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

Authors: Zhao, Xiaoqing | Xu, Miaomiao | Li, Yanbing | Huang, Hao | Silamu, Wushour

Article Type: Research Article

Abstract:  This research focuses on Scene Text Recognition (STR), a crucial component in various applications of artificial intelligence such as image retrieval, office automation, and intelligent traffic systems. Recent studies have shown that semantic-aware approaches significantly improve the performance of STR tasks, with context-aware STR methods becoming mainstream. Among these, the fusion of visual and language models has shown remarkable effectiveness. We propose a novel method (PABINet) that incorporates three key components: a Visual-Language Decoder, a Language Model, and a Fusion Model. First, during training, the Visual-Language Decoder masks the original labels in the Transformer decoder using permutation masks, with each …mask being unique. This enhances word memorization and learning through contextual semantic information, resulting in robust semantic knowledge. During the inference stage, the Visual-Language Decoder employs autonomous Autoregressive model (AR) inference to generate results. Subsequently, the Language Model scrutinizes and corrects the output of the Visual-Language Encoder using a cloze mask approach, achieving context-aware, autonomous, bidirectional inference. Finally, the Fusion Model concatenates and refines the outputs of both models through iterative layers.Experimental results demonstrate that our PABINet performs exceptionally well when handling various quality images. When trained with synthetic data, PABINet achieves a new STR benchmark (average accuracy of 92.41%), and when trained with real data, it establishes new state-of-the-art results (average accuracy of 96.28%). Show more

Keywords: Scene text recognition, language model, visual-language decoder

DOI: 10.3233/JIFS-237135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8605-8616, 2024

Authors: Arunagirinathan, Sumithara | Subramanian, Chitra

Article Type: Research Article

Abstract: This paper presents a hybrid approach for optimizing the maximum power point tracking of photovoltaic (PV) systems in electric vehicles. The hybrid technique involves the simultaneous utilization of the Gannet Optimization Algorithm (GOA) and Quantum Neural Network (QNN), collectively referred to as the GOA-QNN technique. The primary aim is to enhance the efficiency and maximize the power output of PV systems. The proposed hybrid methodology boosts the performance of the photovoltaic system by managing the power interface. A high step-up DC/DC converter is employed to adjust the photovoltaic source power and load, ensuring optimal power transfer under various operating conditions. …The proposed method optimally determines the duty cycle of the converter. Subsequently, the model is implemented in the MATLAB/Simulink platform, and its execution is evaluated using established procedures. The results clearly demonstrate the superiority of the proposed method over existing approaches in terms of power quality, settling time, and controller stability. The proposed technique achieves an impressive efficiency level of 95%, exceeding the efficiency of other existing techniques. Show more

Keywords: MPPT, Photovoltaic, high-gain converter, Gannet Optimization Algorithm, Quantum Neural Network, EV

DOI: 10.3233/JIFS-237734

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8617-8637, 2024

Authors: Karthika, K. | Rangasamy, Devi Priya

Article Type: Research Article

Abstract: In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information …is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time. Show more

Keywords: Hash based message authentication code (HMAC), cryptographic hash functions, Diffie Hellman, communications

DOI: 10.3233/JIFS-234641

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8639-8658, 2024

Authors: Wu, Chengding | Xu, Zhaoping | Liu, Liang | Yang, Tao

Article Type: Research Article

Abstract: There are limitations of personalization in Advanced Driver Assistance Systems (ADAS) that have a serious impact on driver acceptance and satisfaction. This study investigates driving style recognition method to achieve personalization of longitudinal driving behavior. Currently, driving style recognition algorithms for Personalized Adaptive Cruise Control (PACC) rely on integrated recognition. However, disturbances in the driving cycle may lead to changes in a driver’s integrated driving style. Therefore, the integrated driving style cannot accurately and comprehensively reflect the driver’s driving style. To solve this problem, a new driving style recognition method for PACC is proposed, which considers integrated driving style and …driving cycle. Firstly, the method calculates the constructed feature parameters of driving cycle and style, and then reduces the dimensionality of the feature parameter matrix by principal component analysis (PCA). Secondly, a two-stage clustering algorithm with self-organizing mapping networks and K-means clustering (SOM-K-means) is used to obtain the type labels. Then, a transient recognition model based on random forest (RF) is established and the hyperparameters of this model are optimized by sparrow search algorithm (SSA). Based on this, a comprehensive driving style recognition model is established using analytic hierarchy process (AHP). Finally, the validity of the proposed method is verified by a natural dataset. The method incorporates the driving cycle into driving style recognition and provides guidance for improving the personalization of adaptive cruise control system. Show more

Keywords: Personalized adaptive cruise control, SOM-K-means two-stage clustering, random forest (RF), sparrow search algorithm (SSA), driving style recognition

DOI: 10.3233/JIFS-235045

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8659-8675, 2024

Authors: Wan, Huanyu | Qiu, Dong

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-237319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8677-8695, 2024

Authors: Wang, Jinxin | Wu, Zhanwen | Yang, Longzhi | Hu, Wei | Song, Chaojun | Zhu, Zhaolong | Guo, Xiaolei | Cao, Pingxiang

Article Type: Research Article

Abstract:  Distributed flexible flowshop scheduling is getting more important in the large-scale panel furniture industry. It is vital for a higher manufacturing efficiency and economic profit. The distributed scheduling problem with lot-streaming in a flexible flow shop environment is investigated in this work. Furthermore, the actual constraints of packaging collaborative and machine setup times are considered in the proposed approach. The average order waiting time for packaging and average order delay rate is used as objectives. Non-dominated sorting method is used to handle this bi-objective optimization problem. An improved encoding method was proposed to address the large-scale orders that need to …be divided into sub-lots based on genetic algorithm. The proposed approach is firstly validated by benchmark with other multi-objectives evolutionary algorithms. The results found that the proposed approach had a good convergence and diversity. Besides, the influence of the proportion of large-scale orders priority level and sub-lot size was investigated in a panel furniture manufacturing scenario. The results can be concluded that the enterprise could obtain shorter order average waiting time and delay rate when the sub-lot sizes were set as two and the order priority level was allocated in the proportion of 1:2:3:4:5. Show more

Keywords: Distributed flexible flow shop scheduling, Panel furniture manufacturing, Lot-streaming, Packaging collaborative, Setup time

DOI: 10.3233/JIFS-237378

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8697-8707, 2024

Authors: Qin, Xiwen | Zhang, Siqi | Dong, Xiaogang | Shi, Hongyu | Yuan, Liping

Article Type: Research Article

Abstract: The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem …of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm’s optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods. Show more

Keywords: Biomedical data, mRMR, spectral clustering, SMOTE, marine predators algorithm

DOI: 10.3233/JIFS-237538

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8709-8728, 2024

Authors: Ren, Shujun | Wang, Yuanhong

Article Type: Research Article

Abstract: Image segmentation is critical in medical image processing for lesion detection, localisation, and subsequent diagnosis. Currently, computer-aided diagnosis (CAD) has played a significant role in improving diagnostic efficiency and accuracy. The segmentation task is made more difficult by the hazy lesion boundaries and uneven forms. Because standard convolutional neural networks (CNNs) are incapable of capturing global contextual information, adequate segmentation results are impossible to achieve. We propose a multiscale feature fusion network (MTC-Net) in this paper that integrates deep separable convolution and self-attentive modules in the encoder to achieve better local continuity of images and feature maps. In the decoder, …a multi-branch multi-scale feature fusion module (MSFB) is utilized to improve the network’s feature extraction capability, and it is integrated with a global cooperative aggregation module (GCAM) to learn more contextual information and adaptively fuse multi-scale features. To develop rich hierarchical representations of irregular forms, the suggested detail enhancement module (DEM) adaptively integrates local characteristics with their global dependencies. To validate the effectiveness of the proposed network, we conducted extensive experiments, evaluated on the public datasets of skin, breast, thyroid and gastrointestinal tract with ISIC2018, BUSI, TN3K and Kvasir-SEG. The comparison with the latest methods also verifies the superiority of our proposed MTC-Net in terms of accuracy. Our code on https://github.com/gih23/MTC-Net. Show more

Keywords: Medical image segmentation, multi-scale features, detail enhancement, feature fusion, deep learning

DOI: 10.3233/JIFS-237963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8729-8740, 2024

Authors: Yue, Lizhu | Wang, Qian

Article Type: Research Article

Abstract: With the rapid development of big data and continuous optimization of online shopping platforms, personalized recommendation has become a standard feature of recommendation methods. In order to effectively provide personalized recommendations to customers, improve recommendation accuracy, and customer satisfaction, it is necessary to consider customers’ preferences for multiple product attributes when making product recommendations. However, existing recommendation methods require precise calculation of product attribute weights, which is computationally expensive, complex, and often results in unstable weight values. This paper proposes a multi-attribute recommendation method based on consumer decision preference information that overcomes the need for weights and reflects personalized customer …preferences. Based on the acquisition of customer product attribute preference sequences, a partial order relation for recommended products is constructed using partial order set theory. Finally, the recommended products are determined through the partial order Hasse diagram, where the top layer elements of the Hasse diagram represent the recommended product set. This method addresses challenges that traditional content-based recommendations cannot overcome. The experiment in this paper uses a dataset of 30,000 records from Beeradvocate beer reviews. The experimental results show that, compared to traditional multi-attribute recommendation methods, this method only requires decision-maker preference information to complete product recommendations, requiring less information and having lower computational costs, resulting in more robust results. Show more

Keywords: Multi-attribute recommendation, partial order set, decision preference, hasse diagram, personalization

DOI: 10.3233/JIFS-231724

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8741-8754, 2024

Authors: Badshah, Noor | Begum, Nasra | Rada, Lavdie | Ashfaq, Muniba | Atta, Hadia

Article Type: Research Article

Abstract: Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a …new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively. Show more

