Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Gao, Danna | Zhang, Yin | Qiu, Hongjun

Article Type: Research Article

Abstract: Due to the large number of frames and low video resolution, tennis match videos cannot accurately identify and extract effective data, which reduces the level of fine analysis of tennis matches. In order to solve the problem of poor detection effect of small targets in tennis video, an automatic detection method of small targets in tennis video based on deep learning is proposed. Non-maximum suppression algorithm is used to determine the position of the target between different adjacent video image sequences, and SVM classifier is used to train a large number of target behaviors. According to the hierarchical structure of …dataset annotation, the hierarchical structure of tennis video for deep learning is optimized. The reconstruction algorithm is used to enhance the video image in the input VOC data set and improve the fine segmentation effect of the image. The difference video image is binarized to complete the automatic detection of small targets in tennis video. The experimental results show that the proposed method has high integrity of tennis video information collection, high recognition accuracy and short detection time. Show more

Keywords: Deep learning, tennis match, automatic target detection

DOI: 10.3233/JIFS-231167

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9199-9209, 2023

Authors: Chang, Jingjing | Zhao, Hui

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Abstract: This article has been retracted.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-231778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9211-9221, 2023

Authors: Liu, Yang | Yi, Fulong | Ma, Yuhua | Wang, Yongfu

Article Type: Research Article

Abstract: To solve the problem that the existing semantic segmentation method is challenging to balance the two indicators of reasoning speed and accuracy under low-light conditions, a lightweight real-time semantic segmentation method that can run on embedded vehicle terminals under low-light conditions, namely, LCG-BiSeNetV2 is proposed. Firstly, the Zero-DCE++ method is introduced to preprocess the low-light video frames; Secondly, by improving the module of the BiSeNetV2 method and optimizing the downsampling convolution in the encoder structure, the semantic feature extraction ability of the BiSeNetV2 method has been significantly improved; Finally, the self-made road scene semantic segmentation dataset and the CAMVID dataset …were used for training and testing, the mIoU coefficient reaches 72.37% and 76.73%, and the reasoning speed reaches 148.91 Frames Per Second (FPS) and 136.75 FPS, which shows that the LCG-BiSeNetV2 method has achieved fast reasoning and precise semantic segmentation. Show more

Keywords: End-to-end, real-time, semantic segmentation, autonomous driving system, image enhancement

DOI: 10.3233/JIFS-230643

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9223-9237, 2023

Authors: Xie, Zhixin | Wang, Xiaofeng | Yang, Lan | Pang, Lichao | Zhao, Xingyu | Yang, Yi

Article Type: Research Article

Abstract: The survey propagation algorithm is the most effective information propagation algorithm for solving the 3-SAT problem. It can effectively solve the satisfiability problem when it converges. However, when the factor graph structure is complex, the algorithm often does not converge and the solution fails. In order to give a theoretical explanation to this phenomenon and to analyze the convergence of the survey propagation algorithm effectively, a connected treewidth model of the propositional formula was constructed by using the connected tree decomposition method, and the connected treewidth of the factor graph was calculated. The relationship between the connected treewidth and the …convergence of the survey propagation algorithm is established, and the convergence judgment condition of the survey propagation algorithm based on the connected tree width is given. Through experimental analysis, the results show that the method is effective, which is of great significance for analyzing the convergence analysis of other information propagation algorithms. Show more

Keywords: Survey propagation algorithm, convergence, connected treewidth, propositional formula, satisfiability problem

DOI: 10.3233/JIFS-223779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9239-9252, 2023

Authors: Zhang, Ning | Jung, Kwansue

Article Type: Research Article

Abstract: High-quality development of socio-economic, ecological environment, tourism industry and natural resources in the Yellow River Basin is a major strategic task for national development. High-quality development in the Yellow River Basin still has a lot of room for improvement, so it is important for the coupled and coordinated development of water resources-socio-economic-ecological environment-tourism industry in the Yellow River Basin region. In this study, we focus on the major strategic issues of high quality development in the Yellow River Basin in the context of the above-mentioned background and the current forms and policies of sustainable development in China, and conduct research …on the theory and methods of assessing the sustainable development of regional water resources-socio-economic-ecological environment-tourism industry. However, by analyzing the existing research literature on sustainable development assessment, we find that most of them focus on a single area or two combinations of modal development studies, and focus on exploring micro-coupling mechanisms, so the generality of macro policy support is rather mediocre, which we fill this gap through this study. The study relies on an innovative macroscopic research perspective that draws pioneeringly on the quality function deployment (QFD) theory in the field of product quality management in marketing, which, it is worth stating, allows for a framework of research perspectives from a systematic and holistic perspective. In this framework, we will propose a G1-entropy value method for indicator importance (weight) assessment. It is worth stating that the G1 method used in this paper is different from the traditional G1 method in that we will introduce the identity information weights of experts to improve the reliability of the subjective assignment method. We will also construct an indicator system for studying the regional sustainable development issues in the Yellow River Basin on this basis, so as to complete the ranking and analysis of the nine provinces and the importance of indicators in the Yellow River Basin. This study will provide a comprehensive theoretical basis for Chinese government and related departments to formulate policies for the high-quality development of water resources, socio-economic, ecological environment and tourism industry in the Yellow River Basin, and also provide theoretical and empirical references for the analysis and assessment of similar international regional sustainability cases. Show more

