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

Authors: Sathish, E. | Muthukumar, R.

Article Type: Research Article

Abstract: In agriculture, selecting an “appropriate plant for an appropriate soil” is a crucial stage for all sorts of lands. There are different types of soil found in India. It is necessary to understand the features of the soil type to predict the types of crops cultivated in a particular soil. This leads to significant inconsistencies and errors in large-scale soil mapping. However, manually analyzing the soil type in the laboratory is cost-effective and time-consuming, yet it produces an inaccurate classification result. To overcome these challenges, a novel AQU-FRC Net (Aquila – Faster Regional Convolutional Neural Neural) is proposed for the …automatic prediction of soil and recommending suitable crops based on a soil-crop relationship database. The soil images were pre-processed using a Scalable Range-based Adaptive Bilateral Filter (SCRAB) for eliminating the noise artifacts from the images. The pre-processed images were classified using Faster-RCNN, which utilized MobileNet as a feature extraction network. The classification results were optimized by the Aquila optimization (AQU) algorithm that normalizes the parameters of the network to achieve better results. The proposed AQU-FRC Net achieves a high accuracy of 98.16% for predicting soil. The experimental results demonstrate that the model successfully predicts the soil when compared to other meta-heuristic-based methods. Show more

Keywords: MobileNet, Aquila – Faster RCNN, Faster-RCNN, meta-heuristic, aquila optimization

DOI: 10.3233/JIFS-230408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 167-180, 2024

Authors: Meena, Rakesh | Joshi, Sunil | Raghuwanshi, Sandeep

Article Type: Research Article

Abstract: Rice is a staple meal that helps people worldwide access sufficient food. However, this crop has several illnesses, significantly lowering its production and quality. Because of this, it is imperative to conduct early disease detection to halt the spread of infections. Because of this, it is desirable to develop an automatic system that will help agronomists, pathologists, and indeed growers in directly diagnosing rice diseases. This would allow for preventative measures to be done as quickly as feasible. In this day and age of artificial intelligence, researchers have experimented with various learning approaches to discover diseases that can affect rice …plants. Deep learning has recently seen considerable use in many computer vision and image analysis fields, becoming one of the most prominent machine learning algorithms. Deep learning has also recently found substantial usage in many computer vision and picture analysis fields. On the other hand, deep learning methods have seen very little application in plant disease recognition, except for some ongoing research centered on the problem and using a public dataset of pictures magnified to show plant leaves. Because of their high computational complexity, which requires a huge memory cost, and the complexity of experimental materials’ backgrounds, which makes it difficult to train an efficient model, deep learning methods have only seen limited use in plant disease recognition. This is due to several factors, including the following: The Inception module was improved to recognise and detect rice plant illnesses in this research by substituting the original convolutions with architecture based on modified-Xception (M-Xception). In addition, ResNet extracts features by prioritising logarithm calculations over softmax calculations to get more consistent classification outcomes. The model’s training utilised a two-stage transfer learning process to produce an effective model. The results of the experiments reveal that the suggested approach can achieve the specified level of performance, with an average recognition fineness of 99.73% on the public dataset and 98.05% on the domestic dataset, respectively. Our proposed work is better as per existing methods and models. Show more

Keywords: Deep learning, rice crop, disease detection, feature extraction, M-Xception model

DOI: 10.3233/JIFS-230655

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 181-198, 2024

Authors: Li, Mengyang | Wang, Nan | Fu, Zhumu | Tao, Fazhan | Zhou, Tao

Article Type: Research Article

Abstract: In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a …simulation example is shown to confirm effectiveness of the proposed design scheme of this paper. Show more

Keywords: Nonlinear systems, coprime factorization, robust stability, unknown perturbation, fuzzy control, robust control

DOI: 10.3233/JIFS-231879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 199-207, 2024

Authors: Wang, Jia | Zhang, Ke | Li, Jingyuan

Article Type: Research Article

Abstract: Awareness of Network Security Situation (abbreviated as NSS for short) technology is in a period of vigorous development recently. NSS technology means network security situational awareness technology. It refers to the technology of collecting, processing, and analyzing various real-time information in the network to understand and evaluate the current network security status. It can not only find network security threats, but also reflect the NSS in the system security metrics, and provide users with targeted security protection measures. Based on data mining methods, this paper analyzed and models perceived threats and security events with data mining algorithms, and improved information …security measurement methods based on association analysis. This paper proposed network security information analysis and NSS based on data mining, and analyzed the experimental results of network awareness of NSS information security measurement. The experimental results showed that when the Timer was 8, the accuracy of the awareness of NSS information security measurement method based on data mining can reach 92.89%. The data mining model had the highest accuracy of 93.14% in situation understanding and evaluation of KDDCup-99 dataset. The results showed that the model can accurately predict the NSS. When Timer was 6, the highest accuracy of the model was 92.71%. In general, the NSS prediction mining model based on KDDCup-99 can better understand, evaluate and predict the situation. Show more

Keywords: Network security situation, data mining, information security, situation awareness

DOI: 10.3233/JIFS-233390

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 209-219, 2024

Authors: Lv, Zhenzhe | Liu, Qicheng

Article Type: Research Article

Abstract: In the era of big data, the complexity of data is increasing. Problems such as data imbalance and class overlap pose challenges to traditional classifiers. Meanwhile, the importance of imbalanced data has become increasingly prominent, it is necessary to find appropriate methods to enhance classification performance of classifiers on such datasets. In response, this paper proposes a mixed sampling method (ISODF-ENN) based on iterative self-organizing (ISODATA) denoising diffusion algorithm and edited nearest neighbors (ENN) data cleaning algorithm. The algorithm first uses iterative self-organizing clustering algorithm to divide minority class into different sub-clusters, then it uses denoising diffusion algorithm to generate …new minority class data for each sub-cluster, and finally it uses ENN algorithm to preprocess majority class data to remove the overlap with the minority class data. Each sub-cluster is oversampled according to sampling ratio, so that the oversampled minority class data also conforms to the distribution of original minority class data. Experimental results on keel datasets demonstrate that the proposed method outperforms other methods in terms of F-value and AUC, effectively addressing the issues of class imbalance and class overlap. Show more

Keywords: Imbalanced data, diffusion model, mixed-sampling, ISODATA, ENN

DOI: 10.3233/JIFS-233886

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 221-235, 2024

Authors: Jiang, Le | Liu, Hongbin

Article Type: Research Article

Abstract: Some risky multi-criteria group decision making problems include payoff and probability information. To deal with these problems, this study introduces a large scale multi-criteria group decision making model based on focus theory of choice. In this model, a group of experts’ linguistic evaluations on multiple criteria are first collected to form linguistic distributions. The positive foci of the linguistic distributions are computed and aggregated into the alternatives’ scores. It is noted that in this process the linguistic terms and probabilities are aggregated by using different rules. The positive foci of the alternatives’ scores are computed and the optimal alternative is …selected. A pollution treatment evaluation problem is solved by using the proposed model, and simulation experiments and comparative analysis are given. Show more

Keywords: Focus theory of choice, linguistic distribution, multi-criteria group decision making, positive foci

DOI: 10.3233/JIFS-234310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 237-246, 2024

Authors: Dai, Songsong | Song, Haifeng | Xu, Yingying | Du, Lei

Article Type: Research Article

Abstract: This paper introduces the concept of (O , N )-difference, for an overlap function O and a fuzzy negation N . (O , N )-differences are weaker than fuzzy difference constructed from positive and continuous t-norms and fuzzy negations, in the sense that (O , N )-differences do not necessarily satisfy certain properties, as the right neutrality principle, but only weaker versions of these properties. This paper analyzes the main properties satisfied by (O , N )-differences, and provides a characterization of (O , N )-difference.