Keywords: Image segmentation, , , , , image registration, linear curvature (LC), conditional mutual information (CMI), Jaccard similarity index (JSI)

DOI: 10.3233/JIFS-233306

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8755-8770, 2024

Authors: Chen, Zhipeng | Liu, Xiao | Qin, Jianhua

Article Type: Research Article

Abstract: To solve the problem that the walking jitter of quadruped robots leads to the degradation of clarity of visual imaging, a quadruped robot visual imaging jitter compensation algorithm based on the theory of walking jitter is proposed. The D-H coordinate transformation method is used to establish the coordinate system of each joint of the leg. The kinetic equations of the leg are derived from the relationship between the rotational velocity and the moment of the leg joint, and the kinetic equilibrium equations of the quadruped robot body are established based on the spatial moment equilibrium theorem; the spring-mass model of …the leg of the quadruped robot is used to construct the kinetic equations of the leg jittering, and the kinetic equations of the body jittering are derived using the moment equilibrium condition of the body center of gravity position and under the effect of the leg and body jitter to obtain the visual imaging device jitter quantity; finally, the tremor quantity is combined with the jitter quantity and rotation matrix to derive the walking jitter mathematical model of the quadruped robot visual imager, and the jitter compensation algorithm of quadruped robot visual imager is verified. The experimental results show that compared with the traditional Wiener filter algorithm for jitter compensation and the BP neural network jitter compensation algorithm, this algorithm improves the visual imaging by 10.8% and 3.3% in the two evaluation indexes of peak signal-to-noise ratio and structural similarity, respectively, and the de-jittering effect is better. Show more

Keywords: Quadruped robot, visual imaging, walking jitter, compensation algorithm

DOI: 10.3233/JIFS-235345

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8771-8782, 2024

Authors: Xiong, Haoyu | Yang, Leixin | Fang, Gang | Li, Junwei | Xiang, Yu | Zhang, Yaping

Article Type: Research Article

Abstract: Test-time augmentation (TTA) has become a widely adopted technique in the computer vision field, which can improve the prediction performance of models by aggregating the predictions of multiple augmented test samples without additional training or hyperparameter tuning. While previous research has demonstrated the effectiveness of TTA in visual tasks, its application in natural language processing (NLP) tasks remains challenging due to complexities such as varying text lengths, discretization of word elements, and missing word elements. These unfavorable factors make it difficult to preserve the label invariance of the standard TTA method for augmented text samples. Therefore, this paper proposes a …novel TTA technique called Defy, which combines nearest-neighbor anomaly detection algorithm and an adaptive weighting network architecture with a bidirectional KL divergence entropy regularization term between the original sample and the aggregated sample, to encourage the model to make more consistent and reliable predictions for various augmented samples. Additionally, by comparing with Defy, the paper further explores the problem that common TTA methods may impair the semantic meaning of the text during augmentation, leading to a shift in the model’s prediction results from correct to corrupt. Extensive experimental results demonstrate that Defy consistently outperforms existing TTA methods in various text classification tasks and brings consistent improvements across different mainstream models. Show more

Keywords: Test-time augmentation, test-time robustification, text classification, language model, anomaly detection

DOI: 10.3233/JIFS-236010

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8783-8798, 2024

Authors: Vijaya Lakshmi, A. | Vaitheki, K. | Suresh Joseph, K.

Article Type: Research Article

Abstract: Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled …SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. Show more

Keywords: Slender loris optimization algorithm, exploitation and exploration, optimization problems, swarm intelligence algorithm, metaheuristic

DOI: 10.3233/JIFS-236737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8799-8810, 2024

Authors: Chen, Junzhuo | Lu, Zonghan | Kang, Shitong

Article Type: Research Article

Abstract: In the wake of the global spread of monkeypox, accurate disease recognition has become crucial. This study introduces an improved SE-InceptionV3 model, embedding the SENet module and incorporating L2 regularization into the InceptionV3 framework to enhance monkeypox disease detection. Utilizing the Kaggle monkeypox dataset, which includes images of monkeypox and similar skin conditions, our model demonstrates a noteworthy accuracy of 96.71% on the test set, outperforming conventional methods and deep learning models. The SENet module’s channel attention mechanism significantly elevates feature representation, while L2 regularization ensures robust generalization. Extensive experiments validate the model’s superiority in precision, recall, and F1 score, …highlighting its effectiveness in differentiating monkeypox lesions in diverse and complex cases. The study not only provides insights into the application of advanced CNN architectures in medical diagnostics but also opens avenues for further research in model optimization and hyperparameter tuning for enhanced disease recognition. Show more

Keywords: CNN, InceptionV3, SENet, L2 regularization, monkeypox disease, deep learning

DOI: 10.3233/JIFS-237232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8811-8828, 2024

Authors: Zhou, Yinwei | Hu, Jun

Article Type: Research Article

Abstract: The rough set model has been extended to interval rough number decision systems, but the existing studies do not consider interval rough number decision systems with missing values. To this end, a rough set model of incomplete interval rough number decision systems (IIRNDSs) is proposed, and its uncertainty measures are investigated. Firstly, the similarity of two incomplete interval rough numbers (IIRNs) are defined by calculating their optimistic and pessimistic distances of the lower and upper approximation intervals of IIRNs. Then, the rough sets in IIRNDSs are constructed by the induced similarity relation. Next, four uncertainty measures, including approximation accuracy, approximation …roughness, conditional entropy, and decision rough entropy are given, which exhibit a monotonic variation with changes in the size of attribute sets, α, and θ. Finally, the experimental results demonstrate the proposed rough set model of IIRNDSs is feasible and effective. Show more

Keywords: Incomplete interval rough number decision systems, interval rough number, similarity relation, uncertainty measure, rough sets

DOI: 10.3233/JIFS-237320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8829-8843, 2024

Authors: Guo, Hong | Yang, Jin | Yang, Jun

Article Type: Research Article

Abstract: This paper proposes a method of using machine learning and an evolutionary algorithm to solve the flexible job shop problem (FJSP). Specifically, a back propagation (BP) neural network is used as the machine learning method, the most widely used genetic algorithm (GA) is employed as the optimized object to address the machine-selection sub-problem of the FJSP, and particle swarm optimization (PSO) is utilized to solve the operation-order sub-problem of the FJSP. At present, evolutionary algorithms such as the GA, PSO, ant colony algorithm, simulated annealing algorithm, and their optimization algorithms are widely used to solve the FJSP; however, none of …them optimizes the initial solutions. Because each of these algorithms only focuses on solving a single FJSP, they can only use randomly generated initial solutions and cannot determine whether the initial solutions are good or bad. Based on these standard evolutionary algorithms and their optimized versions, the JSON object was introduced in this study to cluster and reconstruct FJSPs such that the machine learning strategies can be used to optimize the initial solutions. Specifically, the BP neural networks are trained so that the generalization of BP neural networks can be used to judge whether the initial solutions of the FJSPs are good or bad. This approach enables the bad solutions to be filtered out and the good solutions to be maintained as the initial solutions. Extensive experiments were performed to test the proposed algorithm. They demonstrated that it was feasible and effective. The contribution of this approach consists of reconstructing the mathematical model of the FJSP so that machine learning strategies can be introduced to optimize the algorithms for the FJSP. This approach seems to be a new direction for introducing more interesting machine learning methodologies to solve the FJSP. Show more

Keywords: Flexible job shop scheduling problem, mechanical engineering, evolutionary algorithms, machine learning

DOI: 10.3233/JIFS-224021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8845-8863, 2024

Authors: Wang, Tianxiong | Xu, Mengmeng | Yang, Liu | Zhou, Meiyu | Sun, Xin

Article Type: Research Article

Abstract:  Kansei Engineering (KE) is a product design method that aims to develop products to meet users’ emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet …are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user’s multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user’s multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design. Show more

Keywords: Text mining, Back propagation neural network (BPNN), Multi-objective evolutionary algorithm (MOEA), Non-dominated sorting genetic algorithm-II (NSGA-II), Kansei engineering

DOI: 10.3233/JIFS-230668

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8865-8885, 2024

Authors: Maleki, Monavareh | Ebrahimi, Mohamad | Davvaz, B.

Article Type: Research Article

Abstract: The concept of entropy and information gain of BE-algebras in scientific disciplines such as information theory, data science, supply chain and machine learning assists us to calculate the uncertanity of the scientific processes of phenomena. In this respect the notion of filter entropy for a transitive BE-algebra is introduced and its properties are investigated. The notion of a dynamical system on a transitive BE-algebra is introduced. The concept of the entropy for a transitive BE-algebra dynamical system is developed and, its characteristics are considered. The notion of equivalent transitive BE-algebra dynamical systems is defined, and it is proved the fact …that two equivalent BE-algebra dynamical systems have the same entropy. Theorems to help calculate the entropy are given. Specifically, a new version of Kolmogorov– Sinai Theorem has been proved. The study introduces the concept of information gain of a transitive BE-algebra with respect to its filters and investigates its properties. This study proposes the use of filter entropy to approximate the level of risk introduced by a BE-algebra dynamical system. This aim is reached by defining the information gain with respect to the filters of a BE-algebra. This methodology is well developed for use in engineering, especially in industrial networks. This paper proposes a novel approach to assess the quantity of uncertainty, and the impact of information gain of a BE-algebra dynamical system. Show more

Keywords: Generator, transitive BE-algebra, dynamical system, entropy, information gain

DOI: 10.3233/JIFS-232363

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8887-8901, 2024

Authors: Ma, Ping | Ni, Zhengwei

Article Type: Research Article

Abstract: Time series forecasting has a wide range of applications in various fields. To eliminate the need for time series data volume, a meta-learning-based few-shot time series forecasting method is proposed. This method uses a residual stack module as its backbone and connects the residuals forward and backward through a multilayer fully connected network so that the model and the meta-learning framework can be seamlessly combined. The Empirical knowledge of different time-sequence tasks is obtained through meta-training. To enable fast adaptation to new prediction tasks, a small meta-network is introduced to adaptively and dynamically generate the learning rate and weight decay …coefficient of each step in the network. This method can use sequences of different data distribution characteristics for cross-task learning, and each training task only needs a small number of time series to achieve sequence prediction for the target task. The results show that compared with the two baselines, the proposed method has improved performance on 67.07% and 58.53% of the evaluated tasks. Thus, this method can effectively alleviate the problems caused by insufficient data during training and has broad application prospects in the field of time series. Show more