Keywords: Analytical theory of sustainable development, quality function deployment, G1-entropy method, yellow river basin

DOI: 10.3233/JIFS-230920

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9253-9276, 2023

Authors: Nguyen, Phu Xuan-Vinh | Nguyen, Thu Hoang-Thien | Van Nguyen, Kiet | Nguyen, Ngan Luu-Thuy

Article Type: Research Article

Abstract: Multilingual pre-trained language models have achieved impressive results on most natural language processing tasks. However, the performance is inhibited due to capacity limitations and their under-representation of pre-training data, especially for languages with limited resources. This has led to the creation of tailored pre-trained language models, in which the models are pre-trained on large amounts of monolingual data or domain specific corpus. Nevertheless, compared to relying on multiple monolingual models, utilizing multilingual models offers the advantage of multilinguality, such as generalization on cross-lingual resources. To combine the advantages of both multilingual and monolingual models, we propose KDDA - a framework …that leverages monolingual models to a single multilingual model with the aim to improve sentence representation for Vietnamese. KDDA employs teacher-student framework and cross-lingual transfer that aims to adopt knowledge from two monolingual models (teachers) and transfers them into a unified multilingual model (student). Since the representations from the teachers and the student lie on disparate semantic spaces, we measure discrepancy between their distributions by using Sinkhorn Divergence - an optimal transport distance. We conduct experiments on two Vietnamese natural language understanding tasks, including machine reading comprehension and natural language inference. Experimental results show that our model outperforms other state-of-the-art models and yields competitive performances. Show more

Keywords: Natural language understanding, machine reading comprehension, natural language inference, knowledge distillation, optimal transport

DOI: 10.3233/JIFS-231485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9277-9288, 2023

Authors: He, Xiaoquan | Dong, Fang

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-233020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9289-9302, 2023

Authors: Ma, Qian | Jiang, Shuhao | Yang, Dandan | Cheng, Guangtao

Article Type: Research Article

Abstract: In recent years, the problem of influential spreader identification in complex networks has attracted extensive attention as its fundamental role in social network analysis, rumor controlling, viral marketing and other related fields. Centrality measures that consider different scales of neighborhood are commonly utilized for ranking node influence. The 2-hop neighborhood of the target node is deemed a suitable evaluation metric. However, as the network scale expands, only considering 2-hop neighborhood is overly restrictive. Furthermore, the interconnections among nodes are often disregarded. In this article, a new method named Limited Spreading Domain (LSD) is proposed to identify influential spreaders. LSD defines …the target node’s 2-hop neighborhood as basic domain and takes the neighbors who are 3–6 hops away from target node as extended domain. The influence of target node is modeled as diffusion along the paths with limited length in basic domain and extended domain. A series of experiments are conducted in eight real complex networks and results demonstrate that LSD outperforms common centralities in terms of accuracy, stability,distinguishability and scalability. Show more

Keywords: Complex network, influential spreaders, SIR model, centrality measure

DOI: 10.3233/JIFS-230258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9303-9314, 2023

Authors: Xinyi, Zhang

Article Type: Research Article

Abstract: Visual art was originally measured by viewing and appreciating graphic works, and there was no previous research into ways to improve the quality of visual art. With the rapid development of visual arts and technology, the question of how to improve quality has become an urgent one. As the most cutting-edge and hottest concept in the international arena today, the development and application of metaverse technology has widely drawn the close attention of various industries, including management, economy, education, and art. However, there is no in-depth and clear research on the concept of metaverse in the field of art, especially …in the field of visual art. We believe that the creation of visual art in the context of metaverse will be an important direction for art development in the future, and can also greatly contribute to the improvement of the quality of metaverse visual art presentation. Therefore, we focus on the issue of visual art quality assessment in our research, and propose a theory and method of metaverse-oriented future visual art quality assessment. The method focuses on the G1-entropy value method to calculate the weights in visual arts, combines qualitative research with quantitative research, and proposes the improvement path and countermeasures for visual arts. In summary, our research aims to address the theoretical approaches to the design of the metaverse field architecture and the assessment of art quality for the future introduction of the metaverse. The main contributions of our research are focused on the following three aspects: 1. The construction of the visual art field architecture draws on the functional requirements analysis method of system science simulation, considering that the entire visual art metaverse field architecture is constructed at three levels: the bottom data support layer, the middle technical support layer and the upper technical application layer. 2. The G1-entropy combination weighting method is used to derive the importance ranking of visual art quality indicators and identify key factors, and to derive suggestions for quality improvement based on the key indicator factors. More importantly, we also build a field architecture for future-oriented visual arts in this study, which bridges the gap in the structural design of visual arts after the introduction of the future concept. Our present study makes a great contribution to the application of visual art quality enhancement, focusing on the analysis of new concepts and the improvement of old methods, building a new scene of organic combination of new technologies and traditional visual art, with practical research theoretical support for the promotion and progress of the disciplinary field. Show more