Keywords: Fuzzy conjunction, fuzzy difference, overlap function, t-norm

DOI: 10.3233/JIFS-234501

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 247-255, 2024

Authors: Jahanpanah, Sirus | Hamidi, Mohammad

Article Type: Research Article

Abstract: Fuzzy graphs as labeled graphs (fuzzy vertex labeling and fuzzy edge labeling) have many applications in real life such as complex networks, coding theory, medical sciences, communication networks, and management sciences. Also, triangular norms as a special class of functions, have many applications in fuzzy set theory, probability and statistics, and other areas. This paper considers the notations of an inverse fuzzy graph and triangular norms to introduce the new type of graphs as valued-inverse Dombi fuzzy graphs. The valued-inverse Dombi fuzzy graphs are a generalization of inverse fuzzy graphs and are dual to Dombi fuzzy graphs. For any given …greater than or equal to one real number, we construct a type of Dombi inverse fuzzy graph and investigate some conditions such that the product and union of Dombi inverse fuzzy graphs be a Dombi inverse fuzzy graph. Show more

Keywords: Fuzzy subset, Dombi triangular operator, valued-Dombi inverse fuzzy graph, Mathematics Subject Classification: 03E72, 05C72

DOI: 10.3233/JIFS-231535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 257-268, 2024

Authors: Dandugala, Lakshmi Srinivasulu | Vani, Koneru Suvarna

Article Type: Research Article

Abstract: Big data analytics (BDA) is a systematic way to analyze and detect various patterns, relationships, and trends in vast amounts of data. Big data analysis and processing require significant effort, techniques, and equipment. The Hadoop framework software uses the MapReduce approach to do large-scale data analysis using parallel processing in order to generate results as soon as possible. Due to the traditional algorithm’s longer execution time and difficulty in processing big amounts of data, this is one of the main issues. Clusters are highly correlated inside each other but are not highly correlated with one another. The technique of effectively …allocating limited resources is known as an optimization algorithm for clustering. For processing large amounts of data with several dimensions, the conventional optimization approach is insufficient. By using a fuzzy method, this can be prevented. In this paper, we proposed Fuzzy based energy efficient clustering approach to enhance the clustering mechanism. In summary, Fuzzy based energy efficient clustering introduces a function that measures the distance between the cluster center and the instance, which aids in improved clustering, and we then present the MobileNet V2 model to improve efficiency and speed up computation. To enhance the method’s performance and reduce its time complexity, the distributed database simulates the shared memory space and parallelizes on the MapReduce framework on the Hadoop cloud computing platform. The proposed approach is evaluated using performance metrics such as Accuracy, Precision, Adjusted Rand Index (ARI), Recall, F1-Score, and Normalized Mutual Information (NMI). The experimental findings indicate that the proposed approach outperforms the existing techniques in terms of clustering accuracy. Show more

Keywords: Big data analytics (BDA), Hadoop, cloud computing, fuzzy based energy efficient clustering, MobileNet V2, MapReduce

DOI: 10.3233/JIFS-230387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 269-284, 2024

Authors: Wang, Yibo

Article Type: Research Article

Abstract: With the development of digital creative industry and the use of more emerging digital technologies, the forms of digital cultural and creative design products are also increasingly diversified. Unlike traditional cultural and creative design products, digital cultural and creative design products are no longer limited to physical products, but appear more in the field of exhibition, virtual reality and product visualization. At the initial stage of the combination of digital information technology and cultural and creative content, digital cultural and creative design products, unlike ordinary cultural and creative design products, opened a new vision for users. The design quality evaluation …of digital cultural and creative design products is viewed as a multi-criteria group decision-making (MCGDM). The single-value neutrosophic sets (SVNSs) concept and its interval-valued version (Interval-valued neutrosophic sets, IVNSs) are within the recent rapid developments for managing the uncertain representation problem in MCGDM. In SVNSs, decision makers (DMs) could portray membership, non-membership and hesitancy. IVNSs expands this useful feature through portraying intervals to these three information decision degrees. In this manner, the uncertainty, ambiguity and vagueness hidden in human judgements could be quantified more efficiently. IVNSs have been widely employed and researched in MCGDM. The main purpose of this paper is to proposed the Interval-valued neutrosophic number MABAC (IVNN-MABAC) technique based on prospect theory (PT) to address the MCGDM. Eventually, an example for design quality evaluation of digital cultural and creative design products and some comparative analysis was employed to demonstrate the superiority of the designed technique. Show more

Keywords: MCGDM, IVNSs, MABAC technique, design quality evaluation, digital cultural, creative product

DOI: 10.3233/JIFS-230520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 285-296, 2024

Authors: Sun, Peixi | Cui, Tong | Qi, Shixin

Article Type: Research Article

Abstract: Corporate culture is an objective existence that arises with the rise and development of enterprises. It originates from enterprise practice and influences the behavior of employees. Whether it is intentional identification or unintentional avoidance, corporate culture is not a question of absence, but a question of quality; It’s not about non-existent issues, but about the magnitude of their influence. Therefore, building a corporate culture that conforms to the characteristics of the enterprise and is recognized by the majority of employees, continuously enhancing the influence of corporate culture, is a very important topic in the construction of corporate culture. The corporate …culture influence evaluation is looked as the multiple attribute group decision-making (MAGDM) problem. The intuitionistic fuzzy sets (IFSs) are easy to depict the uncertain information during the corporate culture influence evaluation. Then, intuitionistic fuzzy Combined Compromise Solution (IF-CoCoSo) method is designed under IFSs. Furthermore, IF-CoCoSo is used to cope with the MAGDM. At last, an example is supplied for corporate culture influence evaluation to prove the practicability of the IF-CoCoSo method and some comparative analysis are conducted to verify the effectiveness of IF-CoCoSo method. Thus, the main objectives of this paper are outlined as follows: (1) the CRITIC method is used to obtain the weight information; (2) intuitionistic fuzzy Combined Compromise Solution (IF-CoCoSo) method is designed under IFSs; (3) IF-CoCoSo is used to cope with the MAGDM based on CRITIC weight information and Euclidean distance; (4) At last, an example is supplied for corporate culture influence evaluation to prove the practicability of the IF-CoCoSo method and some comparative analysis are conducted to show the effectiveness of IF-CoCoSo method. Show more

Keywords: Multiple attribute group decision-making (MAGDM), intuitionistic fuzzy sets (IFSs), IF-CoCoSo method, CRITIC weight method, corporate culture influence evaluation

DOI: 10.3233/JIFS-232044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 297-307, 2024

Authors: Moosavi, Seyyed Mohammad Reza Hashemi | Araghi, Mohammad Ali Fariborzi | Ziari, Shokrollah

Article Type: Research Article

Abstract: Mathematical modeling of many natural and physical phenomena in industry, engineering sciences and basic sciences lead to linear and non-linear devices. In many cases, the coefficients of these devices, taking into account qualitative or linguistic concepts, show their complexity in the form of Z -numbers. Since Z -number involves both fuzziness and reliability or probabilistic uncertainty, it is difficult to obtain the exact solution to the problems with Z -number. In this work, a method and an algorithm are proposed for the approximate solution of a Z -number linear system of equations as an important case of such problems. The …paper is devoted to solving linear systems where the coefficients of the variables and right hand side values are Z -numbers. An algorithm is presented based on a ranking scheme and the neural network technique to solve the obtained system. Moreover, two examples are included to describe the procedure of the method and results. Show more

Keywords: Z-numbers, fuzzy number, linear systems of equations, artificial neural networks