Keywords: Time series forecasting, few-shot learning, meta learning, residual stack model

DOI: 10.3233/JIFS-233520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8903-8916, 2024

Authors: Gul, Rimsha | Bashir, Maryam

Article Type: Research Article

Abstract: As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some …studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach. Show more

Keywords: Feature selection, sentiment analysis, multi-objective optimization, evolutionary algorithms

DOI: 10.3233/JIFS-234615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8917-8932, 2024

Authors: Yang, Jiyun | Gui, Can

Article Type: Research Article

Abstract: Malware attack is a growing problem on the Android mobile platform due to its popularity and openness. Although numerous malware detection approaches have been proposed, it still remains challenging for malware detection due to a large amount of constantly mutating apps. The opcode, as the most fundamental part of Android app, possesses good resistance against obfuscation and Android version updates. Due to the large number of opcodes, most opcode-based methods employ statistical-based feature selection, which disrupts the correlation and semantic information among opcodes. In this paper, we propose an Android malware detection framework based on sensitive opcodes and deep reinforcement …learning. Firstly, we extract sensitive opcode fragments based on sensitive elements and then encode the features using n -gram. Next, we use deep reinforcement learning to select the optimal subset of features. During the process of handling opcodes, we focus on preserving semantic information and the correlation among opcodes. Finally, our experimental results show an accuracy of 0.9670 by using the 25 opcode features we obtained. Show more

Keywords: Android malware, deep reinforcement learning, feature selection, machine learning

DOI: 10.3233/JIFS-235767

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8933-8942, 2024

Authors: Chen, Hongan | Zhang, Zongfu | Luo, Qingjia | Chen, Rongbin | Zhao, Yang

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-235945

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8943-8959, 2024

Authors: Jianping, Liu | Yingfei, Wang | Jian, Wang | Meng, Wang | Xintao, Chu

Article Type: Research Article

Abstract: To better understand users’ behavior patterns in web search, numerous click models are proposed to extract the implicit interaction feedback. Most existing click models are heavily based on the implicit information to model user behaviors, ignoring the impact of explicit information between queries and documents in search sessions. In this paper, we fully consider the topic relevance between queries and documents in search sessions and propose a novel topic relevance-aware click model (TRA-CM) for web search. TRA-CM consists of a relevance estimator and an examination predictor. The relevance estimator consists of a topic relevance predictor and a click context encoder. …In the topic relevance predictor, we utilize the pre-trained BERT model to model the content information of queries and documents in search sessions. Meanwhile, we use transformer to encode users’ click behaviors in the click context encoder. We further apply a two-stage fusion strategy to obtain the final relevance scores. The examination predictor estimates the examination probability of each document. We further utilize learnable filters to attenuate log noise and obtain purer input features in both relevance estimator and examination predictor, and investigate different combination functions to integrate relevance scores and examination probabilities into click prediction. Extensive experiment results on two real-world session datasets prove that TRA-CM outperforms existing click models in both click prediction and relevance estimation tasks. Show more

Keywords: BERT, click model, click prediction, deep learning, web search

DOI: 10.3233/JIFS-236894

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8961-8974, 2024

Authors: Sharmila Joseph, J. | Vidyarthi, Abhay

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-231154

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8975-8992, 2024

Authors: Alqudah, Rajaa | Al-Mousa, Amjed | Faza, Ayman

Article Type: Research Article

Abstract: Traffic on highways has increased significantly in the past few years. Consequently, this has caused delays for the drivers in reaching their final destination and increased the highway’s congestion level. Many options have been proposed to ease these issues. In this paper, a model of the highway drivers’ population was built based on several factors, including the behavioral patterns of the drivers, like drivers’ time flexibility to reach the destination, their carpool eligibility, and their tolerance to pay the toll price, in addition to the traffic information from the system. A fuzzy logic decision-making model is presented to emulate how …drivers would choose the lane to use based on the aforementioned factors and the current congestion levels of all the lanes on the highway. The presented model, along with the simulation results from applying the model to different simulation scenarios, show the usefulness of such a model in predicting an optimal toll value. Such optimal value would reduce congestion on the highway at one end while maximizing the revenue for the toll company. Show more

Keywords: Fuzzy logic, decision-making, probabilistic model, toll pricing, traffic management

DOI: 10.3233/JIFS-231352

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8993-9006, 2024

Authors: Singh, Surender | Sharma, Sonam

Article Type: Research Article

Abstract: A Single-valued neutrosophic set (SVNS) has recently been explored as a comprehensive tool to assess uncertain information due to varied human cognition. This notion stretches the domain of application of the classical fuzzy set and its extended versions. Various comparison measures based on SVNSs like distance measure, similarity measure, and, divergence measure have practical significance in the study of clustering analysis, pattern recognition, machine learning, and computer vision-related problems. Existing measures have some drawbacks in terms of precision and exclusion of information and produce unreasonable results in categorization problems. In this paper, we propose a generic method to define new …divergence measures based on common aggregation operators and discuss some algebraic properties of the proposed divergence measures. To further appreciate the proposed divergence measures, their application to pattern recognition has been investigated in conjunction with the prominent existing comparison measures based on SVNSs. The comparative assessment sensitivity analysis of the proposed measures establishes their edge over the existing ones because of appropriate classification results. Show more

Keywords: Single-valued neutrosophic set, aggregation operator, pattern recognition, divergence measure

DOI: 10.3233/JIFS-232369

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9007-9020, 2024

Authors: Dai, Songsong

Article Type: Research Article

Abstract:  The well-known iterative boolean-like law a  →(a  → b ) = a  → b can be generalized to the functional equation I (x , I (x , y )) = I (x , y ), where I is a fuzzy implication. In this paper, we discuss an approximation of the equation, I (x , I (x , y )) ≈ I (x , y ), i.e., the law is approximately valid. Furthermore, we study the property of approximation preserving with respect to compositions of fuzzy implications. Finally, we give a necessary condition and a sufficient condition for the approximate equation of (S , N )-implications.

Keywords: Functional equation, iterative boolean-like law, fuzzy implication, (S, N)-implication

DOI: 10.3233/JIFS-233435

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9021-9028, 2024

Authors: Li, Xiaoli | Du, Linhui | Yu, Xiaowei | Wang, Kang | Hu, Yongkang

Article Type: Research Article

Abstract: During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict …HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R ) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods. Show more

Keywords: HVAC, energy consumption, weighted similarity measure, deep neural network, Just-in-Time learning

DOI: 10.3233/JIFS-233544

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9029-9042, 2024

Authors: Mohammed Mustafa, M. | Kalpana Devi, S. | Althaf Ali, A. | Gunavathie, M.A.

Article Type: Research Article

Abstract: Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with …ZigBee and RA-TDMA & PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate. Show more

Keywords: Location points, real time scheduling, RATDMA, BSN

DOI: 10.3233/JIFS-234275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9043-9055, 2024

Authors: Qu, Ying | Wang, Xuming

Article Type: Research Article

Abstract:  In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of …the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention. Show more

Keywords: 24model, decision tree model, association rule, gas explosion

DOI: 10.3233/JIFS-234372

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9057-9068, 2024

Authors: Tomy, Navin | Johnson, T.P.

Article Type: Research Article

Abstract: This paper deals with lattice isomorphic L -topological spaces. We are concerned with a question: Under what conditions will a lattice isomorphic L -topological spaces be L -homeomorphic. We give contributions to this question in three different ways.

Keywords: L-homeomorphism, quasi L-homeomorphism, lattice isomorphism, pL-homeomorphism

DOI: 10.3233/JIFS-234375

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9069-9082, 2024

Authors: Liu, Gan | Qi, Guirong | Wan, Sanyu

Article Type: Research Article

Abstract: Imbalanced data is a serious binary classification difficulty in forecasting the well-being of the elderly. This paper improves the Smote algorithm from the algorithm and sample dimensions to tackle the issue of imbalanced distribution of questionnaire data. The k-means Smote is combined with RBFNN as K-RBFNN Smote in the algorithm dimension and add FCM link to resample the minority set in the sample dimension as FCM K-RBFNN Smote. In order to improve the generalization of models, the RUS module is added to the algorithm. Experiments are carried out on four improved Smote technologies and two existing Smote technologies combined with …XGBoost, which is superior than the other five conventional classification models. The experimental results indicate that the performance order is RUS FCM K-RBFNN Smote > K-RBFNN Smote > FCM K-RBFNN Smote > RUS K-RBFNN Smote > K-Means Smote > FCM Smote. The RUS FCM K-RBFNN method has been identified as the optimal approach for enhancing performance, resulting in a 98.58% accuracy rate. In conclusion, Smote algorithm undergoes the implementation of K-RBFNN shows greater performance and the enhancement of FCM and RUS relies on the structure of sampling. Show more

Keywords: RUS FCM K-RBFNN Smote, XGBoost, imbalanced data, elderly well-being classification

DOI: 10.3233/JIFS-235213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9083-9102, 2024

Authors: Abraham, Asha | Kayalvizhi, R. | Mohideen, Habeeb Shaik

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-236197

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9103-9117, 2024

Authors: Jumde, Amol | Keskar, Ravindra

Article Type: Research Article

Abstract: With tremendous evolution in the internet world, the internet has become a household thing. Internet users use search engines or personal assistants to request information from the internet. Search results are greatly dependent on the entered keywords. Casual users may enter a vague query due to lack of knowledge of the domain-specific words. We propose a query reformulation system that determines the context of the query, decides on keywords to be replaced and outputs a better-modified query. We propose strategies for keyword replacements and metrics for query betterment checks. We have found that if we project keywords into the vector …space of word projection using word embedding techniques and if the keyword replacement is correct, clusters of a new set of keywords become more cohesive. This assumption forms the basis of our proposed work. To prove the effectiveness of the proposed system, we applied it to the ad-hoc retrieval tasks over two benchmark corpora viz TREC-CDS 2014 and OHSUMED corpus. We indexed Whoosh search engine on these corpora and evaluated based on the given queries provided along with the corpus. Experimental results show that the proposed techniques achieved 9 to 11% improvement in precision and recall scores. Using Google’s popularity index, we also prove that the reformulated queries are not only more accurate but also more popular. The proposed system also applies to Conversational AI chatbots like ChatGPT, where users must rephrase their queries to obtain better results. Show more

Keywords: Query reformulation, WordNet, word embedding, whoosh, TREC

DOI: 10.3233/JIFS-236296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9119-9137, 2024

Authors: Selvakumar, B. | Abinaya, P. | Lakshmanan, B. | Sheron, S. | Smitha Rajini, T.