Keywords: Visual art, metaverse, field structure, G1 entropy method, index weights

DOI: 10.3233/JIFS-224571

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9315-9333, 2023

Authors: Liu, Shaohua | Xia, Caichu | Ye, Jun

Article Type: Research Article

Abstract: In large-section tunnel engineering, there is uncertain and inconsistent information in risk factors, due to complex geological, irregular hydrological conditions, limited survey technology, and inexperience of construction technicians. However, it is difficult for existing risk assessment methods to consider and express this uncertain information comprehensively, which will affect the accuracy of tunnel risk assessment. Hence, this paper develops a method of neutrosophic numbers similarity measure based on arcsine function to address the expression of uncertain information in the risk factors of large-section tunnel. The uncertain information in risk factors is considered comprehensively and the comprehensive weight of tunnel risk factors …is calculated by the analytic hierarchy process and the entropy weight method in this method, in order to provide a reference for similar tunnels. Show more

Keywords: Large-section tunnel, risk assessment, neutrosophic numbers, similarity measure, arcsine function

DOI: 10.3233/JIFS-231475

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9335-9345, 2023

Authors: Janakiraman, Bhavithra | Prabu, S. | Senthil Vadivu, M. | Krishnan, Dhineshkumar

Article Type: Research Article

Abstract: Having one’s life threatened by a disease like ovarian cancer is the single most crucial thing in the whole world. It is difficult to achieve high performance without sacrificing computational efficiency; the results of the denoising process are not as good as they could be; the proposed models are nonconvex and involve several manually chosen parameters, which provides some leeway to boost denoising performance; the methods generally involve a complex optimisation problem in the testing stage; Here at DnCNN, we’ve developed our own version of the deep ii learning model, a discriminative learning technique. The goal was to eliminate the …need for the iterative optimisation technique at the time it was being evaluated. The goal was to avoid having to go through testing altogether, thus this was done. It is highly advised to use a Deep CNN model, the efficacy of which can be evaluated by comparing it to that of more traditional filters and pre-trained DnCNN. The Deep CNN strategy has been shown to be the best solution to minimise noise when an image is destroyed by Gaussian or speckle noise with known or unknown noise levels. This is because Deep CNN uses convolutional neural networks, which are trained using data. This is because convolutional neural networks, which are the foundation of Deep CNN, are designed to learn from data and then use that learning to make predictions. Deep CNN achieves a 98.45% accuracy rate during testing, with an error rate of just 0.002%. Show more

Keywords: Ovarian follicles, cancer, deep learning, hybrid optimization, noise levels, magnetic resonance imaging

DOI: 10.3233/JIFS-231322

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9347-9362, 2023

Authors: Noor, Mah | Jamil, Muhammad Kamran | Ullah, Kifayat | Azeem, Muhammad | Pamucar, Dragan | Almohsen, Bandar

Article Type: Research Article

Abstract: A T-spherical fuzzy set (TSFS) is an extended and logical algebraic representation to handle uncertainty, with the help of four functions describing four possible aspects of uncertain information. Aczel-Alsina triangular norm (TN) and conorm (TCN) are novel and proved to be more efficient than other existing TNs and TCNs. In our article, we establish the concept of a T-spherical fuzzy Aczel-Alsina graph (TSFAAG). We described the energy of TSFAAG along with the splitting and shadow energy of TSFAAG. Furthermore, we figured out the Randić energy of TSFAAG and obtained some useful results. Moreover, we give the notion of the Aczel-Alsina …digraph (TSFAADG). To see the significance of the proposed TSFAADGs, we employed the energy and Randić energy of TSFAADGs for solving the problem of selecting the best investing company by using a decision-making algorithm. The sensitivity analysis of the variable parameters is also discussed and where the effect on ranking results is studied. To see the effectiveness of the proposed work, we did a comparative study and established some remarks. Show more

Keywords: T-spherical fuzzy sets, T-spherical fuzzy Aczel-Alsina graph, energy, splitting graph, shadow graph, randić energy, decision making