DOI: 10.3233/JIFS-232452

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 309-320, 2024

Authors: Wang, Yongjie | Lu, Chang-e | Cheng, Zhihong | Wang, Juan

Article Type: Research Article

Abstract: Optimizing the allocation of preschool education resources and improving the efficiency of resource allocation is of great strategic significance for the universal and inclusive development of preschool education and the realization of “education for young children". In recent years, the shift from high-speed development to high-quality development of the social economy has significantly improved the balanced development level of China’s preschool education industry. However, preschool education remains the weakest link in China’s education system and the most unfavorable aspect of educational resource allocation. Problems such as shortage of preschool education resources, insufficient investment, uneven regional development, imbalanced supply and demand …structure, low resource allocation efficiency, and “difficult to enter, expensive to enter” are still prominent. How to optimize resource allocation and improve resource utilization efficiency in the limited resources of preschool education is the key to achieving balanced, fair, coordinated, and high-quality development of preschool education. The county preschool education resource allocation level evaluation is MAGDM problems. Recently, the TODIM and TOPSIS technique was employed to cope with MAGDM issues. The interval-valued Pythagorean fuzzy sets (IVPFSs) are employed as a tool for characterizing uncertain information during the county preschool education resource allocation level evaluation. In this manuscript, the interval-valued Pythagorean fuzzy TODIM-TOPSIS (IVPF-TODIM-TOPSIS) technique is built to solve the MAGDM under IVPFSs. Finally, a numerical case study for county preschool education resource allocation level evaluation is given to validate the proposed technique. The main contribution of this paper is managed: (1) the TODIM and TOPSIS technique was extended to IVPFSs; (2) Information Entropy is employed to manage the weight values under IVPFSs. (3) the IVPF-TODIM-TOPSIS technique is founded to manage the MAGDM under IVPFSs; (4) Algorithm analysis for county preschool education resource allocation level evaluation and comparison analysis are constructed based on one numerical example to verify the feasibility and effectiveness of the IVPF-TODIM-TOPSIS technique. Show more

Keywords: Multiple attribute group decision making (MAGDM), interval-valued Pythagorean fuzzy sets (IVPFSs), TODIM technique, TOPSIS technique, education resource allocation level evaluation

DOI: 10.3233/JIFS-233742

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 321-338, 2024

Authors: Ghavidel, Motahare | Yadollahzadeh-Tabari, Meisam | GolsorkhTabariAmiri, Mehdi

Article Type: Research Article

Abstract: In this paper, we proposed classification and clustering algorithms that are proper for analyzing customer-related datasets, which are mostly high-dimensional with too many instances. For the clustering purpose, This paper presents a Cuckoo-Search-based Variable Weighting (CSVW) Clustering algorithm to obtain optimal variable weights of high-dimensional data for each cluster. This paper also proposes a deep Inferarer Classifier for categorizing customers using Bi-Directional Long Short-Term Memory (Bi-LSTM) neural network, which uses a Fuzzy Inferential Classifier on its last layer. The Insurance Company (TIC) and InstaCart datasets are utilized for the experiments and performance evaluation. Simulation results reveal that the proposed clustering …algorithm generates appropriate Silhouette and Elbow criteria scores in a few cycles of execution in comparison to ordinal clustering algorithms. Also, the proposed classification algorithm with fuzzy soft-max classifier hits the better Classification Criteria in comparison. Show more

Keywords: Customer clustering, Cuckoo optimization, variable-sensitive clustering, deep learning

DOI: 10.3233/JIFS-230675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 339-353, 2024

Authors: Li, Weidong | Li, Zhenying | Wang, Chisheng | Zhang, Xuehai | Duan, Jinlong

Article Type: Research Article

Abstract: Accurate identification and monitoring of aircraft on the airport surface can assist managers in rational scheduling and reduce the probability of aircraft conflicts, an important application value for constructing a "smart airport." For the airport surface video monitoring, there are small aircraft targets, aircraft obscuring each other, and affected by different weather, the aircraft target clarity is low, and other complex monitoring problems. In this paper, a lightweight model network for video aircraft recognition in airport field video in complex environments is proposed based on SSD network incorporating coordinate attention mechanism. First, the model designs a lightweight feature extraction network …with five feature extraction layers. Each feature extraction layer consists of two modules, Block_A and Block_I. The Block_A module incorporates the coordinate attention mechanism and the channel attention mechanism to improve the detection of obscured aircraft and to enhance the detection of small targets. The Block_I module uses multi-scale feature fusion to extract feature information with rich semantic meaning to enhance the feature extraction capability of the network in complex environments. Then, the designed feature extraction network is applied to the improved SSD detection algorithm, which enhances the recognition accuracy of airport field aircraft in complex environments. It was tested and subjected to ablation experiments under different complex weather conditions. The results show that compared with the Faster R-CNN, SSD, and YOLOv3 models, the detection accuracy of the improved model has been increased by 3.2%, 14.3%, and 10.9%, respectively, and the model parameters have been reduced by 83.9%, 73.1%, and 78.2% respectively. Compared with the YOLOv5 model, the model parameters are reduced by 38.9% when the detection accuracy is close, and the detection speed is increased by 24.4%, reaching 38.2fps, which can well meet the demand for real-time detection of aircraft on airport surfaces. Show more

Keywords: Complex environment, airport surface, aircraft recognition, SSD network, coordinate attention

DOI: 10.3233/JIFS-231423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 355-368, 2024

Authors: Peng, Li-Ling | Bi, Xiao-Feng | Fan, Guo-Feng | Wang, Ze-Ping | Hong, Wei-Chiang

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-231588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 369-388, 2024

Authors: Li, Yundong | Yan, Yunlong | Wang, Xiang

Article Type: Research Article

Abstract: Timely detection of building damage after a disaster can provide support and help in saving lives and reducing losses. The emergence of transfer learning can solve the problem of difficulty in obtaining several labeled samples to train deep models. However, some degree of differences exists among different scenarios, which may affect the transfer performance. Furthermore, in reality, data can be collected from multiple historical scenarios but cannot be directly combined using single-source domain adaptation methods. Therefore, this study proposes a multi-source variational domain adaptation (MVDA) method to complete the task of post-disaster building assessment. The MVDA method consists of two …stages: first, the distributions of each pair of source and target domains in specific feature spaces are aligned separately; second, the outputs of the pre-trained classifiers are aligned using domain-specific decision boundaries. This method maximizes the relevant information in the historical scene, solves the problem of inconsistent image classification in the current scene, and improves the migration efficiency from the history to the current disaster scene. The proposed approach is validated by two challenging multi-source transfer tasks using the post-disaster hurricane datasets. The average accuracy rate of 83.3% for the two tasks is achieved, obtaining an improvement of 0.9% compared with the state-of-the-art methods. Show more

Keywords: Building damage detection, domain adaptation, multi-source domain, transfer learning, remote sensing

DOI: 10.3233/JIFS-232613

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 389-404, 2024

Authors: An, Xiaogang | Chen, Mingming

Article Type: Research Article

Abstract: This paper explores the relationship between fuzzy logic algebra and non associative groupoid. As a groupoid which can satisfy type-2 cyclic associative (T2CA) law, T2CA-groupoid is characterized by generalized symmetry. Fuzzy logic algebra is a major direction in the study of fuzzy logic. Residuated lattices are a class of fuzzy logic algebras with widespread applications. The inflationary pseudo general residuated lattice (IPGRL), a generalization of the residuated lattice, does not need to satisfy the associative law and commutative law. Moreover, the greatest element of IPGRL is no longer the identity element. In this paper, the notion of T2CA-IPGRL (IPGRL in …T2CA-groupoid) is proposed and its properties are investigated in combination with the study of IPGRL and T2CA-groupoid. In addition, the generalized symmetry and regularity of T2CA-groupoid are investigated based on the characteristics of commutative elements. Meanwhile, the decomposition of T2CA-root of band with T2CA-unipotent radical is studied as well. The result shows that every T2CA-root of band is the disjoint union of T2CA-unipotent radicals. Show more

Keywords: Semigroup, cyclic associative groupoid, generalized regular T2CA-groupoid, fuzzy logic, pseudo general residuated lattice