Article Type: Research Article

Abstract: Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to …hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparatively more data than other recent works with lower Mean Square Error value of 4748.12 which is the main factor used to determine how well the data is hidden and cannot be interpreted easily. Also, it achieved a Peak Signal-Noise Ratio (PSNR) value of 71.34 dB, which is superior than other recent works, verifying that the image quality remains uncompromising even after being encrypted. Show more

Keywords: Hybrid image encryption, least significant bit algorithm, arithmetic division operation, canny edge detection algorithm, chaotic maps, DNA encoding

DOI: 10.3233/JIFS-236637

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9139-9153, 2024

Authors: Zhong, Yu | Shen, Bo | Wang, Tao

Article Type: Research Article

Abstract: Document-level relation extraction aims to uncover relations between entities by harnessing the intricate information spread throughout a document. Previous research involved constructing discrete syntactic matrices to capture syntactic relationships within documents. However, these methods are significantly influenced by dependency parsing errors, leaving much of the latent syntactic information untapped. Moreover, prior research has mainly focused on modeling two-hop reasoning between entity pairs, which has limited applicability in scenarios requiring multi-hop reasoning. To tackle these challenges, a syntax-enhanced multi-hop reasoning network (SEMHRN) is proposed. Specifically, the approach begins by using a dependency probability matrix that incorporates richer grammatical information instead of …a sparse syntactic parsing matrix to build the syntactic graph. This effectively reduces syntactic parsing errors and enhances the model’s robustness. To fully leverage dependency information, dependency-type-aware attention is introduced to refine edge weights based on connecting edge types. Additionally, a part-of-speech prediction task is included to regularize word embeddings. Unrelated entity pairs can disrupt the model’s focus, reducing its efficiency. To concentrate the model’s attention on related entity pairs, these related pairs are extracted, and a multi-hop reasoning graph attention network is employed to capture the multi-hop dependencies among them. Experimental results on three public document-level relation extraction datasets validate that SEMHRN achieves a competitive F1 score compared to the current state-of-the-art methods. Show more

Keywords: Attention mechanism, document-level relation extraction, syntactic information, multi-hop reasoning

DOI: 10.3233/JIFS-237167

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9155-9171, 2024

Authors: Amiri-Bideshki, M. | Hoskova-Mayerova, S. | Ameri, R.

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-237912

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9173-9178, 2024

Authors: Khan, Younas | Ashraf, Shahzaib | Farman, Muhammad | Abdallah, Suhad Ali Osman

Article Type: Research Article

Abstract: Achieving household food security is the tumbling issue of the century. This article explores the factors affecting household food security and solutions by utilizing a synergy of statistical and mathematical models. The methodology section is divided into two portions namely sociological and mathematical methods. Sociologically, 379 household heads were interviewed through structured questions and further analyzed in terms of descriptive and binary logistic regression. The study found that 4 independent variables (poverty, poor governance, militancy, and social stratification) showed a significant association (P  = 0.000) to explain variations in the dependent variable (household FS). The Omnibus test value (χ2 = 102.386; P … = 0.000) demonstrated that the test for the entire model against constant was statistically significant. Therefore, the set of predictor variables could better distinguish the variation in household FS. The Nagelkerke’s R Square (R2  = .333) helps to interpret that the prediction variable and the group variables had a strong relationship. Moreover, 23% to 33% variation in FS was explained by the grouping variables (Cox and Snell R2  = 0.237 and Nagelkerke’s R2  = 0.333). The significant value of Wald test results for each variable confirmed that the grouping variables (poor governance P  = 0.004, militancy P  = 0.000, social stratification P  = 0.021 and poverty P  = 0.000) significantly predicted FS at the household level. Mathematically, all the statistics were validated further through the application of spherical fuzzy mathematics (TOPIS and MADM) to explore what factors are affecting household FS. Thus, the study found that F 3  (poverty ) > F 2  (militancy) > F 4  (social stratification) > F 1 (poor governance) respectively. Thus, it could be concluded from these findings that the prevalence of poverty dysfunctional all the channels of household FS at the macro and micro levels. Therefore, a sound and workable model to eradicate poverty in the study area by ensuring social safety nets for the locals was put forward some of the policy implications for the government are the order of the day. Show more

Keywords: Food security, militancy, poor governance, social stratification, poverty, logistic regression, TOPIS, MADM, spherical fuzzy set

DOI: 10.3233/JIFS-237938

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9179-9195, 2024

Authors: Seethappan, K. | Premalatha, K.

Article Type: Research Article

Abstract: Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to present a system that can automatically classify sarcastic phrases in multi-domain data. This multi-domain dataset consisting of 67850 sarcastic and non-sarcastic data is collected from various websites to identify sarcastic or non-sarcastic utterances. Multiple approaches are examined in this work to improve sarcasm identification: 1. A Combination of fasttext embedding, syntactic, semantic, lexical n-gram, and oxymoron features 2. TF-IDF feature weighting scheme 3. Three machine learning algorithms …(SVM, Multinomial Naïve Bayes, and Random Forest), three deep learning algorithms (CNN, LSTM, MLP), and one ensemble model (CNN + LSTM) The CNN + LSTM model achieves a Precision of 91.32%, Recall of 92.85%, F-Score of 92.08%, accuracy of 92.01%, and Kappa of 0.84 by combining the fasttext embedding, bigram, syntactic, semantic, and oxymoron features with TF-IDF method. These experimental results show CNN + LSTM with a combination of all features outperforms the other algorithms in classifying the sarcasm in both datasets. The sarcasm classification performance of our dataset and another sarcasm news dataset was compared while applying the above model. Show more

Keywords: Natural language processing, sarcasm, figurative language, deep learning, CNN, oxymoron

DOI: 10.3233/JIFS-224110

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9197-9207, 2024

Authors: Sangeetha, R. | Kuriakose, Bessy M. | Naveen, V. Edward | Jenefa, A. | Lincy, A.

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-231113

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9209-9223, 2024

Authors: Rezaei, Reza | Shahidi, Seyed-Ahmad | Abdollahzadeh, Sohrab | Ghorbani-Hasansaraei, Azade | Raeisi, Shahram Naghizadeh | Hayati, Jamileh

Article Type: Research Article

Abstract: Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people’s health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a …multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry’s risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach. Show more

Keywords: Fuzzy cognitive map, beverage industry failures, risk evaluation, FMEA

DOI: 10.3233/JIFS-233277

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9225-9247, 2024

Authors: Vasanthamani, K. | Pavai Madheswari, S.

Article Type: Research Article

Abstract: This paper deals with a discrete-time Geo/G/1 queue with repeated attempts and starting failure. If the server fails to start, it is sent for repair. During a repair process, alterations in the repair times is permitted based on current requirements. Customers are served on priority by the pre-emptive resume queue discipline. The distributions of the various system states when the system is in stable are analysed using the generating function technique. Analytical expressions are supported by numerical illustrations to exhibit the influence of the various parameters of the system on the performancemeasures.

Keywords: Discrete-time retrial queues, general retrial times, unreliable server, impatience, priority, replacement in repair times

DOI: 10.3233/JIFS-233406

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9249-9259, 2024

Authors: Selvy, R. | Vinod Kumar, P.B.

Article Type: Research Article

Abstract: It is observed that IFSs are defined based on the concept that the iterates take only an integer number of times. This work studies the dynamics of functions, where a function can iterate r times for every r  ∈ R . Utilizing concepts from fuzzy sets, r -times iterates of a function are defined for r  ∈ R . The study demonstrates that the chaotic property can be generalized to this new iterative concept. The chaotic behavior of a function is then extended using this iterative concept.