DOI: 10.3233/JIFS-231086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9363-9385, 2023

Authors: Lang, Liping | Thuente, David | Ma, Xiao

Article Type: Research Article

Abstract: In order to better evaluate and promote human health, this paper analyzes the influence of different inertial-measurement-unit signals, different sensor locations, different activity intensities and different signal fusion schemes on the accuracy of physical strength consumption estimation during walking and running activities. Different pattern recognition methods, such as the Counts-based linear regression model, the typical non-linear model based on decision tree and artificial neural network, and the end-to-end convolutional neural network model, are analyzed and compared. Our findings are as follows: 1) For the locations of sensors during walking and running activities, the physical strength consumption prediction accuracy at the …ankle location is higher than that at the hip location. Therefore, wearing an inertial-measurement-unit at the ankle can improve the accuracy of the model. 2) Regarding the types of activity signals during walking and running activities, the impact of accelerometer signals on hip and ankle prediction accuracy is not significantly different, while the gyroscope model is more sensitive to the location, with higher prediction accuracy at the ankle than at the hip. In addition, the physical strength consumption prediction accuracy of accelerometer signals is higher than that of gyroscope signals, and fusion of accelerometer and gyroscope signals can improve the accuracy of physical strength consumption prediction. 3) For different data analysis models during walking and running activities, the artificial neural network model that integrates different sensor locations and inertial-measurement-unit signals with different activity intensities has the lowest mean squared error for the measurement of physical strength consumption. The non-linear models based on decision tree and artificial neural network have better physical strength consumption prediction capabilities than the Counts-based linear regression model, especially for high-intensity activity energy consumption prediction. In addition, feature engineering models are generally better than convolutional neural network model in terms of overall performance and prediction results under the three different activity intensities. Furthermore, as the activity intensity increases, the performance of all physical strength consumption calculation models decreases. We recommend using the artificial neural network model based on multi-signal fusion to estimate physical strength consumption during walking and running activities because this model exhibits strong generalization ability in cross-validation and test results, and its stability under different activity intensities is better than that of the other three models. To the best of our knowledge, this paper is the first to delve deeply and in detail into methods for estimating physical strength consumption. Undoubtedly, our paper will have an impact on research related to topics such as intelligent wearable devices and subsequent methods for estimating physical strength consumption, which are directly related to physical health. Show more

Keywords: Human health, intelligent wearable devices, strength consumption estimation, pattern recognition

DOI: 10.3233/JIFS-231691

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9387-9402, 2023

Authors: Li, Dongjie | Wang, Mingrui | Zhang, Yu | Zhai, Changhe

Article Type: Research Article

Abstract: Although various automatic or semi-automatic recognition algorithms have been proposed for tiny part recognition, most of them are limited to expert knowledge base-based target recognition techniques, which have high false detection rates, low recognition accuracy and low efficiency, which largely limit the quality as well as efficiency of tiny part assembly. Therefore, this paper proposes a precision part image preprocessing method based on histogram equalization algorithm and an improved convolutional neural network (i.e. Region Proposal Network(RPN), Visual Geometry Group(VGG)) model for precision recognition of tiny parts. Firstly, the image is restricted to adaptive histogram equalization for the problem of poor …contrast between part features and the image background. Second, a custom central loss function is added to the recommended frame extraction RPN network to reduce problems such as excessive intra-class spacing during classification. Finally, the local response normalization function is added after the nonlinear activation function and pooling layer in the VGG network, and the original activation function is replaced by the Relu function to overcome the problems such as high nonlinearity and serious overfitting of the original model. Experiments show that the improved VGG model achieves 95.8% accuracy in precision part recognition and has a faster recognition speed than most existing convolutional networks trained on the same test set. Show more

Keywords: Precision parts, histogram equalization, image recognition, VGG, RPN

DOI: 10.3233/JIFS-231730

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9403-9419, 2023

Authors: Zhang, Longji | zhao, Hui

Article Type: Research Article

Abstract: Traditional graph convolutional neural networks (GCN) utilizing linear feature combination methods have limited capacity to capture the interaction between complex features. While current research has extensively investigated various syntactic dependency tree structures, the optimization of GCN algorithms has often been overlooked, leading to suboptimal efficiency in practical applications. To address this issue, this paper proposes a cross-feature method that utilizes feature vector multiplication to construct non-linear combinations of GCN features and enhance the model’s capability to extract complex feature correlations. Experimental results demonstrate the superiority of the proposed method, with our models outperforming state-of-the-art methods and achieving significant improvements on …three standard benchmark datasets. These results suggest that the cross-feature method can effectively extract potential connections between features, highlighting its potential for improving the performance of GCN-based models in real-world applications. Show more