DOI: 10.3233/JIFS-232966

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 405-418, 2024

Authors: Wang, Tianhui | Liu, Renjing | Liu, Jiaohui | Qi, Guohua

Article Type: Research Article

Abstract: With the development of artificial intelligence technology, the assessment method based on machine learning, especially the ensemble learning method, has attracted more and more attention in the field of credit assessment. However, most of the ensemble assessment models are complex in structure and costly in time for parameter tuning, few of them break through the limitations of lightweight, universal and efficient. This paper present a new ensemble model for personal credit assessment. First, considering the conflicts and differences among multiple sources of information, a new method is proposed to correct the category prior information by using the difference measure. Then, …the revised prior information is fused with the current sample information with the help of Bayesian data fusion theory. The model can integrate the advantages of multiple benchmark classifiers to reduce the interference of uncertain information. To verify the effectiveness of the proposed model, several typical ensemble classification models are selected and empirically studied using real customer credit data from a commercial bank in China, and the results show that among various assessment criteria: the proposed model not only effectively improves the multi-class classification performance, but also outperforms other advanced multi-class classification credit assessment models in terms of parameter tuning and generalizability. This paper supports commercial banks and other financial institutions examination and approval work. Show more

Keywords: Ensemble model, multi-class credit assessment, information fusion theory

DOI: 10.3233/JIFS-233141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 419-431, 2024

Authors: Cui, Wei | Zhang, Xuerui | Shang, Mingsheng

Article Type: Research Article

Abstract: An increasing number of fake news combining text, images and other forms of multimedia are spreading rapidly across social platforms, leading to misinformation and negative impacts. Therefore, the automatic identification of multimodal fake news has become an important research hotspot in academia and industry. The key to multimedia fake news detection is to accurately extract features of both text and visual information, as well as to mine the correlation between them. However, most of the existing methods merely fuse the features of different modal information without fully extracting intra- and inter-modal connections and complementary information. In this work, we learn …physical tampered cues for images in the frequency domain to supplement information in the image space domain, and propose a novel multimodal frequency-aware cross-attention network (MFCAN) that fuses the representations of text and image by jointly modelling intra- and inter-modal relationships between text and visual information whin a unified deep framework. In addition, we devise a new cross-modal fusion block based on the cross-attention mechanism that can leverage inter-modal relationships as well as intra-modal relationships to complement and enhance the features matching of text and image for fake news detection. We evaluated our approach on two publicly available datasets and the experimental results show that our proposed model outperforms existing baseline methods. Show more

Keywords: Fake news detection, multimoal, cross attention, frequency domain

DOI: 10.3233/JIFS-233193

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 433-455, 2024

Authors: Prabu, Saranya | Padmanabhan, Jayashree

Article Type: Research Article

Abstract: Software-Defined Networking (SDN) is a strategy that leads the network via software by separating its control plane from the underlying forwarding plane. In support of a global digital network, multi-domain SDN architecture emerges as a viable solution. However, the complex and ever-evolving nature of network threats in a multi-domain environment presents a significant security challenge for controllers in detecting abnormalities. Moreover, multi-domain anomaly detection poses a daunting problem due to the need to process vast amounts of data from diverse domains. Deep learning models have gained popularity for extracting high-level feature representations from massive datasets. In this work, a novel …deep neural network architecture, supervised learning based LD-BiHGA (Low Dimensional Bi-channel Hybrid GAN Attention) system is designed to learn class-specific features for accurate anomaly detection. Two asymmetric GANs are employed for learning the normal and abnormal network flows separately. Then, to extract more relevant features, a bi-channel attention mechanism is added. This is the first study to introduce an innovative hybrid architecture that merges bi-channel hybrid GANs with attention models for the purpose of anomaly detection in a multi-domain SDN environment that effectively handles real-time unbalanced data. The suggested architecture demonstrates its effectiveness on three benchmark datasets, achieving an average accuracy improvement of 7.225% on balanced datasets and 3.335% on imbalanced datasets compared to previous intrusion detection system (IDS) architectures in the literature. Show more

Keywords: Hybrid GAN, intrusion detection, deep learning, attention model, dimensionality reduction, denoising autoencoder

DOI: 10.3233/JIFS-233668

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 457-478, 2024

Authors: Ren, Jianji | Yang, Donghao | Yuan, Yongliang | Liu, Haiqing | Hao, Bin | Zhang, Longlie

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-233990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 479-492, 2024

Authors: Yuan, Hao | Yang, Hao | Li, Ruiqi | Wang, Jun | Tian, Lin

Article Type: Research Article

Abstract: For the purpose of real-time monitoring the hazard information on the electric power construction site, a personal safety monitoring system based on Artificial intelligence internet of things (AIoT) technology is designed. After the system sensing layer collects the gas information of the construction site through the gas sensor, limit current oxygen sensor and DS1820B temperature sensor, the edge computing device of the edge layer directly stores its calculation in the database of the platform layer through the data gateway. The Artificial Intelligence (AI) analysis module of this layer invokes the monitoring data of the power construction site of the database, …and uses the personal safety identification method of the power construction site based on artificial intelligence technology, to complete the abnormal identification of monitoring data and realize personal safety monitoring. In addition, the system is also equipped with a power-fail detection module, which can collect the working voltage through the voltage transformer and compare it with the mains power standard to judge whether there is a power-fail risk, so as to prevent the problem of threatening personal safety due to the power-fail of the energized equipment. After testing, the system can monitor the operation status of the construction site in real time to protect personal safety. Show more

Keywords: AIoT technology, power construction, operation site, personal safety, monitoring system

DOI: 10.3233/JIFS-235087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 493-504, 2024

Authors: Rahim, Muhammad | Amin, Fazli | Tag Eldin, ElSayed M. | Abd El-Wahed Khalifa, Hamiden | Ahmad, Sadique

Article Type: Research Article

Abstract: The selection of an appropriate third-party logistics (3PL) provider has become an inescapable option for shippers in today’s business landscape, as the outsourcing of logistics activities continues to increase. Choosing the 3PL supplier that best meets their requirements is one of the most difficult difficulties that logistics consumers face. Effective decision-making (DM) is critical in dealing with such scenarios, allowing shippers to make well-informed decisions within a restricted timeframe. The importance of DM arises from the possible financial repercussions of poor decisions, which can result in significant financial losses. In this regard, we introduce p, q-spherical fuzzy set (p, q …-SFS), a novel concept that extends the concept of T-spherical fuzzy sets (T-SFSs). p, q- SFS is a comprehensive representation tool for capturing imprecise information. The main contribution of this article is to define the basic operations and a series of averaging and geometric AOs under p, q -spherical fuzzy (p, q -SF) environment. In addition, we establish several fundamental properties of the proposed aggregation operators (AOs). Based on these AOs, we propose a stepwise algorithm for multi-criteria DM (MCDM) problems. Finally, a real-life case study involving the selection of a 3PL provider is shown to validate the applicability of the proposed approach. Show more

Keywords: T-spherical fuzzy set, aggregation operators, decision-making, p, q-spherical fuzzy set, multi-criteria decision-making

DOI: 10.3233/JIFS-235297

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 505-528, 2024

Authors: Sánchez-DelaCruz, Eddy | Abdul-Kareem, Sameem | Pozos-Parra, Pilar

Article Type: Research Article

Abstract: Background: Many neurodegenerative diseases affect human gait. Gait analysis is an example of a non-invasive manner to diagnose these diseases. Nevertheless, gait analysis is difficult to do because patients with different neurodegenerative diseases may have similar human gaits. Machine learning algorithms may improve the correct identification of these pathologies. However, the problem with many classification algorithms is a lack of transparency and interpretability for the final user. Methods: In this study, we implemented the PS -Merge operator for the classification, employing gait biomarkers of a public dataset. Results: The highest classification percentage was 83.77%, which means …an acceptable degree of reliability. Conclusions: Our results show that PS -Merge has the ability to explain how the algorithm chooses an option, i.e., the operator can be seen as a first step to obtaining an eXplainable Artificial Intelligence (XAI). Show more