Keywords: Iterated function systems, fuzzy functions, chaotic functions

DOI: 10.3233/JIFS-236563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9261-9270, 2024

Authors: Arunkumar, N. | Nagaraj, Balakrishnan | Keziah, M. Ruth

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-236145

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9271-9290, 2024

Authors: Huang, Mengtao | Wang, Jiaxuan

Article Type: Research Article

Abstract: Pedestrian trajectory prediction plays a crucial role in autonomous driving, as its accuracy directly affects the autonomous driving system’s comprehension of the environment and subsequent decision-making processes. Current trajectory prediction methods tend to oversimplify pedestrians to mere point coordinates, utilizing positional information to infer interactions among individuals while overlooking the temporal correlations between them, thereby excessively simplifying pedestrian characteristics. To address the aforementioned issues, this paper proposes a trajectory prediction model for autonomous driving applications, that takes into account both pedestrian motion characteristics and scene interaction. The model optimizes the LSTM unit structure twice, serving to learn correlations among long …trajectories of pedestrians and to integrate multiple forms of information into the neighborhood interaction module. Furthermore, our model introduces dual attention mechanisms for individuals and scenes, focusing on the key motion points of individual pedestrians and their interactive behavior with others in busy scenarios. The efficacy of the model was validated on the MOT16 pedestrian dataset and the Daimler pedestrian path prediction dataset, outperforming mainstream methods with 8% and 10% reductions in Average Displacement Error and Final Displacement Error respectively. Show more

Keywords: Trajectory prediction, automated driving, CNN-LSTM, deep learning

DOI: 10.3233/JIFS-236271

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9291-9310, 2024

Authors: Wu, Hui-Yong | Zhou, Zi-Wei | Li, Hong-Kun | Yang, Tong-Tong

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-236948

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9311-9328, 2024

Authors: Huang, Jui-Chan | Shu, Ming-Hung | Lin, Hsiang-Tsen | Day, Jen-Der

Article Type: Research Article

Abstract: With the fast advances of new energy vehicles, the EV battery technology needs to be further improved to follow the step. How to effectively diagnose the electric vehicle’s lithium battery fault becomes a hotspot in the academic circle. This study has proposed new method that uses the state of charge of the battery and self-coder depth to detect faults in the lithium battery group of electric vehicles. First, the study investigates the single lithium battery faults. Then, it builds a lithium battery group fault diagnosis model by integrating the battery charge state and denoising converter network. Finally, it uses a …dataset and retired battery group to validate the model’s performance. The results show that the proposed model achieves an accuracy of 93.18% and a recall rate of 93.73% in identifying the faults in the lithium batteries of the electric vehicles and its F1 value is as high as 0.95. Moreover, the modeling method has the lowest prediction error, indicating its high accuracy and robustness in diagnosing the faults of battery packs. This study aims to provide an effective solution for the fault diagnosis of lithium battery packs in electric vehicles. Show more

Keywords: Transformer framework, DAE, electric vehicle, lithium battery, fault diagnosis

DOI: 10.3233/JIFS-237796

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9329-9341, 2024

Authors: Razzaque, Huzaira | Ashraf, Shahzaib | Sohail, Muhammad | Abdeljawad, Thabet

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-235397

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9343-9366, 2024

Authors: Li, Chunling | Zhang, Yi

Article Type: Research Article

Abstract: The existing negative selection algorithms can not improve their detection performance by human intervention during the testing process. This paper proposes a negative selection algorithm with human-in-the-loop for anomaly detection. It uses self-sample clusters to train detectors with a nonrandom strategy. Its detectors and self-sample clusters fully cover state space without overlapping each other. It locally adjusts detectors and self-sample clusters with human intervention to improve its detection performance during the testing process. Experiments were performed on two synthetic datasets and the Iris dataset from the UCI repository to assess its performance. The results show that it outperforms the other …anomaly detection methods in most cases. Show more

Keywords: Negative selection algorithm, human-in-the-loop, anomaly detection, artificial immune algorithm, artificial immune system

DOI: 10.3233/JIFS-235724

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9367-9380, 2024

Authors: Wu, Xiaogang

Article Type: Research Article

Abstract:  The similarity measure of intuitionistic fuzzy sets is a primary method for dealing with uncertainty and fuzzy problems and is commonly used in fuzzy decision-making and pattern recognition. The current mainstream similarity measure is based on the classical fuzzy set with only one negation, which often violates the intuitionistic problem in applications because the actual semantics of multiple negations are not considered. To solve the inconsistency and irrationality problems in the classical similarity methods, we introduce three negations (contradiction negation, opposition negation, and mediation negation) in the intuitionistic fuzzy set to obtain the generalized intuitionistic fuzzy set and prove its …related property theorem. On this basis, our similarity measure adopts a mediational negation to represent non-membership, which fully utilizes the multiple negation information of non-membership and hesitancy and avoids the loss of fuzzy information. We verify the method’s rationality, validity, and originality through pattern recognition experiments and numerical examples, which improves the performance of intuitionistic fuzzy set similarity in practical applications and provides a new approach for future research on intuitionistic fuzzy inference. Show more

Keywords: Generalized intuitionistic fuzzy sets (GIFS), three kinds of negation, similarity measure, fuzzy decision

DOI: 10.3233/JIFS-236510

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9381-9391, 2024

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

Authors: Liu, Zhanpeng | Xiao, Wensheng | Cui, Junguo | Mei, Lianpeng

Article Type: Research Article

Abstract: The drilling permanent magnet synchronous motor (DPMSM) contains multiple subsystems with identical structures and has a high probability of failure because the downhole working conditions are harsh. Therefore, the quick localization of faults is difficult to determine although the fault type may be identified in time. The system diagnostic model based on the Bayesian network (BN) can be used for fault diagnosis and localization for components in subsystems, but it is difficult to build and modify due to the complex system in practice. New methods are necessary to reduce the difficulty of building and modifying models. In this study, object-oriented …ideas are introduced into the BN to establish a system diagnostic model based on an Object-oriented Bayesian network (OOBN) for the DPMSM. First, the fault diagnostic models for subsystems based on BN are established, respectively. Then, submodels of forward and backward based on BN are instantiated as instance nodes. Next, instance nodes are connected through input nodes and output nodes to establish the OOBN-based system diagnosis model. Finally, the system diagnosis model is validated by sensitivity analysis and the effectiveness is discussed in Cases. The system diagnosis model can effectively reduce the difficulties of modeling and modifying. Show more

Keywords: Object-oriented Bayesian network, fault diagnosis, instance nodes, sensitivity analysis

DOI: 10.3233/JIFS-236850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9559-9576, 2024

Authors: Hu, Yuanyuan

Article Type: Research Article

Abstract: Music education has a rich historical background. Nevertheless, the introduction of modern teaching methods is relatively delayed. In recent years, there has been a remarkable acceleration in the advancement of music education. A promising tool that has emerged to revolutionize education as a whole is Virtual Reality (VR) technology, which offers immersive and interactive experiences across various disciplines. At the university level, integrating VR technology into music education opens up exciting opportunities to enhance practical teaching methods and provide students with enriched musical experiences. Virtual Reality together with Internet of Things (IoT) demonstrates its capabilities in various tasks, but its …widespread availability in online learning remainders a pressing challenge that needs to be addressed. In pre-processing, it removes noise data using Dynamic Context-Sensitive Filtering (DCSF). VR technology creates an unparalleled learning environment, it transporting students to virtual concert halls, recording studios, or historical music venues. Hence the Multiscale deep bidirectional gated recurrent neural Network (MDBGNN) improves the practical teaching of music course concept, like Music theory, harmony, and rhythm can be visualized and experienced in VR. Finally, Dung Beetle Optimization Algorithm (DBOA) is employed to optimize the weight parameters of MDBGNN. The proposed MDBGNN-DBO-UMC-VRT is implemented in Python. The proposed method is analysed with the help of performance metrics, like precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time and RoC. The proposed MDBGNN-DBO-UMC-VRT method attains 13.11%, 18.12% and 18.73% high specificity, 11.13%, 11.04% and 19.51% lower computation Time, 15.29%, 15.365% and 14.551% higher ROC and 13.65%, 15.98%, and 17.15% higher Accuracy compared with existing methods, such as Enhancing Vocal Music Teaching through the Fusion of Artificial Intelligence Algorithms and VR Technology (CNN-UMC-VRT), Exploring the Efficacy of VR Technology in Augmenting Music Art Teaching (BPNN-UMC-VRT) and Implementing an Interactive Music-Assisted Teaching System Using VR Technology (DNN-UMC-VRT) respectively. Show more

Keywords: Dung Beetle optimization, Dynamic Context-Sensitive Filtering, multiscale deep bidirectional gated recurrent neural network, Virtual Reality, music course

DOI: 10.3233/JIFS-236893

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9577-9590, 2024

Authors: Zhao, Xue | Li, Qiaoyan | Xing, Zhiwei | Dai, Xuezhen

Article Type: Research Article

Abstract: Traditional multi-label feature selection is usually performed under the condition of given label information, but nowadays, labeling multi-label data is a huge project, which is both time-consuming and labor-intensive, but if there is no label information condition, it will lead to poor feature selection, in order to solve this problem, this paper proposes a new semi-supervised multi-label feature selection method, i.e., semi-supervised multi-label feature selection algorithm based on dual dynamic graph. In this paper, a semi-supervised multi-label feature selection algorithm is proposed by constructing a dual dynamic graph. First, the method selects the most discriminative features for dimensionality reduction through …the feature selection method of least squares regression, combined with the redundancy penalty of highly correlated features. Second, the label information is added to the construction of sample matrix similarity to learn the similarity. A semi-supervised multi-label feature selection framework is constructed by designing iterative updates of dual dynamic graphs to learn more accurate pseudo-label matrices to guide feature selection. Finally, the paper validates the above model using the alternating iteration optimization algorithm and verifies the effectiveness of the algorithm through experiments. Show more

Keywords: Multi-label learning, semi-supervised, feature selection, dual dynamic graph, redundant regular terms

DOI: 10.3233/JIFS-237146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9591-9605, 2024

Authors: Kang, Xiaoqiang

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-238284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9607-9623, 2024

Authors: Yu, Peng | Jing, Fengwei | Guo, Jin

Article Type: Research Article

Abstract: This paper studies the security control problem of semi-Markov jump systems. First, the parameter uncertainty, the time-varying delay, the nonlinear function and the cyber attack are considered in the system. Second, the nonlinear function is linearized by the fuzzy logic rule. A sliding mode surface is designed to obtain an equivalent controller and get a sliding mode dynamic system. By constructing Lyapunov functions of the mode dependence, a sufficient condition for H ∞ asymptotic stability of the system is obtained. Then, an adaptive sliding mode controller is established, and the original system reaches the sliding mode surface in a …finite time. Finally, two examples verify the correctness and practicality of the proposed theory. Show more

Keywords: Semi-Markov jump system, sliding mode control, cyber attack, fuzzy logic