Keywords: Aspect-based sentiment analysis, syntactic dependency tree, graph convolutional neural networks, cross-feature

DOI: 10.3233/JIFS-221687

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9421-9432, 2023

Authors: Zhou, Chunrong | Jiang, Zhenghong

Article Type: Research Article

Abstract: Load balancing in cloud computing refers to dividing computing characteristics and workloads. Distributing resources among servers, networks, or computers enables enterprises to manage workload demands. This paper proposes a novel load-balancing method based on the Two-Level Particle Swarm Optimization (TLPSO). The proposed TLPSO-based load-balancing method can effectively solve the problem of dynamic load-balancing in cloud computing, as it can quickly and accurately adjust the computing resource distribution in order to optimize the system performance. The upper level aims to improve the population’s diversity and escape from the local optimum. The lower level enhances the rate of population convergence to the …global optimum while obtaining feasible solutions. Moreover, the lower level optimizes the solution search process by increasing the convergence speed and improving the quality of solutions. According to the simulation results, TLPSO beats other methods regarding resource utilization, makespan, and average waiting time. Show more

Keywords: Load balancing, cloud computing, virtualization, particle swarm optimization algorithm

DOI: 10.3233/JIFS-230828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9433-9444, 2023

Authors: Yu, Ying | Yu, Jiamao | Qian, Jin | Zhu, Zhiliang | Han, Xing

Article Type: Research Article

Abstract: Crowd counting aims to estimate the number, density, and distribution of crowds in an image. The current mainstream approach, based on CNN, has been highly successful. However, CNN is not without its flaws. Its limited receptive field hampers the modeling of global contextual information, and it struggles to effectively handle scale variation and background complexity. In this paper, we propose a Multi-scale Hybrid Attention Network called MHANet to solve crowd counting challenges more effectively. To address the issue of scale variation, we have developed a Multi-scale Aware Module (MAM) that incorporates multiple sets of dilated convolutions with varying dilation rates. …The MAM significantly improves the network’s ability to extract information at multiple scales. To tackle the problem of background complexity, we have introduced a Hybrid Attention Module (HAM) that combines spatial attention and channel attention. The HAM effectively directs attention to the crowd region while suppressing background interference, resulting in more accurate counting. MHANet has been extensively experimented on four benchmark datasets and compared against state-of-the-art algorithms. It consistently achieves superior performance in terms of the MAE evaluation metric. MHANet outperforms the current state-of-the-art methods by margins of 1.9%, 5.4%, 0.4%, and 0.8% on the ShanghaiTech Part_A, ShanghaiTech Part_B, UCF-QNRF, and UCF_CC_50 datasets, respectively. Furthermore, a series of ablation experiments targeting MAM and HAM were conducted in this paper, and the experimental results fully demonstrate that MAM and HAM can effectively address the challenges of scale variation and background complexity, ultimately enhancing the accuracy and robustness of the network. Show more

Keywords: CNN, crowd counting, multi-scale, hybrid attention

DOI: 10.3233/JIFS-232065

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9445-9455, 2023

Authors: Jiang, Zhuo | Huang, Xiao | Wang, Rongbin

Article Type: Research Article

Abstract: Aiming at anomaly detection upon a high-dimensional space, this paper proposed a novel autoencoder-support vector machine. The key thought is that using the autoencoder extracts the features from high-dimensional data, and then the support vector machine achieves the separation of abnormal features and normal features. To increase the precision of identifying anomalies, Chebyshev’s theorem was used to estimate the upper of the number of abnormal features. Meanwhile, the dot product operation was implemented in order to strengthen the learning of the model for class labels. Experiment results show that the detected accuracy of the proposed method is 0.766 when the …data dimensionality is 5408, and also wins over competitors in detected performance for the considered cases. We also demonstrate that the strengthened learning of class labels can improve the ability of the model to detect anomalies. In terms of noise resistance and overcoming the curse of dimensionality, the former can carry out more efforts than the latter. Show more

Keywords: Anomaly detection, Chebyshev’s theorem, high-dimensional data

DOI: 10.3233/JIFS-231735

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9457-9469, 2023

Authors: Liang, Xingzhu | Liu, Wen | Bi, Feilong | Yan, Xinyun | Zhang, Chunjiong