Keywords: PS-Merge, Classification, Neurodegenerative diseases, XAI

DOI: 10.3233/JIFS-235053

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 529-541, 2024

Authors: Vidya, S. | Jagannathan, Veeraraghavan | Guhan, T. | Kumar, Jogendra

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-235798

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 543-561, 2024

Authors: He, Jialin

Article Type: Research Article

Abstract: With the rapid development of information technology, software products are playing an increasingly important role in people’s production and life, and have penetrated into many industries. Software quality is the degree to which the software meets the specified requirements, and is an important indicator to evaluate the quality of the products used. At present, the scale of software is increasing, and the complexity is increasing. It is an urgent problem to reasonably grasp and ensure the product quality. The measurement and evaluation of Software quality characteristics is an effective means to improve Software quality. Faced with the complex system of …software, there are many factors that affect product quality. Current research mainly measures software product quality from a qualitative perspective. The computer software quality evaluation is a classical multi-attribute group decision making (MAGDM). Type-2 Neutrosophic Numbers (T2NNs) is a popular set in the field of MAGDM and many scholars have expanded the traditional MAGDM to this T2NNs in recent years. In this paper, two new similarity measures based on sine function for T2NN is proposed under T2NNs. These two new methods are built for MAGDM based on the sine similarity measures for T2NN (SST) and sine similarity weighted measures for T2NN (SSWT). At the end of this paper, Finally, a practical case study for computer software quality evaluation is constructed to validate the proposed method and some comparative studies are constructed to verify the applicability. Thus, the main research contribution of this work is constructed: (1) two new similarity measures based on sine function for T2NN is proposed under T2NNs; (2) These two new methods are built for MAGDM based on the sine similarity measures for T2NN (SST) and sine similarity weighted measures for T2NN (SSWT); (3) an example for computer software quality evaluation is employed to verify the constructed techniques and several decision comparative analysis are employed to verify the constructed techniques. Show more

Keywords: Multi-attribute decision making (MAGDM), Type-2 neutrosophic numbers (T2NNs), similarity measure, sine function, computer software quality evaluation

DOI: 10.3233/JIFS-233407

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 563-578, 2024

Authors: Sharma, Shamneesh | Mishra, Nidhi

Article Type: Research Article

Abstract: The expeditious advancement and widespread implementation of intelligent urban infrastructure have yielded manifold advantages, albeit concurrently engendering novel security predicaments. Examining current patterns in the security of smart cities is paramount in comprehending nascent risks and formulating efficacious preventative measures. The present study suggests the utilization of Latent Semantic Analysis (LSA) as a means to scrutinize and reveal implicit semantic associations within a collection of textual materials pertaining to the security of smart cities. Through the process of gathering and pre-processing pertinent textual data, constructing a matrix that represents the frequency of terms within documents, and utilizing techniques that reduce …the number of dimensions, Latent Semantic Analysis (LSA) has the ability to uncover concealed patterns and associations among concepts related to security. This study proposes five recommendations for future research that employ a topic modeling technique to investigate the often-explored subjects related to smart city security. This discovery provides additional evidence in favor of the proposition that a robust blockchain-driven framework is vital for the advancement of smart cities. Latent Semantic Analysis (LSA) offers important insights into the dynamic landscape of smart city security by employing several techniques such as pattern recognition, document or phrase clustering, and result visualization. Through the examination of patterns and developments, individuals in positions of political authority, urban planning, and security knowledge possess the ability to uphold their proficiency, render judicious choices substantiated by empirical data, and establish proactive strategies aimed at preserving the security, privacy, and sustainability of intelligent urban environments. Show more

Keywords: Smart cities, security in smart cities, Latent Semantic Analysis (LSA), trends in smart cities, natural language processing

DOI: 10.3233/JIFS-235210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 579-596, 2024

Authors: Aruna Jasmine, J. | Heltin Genitha, C.

Article Type: Research Article

Abstract: Predicting the landslide-prone area is critical for various applications, including emergency response, land planning, and disaster mitigation. There needs to be a thorough landslide inventory in current studies and appropriate sampling uncertainty issues. Landslide risk mapping has expanded significantly as machine learning techniques have developed. However, one of the primary issues in Landslide Prediction is data imbalance (DI). This is problematic since it is challenging or expensive to generate an accurate inventory map of landslides based on previous data. This study proposes a novel landslide prediction method using Generative Adversarial Networks (GAN) for generating the synthetic data, Synthetic Minority Oversampling …Technique (SMOTE) for overcoming the data imbalance problem, and Bee Collecting Pollen Algorithm (BCPA) for feature extraction. Combining 184 landslides and ten criteria, including topographic wetness index (TWI), aspect, distance from the road, total curvature, sediment transport index (STI), height, slope, stream, lithology, and slope length, a geographical database was produced. The data was generated using GAN, a Deep Convolutional Neural Network (DCNN) technique to populate the dataset. The proposed DCNN-BCPA approach findings were merged with current machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM), logistic regression (LR). The model’s accuracy, precision, recall, f-score, and RMSE were measured using the following metrics: 92.675%, 96.298%, 90.536%, 96.637%, and 45.623%. This study suggests that harmonizing landslide data may have a substantial impact on the predictive capabilities of machine learning models. Show more

Keywords: Bee collecting pollen algorithm, data balancing, generative adversarial network, landslide susceptibility, synthetic data

DOI: 10.3233/JIFS-234924

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 597-617, 2024

Authors: Choe, Kwang-Il | Huang, Xiaoxia | Ma, Di

Article Type: Research Article

Abstract: To achieve the carbon neutrality goal, enterprises should consider not only the development of new low-carbon emission projects but also the adjustment of the existing high-carbon emission projects. This paper discusses a multi-period project adjustment and selection (MPPAS) problem under the carbon tax and carbon quota policies. First, we propose an uncertain mean-chance MPPAS model for maximizing the profit of the project portfolio under the carbon tax and carbon quota policies. Then, we provide the deterministic equivalent of the proposed model and conduct the theoretical analysis of the impact of carbon tax and carbon quota policies. Next, we propose an …improved adaptive genetic algorithm to solve the proposed model. Finally, we give numerical experiments to verify the proposed algorithm’s performance and show the proposed model’s applicability. Research has shown that the government can achieve the carbon neutrality goal by determining reasonable carbon tax and carbon quota policies, and companies can make the optimal investment decisions for the project portfolio by the proposed model. In addition, the proposed algorithm has good performances in robustness, convergence speed, and global convergence. Show more

Keywords: Project portfolio, uncertainty theory, carbon emission reduction, adaptive genetic algorithm

DOI: 10.3233/JIFS-231970

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 619-637, 2024

Authors: Cai, Mingqian | Zhou, Ligang | Chen, Mingxian | Chen, Huayou

Article Type: Research Article

Abstract: Linguistic q-rung orthpair fuzzy sets (Lq-ROFSs) can facilitate describing the uncertainty and the vagueness information existing in the real world. Based on the advantages of Lq-ROFSs, this paper innovatively puts forward a new method to solve the multi-attribute group decision-making (MAGDM) problems when the attribute weight is completely unknown, and proves the feasibility and effectiveness of this method through illustrative examples. Firstly, we propose the linguistic q-rung orthopair fuzzy generalized power average (Lq-ROFGPA) operator, which considers not only the importance of the data itself, but also the interaction between the data, and prove its properties. In particular, the linguistic q-rung …orthopair fuzzy weighted generalized power average (Lq-ROFWGPA) operator takes into account the weight between data, which can better aggregate evaluation information. Then, we introduce decision making trial and evaluation laboratory (DEMATEL) method of the linguistic q-rung orthpair fuzzy numbers (Lq-ROFNs) to analyze the causal relationship and key elements of complex systems. Based on DEMATEL method, we further develop a weight model to calculate the attribute weights, which can make up for the deficiency which is the influence of the interaction between attributes that the existing weight determination method for Lq-ROFNs does not consider. Finally, we present a new MAGDM method based on the Lq-ROFWGPA operator and DEMATEL method. Further, several practical examples are given to illustrate the effectiveness and superiority of this new method in comparison with other existing MAGDM methods. Show more

Keywords: Multiple-attribute group decision-making, linguistic q-rung orthopair fuzzy numbers, generalized power average operator, DEMATEL

DOI: 10.3233/JIFS-230712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 639-658, 2024

