. Finally, the numerical example for education quality management evaluation in higher education institutions was constructed and some comparisons is employed to verify advantages of IVNN-CE technique. The main contribution of this paper is constructed: (1) the cross-entropy model is extended to IVNSs; (1) the CRITIC technique is employed to construct the attribute weights under IVNSs; (3) the IVNN-CE technique is constructed to manage the MADM under IVNSs; (4) a case study about education quality management evaluation in higher education institutions is constructed to show the built technique; (5) some comparative algorithms are constructed to verify the rationality of IVNN-CE technique. Show more

Keywords: Multiple attribute decision making (MADM), interval-valued neutrosophic sets (IVNSs), cross-entropy technique, education quality management evaluation

DOI: 10.3233/JIFS-233481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1275-1286, 2024

Authors: Liu, Yayun | Ning, Kuangfeng

Article Type: Research Article

Abstract: The adaptive fusion module with an attention mechanism functions by employing a dual-channel graph convolutional network to aggregate neighborhood information. The resulting embeddings are then utilized to calculate interaction terms, thereby incorporating additional information. To enhance the relevance of fusion information, an adaptive fusion module with an attention mechanism is constructed. This module selectively combines the neighborhood aggregation and interaction terms, prioritizing the most pertinent information. Through this adaptive fusion process, the algorithm effectively captures both neighborhood features and other nonlinear information, leading to improved overall performance. Neighborhood Aggregation Interaction Graph Convolutional Network Adaptive Fusion (NAIGCNAF) is a graph representation …learning algorithm designed to obtain low-dimensional node representations while preserving graph properties. It addresses the limitations of existing algorithms, which tend to focus solely on aggregating neighborhood features and overlook other nonlinear information. NAIGCNAF utilizes a dual-channel graph convolutional network for neighborhood aggregation and calculates interaction terms based on the resulting embeddings. Additionally, it incorporates an adaptive fusion module with an attention mechanism to enhance the relevance of fusion information. Extensive evaluations on three citation datasets demonstrate that NAIGCNAF outperforms other algorithms such as GCN, Neighborhood Aggregation, and AIR-GCN. NAIGCNAF achieves notable improvements in classification accuracy, ranging from 1.0 to 1.6 percentage points on the Cora dataset, 1.1 to 2.4 percentage points on the Citeseer dataset, and 0.3 to 0.9 percentage points on the Pubmed dataset. Moreover, in visualization tasks, NAIGCNAF exhibits clearer boundaries and stronger aggregation within clusters, enhancing its effectiveness. Additionally, the algorithm showcases faster convergence rates and smoother accuracy curves, further emphasizing its ability to improve benchmark algorithm performance. Show more

Keywords: Graph representation learning, graph convolutional neural network (GCNN), attention mechanism, node classification

DOI: 10.3233/JIFS-234086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1287-1314, 2024

Authors: Lv, Jingjing

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-234212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1315-1328, 2024

Authors: Jiang, Zhujun | Zhou, Jieyong | He, Qixiang

Article Type: Research Article

Abstract: Fuzzy singular Lyapunov matrix equations have many applications, but feasible numerical methods to solve them are absent. In this paper, we propose an efficient numerical method for fuzzy singular Lyapunov matrix equations, where A is crisp and semi-stable. In our method, we transform fuzzy singular Lyapunov matrix equation into two crisp Lyapunov matrix equations. Then we solve the least squares solutions of the two crisp Lyapunov matrix equations, respectively. The existence of fuzzy solution is also considered. At last, two small examples are presented to illustrate the validate of the method and two large scale examples that the existing method …fails to slove are presented to show the efficiency of the method. Show more

Keywords: Fuzzy, singular Lyapunov matrix equations, semi-stable, Extension method, the least squares solutions

DOI: 10.3233/JIFS-230990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1329-1340, 2024

Authors: Geetha, M.P. | Karthika Renuka, D.