Article Type: Research Article

Abstract: Online knowledge distillation breaks the pre-determined strong and weak teacher-student models, it provides a new way of thinking about knowledge distillation. However, the current online methods often use the Logits-based prediction distribution, and the features containing rich semantic information are rarely used. Even if the feature-based methods are used, they only operate on the last layer of the network, without further exploring the representation knowledge of the middle layer feature map. To address the above issues, we propose an innovative feature early fusion and reconstruction (FEFR) method for online knowledge distillation which entails four essential components: multi-scale feature extraction and …intermediate layer feature early fusion, reconstruction of features, dual-attention and overall fusion module in this paper. We propose early fusion by “sum” operation for feature matrices between different layers and advance fusion to improve the feature map representation. In order to enhance the communication ability between groups to obtain features, the features were reconstructed. We create a dual-attention to enhance the critical channel and spatial regions adaptively in order to collect more accurate information. The previously processed feature maps are combined and fused using feature fusion, which also aids in student models training. A study of the network architectures of CIFAR-10, CIFAR-100, CINIC-10 and ImageNet 2012 shows that FEFR provides more useful characterization knowledge for refinement and improves accuracy by about 0.5% compared to other methods. Show more

Keywords: Online knowledge distillation, teacher-student models, multi-scale, feature early fusion

DOI: 10.3233/JIFS-232626

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9471-9482, 2023

Authors: Mihi, Soukaina | Ait Benali, Brahim | Laachfoubi, Nabil

Article Type: Research Article

Abstract: Sentiment analysis has become a prevalent issue in the research community, with researchers employing data mining and artificial intelligence approaches to extract insights from textual data. Sentiment analysis has progressed from simply classifying evaluations as positive or negative to a sophisticated task requiring a fine-grained multimodal analysis of emotions, manifestations of sarcasm, aggression, hatred, and racism. Sarcasm occurs when the intended message differs from the literal meaning of the words employed. Generally, the content of the utterance is the opposite of the context. Sentiment analysis tasks are hampered when a sarcastic tone is recognized in user-generated content. Thus, automatic sarcasm …detection in textual data dramatically impacts the performance of sentiment analysis models. This study aims to explain the basic architecture of a sarcasm detection system and the most effective techniques for extracting sarcasm. Then, for the Arabic language, determining the gap and challenges. Show more

Keywords: Sarcasm, NLP, sentiment analysis, Arabic, deep learning, machine learning

DOI: 10.3233/JIFS-224514

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9483-9497, 2023

Authors: Thirugnanam, G. | Sahul Hameed, Jennathu Beevi | Bharathidasan, B.

Article Type: Research Article

Abstract: In addition to existing cryptographic systems, watermarking technologies have been developed to add extra security. Digital watermarking utilizes embedding or hiding techniques to protect multimedia files from copyright violations. Fundamental procedures of digital watermarking techniques are embedding and extraction. Singular value decomposition (SVD) based Image watermarking schemes become popular owing to its better trade-off among robustness and imperceptibility. Nevertheless, false positive problem (FPP) is a major issue of SVD-based watermarking schemes. The singular value that is a fixed value and does not contain structural information about image is the primary cause of FPP problem. Therefore, Message Digest algorithm image watermarking …scheme based on Funk Singular Value Decomposition and Fractional-Order Polar Harmonic Transform (FSVD-FOPHT) is proposed in this paper to address this problem. The MD-5 algorithm is used to extract data from the host and watermark imageries and then create secret key. The FSVD-FOPHT method is utilized to hide watermark information in host image. The secret keys are extracted from hided image using inverse process of Fractional-Order Polar Harmonic Transforms with Funk Singular Value Decomposition algorithm. By using the extraction procedure, watermark image is extracted, and then reconstructs original watermarked image. During extraction procedure, the secret key is used for authentication to address FPP. Then, the proposed method is implemented in MATLAB and performance is analyzed with evaluation metrics, such as Embedding capacity, MSE, PSNR, and NC. The proposed method provide 14.6%, 17.34%, 19.53%, 21.46% and 23.89% high PSNR for cold-snow-landscape-water test image, 14.29%, 16.47%, 18.39%, 20.16% and 21.93% high PSNR for landscape-nature-sky-blue Test image, 16.85%, 19.99%, 22.70%, 27.22% and 29.16% high Embedding Capacity for cold-snow-landscape-water test image 22.83%, 24.64%, 27.92%, 29.60% and 31.77% high Embedding Capacity for landscape-nature-sky-blue Test image 35.38%, 32.63%, 30.95%, 28.61% and 26.08% low extraction time compared with existing methods SVD-CMSF-SIW, FE-IWS-DNN, AR-IWS-DNN, BBET-SHA1-SIW and LSB-DWT-SIW respectively. Show more

Keywords: Fractional-order polar harmonic transforms funk singular value decomposition embedding and extraction, message digest algorithm, secure image watermarking

DOI: 10.3233/JIFS-222182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9499-9521, 2023