Authors: Wang, Pingping | Chen, Jiahua

Article Type: Research Article

Abstract: As a decision information preference which includes membership degree (MD), non-membership degree (NMD), and probability, the probabilistic dual hesitant fuzzy set (PDHFS) is a crucial tool for effectively expressing uncertain information. In the domains of multi-attribute decision making (MADM) and multi-attribute group decision making (MAGDM), distance measures are extremely helpful tools. In this study, a novel PDHFS distance measure is put out, on which a MAGDM method that takes decision-makers’ (DMs’) psychological behavior into account is proposed. First, a novel distance measure is put forward to effectively assess the difference between different PDHFSs by adding consideration of the distances between …MDs and between NMDs. Second, a similarity-trust analysis method based on the new distance measure is employed to calculate expert weights for integrating group decisions, and the group satisfaction index and regret theory are extended to a probabilistic dual hesitant fuzzy information environment. A MAGDM method based on the novel distance measure and regret theory is proposed. Finally, the proposed method is applied to the selection of radiation protection strategies in nuclear power plants, and it is also determined through parametric analysis that DMs’ tendency to avoid regret has an impact on the outcomes of decisions. When the method proposed in this study is compared to existing approaches, the findings demonstrate that the method’s performance in resolving MAGDM issues in a PDHFS environment is superior. Show more

Keywords: Multi-attribute group decision making, probabilistic dual hesitant fuzzy set, distance measures, regret theory, EDAS, similarity-trust analysis

DOI: 10.3233/JIFS-233148

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 659-675, 2024

Authors: Quyang,

Article Type: Research Article

Abstract: The completion degree of sports training can not reach the corresponding standard, and the training effect will be greatly weakened. In order to improve the effect of sports training, the evaluation method of sports training completion degree based on deep residual network is studied. The image collector based on ARM is used to collect the action images of athletes in sports training, and the collected action images are preprocessed based on spatial scale filtering and regression factors. Construct a depth residual network, learn the implicit relationship between athletes’ state and the dynamic change process of sports training actions through off-line …training, and train the model; In the online application process, the preprocessed action images will be input into the trained evaluation model to evaluate the athletes’ sports training action completion in real time. At the same time, residual shrinkage unit and attention mechanism are used to optimize the depth residual network, which improves the training efficiency and evaluation performance of the network. The experimental results show that this method has good evaluation performance under the condition of setting parameters, and can effectively improve the effect of physical training. Show more

Keywords: Deep residual network, sports training, action completion degree, image acquisition, image denoising

DOI: 10.3233/JIFS-233773

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 677-691, 2024

Authors: Naik, N.V. | Hyma, J. | Prasad Reddy, P.V.G.D.

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-235991

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 693-709, 2024

Authors: Chunhong, Zhao | Jinglei, Nie | Shuwen, Yin | Dingyu, Zhang | Chengmo, Li

Article Type: Research Article

Abstract: The design and implementation of teachers’ classroom teaching strategies is the key to the success of second language classroom teaching. In order to improve the quality of second-language classroom teaching in universities and enhance the interactivity in the teaching process, the application of virtual reality technology in second-language classroom teaching in universities is studied. Firstly, an integrated ware platform is designed for second language classroom teaching in universities, which consists of an integrated ware library and a database. Then virtual reality technology is used to design an integrated ware library within the platform and collect information such as various teaching …media resources designed in the classroom teaching content into the integrated ware library; Utilize 3DS Max software in virtual reality technology to construct three-dimensional models of teaching scenes and entities; Choose to use linear difference method to render 3D model colors; From a visual perspective, enhance the realism of the rendered model through image enhancement technology and color contrast enhancement technology. According to the functions of the physical object, various interactive events are added to the created teaching scene and the three-dimensional model of the entity and stored in the integrable ware library to achieve panoramic roaming of the second language classroom teaching scene in universities. The experimental results show that the teaching platform designed by this method can accurately construct three-dimensional models of teaching scenes and objects with good visual effects, providing users with a more realistic sensory experience and effectively improving students’ mastery of teaching content. Show more

Keywords: Virtual reality technology, classroom teaching, 3D model, color rendering, panoramic walkthrough

DOI: 10.3233/JIFS-233210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 711-722, 2024

Authors: Kaijun, Zhao

Article Type: Research Article

Abstract: To enhance the psychological resilience of athletes, a method for evaluating the psychological resilience of High-intensity Interval Training (HIIT) athletes based on evolutionary neural networks is studied. From the six criteria of frustration coping, personal characteristics, self-promotion, self-regulation, internal protection and external protection, the evaluation index of psychological resilience of athletes in sports High-intensity Interval Training is selected; the audition indicators are qualitatively analyzed according to the principle of indicator selection, and the indicators that do not meet the requirements are eliminated; Cluster analysis and coefficient of variation analysis are used to carry out quantitative analysis on the remaining evaluation …indicators after qualitative analysis; the indicators after quantitative analysis are improved, to build the assessment index system of psychological resilience of athletes in high-intensity sports training. The Back Propagation (BP) neural network is optimized by a genetic algorithm, and the evolutionary neural network is constructed. The index data set is input into the evolutionary neural network as a sample, and the index weight value is output through training. The evaluation result and corresponding evaluation grade are determined based on the index weight value and membership degree. The experimental results show that when the number of hidden layers is 3, the calculation of evaluation index weights is the best; The weight of personal traits obtained from the evaluation results is the highest (0.206), while the weight of external protection is the lowest (0.151), and the evaluation results are basically consistent with the expert results. The above results show that this method can accurately evaluate the psychological resilience of athletes and significantly enhance their psychological resilience. Show more

Keywords: Evolutionary neural network, evaluation of psychological resilience, index system construction, genetic algorithm, weight calculation

DOI: 10.3233/JIFS-233299

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 723-737, 2024

Authors: Lei, Hongquan | Li, Diquan | Jiang, Haidong

Article Type: Research Article

Abstract: Traditional sonar image target detection analysis has problems such as long detection time, low detection accuracy and slow detection speed. To solve these problems, this paper will use the multi-feature fusion sonar image target detection algorithm based on the particle swarm optimization algorithm to analyze the sonar image. This algorithm uses the particle swarm algorithm to optimize the combination of multiple feature vectors and realizes the adaptive selection and combination of features, thus improving the accuracy and efficiency of sonar image target detection. The results show that: when other conditions are the same, under the particle group optimization algorithm, the …sonar image multiple feature detection algorithm for three sonar image detection time between 4s-9.9s, and the sonar image single feature detection algorithm of three sonar image detection time between 12s-20.9s, shows that the PSO in multiple feature fusion sonar image target detection with better performance and practicability, can be effectively applied to the sonar image target detection field. Show more

Keywords: Sonar images, particle swarm optimization algorithm, target detection, multi-feature fusion, single multi-feature fusion

DOI: 10.3233/JIFS-234876

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 739-751, 2024

Authors: Kavitha, J.C. | Subitha, D.

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-235990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 753-767, 2024

Authors: Anisha, C.D. | Arulanand, N.