Article Type: Research Article

Abstract: A recommendation system serves as a distributed information filter, predicting customer preferences in reviews, ratings, and comments. Analysing customer behaviour aids in understanding needs and predicting intentions. E-commerce tracks product usage and sentiment to provide a personalized network based on consumer preference modelling. The challenge lies in optimizing item selection for suitable consumers to enhance performance. To address this, an imperative is the item recommendation approach for modelling future consumer behaviour. However, traditional machine learning methods often overlook dynamic product recommendations due to evolving user interests and changes in preferences reflected in customer ratings, causing cold-start issues. To overcome these …challenges, a comprehensive deep learning approach is introduced. This approach incorporates a deep neural network for consumer preference prediction, utilizing a multi-task learning paradigm to accommodate variations in consumer ratings. The research contribution lies in applying this network to predict consumer preference scores based on latent multimodal information and item characteristics. Initially, the architecture manages changing consumer aspects and preferences by extracting features and latent factors from customer review rating data. These latent factors include customer demographic information and other concealed features that signify preferences based on experiences and behaviours. Extracted latent features are processed using a sentiment analysis model to generate embedding latent features. A finely-tuned deep neural network with hyper-parameter adjustments serves as a prediction network, forming a customer performance-oriented recommendation system. It processes embedded latent features along with associated sentiments to achieve high prediction accuracy, reliability, and latency. The deep learning architecture, enriched with consumer-specific discriminative information, generates an objective function for item recommendations with minimal error, significantly enhancing predictive performance. Empirical experiments on Amazon review datasets validate the proposed model’s performance, showcasing its enhanced effectiveness and scalability in handling substantial data volumes. Show more

Keywords: Product recommendation, multitask learning, consumer buying behaviour analysis, user preference modelling

DOI: 10.3233/JIFS-231116

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1341-1357, 2024

Authors: Liu, Feng-Lang | Chien, Li-Chih | Chang, Ting-Yu | Ku, Cooper Cheng-Yuan | Chang, Ching-Ter

Article Type: Research Article

Abstract: Improving technological innovation (TI) capabilities is an integral component of government policies aimed at improving the competitiveness of small and medium enterprises (SMEs). This study aims to address implementation challenges arising from the use of Qualitative Forecasting Method (QFM) in new product development programs and proposes a novel method to aid decision makers (DMs) in their decision-making process. To tackle this issue, a hybrid method is proposed, incorporating Fuzzy Delphi method (FDM), Fuzzy Analytic Hierarchy Process (FAHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Multi-Choice Goal Programming with utility function (MCGP-U), while introducing prospect theory …as a novel approach. is proposed. The proposed method offers several advantages, including effective early planning, accurate identification of key success factors (KSFs), selection of the most suitable project leader, and estimation of the most reasonable resource investment, all of which are critical factors for success in TI for enterprises. The research results show that (1) the proposed method reduces project execution time by 20% compared to the original manual planning, (2) it facilitates the acquisition of KSFs using a rational approach to ensure project success, and (3) it increases the financial returns of the company by 17% compared to the company’s forecast. In summary, this paper makes a significant contribution to practical applications and additionally contributes to decision-making field by introducing prospect theory into the proposed hybrid method. Show more

Keywords: Technological innovation, decision-making model, fuzzy, MCGP

DOI: 10.3233/JIFS-234327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1359-1378, 2024

Authors: Liu, Die | Xu, MengDie | Li, ZhiTing | He, Yingying | Zheng, Long | Xue, Pengpeng | Wu, Xiaodong

