Journal of Intelligent & Fuzzy Systems - Volume 46, issue 4

Authors: Balasubramaniyan, M. | Navaneethan, C.

Article Type: Research Article

Abstract: Artificial intelligence has played a significant role in the expansion of the agriculture industry in recent times by evaluating data and making recommendations for better production. An automated method for determining significant information in seed quality analysis is the peanut maturity analysis in image processing through sensory images. The majority of the time, changes in picture intensity result in feature independence and precise maturity level determination. Therefore, agricultural precision in identifying essential features is low. To address this issue, we suggest employing a Cross-Layer Multi-Perception Neural Network (CLMPNN) for hyperspectral sensory image feature observation in order to determine the optimal …assessment of peanut maturity in agriculture. The sensing unit first determines the angular cascade projection’s (ACP) structural dependencies for the peanut pod structure. With the aid of color-intensive saturation, the entity projection of pod growth is found using the Slicing Fragment Segmentation (SFS) technique. This generates the various entity variations by integrating relational maturity and non-maturity findings with spectral values. Next, cross-layer multi-perception neural networks are trained with hyperspectral values optimized by LSTM to distinguish between mature and immature pods. In comparison to the other system, this one does exceptionally well in precision agriculture, with a 98.6 well recall rate, a 97.3% classification accuracy, and a 98.9% production accuracy. Show more

Keywords: Peanut maturity, feature selection and classification, deep learning, cascade projection, slicing segmentation

DOI: 10.3233/JIFS-239332

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9393-9407, 2024

Authors: Xiao, Yanjun | Li, Rui | Zhao, Yue | Wang, Xiaoliang | Liu, Weiling | Peng, Kai | Wan, Feng

Article Type: Research Article

Abstract: The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from “regular maintenance” and “post-event maintenance” to “predictive maintenance”. In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of “combining the real with the virtual and controlling the real”. Firstly, a digital twin system …structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom. Show more

Keywords: Digital twin, predictive maintenance, deep learning, rapier loom

DOI: 10.3233/JIFS-233863

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9409-9430, 2024

Authors: Zhou, Ning | Liu, Bin | Cao, Jiawei

Article Type: Research Article

Abstract: Facial expression recognition has long been an area of great interest across a wide range of fields. Deep learning is commonly employed in facial expression recognition and demonstrates excellent performance in large-sample classification tasks. However, deep learning models often encounter challenges when confronted with small-sample expression classification problems, as they struggle to extract sufficient relevant features from limited data, resulting in subpar performance. This paper presents a novel approach called the Multi-CNN Logical Reasoning System, which is based on local area recognition and logical reasoning. It initiates the process by partitioning facial expression images into two distinct components: eye action …and mouth action. Subsequently, it utilizes logical reasoning based on the inherent relationship between local actions and global expressions to facilitate facial expression recognition. Throughout the reasoning process, it not only incorporates manually curated knowledge but also acquires hidden knowledge from the raw data. Experimental results conducted on two small-sample datasets derived from the KDEF and RaFD datasets demonstrate that the proposed approach exhibits faster convergence and higher prediction accuracy when compared to classical deep learning-based algorithms. Show more

Keywords: Facial expression recognition, logic reasoning, few-shot learning, local area recognition

DOI: 10.3233/JIFS-233988

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9431-9447, 2024

Authors: Zhang, Yongzhi | He, Keren | Ge, Jue

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-235386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9449-9464, 2024

Authors: Li, Junwei | Lian, Mengmeng | Jin, Yong | Xia, Miaomiao | Hou, Huaibin

Article Type: Research Article

Abstract: To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and …information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively. Show more

Keywords: Probabilistic linguistic term set (PLTS), multi-attribute group decision-making (MAGDM), expert weights, attribute weights, correlation coefficient

DOI: 10.3233/JIFS-235593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9465-9478, 2024

Authors: Mahmood, Tahir | Hussain, Kashif | Ahmmad, Jabbar | Shahab, Sana | ur Rehman, Ubaid | Anjum, Mohd

Article Type: Research Article

Abstract: The notion of a T-bipolar soft set (T  - BS ft S ) is the structure that has the ability to discuss the two-sided aspects of certain situations like the effects and side effects of a medicine. Moreover, T  - BS ft S has the ability to discuss the parametrization tool as well. Also, notice that a group is an algebraic structure that is the key tool in many branches of mathematics. In many decision-making situations, we have to discuss the two-sided aspects of a certain situation and we can see that T  - BS ft S is …the only structure that can handle it. So based on a characteristic of T  - BS ft S and groups theory there is a need to define the combined notion of T  - BS ft S and group. So, based on these valuable structures, in this manuscript, we aim to introduce the notion of T-bipolar soft groups by generalizing T-bipolar soft sets. Based on this newly defined structure, we have defined the basic operational laws like extended union, extended intersection, restricted union, restricted intersection, AND product, and OR product for T-bipolar soft groups. Moreover, we have observed the impact of these newly defined notions on T-bipolar soft groups. Show more

Keywords: Soft set, soft groups, T-bipolar soft set, T-bipolar soft groups

DOI: 10.3233/JIFS-236150

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9479-9490, 2024

Authors: Madhubala, P. | Ghanimi, Hayder M.A. | Sengan, Sudhakar | Abhishek, Kumar