Authors: Liu, Hong | Wang, Gaihua | Li, Qi | Wang, Nengyuan

Article Type: Research Article

Abstract: The detection of magnetic tile quality is an essential link before the assembly of permanent magnet motor. In order to meet the high standard of magnetic tile surface defect detection and realize the rapid and automatic segmentation of magnetic tile defects, a magnetic tile surface defect segmentation algorithm based on cross self-attention model (CSAM) is proposed. It adopts high-low level semantic feature fusion method to build the dependency relationship between the deep and shallow features. Multiple auxiliary loss functions are used to constrain the network and reduce the noise in the deep features. In addition, an image enhancement method is …also designed to solve the problem of insufficient annotated data. The experimental results show that the network can achieve 79.6% mIoU and 98.5% PA, which can meet the high standard requirements of magnetic tile manufacturing. Show more

Keywords: Defect detection, data enhancement, cross self-attention, multiple auxiliary loss, semantic segmentation

DOI: 10.3233/JIFS-232366

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9523-9532, 2023

Authors: Xiao, Yanjun | Zhao, Yue | Li, ShiFang | Song, Weihan | Wan, Feng

Article Type: Research Article

Abstract: The foundation of textile machinery digitization and intelligence is condition monitoring and identification. Online condition monitoring of looms is of great significance to ensure their long-term stable operation and improve their digital management level. However, the existing loom condition monitoring methods have problems such as insufficient depth of information mining, low condition recognition rate and poor system versatility. As a result, the loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory is studied. To begin, we examine the loom operation mechanism, loom state characterization, and loom state feature data composition. Then, using the fuzzy rough …set method, we analyze and make decisions on the loom state feature data, apply the theory of uncertainty and importance improvement DSmT fusion to solve the uncertainty problem of the rough set method’s decision rules, and build the loom state feature decision network on the embedded terminal using the decision rules. Meanwhile, to collect, communicate, display, and alarm loom characteristic data, this paper employs the STM32F407ZET6 microcontroller and designs a loom system status data collection platform with the AD7730 as the core, as well as tests loom status monitoring data collection and loom status data analysis and decision method based on this platform. The experimental findings show the usefulness of attribute data gathering as well as data analysis and decision-making processes. The technology enhances the precision of loom condition identification and decision making, as well as the safety and quality of manufacturing. It is critical for carrying out applications like as problem detection, remote monitoring, efficiency optimization, and intelligent weaving machine management. Show more

Keywords: Keywords: Rapier loom, dezert-Smarandache, fuzzy rough sets, condition monitoring, attribute reduction

DOI: 10.3233/JIFS-230950

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9533-9553, 2023

Authors: Dong, Yingda | He, Chunguang | Qin, Yanping | Yuan, Yunmei | Gao, Fan | Duo, Huaqiong | Wang, Ximing

Article Type: Research Article

Abstract: A novel enhancement method to improve resolution and contrast has been proposed to address the issues of blurring and distortion commonly encountered in traditional patterns. Initially, a discrete wavelet transform, a stationary wavelet transform, and an interpolation algorithm are used to obtain high-resolution images of traditional patterns. Subsequently, improved singular value matrix coefficients and reconstructed gamma function are used to enhance the image contrast to obtain high-resolution and contrast-enhanced patterns. Experimental results demonstrate the efficacy of this method, as evidenced by improved evaluation indexes, such as mean square error, peak signal-to-noise ratio, and structural similarity, in comparison to other existing …methods. The proposed method effectively improves the quality of traditional patterns and offers significant contributions to research on the restoration and protection of traditional patterns. Show more

Keywords: Traditional pattern enhancement, stationary wavelet transform, discrete wavelet transform, singular value matrix, gamma function

DOI: 10.3233/JIFS-232169

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9555-9569, 2023

Authors: Shenbagavalli, S.T. | Shanthi, D.

Article Type: Research Article

Abstract: Due to the vast amount of patient health data, automated healthcare systems still struggle to classify and diagnose various ailments. Learning redundant data also reduces categorization accuracy. A Deep Belief Network (DBN) has been used to precisely extract the most important aspects from clinical data by ignoring irrelevant/redundant features. Due of many learning variables, training is complicated. Similarly, the hybrid model has been employed by ensemble Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to categorize diseases. But, its efficiency depends on the proper choice of kernels and hyper-parameters. Therefore, this paper develops an efficient …feature extraction and classification model for healthcare systems. First, several medical data related to the patient’s health are collected. Then, an Optimized DBN (ODBN) model is presented for maximizing the accurateness of DBN by optimizing the learning variables depends on the Ant Lion Optimization (ALO) algorithm. With learning ODBN, the most relevant features are extracted with reduced computational complexity. After that, the CNN-LSTM with Unsupervised Fine-tuned Deep Self-Organizing Map (UFDSOM)-based classifier model is designed to categorize the extracted features into categories of illnesses. In this novel classifier, dropout normalization and parameter tuning processes are applied to avoid overfitting and optimize the hyper-parameters, which results in a less training period. In the end, studies utilizing publically accessible datasets show that the ODBN with CNN-LSTM-UFDSOM system outperforms classical models by 98.23%. Show more