DOI: 10.3233/JIFS-238994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9625-9638, 2024

Authors: Hussain, Abrar | Amjad, Alina | Ullah, Kifayat | Pamucar, Dragan | Ali, Zeeshan | Al-Quran, Ashraf

Article Type: Research Article

Abstract: Supplier selection is a very crucial process within a business or commercial enterprise because it depends upon different components like reliability, customer need, services, cost and reputation. A suitable supplier is familiar with developing a relationship between customer needs and business. To serve this purpose, the multiple attribute group decision-making (MAGDM) technique is a well-known and efficient aggregation model used to evaluate flexible optimal options by considering some appropriate criteria or attributes. Experts face some sophisticated challenges during the decision-making process due to uncertain and ambiguous information about human opinions. To address such conditions, we explore the notion of spherical …fuzzy sets (SFS) and their reliable operations. Some flexible operational laws of Dombi t-norms are also developed in light of spherical fuzzy (SF) information. Combining the theory of Hamy mean (HM) models and Dombi aggregation tools, some robust strategies are also studied in this research work. The main objectives of this article are to propose some dominant strategies in the presence of SF information including spherical fuzzy Dombi Hamy mean (SFDHM), spherical fuzzy Dombi weighted Hamy mean (SFDWHM), spherical fuzzy Dombi Dual Hamy mean (SFDDHM) and spherical fuzzy Dombi weighted Dual Hamy mean (SFDWDHM) operators. The MAGDM techniques are utilized to evaluate the flexibility of our derived methodologies under considering SF information. An experimental case study is utilized to evaluate a notable supplier enterprise under consideration of our developed methodologies. Finally, a comprehensive overview of our research work is also presented. Show more

Keywords: Spherical fuzzy values, Dombi aggregation tools, Hamy mean and Dual Hamy mean, multi-attribute group decision-making system

DOI: 10.3233/JIFS-234514

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9639-9662, 2024

Authors: Indupalli, Manjula Rani | Pradeepini, Gera

Article Type: Research Article

Abstract: Symptom-based disease identification is crucial to the diagnosis of the disease at the early stage. Usage of traditional stacking and blending models i.e., with default values of the models cannot handle the multi-classification data properly. Some of the existing researchers also implemented tuning with the help of a grid search approach but it consumes more time because it checks all the possible combinations. Suppose if the model has n estimators with k values it needs to check (n*k)! elements combination, this makes the learning time high. The proposed model chooses the estimators to train the model with in a considerable …amount of time using an advanced tuning technique known as “Bayes-Search” on an ensemble random forest and traditional, support vector machine. The advantage of this model is its capability to store the best evaluation metrics from the previous model and utilise them to store the new model. This model chooses the values of the estimator based on the probability of selection, which reduces the elements in search space i.e., (< (n-k)!). The proposed model defines the objective function with a minimum error rate and predicts the error rate with the selected estimators for different distributions. The model depending on the predicted value decides whether to store the value or to return the value to the optimizer. The Bayes search optimization has achieved +9.21% accuracy than the grid search approach. Among the two approaches random forest has achieved good accuracy and less loss using Bayes search with cross-validation. Show more

Keywords: Grid search, bayes search, objective function, error minimization, search space

DOI: 10.3233/JIFS-236137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9663-9676, 2024

Authors: Pang, Yufeng | Li, Xiaojuan

Article Type: Research Article

Abstract: Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also …proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency. Show more

Keywords: Acoustic signal, bearing fault detection, fast-unoriented SIFT algorithm, feature extraction, word bag model

DOI: 10.3233/JIFS-237331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9677-9691, 2024

Authors: Shen, Xiaohui

Article Type: Research Article

Abstract: Analyzing Lexical Semantic Changes (LSC) in Educational Texts (ET) refers to examining how the meanings of words, terms, or phrases used in ET have evolved. It involves learning shifts in the semantic content, connotations, and language associations within educational resources such as textbooks, research articles, and instructional content. The analysis can reveal how educational models, pedagogical methods, and terminology have transformed in response to technological innovations, societal changes, and pedagogical developments. This analysis provides visions into the dynamic nature of educational discourse, helping researchers, educators, and policymakers understand how language has adapted to reflect changes in educational paradigms and the …broader educational context. This research investigates the semantic analysis and classification performance within ET, employing the innovative Decision Tree + Feed Forward Neural Networks (DT + FFNNs) framework. This research shows the dynamic semantic relationships inherent in educational terminology by diverse semantic similarity measures and contextualized embeddings. It looks at how educational language changes to reflect changes in society, technology, and pedagogy. The study uses a DT + FFNN framework for semantic analysis and classification. The study uses several embeddings and semantic similarity metrics, and Spearman’s Correlation Coefficient (SCC) is employed to evaluate their effectiveness. This study highlights the DT + FFNN framework’s capacity to capture complex semantics in an educational setting and offers insights into the adaptive nature of educational discourse. SCC serves as a guiding metric, offering insights into the efficiency of several embeddings and measures. The findings show the pivotal role of fine-tuning in significantly enhancing the accuracy of DT + FFNNs across measures, revealing its remarkable potential in capturing semantics within an educational context. Show more

Keywords: Semantic analysis, education, spearman correlation, machine learning, decision tree, and accuracy

DOI: 10.3233/JIFS-237410

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9693-9707, 2024

Authors: Zhang, Weidong | Tan, Huadi

Article Type: Research Article

Abstract: Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for …ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation. Show more

Keywords: IoT, 5G, machine learning, smart farming, accuracy, plant disease prediction, WSN

DOI: 10.3233/JIFS-237482

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9709-9726, 2024

Authors: Zhang, Guili | Li, Pengxi | Zhang, Hanyue | Yu, Yinglong | Liang, Zhao

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-237768

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9727-9738, 2024

Authors: He, Liu | Zhu, Yuanguo | Ye, Tingqing

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-237977

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9739-9753, 2024

Authors: Sung, Tien-Wen | Zhao, Baohua | Zhang, Xin | Lee, Chao-Yang | Fang, Qingjun

Article Type: Research Article

Abstract: Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of …different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm’s practicability. Show more

Keywords: QUATRE algorithm, swarm-based optimization, fixed dimension updating, 3D path planning, unmanned aerial vehicle

DOI: 10.3233/JIFS-230928

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9755-9781, 2024

Authors: Amsaprabhaa, M.

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-233286

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9783-9805, 2024

Authors: Cui, Wanqiu

Article Type: Research Article

Abstract: Graph data storage has a promising prospect due to the surge of graph-structure data. Especially in social networks, it is widely used because hot public opinions trigger some network structures consisting of massively associated entities. However, the current storage model suffers from slow processing speed in this dense association graph data. Thus, we propose a new storage model for dense graph data in social networks to improve data processing efficiency. First, we identify the public opinion network formed by hot topics or events. Second, we design the germ elements and public opinion bunching mapping relationship based on equivalence partition. Finally, …the Public Opinion Bunching Storage(POBS) model is constructed to implement dense graph data storage effectively. Extensive experiments on Twitter datasets demonstrate that the proposed POBS performs favorably against the state-of-the-art graph data models for storage and processing. Show more

Keywords: Graph data storage, social networks, topic cluster, equivalent partition

DOI: 10.3233/JIFS-233540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9807-9818, 2024

Authors: Narendiranath Babu, T. | Kothari, Ayush Jain | Rama Prabha, D. | Mokashe, Rohan | Kagita, Krish Babu | Raj kumar, E.

Article Type: Research Article

Abstract: In the modern world, condition monitoring is crucial to the predictive maintenance of machinery. Gearboxes are widely used in machineries and auto motives to achieve the variable speeds. The major problem in gearbox is catastrophic failure due to heavy loads, corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The main purpose of this study to use Machine Learning (ML) algorithms and Artificial Neural Network (ANN) which are very powerful and reliable tool for fault detection and …its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. To do the vibration study, an experimental setup was created, and various faults were induced faults of various kinds that usually occurred in the gearbox. The gear in the gear train was subjected to vibration analysis which was captured via a sensor. Signal processing was carried out using MATLAB Toolbox. To automatically identify the flaws in the helical gearbox, an artificial neural network (ANN) and several machines learning methods, including KNN, decision tree, random forest, and SMV, were trained by creating a database from the experiment conducted. The outcomes showed potential in accurately classifying the faults. The results show that ANN has the highest accuracy of 99.6% with a 6.5662 seconds computational time while SVM has the lowest accuracy of 96% among them along with the highest computational time of 21.324 seconds. Show more

Keywords: Helical gearbox, vibration analysis, signal processing, fault diagnosis, artificial neural network, K-nearest neighbor, support vector machine, decision tree, random forest

DOI: 10.3233/JIFS-233602

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9819-9840, 2024

Authors: Guo, Sheng | Tan, Mian | Cai, Shan | Zhang, Zaijun | Liang, Yihui | Feng, Hongxi | Zou, Xue | Wang, Lin

Article Type: Research Article

Abstract: Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the …local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. Show more

Keywords: Facial expression recognition, deep convolution, multi-feature convolution module, local cross-attention module

DOI: 10.3233/JIFS-233748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9841-9856, 2024

Authors: Geng, Xiuli | Du, Yuanhao | Cao, Shuyuan | Cheng, Sheng

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-235074

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9857-9871, 2024

Authors: Ratmele, Ankur | Thakur, Ramesh

Article Type: Research Article

Abstract: As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers’ decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the …textual content of reviews and classify buyer’s opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product’s features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer’s opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice. Show more

Keywords: Opinions, Opinion Extraction (OE), product features, decision making, hierarchical attention mechanism, GloVe