Article Type: Research Article

Abstract: The medical domain faces unique challenges in Information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Methods (KBM) have limitations, the integration of semantic knowledge bases and concept mapping techniques enhances data organization and retrieval. Addressing the growing demands in the biomedical field, a novel medical Information Retrieval System (IRS) is proposed that employs Deep Learning (DL) and KBM. This system comprises five core steps: pre-processing of texts, document indexing using DL (ELMo) and KBM, advanced query processing, a BiLSTM-based retrieval network for contextual representation, and a KR-R …re-ranking algorithm to refine document relevance. The purpose of the system is to give users improved biomedical search results through the integration of all of these techniques into a method that takes into consideration the semantic problems of medical records. An in-depth examination of the TREC-PM track samples from 2017 to 2019 observed an impressive leading MRR score of 0.605 in 2017 and a best-in-class rPrec score of 0.350 in 2019, proving how well able the system is to detect and rank relevant medical records accurately. Show more

Keywords: Biomedical information retrieval, BiLSTM, DL, accuracy, query semantics, kernel ridge regression

DOI: 10.3233/JIFS-237056

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9491-9510, 2024

Authors: Gao, Yanbing | Ma, Rui

Article Type: Research Article

Abstract: With the deepening development of the financial market, the role of regulatory systems in ensuring green and safe financial environment is becoming increasingly prominent. The traditional intelligent financial regulatory systems on the market lack precise and effective real-time monitoring and recognition capabilities, making it difficult to effectively process and analyze large-scale financial data. In order to improve the real-time recognition of abnormal situations or potential risks, achieve automation and intelligence of supervision, this article combines deep learning technology to study the deep practice of IoT image recognition technology in intelligent financial supervision systems. In response to the “data silos” and …cross regional linkage issues faced by financial industry regulation, this article designs and implements an intelligent regulatory system based on IoT image recognition technology through deep learning. Using Convolutional Neural Network (CNN) algorithm to classify and analyze system images for regulatory and risk control purposes. The research results indicate that the intelligent financial regulatory system constructed in this article has high stability and responsiveness, which can effectively meet the real-time regulatory needs of finance and help promote the healthy development of the financial market. Show more

Keywords: Financial supervision system, internet of things, image recognition technology, deep learning, artificial intelligence

DOI: 10.3233/JIFS-237692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9511-9523, 2024

Authors: Ren, Zhenxing | Zhang, Jia | Zhou, Yu | Ji, Xinxin

Article Type: Research Article

Abstract: Over the past several decades, several air pollution prevention measures have been developed in response to the growing concern over air pollution. Using models to anticipate air pollution accurately aids in the timely prevention and management of air pollution. However, the spatial-temporal air quality aspects were not properly taken into account during the prior model construction. In this study, the distance correlation coefficient (DC) between measurements made in various monitoring stations is used to identify appropriate correlated monitoring stations. To derive spatial-temporal correlations for modeling, the causality relationship between measurements made in various monitoring stations is analyzed using Transfer Entropy …(TE). This work explores the process of identifying a piecewise affine (PWA) model using a larger dataset and suggests a unique hierarchical clustering-based identification technique with model structure selection. This work improves the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing Kullback-Leibler (KL) Divergence as the dissimilarity between clusters for handling clusters with arbitrary shapes. The number of clusters is automatically determined using a cluster validity metric. The task is formulated as a sparse optimization problem, and the model structure is selected using parameter estimations. Beijing air quality data is used to demonstrate the method, and the results show that the proposed strategy may produce acceptable forecast performance. Show more

Keywords: PWA model, prediction of air pollutants, spatial-temporal features, hierarchical clustering-based identification

DOI: 10.3233/JIFS-238920

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9525-9542, 2024

Authors: Yang, Xingyao | Chang, Mengxue | Yu, Jiong | Wang, Dongxiao | Dang, Zibo

Article Type: Research Article

Abstract: Social recommendations enhance the quality of recommendations by integrating social network information. Existing methods predominantly rely on pairwise relationships to uncover potential user preferences. However, they usually overlook the exploration of higher-order user relations. Moreover, because social relation graphs often exhibit scale-free graph structures, directly embedding them in Euclidean space will lead to significant distortion. To this end, we propose a novel graph neural network framework with hypergraph and hyperbolic embedding learning, namely HMGCN. Specifically, we first construct hypergraphs over user-item interactions and social networks, and then perform graph convolution on the hypergraphs. At the same time, a multi-channel setting …is employed in the convolutional network, with each channel encoding its corresponding hypergraph to capture different high-order user relation patterns. In addition, we feed the item embeddings and the obtained high-order user embeddings into a hyperbolic graph convolutional network to extract user and item representations, enabling the model to better capture the hierarchical structure of their complex relationships. Experimental results on three public datasets, namely FilmTrust, LastFM, and Yelp, demonstrate that the model achieves more comprehensive user and item representations, more accurate fitting and processing of graph data, and effectively addresses the issues of insufficient user relationship extraction and data embedding distortion in social recommendation models. Show more

Keywords: Social recommendation, hypergraph learning, hyperbolic embedding, graph convolutional network, data mining

DOI: 10.3233/JIFS-235266

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9543-9557, 2024