Article Type: Research Article

Abstract: The Spiral Drawing Test (SDT) has become a prominent clinical marker for the early diagnosis of Parkinson’s Disorder (PD) by capturing tremor symptoms. The integration of AI algorithms into a PD diagnosis system has proven to be a breakthrough objective assessment that aids professionals in decision-making. However, there is a need for improvisation of the workflow architectures of AI models to optimize the diagnosis system by reducing the misdiagnosis rate. The proposed system presents PD prediction using a Spiral Drawing Test (SDT) image modality integrated with an Artificial Intelligence (AI) algorithm. The proposed study presents three hybrid workflow architectures formed …by integrating three core layers: a data augmentation layer, Transfer Layer (TL)-based feature extraction layer, and Deep Learning (DL)-based classification layer. The results were analyzed by conducting 18 experiments based on the hyperparameter values and workflow architectures. The highest accuracy obtained by the proposed study is 98% for Hybrid Workflow Architecture II. Show more

Keywords: Parkinson disorder, transfer learning

DOI: 10.3233/JIFS-231202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 769-787, 2024

Authors: Li, Zehao | Wang, Shunli | Yu, Chunmei | Qi, Chuangshi | Shen, Xianfeng | Fernandez, Carlos

Article Type: Research Article

Abstract: The development of a secure battery management system (BMS) for electric vehicles depends heavily on the correct assessment of the online state-of-charge (SOC) of Li-ion batteries. The ternary lithium battery is used as the research object in this paper, and a second-order RC equivalent circuit model is developed to characterize the dynamic operating characteristics of the battery. In order to solve the problem that the adaptive unscented Kalman filter (AUKF) algorithm is easy to fail SOC estimation because the error covariance matrix is not positively definite due to the incomplete accuracy of the equivalent circuit model, a corresponding solution is …proposed. Considering the poor real-time battery SOC estimate caused by the battery model’s fixed parameters, therefore we propose the Variable Forgetting Factor Recursive Least Squares (VFFRLS) algorithm for joint estimation of Li-battery SOC and the Singular Value Decomposition-AUKF (SVD-AUKF) algorithm. The SVD-AUKF algorithm can accurately estimate the SOC of the battery when the error covariance is negative. The algorithm can be adaptively adjusted in both the parameter identification and SOC estimation stages, which can effectively solve the problem of poor estimation accuracy caused by fixed parameters. According to experiments, under two separate dynamic operating situations, the joint estimation algorithm’s error is less than 2%, and its stability has also been greatly enhanced. At the same time, when the initial SOC value is set incorrectly, the convergence time of the algorithm proposed in this paper can reach within 2.1 seconds for BBDST and DST conditions, which can be well adapted to complex working conditions. Show more

Keywords: Lithium-ion battery, second-order RC equivalent circuit model, charge state, adaptive unscented Kalman filter algorithm, variable forgetting factor recursive least squares, singular value decomposition, error covariance

DOI: 10.3233/JIFS-231433

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 789-803, 2024

Authors: Li, Yue | Cai, Qiang | Wei, Guiwu

Article Type: Research Article

Abstract: In this paper, the author propose a unique multi-attribute group decision making(MAGDM) method SVN-CPT-GRA. The method takes the single-value neutrosophic environment as the decision-making environment and uses the entropy weighted-grey relational analysis method under cumulative prospect theory. First, based on the evaluation of decision-makers, the single-value neutrosophic decision matrix was obtained. The entropy weight method was used to calculate the attribute weights. Next, according to the distance between each SVNN and the negative ideal value, combining the gray relation analysis and the cumulative prospect theory, the correlation between each solution and the attribute is compared to determine the advantages and …disadvantages of each solution. Finally, the extended gray relational analysis method is demonstrated to be effectively applied to the decision-making process through a case study of investment choices in new energy vehicles and a comparison with other methods. The main innovations in this paper can be summarized as follows. Firstly, combining the cumulative prospect theory with the gray relational analysis for decision making can better reflect and represent the psychological changes and risk sensitivity of decision makers. Secondly, the entropy weight method is used to determine the attribute weights according to the distance between SVNN and the negative ideal value, which makes the attribute weights more objective and ensures the scientificity and reasonableness of the attribute weights. Thirdly, applying GRA method to the single-value neutrosophic environment, the original simple and practical GRA method to be more widely applied to the fuzzy environment, expanding the scope of application. Overall, the extended GRA method proposed in this paper can be more efficiently and scientifically adapted to MAGDM in fuzzy environments, providing more choices for decision-makers. Show more

Keywords: Single-valued neutrosophic sets (SVNSs), grey relational analysis (GRA), multi-attribute group decision making (MAGDM), CRITIC, cumulative prospect theory (CPT)

DOI: 10.3233/JIFS-231630

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 805-819, 2024

Authors: Zhang, Yanyu | Liu, Chunyang | Rao, Xinpeng | Zhang, Xibeng | Zhou, Yi

Article Type: Research Article

Abstract: Accurate forecasting of the load of electric vehicle (EV) charging stations is critical for EV users to choose the optimal charging stations and ensure the safe and efficient operation of the power grid. The charging load of different charging stations in the same area is interrelated. However, forecasting the charging load of individual charging station using traditional time series methods is insufficient. To fully consider the spatial-temporal correlation between charging stations, this paper proposes a new charging load forecasting framework based on the Adaptive Spatial-temporal Graph Neural Network with Transformer (ASTNet-T). First, an adaptive graph is constructed based on the …spatial relationship and historical information between charging stations, and the local spatial-temporal dependencies hidden therein are captured by the spatio-temporal convolutional network. Then, a Transformer network is introduced to capture the global spatial-temporal dependencies of charging loads and predict the future multilevel charging loads of charging stations. Finally, extensive experiments are conducted on two real-world charging load datasets. The effectiveness and robustness of the proposed algorithm are verified by experiments. In the Dundee City dataset, the MAE, MAPE, and RMSE values of the proposed model are improved by approximately 71%, 90%, and 67%, respectively, compared to the suboptimal baseline model, demonstrating that the proposed algorithm significantly improves the accuracy of load forecasting. Show more

Keywords: Electric vehicle, load forecasting, graph convolutional network, temporal convolutional network, transformer

DOI: 10.3233/JIFS-231775

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 821-836, 2024

Authors: Hong, Jiajun | Tsai, Rong-Guei | Chen, Xiaolan | Lin, Di | Yu, Yicong | Lin, Ying | Li, Ronghao

Article Type: Research Article

Abstract: Marine debris is a serious global problem that is not limited to areas where humans live but also drifts around the world with wind and currents. More than 10 million tons of plastic waste flow into the ocean every year, posing a major threat to humanity. This study designs a path planning algorithm for surface garbage-cleaning robots called U*, which aims to improve the efficiency of salvaging marine debris and reduce labor and time costs. The U* algorithm consists of two procedures: exploration and path-planning. The exploration procedure searches for marine debris, while the path-planning procedure predicts the possible location …of marine debris using the velocity and direction of ocean currents and finds the shortest path by using a genetic algorithm (GA) to collect the found marine debris. According to the experimental results, the U* method is more efficient in terms of reducing path length and time costs. Show more

Keywords: Path planning, shorted path, genetic algorithm, surface garbage-cleaning robots

DOI: 10.3233/JIFS-232137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 837-850, 2024

Authors: Gao, Yuchen | Yang, Qing | Meng, Huijuan | Gao, Dexin

Article Type: Research Article

Abstract: Flame and smoke detection is a critical issue that has been widely used in various unmanned security monitoring scenarios. However, existing flame smoke detection methods suffer from low accuracy and slow speed, and these problems reduce the efficiency of real-time detection. To solve the above problems, we propose an improved YOLOv7(You Only Look Once) algorithm for flame smoke mobile detection. The algorithm uses the Kmeans algorithm to cluster the prior frames in the dataset and uses a lightweight CNeB(ConvNext Block) module to replace part of the traditional ELAN module to accelerate the detection speed while ensuring high accuracy. In addition, …we propose an improved CIoU loss function to further enhance the detection effect. The experimental results show that, compared with the original algorithm, our algorithm improves the accuracy by 4.5% and the speed by 39.87%. This indicates that our algorithm meets the real-time monitoring requirements and can be practically applied to field detection on mobile edge computing devices. Show more

Keywords: YOLO, fire detect, smoke detect, NVIDIA Jetson

DOI: 10.3233/JIFS-232650

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 851-861, 2024

Authors: Monteiro, Ana Shirley | Santiago, Regivan | Bedregal, Benjamín | Palmeira, Eduardo | Araújo, Juscelino

Article Type: Research Article

Abstract: Saminger-Platz, Klement, and Mesiar (2008) extended t -norms from a complete sublattice to its respective lattice using the conventional definition of sublattice. In contrast, Palmeira and Bedregal (2012) introduced a more inclusive sublattice definition, via retractions. They expanded various important mathematical operators, including t -norms, t -conorms, fuzzy negations, and automorphisms. They also introduced De Morgan triples (semi-triples) for these operators and provided their extensions in their groundbreaking work. In this paper, we propose a method of extending quasi-overlap functions and quasi-grouping functions defined on bounded sublattices (in a broad sense) to a bounded superlattice. To achieve that, we use …the technique proposed by Palmeira and Bedregal. We also define: quasi-overlap (resp . quasi-grouping) functions generated from quasi-grouping (resp . quasi-overlap) functions and frontier fuzzy negations, De Morgan (semi)triples for the classes of quasi-overlap functions, quasi-grouping functions and fuzzy negations, as well as its respective extensions. Finally we study properties of all extensions defined. Show more