DOI: 10.3233/JIFS-235389

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9873-9887, 2024

Authors: Mi, Xiaodong | Luo, Qifang | Zhou, Yongquan

Article Type: Research Article

Abstract: Panchromatic and multi-spectral image fusion, called panchromatic sharpening, is the process of combining the spatial and spectral information of the source image into the fused image to give the image a higher spatial and spectral resolution. In order to improve the spatial resolution and spectral information quality of the image, an adaptive multi-spectral image fusion method based on an improved arithmetic optimization algorithm is proposed. This paper proposed improved arithmetic optimization algorithm, which uses dynamic stochastic search technique and oppositional learning operator, to perform local search and behavioral complementation of population individuals, and to improve the ability of population individuals …to jump out of the local optimum. The method combines adaptive methods to calculate the weights of linear combinations of panchromatic and multi-spectral gradients to improve the quality of fused images. This study not only improves the quality and effect of image fusion, but also focuses on optimizing the operation efficiency of the algorithm to have real-time and high efficiency. Experimental results show that the proposed method exhibits strong performance on different datasets, improves the spatial resolution and spectral information quality of the fused images, and has good adaptability and robustness. The source code is available at: https://github.com/starboot/IAOA-For-Image-Fusion . Show more

Keywords: Image fusion, multi-spectral image, panchromatic image, oppositional learning operator, arithmetic optimization algorithm, meta-heuristic

DOI: 10.3233/JIFS-235607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9889-9921, 2024

Authors: Guo, Yan | Tang, Dezhao | Cai, Qiqi | Tang, Wei | Wu, Jinghua | Tang, Qichao

Article Type: Research Article

Abstract: Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the …final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture. Show more

Keywords: Machine learning, precision agriculture, digital agriculture, STL, attentional mechanisms

DOI: 10.3233/JIFS-235843

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9923-9943, 2024

Authors: Chen, Liang-Ching | Chang, Kuei-Hu | Wu, Chia-Heng | Chen, Shin-Chi

Article Type: Research Article

Abstract: Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of …extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data. Show more

Keywords: Corpus, natural language processing (NLP), technical word, advanced general-purpose word list (AGWL), COVID-19

DOI: 10.3233/JIFS-236635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9945-9956, 2024

Authors: de Oliveira, Heveraldo R. | Vieira, Antônio Wilson | Santos, Laércio Ives | Filho, Murilo César Osório Camargos | Ekel, Petr Ya. | D’Angelo, Marcos Flávio S.V.

Article Type: Research Article

Abstract: When providing patient care, healthcare professionals often rely on interpreting laboratory and clinical test results. However, their analysis is constrained by human capacity, leading to uncertainties in diagnoses. Machine learning has the potential to evaluate a larger amount of data and identify patterns and relationships that may otherwise go unnoticed. However, popular machine learning algorithms typically require abundant and labeled data, which is not always available. To address this challenge, the adoption of active learning allows for the selection of the most relevant instances for training, reducing the need for extensive labeling. Additionally, fuzzy logic offers the ability to handle …uncertainties. This paper proposes a novel approach that utilizes fuzzy membership functions to transform data as a pre-processing step for active learning. The objective is to approximate similar instances, specifically for the purpose of prediction, thereby minimizing the workload of human experts in labeling data for model training. The results of this study demonstrate the effectiveness of this approach in predicting heart disease and highlight the potential of using membership functions to enhance machine learning models in the analysis of medical information. By incorporating fuzzy logic and active learning, healthcare professionals can benefit from improved accuracy and efficiency in diagnosing and predicting pacients’ health conditions. Show more

Keywords: Active learning, fuzzy logic, cardiovascular diseases

DOI: 10.3233/JIFS-237047

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9957-9973, 2024

Authors: Ma, Xiyin | Li, Jian

Article Type: Research Article

Abstract: In order to ensure the safety of life and property in large buildings, the design of emergency evacuation routes for large buildings based on cloud computing and GIS big data is studied. Combining cloud computing and GIS big data, a command model for emergency evacuation of large buildings is built. Emergency evacuation functions are realized through the access layer, business logic layer, cloud computing layer and data layer. GIS big data of large buildings is stored in the model data layer. GIS geographic data is clustered through the MapReduce based parallel K-means clustering algorithm in the cloud computing layer. After …clustering, the emergency evacuation road network of large buildings is constructed through GIS in the business logic layer. On the road network, the emergency evacuation route selection method combining Dikstra algorithm and ant colony algorithm is used to realize the optimal route selection of emergency evacuation of large buildings. Experiments show that this method can effectively select the best evacuation path in large buildings, and the evacuation speed of the selected path is fast, which can ensure the safety of people in buildings. Show more

Keywords: Cloud computing, GIS big data, large buildings, emergency evacuation route, K-means clustering, ant colony algorithm

DOI: 10.3233/JIFS-237834

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9975-9986, 2024

Authors: Xiao, Yan | Liang, Jinqian

Article Type: Research Article

Abstract: In many real production scenarios, departmental organizations often exhibit a hierarchical structure, where departments cooperate with subordinate departments to optimize resource allocation and maximize their respective benefits. However, due to a lack of information or data, many model parameters in the allocation process cannot be precisely defined. In response to this challenge, an interval n -person hierarchical resource allocation model is proposed to achieve maximum economic benefit in uncertain environments. Based on the concepts of satisfactory degrees of comparing intervals and interval-valued cores of interval-valued n -person cooperative games, an auxiliary nonlinear programming model and method are developed to solve …the interval-valued cores of such cooperative games. The approach explicitly considers the inclusion and/or overlap relations between intervals, whereas the traditional interval ranking method may not guarantee the existence of interval-valued cores. The proposed method offers cooperative opportunities under uncertain conditions. Finally, the feasibility and applicability of the models and methods are demonstrated through a numerical example and comparison with other methods. Show more

Keywords: Hierarchical structure, resource allocation, uncertain environment, interval n-person cooperative game, nonlinear programming model

DOI: 10.3233/JIFS-191941

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9987-9998, 2024

Authors: Chau, Vinh Huy | Vo, Anh Thu | Ngo, Huu Phuc

Article Type: Research Article

Abstract: This paper discusses the use of an improved random forest regression algorithm (RFRA) to predict the total score of powerlifters. The paper collected the age, weight, and total score of multiple powerlifters, and then used an improved RFRA to build a predictive model. The parameters of this model are optimized by a differential squirrel search algorithm. The experimental results show that our proposed method can effectively predict the total score of powerlifters with an error of less than 10%, which can provide a reference for experts and athletes before training or competition.

Keywords: Artificial intelligence, random forest, powerlifting, total score

DOI: 10.3233/JIFS-230032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9999-10004, 2024

Authors: Li, Guofa | Wang, Jinfu | He, Jialong | Wang, Jili | Hou, Tianwei

Article Type: Research Article

Abstract: The reliability of machine tool components, particularly the tool magazine manipulator, significantly affects the overall performance of the machine tool. To address the challenge of accurately evaluating the manipulator’s health status using a single performance indicator, this study proposes a method that combines Fuzzy Comprehensive Evaluation (FCE) and a Combined Weighting Method (CWM). By considering both subjective and objective factors, this method provides a comprehensive evaluation of the manipulator’s health status, enhancing the accuracy and reliability of the assessment. The method utilizes fuzzy distribution to construct membership matrices for different health levels and adopts the CWM that combines the Entropy …Weight Method (EWM) and Analytic Hierarchy Process (AHP) to determine the combined weights of the health evaluation indices. This approach improves the accuracy and reliability by considering multiple indicators and objectively weighting them based on their importance. The current health status of the manipulator is evaluated using the fuzzy weighted average operator and the maximum membership principle. Moreover, a fault prediction method based on Particle Swarm Optimization (PSO) and GM(1,1) is proposed to overcome the information gap and small sample problems. The proposed model’s prediction accuracy is verified by comparing it with other models, demonstrating its effectiveness and reliability. Show more

Keywords: Health status evaluation, fault prediction, fuzzy comprehensive evaluation, grey model, particle swarm optimization

DOI: 10.3233/JIFS-233028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10005-10018, 2024

Authors: Zhang, Min

Article Type: Research Article

Abstract: Vehicle safety on roadsides is vital for preventing collisions, controlling failures and accidents, and ensuring driver and passenger wellness. Finite Element Analysis (FEA) in vehicle safety relies on the vehicle’s physical attributes for predicting and preventing collisions. This article introduces a Differential FEA (DFEA) model for predicting vehicle collisions regardless of the speed and direction for driver/ passenger safety. The proposed model induces a vehicle’s speed, direction, and displacement from two perspectives: self and approaching vehicle. The collision probability with the trailing or persuading vehicle is calculated by distinguishing the forward and rear features. The differential calculus for the point …of deviation and distance metrics are significantly estimated for a vehicle’s front and rear ends. Such calculus generates a maximum and minimum possibility for self and approaching vehicle contact. This contact is further split based on the collision threshold; the threshold is determined using the safe distance between two vehicles for collision-less driving. The threshold exceeding vehicles are alerted for their change in direction/ speed through contact point (rear/front) differential derivatives. This ensures collision detection under fewer contact errors, leveraging the safety of the duo vehicles. Show more

Keywords: Collision, contact threshold, differential equation, finite element analysis, vehicle safety

DOI: 10.3233/JIFS-233628

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10019-10034, 2024

Authors: Nagamani, T. | Logeswari, S.