Article Type: Research Article

Abstract: Concrete surface crack detection plays a crucial role in ensuring concrete safety. However, manual crack detection is time-consuming, necessitating the development of an automatic method to streamline the process. Nonetheless, detecting concrete cracks automatically remains challenging due to the heterogeneous strength of cracks and the complex background. To address this issue, we propose a multi-scale residual encoding network for concrete crack segmentation. This network leverages the U-NET basic network structure to merge feature maps from different levels into low-level features, thus enhancing the utilization of predicted feature maps. The primary contribution of this research is the enhancement of the U-NET …coding network through the incorporation of a residual structure. This modification improves the coding network’s ability to extract features related to small cracks. Furthermore, an attention mechanism is utilized within the network to enhance the perceptual field information of the crack feature map. The integration of this mechanism enhances the accuracy of crack detection across various scales. Furthermore, we introduce a specially designed loss function tailored to crack datasets to tackle the problem of imbalanced positive and negative samples in concrete crack images caused by data imbalance. This loss function helps improve the prediction accuracy of crack pixels. To demonstrate the superiority and universality of our proposed method, we conducted a comparative evaluation against state-of-the-art edge detection and semantic segmentation methods using a standardized evaluation approach. Experimental results on the SDNET2018 dataset demonstrate the effectiveness of our method, achieving mIOU, F1-score, Precision, and Recall scores of 0.862, 0.941, 0.945, and 0.9394, respectively. Show more

Keywords: Crack segmentation, U-NET, residual structure, attention mechanism

DOI: 10.3233/JIFS-231736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1379-1392, 2024

Authors: Zhang, Bin | Li, Jianqi | Li, Zewen | Sun, Jian | Xia, Yixiang | Zou, Pinlong

Article Type: Research Article

Abstract: The prediction of power demand for unmanned aerial vehicles (UAV) is an essential basis to ensure the rational distribution of the energy system and stable economic flight. In order to accurately predict the demand power of oil-electric hybrid UAV, a method based on variational mode decomposition (VMD) and Sparrow Search Algorithm (SSA) is proposed to optimize the hybrid prediction model composed of long-short term memory (LSTM) and Least Squares Support Vector Machine (LSSVM). Firstly, perform VMD decomposition on the raw demand power data and use the sample entropy method to classify the feature-distinct mode components into high-frequency and low-frequency categories. …Then, each modality component was separately input into the mixed model for rolling prediction. The LSSVM model and LSTM model were used to process low-frequency and high-frequency components, respectively. Finally, the predicted values for each modal component are linearly combined to obtain the final predicted value for power demand. Compared with the current models, the prediction model constructed in this paper stands out for its superior ability to track the changing trends of power demand and achieve the highest level of prediction accuracy. Show more

Keywords: UAV demand power, variational mode decomposition, sparrow search algorithm, long-short term memory, long-short term memory

DOI: 10.3233/JIFS-234263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1393-1406, 2024

Authors: Hou, Junjian | Xu, Yaxiong | He, Wenbin | Zhong, Yudong | Zhao, Dengfeng | Zhou, Fang | Zhao, Mingyuan | Dong, Shesen

Article Type: Research Article

Abstract: Fatigue driving is one of the primary causative factors of road accidents. It is of great significance to discern, identify and warn drivers in time for traffic safety and reduce traffic accidents. In this paper, a systematic review for the fatigue driving behavior recognition method is developed to analyze its research status and development trends. Firstly, the data information and its application scenarios related to fatigue driving is detailed. Three driving behavior recognition methods based on different types of signal data are summarized and analyzed, and this signal data can be divided into physiological signal characteristics, visual signal characteristics, vehicle …sensor data characteristics and multi-data information fusion. By summarizing and comparing the recognition effect of existing fatigue driving recognition methods, combined with deep learning technology, the paper concludes the fatigue driving behavior recognition method based on single data source has some shortcomings such as low accuracy and easy to be affected by external factors, but the recognition method based on multi-feature information fusion can achieve a exhilarated recognition result. Finally, some prospects are given to analyze the development trend of fatigue driving behavior recognition in the future. Show more

Keywords: Fatigue driving, information fusion, physiological signals, deep learning, vehicle sensors

DOI: 10.3233/JIFS-235075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1407-1427, 2024