Keywords: Retractions, extensions, quasi-overlap, quasi-grouping, bounded lattices

DOI: 10.3233/JIFS-232805

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 863-877, 2024

Authors: Hao, Xiaofan

Article Type: Research Article

Abstract: From a management perspective, performance is the desired outcome of an organization, and it is an effective output that an organization exhibits at different levels to achieve its goals. Sports event performance refers to the results and effects generated by sports events, and is a comprehensive assessment category in sports event management. It refers not only to the concept of economic level, but also to the public satisfaction of sports events and a series of social effects caemployed by them. It focuses not only on the quality and economic value of sports events themselves, but also on the achievements and …effects of sports events and society, sports events and citizens, sports events and the environment. The performance evaluation of intangible assets operation and management (IAOM) in sports events is the MAGDM. Recently, the TODIM and TOPSIS technique has been employed to manage MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are employed as a useful tool for depicting uncertain information during the performance evaluation of IAOM in sports events. In this paper, the interval-valued intuitionistic fuzzy TODIM-TOPSIS (IVIF-TODIM-TOPSIS) technique is built to manage the MAGDM under IVIFSs. At last, the numerical example for sports events performance evaluation of IAOM is employed to show the IVIF-TODIM-TOPSIS decision technique. The main contribution of this paper is outlined: (1) the TODIM technique based on TOPSIS has been extended to IVIFSs based on information Entropy; (2) the information Entropy technique is employed to derive weight based on core values under IVIFSs. (3) the IVIF-TODIM-TOPSIS technique is founded to manage the MAGDM under IVIFSs; (4) a numerical case study for performance evaluation of IAOM in sports events and some comparative analysis is supplied to validate the proposed technique. Show more

Keywords: Multiple-attribute group decision-making (MAGDM), interval-valued intuitionistic fuzzy sets (IVIFSs), TODIM, TOPSIS, performance evaluation

DOI: 10.3233/JIFS-233465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 879-890, 2024

Authors: Cui, Qian | Rong, Shuai | Zhang, Fei | Wang, Xiaodan

Article Type: Research Article

Abstract: The consumer price index (CPI) is an important indicator to measure inflation or deflation, which is closely related to residents’ lives and affects the direction of national macroeconomic policy formulation. It is a common method to discuss CPI from the perspective of economic analysis, but the statistical principles and influencing factors related to CPI are often ignored. Thus, the impact of different types of CPI on China’s overall CPI was discussed from three aspects: statistical simulation, machine learning prediction and correlation analysis of various types of influencing factors and CPI in this study. Realistic data from the National Bureau of …Statistics from 2010 to 2022 were selected as the analysis object. The Statistical analysis showed that in 2015 and 2020, CPI had a fluctuating trend due to the impact of education and transportation. Four types of statistical models including Gauss, Lorentz, Extreme and Pearson were compared. It was determined that the R2 fitted by Extreme model was higher (R2  = 0.81), and the optimal year of simulation was around 2019, which was close to reality. To accurately predict the CPI, the results of Support Vector Machine, Regression decision tree and Gaussian regression (GPR) were compared, and the GPR was determined to be the optimal model (R2  = 0.99). In addition, Spearman matrix analyzed the correlation between CPI and various influencing factors. Herein, this study provided a new method to determine and predict the changing trend of CPI by using big data analysis. Show more

Keywords: Consumer price index, statistics, mathematical, machine learning, Spearman

DOI: 10.3233/JIFS-234102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 891-901, 2024

Authors: Xu, Yue | Afzal, Mansour

Article Type: Research Article

Abstract: Accurately estimating concrete mechanical parameters using artificial intelligence-based methods can save time and energy. Existing nonlinear relationships between concrete components have entered uncertainty in the estimation of hardness properties of the slump and compressive strength as one of the most important parameters in concrete design. Employing regular approaches to use AI models individually in estimating dependent variables has been adopted in many studies. Therefore, the current study has aimed to develop predictive models in two categories of ensemble and hybrid frameworks to predict the hardness properties of high-performance concrete (HPC). In this regard, models based on Support Vector Regression, Decision …Tree, and AdaBoost Machine learning were coupled with a metaheuristic optimization algorithm Chaos game optimizer (CGO). Linking three predictive models as well as tuning their internal settings via optimization algorithm could generate various types of hybrid and ensemble models. By assessing the results of the proposed models for compressive strength, the performance of ADA-CGO hybrid models was calculated higher than the ensemble model of SVR-ADA-DT, with 1.22% and 166% percent difference in terms of R2 and RMSE, respectively. Also, for predicting Slump, other hybrid models appeared with weaker performance than the ensemble model, with an average difference of 40.66% in terms of the MAE index. Generally, using advanced types of individual models, including ensemble and hybrid, indicated boosted performance accompanied by low-cost modeling processes. Show more

Keywords: High-performance concrete compressive strength and slump, AdaBoost, support vector regression, decision tree, Chaos game optimizer.

DOI: 10.3233/JIFS-234409

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 903-921, 2024

Authors: Shanyong, Xu | Jicheng, Deng | Yourui, Huang | Tao, Han

Article Type: Research Article

Abstract: Aiming at the problems of poor accuracy of insulator defects, bird’s nests and foreign objects detection in transmission lines, and the difficulty of algorithm hardware deployment, this paper proposes an improved YOLOv5s multi-hidden target detection algorithm for transmission lines, firstly, in backbone, the CA attention(Coordinate attention) mechanism is integrated into the C3 module to form the C3CA module, which replaces the C3 module of the sixth and the eighth layers, and enhances the feature fusion capability; secondly, in the neck, the GSConv convolution and VoVGSCSP modules are used to replace the standard convolution and C3 modules to form a BiFPN …network, which reduces the floating-point operations of the network; finally, the improved algorithm is deployed into Raspberry Pi and accelerated by OpenVINO to realize the hardware deployment of the algorithm, which is demonstrated by experiments that: the mAP value of the algorithm is comparable to that of YOLOv3, YOLOv5 and YOLOv7 by 4.7%, 1.1%, and 1.2%, respectively. The model size is 14.2MB, and the average time to detect an image in Raspberry Pi is 78.2 milliseconds, which meets the real-time detection requirements. Show more

Keywords: Improved YOLOv5s, transmission line inspection, GSConv convolutional, raspberry Pi, OpenVINO

DOI: 10.3233/JIFS-234732

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 923-939, 2024

Authors: Yang, Xingyao | Dang, Zibo | Yu, Jiong | Zhong, Zhiqiang | Chang, Mengxue | Zhang, Zulian

Article Type: Research Article

Abstract: In existing sequential recommendation systems, user behavior data are directly used as training data for the model to complete the training process and address recommendation tasks. However, user-generated behavioral data inevitably contains noise, and the use of the Transformer’s recommendation model may lead to overfitting on such noisy data. To address this issue, we introduce a sequence recommendation algorithm model named FAT-Rec, which incorporates fusion filters and converters through joint training. By employing joint training methods, we establish both a transformer prediction layer and a CTR prediction layer. Toward the end of the model, we assign weights and sum up …the losses from the Transformer and CTR prediction layers to derive the final loss function. Experimental results on two widely used datasets, MovieLens and Goodbooks, demonstrate a significant enhancement in the performance of the proposed FAT-Rec recommendation algorithm compared with seven comparative models. This validates the efficacy of the fusion filter and transformer within the context of sequence recommendation tasks under the joint training mechanism. Show more

Keywords: Filter, self-attention mechanism, transformer, joint training, user sequence

DOI: 10.3233/JIFS-235318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 941-953, 2024