Article Type: Research Article

Abstract: A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations …for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy. Show more

Keywords: Adam optimization, coronary artery disease, grid search, one hot encoding, random search, XGBoost

DOI: 10.3233/JIFS-233804

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10035-10044, 2024

Authors: Pan, Hongyan

Article Type: Research Article

Abstract: In addition to providing learners with a large amount of teaching resources, online teaching platforms can also provide learning resources and channels such as video courseware, Q&A tutoring groups, and forums. However, currently, there are still shortcomings in depth and dimensionality in mining student learning behavior data on the platform. In view of this situation, based on the learning interaction behavior, this study established the difficulty similarity model of knowledge points, and used spectral clustering to classify their difficulty. In addition, the study intended to use the maximum frequent subgraph under the Gspan framework to characterize learners’ implicit learning patterns. …The outcomes expressed that the algorithm put forward in the study achieved the highest accuracy index of 98.8%, which was 1.4%, 4.0%, and 8.6% higher than Apriori-based graph mining algorithms, K-means, and frequent subgraph discovery algorithms. In terms of F1 index, the convergence value of the algorithm proposed in the study was 95.5%, which was about 2.5% higher than the last three algorithms. In addition, learners of all three cognitive levels had the highest maximum number of frequent subgraphs with sizes above 100 when the minSup value was 60%. And when the number of clusters was 3, the clustering accuracy of the three learners was the highest. In similarity calculation, the calculation method used in the study was at the minimum in terms of root mean square error and absolute error average index, which were 0.048% and 0.01% respectively. This indicated that the model proposed by the research had better classification effect on the difficulty of knowledge points for learners of different cognitive levels, and had certain application potential. Show more

Keywords: Similarity, knowledge points, clustering, multidimensional, Gspan, accuracy

DOI: 10.3233/JIFS-234274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10045-10058, 2024

Authors: Liu, Jianping | Chu, Xintao | Wang, Jian | Wang, Meng | Wang, Yingfei

Article Type: Research Article

Abstract: Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations …in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model’s robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model’s generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability. Show more

Keywords: Machine reading comprehension, pre-trained model, adversarial training, Bi-GRU, multi-head self-attention mechanism

DOI: 10.3233/JIFS-234417

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10059-10073, 2024

Authors: Zhu, Meng-Meng | Mao, Jun-Jun | Xu, Wei

Article Type: Research Article

Abstract: Linguistic preference relations with self-confidence (LPRs-SC) are the preference relation that can reflect the decision maker’s (DM) confidence psychology and has received widespread attention for their simple form and multiple information. Currently, arithmetic studies of LPRs-SC are conducted separately for preference relations and self-confidence. In addition, personalized individual semantics (PIS) is an important tool in large-scale decision-making to reflect the differences in the semantic understanding of DMs. However, the confidence level in LPRs-SC limits the preference relation to a certain extent and the linguistic representations of these two components are usually different. This means that it is not only necessary …to propose an arithmetic rule that can express the restrictive relationship between the two but also to construct a model that can extract the PIS of preference relation and confidence respectively. Besides, we constructed a two-stage consensus reaching process (CRP) based on the specificity of the LPRs-SC structure when enhancing group harmony. The process takes self-confidence as an independent source of information, delineates the adjusted categories in detail, and builds an adjustment model accordingly. Finally, the example and comparative analyses verify the merits of the proposed PIS in terms of consistency enhancement and CRP in terms of speed and accuracy harmonization. Show more

Keywords: Personalized individual semantics, linguistic preference relations with self-confidence, consensus reaching process, large scale decision making

DOI: 10.3233/JIFS-236552

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10075-10093, 2024

Authors: Peng, Jun long | Liu, Xiao

Article Type: Research Article

Abstract: This study explores the impact of public health events, multi-modal projects, multi-project environments, and multi-capacity resource constraints on project scheduling. It describes the comprehensive resource-constrained project scheduling problem (MCMRCMPSP) specifically for public health events, and proposes two approaches for modelling and solving the problem. The objective is to enhance the practical relevance of project scheduling and enrich the problem itself. To improve efficiency and the algorithm for scheduling problems, an enhanced quantum algorithm based on the quantum particle swarm algorithm (QPSO) is proposed. The enhancements include Gaussian variation and a tournament selection strategy. Furthermore, the article integrates multiple heuristic rules …with the algorithm to minimize illogical computations, improve computational efficiency, and enhance solution quality. The proposed algorithm’s effectiveness is validated through performance tests and practical application experiments. The results show that the algorithm has superior convergence performance and solution accuracy compared with the traditional QPSO, particle swarm algorithm (PSO), genetic algorithm, ant colony algorithm, and cuckoo algorithm. Thus, the algorithm provides a targeted resource scheduling plan for real-world cases. This research contributes to the field of project scheduling problems and proposes a new solution. Show more

Keywords: Public health events, improved quantum algorithm, multi-mode, multi-project, multi-capability resource-constrained project scheduling

DOI: 10.3233/JIFS-236757

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10095-10114, 2024

Authors: Kahraman, Cengiz

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-237389

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10115-10133, 2024

Authors: Chang, Chih-Yung | Yang, Yu-Ting | Zhang, Qiaoyun | Lin, Yi-Ti | Roy, Diptendu Sinha

Article Type: Research Article

Abstract: With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent Citation Recommendation Mechanism), designed to automate the process of suggesting the appropriate number of citations for individual brackets within a document. The proposed ICRM comprises three phases: Coarse-grained Weighted Bag of Word (WCBW), Fine-grained SciBERT (FSB) and Citation Adjustment phases. Firstly, the WCBW phase employs TF-IDF to extract keywords from both target and candidate documents, forming vectors that capture word significance along with metadata like authorship, keywords, and titles. It aims to …identify relevant papers from a database, serving as initial candidates for each bracket. Secondly, the FSB phase employs the SciBERT model to assess the similarity between candidate documents and the local context around brackets, enhancing the precision of recommendations. It refines this selection by analyzing candidate-document relationships within the proximity of the brackets. Lastly, the Citation Adjustment phase tackles overlapping citations and ensures that recommended citation numbers align with user-defined criteria, resolving issues of imbalance. The simulation results demonstrate that the proposed ICRM outperforms existing models significantly in terms of precision, recall and F1-score. Show more

Keywords: Citation recommendation, TF-IDF, weighted bag of word, BERT

DOI: 10.3233/JIFS-237975

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10135-10150, 2024

Authors: Ping, Yang

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-239687

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10151-10165, 2024

Authors: Du, Xin-Feng | Wang, Jie-Sheng | Sun, Wei-Zhen | Zhang, Zhi-Hao | Zhang, Yun-Hao

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-236702

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10167-10186, 2024

Authors: Zong, Xinlu | Li, Hejing | Liu, Aiping | Xu, Hui

Article Type: Research Article

Abstract: Emotion is a crucial factor which influences evacuation effects. However, the studies and quantitative analysis of evacuation emotions, including the emotion generated by external factors and internal personality or cognition levels, emotional contagion evolution, and the regulation mechanism of pedestrians to negative emotion, are still rare. In this paper, an evacuation model based on emotional cognition and contagion (EMECC) is presented. Firstly, individual’s emotion is generated and quantified based on Lazarus’s cognitive theory. Secondly, the emotional contagion between individuals is simulated by SIS (Susceptible Infected Susceptible) infectious disease model. Combining with cellular automata model, an emotion-driven moving rule is proposed …to guide pedestrians move towards the directions with more positive individuals so that positive emotions can be spread effectively. Various experiments on model parameters, obstacles, and emotional contagion process are implemented to verify the effectiveness of the EMECC model. The simulation and experimental results show that emotional regulation mechanism can improve pedestrian’s decision-making ability and contagion of positive emotion can accelerate evacuation process. The EMECC model can simulate emotional changes dynamically and guide pedestrians efficiently and reasonably in emergency evacuation. Show more

Keywords: Emergency evacuation, crowd simulation, emotion, emotional contagion

DOI: 10.3233/JIFS-237147

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10187-10200, 2024

Authors: Wang, Qian

Article Type: Research Article

Abstract: Neuroimaging technology is considered a non-invasive method research the structure and function of the brain which have been widely used in neuroscience, psychiatry, psychology, and other fields. The development of Deep Learning Neural Network (DLNN), based on the deep learning algorithms of neural imaging techniques in brain disease diagnosis plays a more and more important role. In this paper, a deep neural network imaging technology based on Stack Auto-Encoder (SAE) feature extraction is constructed, and then Support Vector Machine (SVM) was used to solve binary classification problems (Alzheimer’s disease [AD] and Mild Cognitive Impairment [MCI]). Four sets of experimental data …were employed to perform the training and testing stages of DLNN. The number of neurons in each of the DLNNs was determined using the grid search technique. Overall, the results of DLNNs performance indicated that the SAE feature extraction was superior over (Accuracy Rate [AR] = 74.9% with structure of 93-171-49-22-93) shallow layer features extraction (AR = 70.8% with structure of 93-22-93) and primary features extraction (AR = 69.2%). Show more

Keywords: Deep learning neural network, neuroimaging technology, brain diseases, disease diagnosis, feature extraction

DOI: 10.3233/JIFS-237979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10201-10212, 2024

Authors: Qiao, Gongzhe | Zhuang, Yi | Ye, Tong | Qiao, Yuan

Article Type: Research Article

Abstract: The intelligent network information systems, such as smart grid systems, face many security problems in the aspects of sensing, communication and computing. Information security risk assessment is an important way to assess the threats faced by information systems before risk events occur and ensure the security of assets. However, a comprehensive risk assessment of the system is a very resource-consuming process. Many existing risk assessment methods rely on a large number of experts and computing resources. Their assessment results are vulnerable to the differences in experts’ subjective judgments. Therefore, we propose FRAMB, a novel man-machine collaborative risk assessment method based …on fitting upper and lower bounds. Firstly, we present a risk assessment criterion including four categories and sixteen risk factors following the ISO/IEC 27005:2018 standard. On this basis, we present the DFAHP and CM-NN assessment models to obtain the upper and lower bounds of the risk assessment value, which provides a reference for expert assessment. FRAMB integrates the experts’ assessment value and the values of upper and lower bounds, and adjusts the weights of these values to give the final risk assessment value. We introduce the risk assessment process of FRAMB in detail through a case study of the smart grid system risk assessment. We evaluate the effectiveness and accuracy of FRAMB through experiments. The experimental results show that FRAMB can effectively and accurately assess the security risks of the intelligent network information systems. Show more

Keywords: Risk assessment, information systems, neural network, analytic hierarchy process, expert evaluation

DOI: 10.3233/JIFS-231880

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10213-10229, 2024