Keywords: Medical data classification, DBN, CNN-LSTM, SVM, Ant lion optimizer

DOI: 10.3233/JIFS-224370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9571-9589, 2023

Authors: Su, Jiafu | Wang, Dan | Xu, Baojian | Zhang, Fengting | Ling, Xu

Article Type: Research Article

Abstract: A crucial step for agricultural product merchants to achieve profitable and sustainable development in the live-streaming e-commerce age is evaluating the risk of the agricultural products live-streaming e-commerce platform. However, there isn’t much reliable research available right now on the risk evaluation of platforms. Therefore, this study suggests an improved risk evaluation method based on interval-valued intuitionistic fuzzy multi-criteria group decision-making (MCGDM). This method determines the decision-maker weight for the risk criterion according to the levels of professionalism of the decision-makers in the risk criterion and uses the VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rate the risk …of the alternative agricultural products live-streaming e-commerce platforms. The viability and dependability of the approach described in this work are demonstrated using a case study. The strengths and weaknesses of this approach are illustrated by a comparative analysis. With the help of this paper, agricultural product merchants will be able to identify the live-streaming e-commerce platform that carries the least amount of overall risk and work toward the paper’s stated objectives of sustainable development in addition to developing and enhancing theoretical research findings in the field. Show more

Keywords: Live-streaming e-commerce platform, risk assessment, MCGDM, interval-valued intuitionistic fuzzy number, professionalism of decision makers

DOI: 10.3233/JIFS-231403

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9591-9604, 2023

Authors: Chen, Liang-Ching | Chang, Kuei-Hu

Article Type: Research Article

Abstract: Within the new era of artificial intelligence (AI), education industry should develop in the direction of intelligence and digitalization. For evaluating learners’ academic performances, English high-stakes test is not only a mere means for measuring what English as a Foreign Language (EFL) stakeholders know or do not know but also likely to bring life-changing consequences. Hence, effective test preparation for English high-stakes test is crucial for those who futures depend on attaining a particular score. However, traditional corpus-based approaches cannot simultaneously take words’ frequency and range variables into consideration when evaluating their importance level, which makes the word sorting results …inaccurate. Thus, to effectively and accurately extract critical words among English high-stakes test for enhancing EFL stakeholders’ test performance, this paper integrates a corpus-based approach and a revised Importance-Performance Analysis (IPA) method to develop a novel frequency-range analysis (FRA) method. Taiwan College Entrance Exam of English Subject (TCEEES) from the year of 2001 to 2022 are adopted as an empirical case of English high stake test and the target corpus for verification. Results indicate that the critical words evaluated by FRA method are concentrated on Quadrant I including 1,576 word types that account for over 60% running words of TCEEES corpus. After compared with the three traditional corpus-based approaches and the Term Frequency-Inverse Document Frequency (TF-IDF) method, the significant contributions include: (1) the FRA method can use a machine-based function words elimination technique to enhance the efficiency; (2) the FRA method can simultaneously take words’ frequency and range variables into consideration; (3) the FRA method can effectively conduct cluster analysis by categorizing the words into the four quadrants that based on their relative importance level. The results will give EFL stakeholders a clearer picture of how to allocate their learning time and education resources into critical words acquisition. Show more

Keywords: Artificial intelligence (AI), English high-stakes test, corpus-based approach, Importance-Performance Analysis (IPA) method, Term Frequency-Inverse Document Frequency (TF-IDF) method, frequency-range analysis (FRA) method

DOI: 10.3233/JIFS-231539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9605-9620, 2023

Authors: Hussain, Abrar | Ullah, Kifayat | Al-Quran, Ashraf | Garg, Harish

Article Type: Research Article

Abstract: Renewable energy sources play an influential role in the world’s climate and reduce the rate of harmful gasses such as carbon dioxide, methane, nitrous oxide, and many other greenhouse gasses that contribute to global warming. The theoretical concept of the T-spherical fuzzy (T-SF) set (T-SFS) is the most suitable model to evaluate energy resources under uncertainty. This article illustrates appropriate operations based on Dombi triangular norm and t-conorm. We derived a series of new aggregation approaches, such as T-SF Dombi Hamy mean (T-SFDHM) and T-SF weighted Dombi Hamy Mean (T-SFDWHM) operators. Further authors illustrated a list of new approaches such …as T-SF Dual Dombi Hamy mean (T-SFDDHM), and T-SF Dombi weighted Dual Hamy mean (T-SFDWDHM) operators. Some exceptional cases and desirable properties of our derived approaches are also studied. We illustrate an application of renewable energy resources to be evaluated using a multi-attribute group decision-making (MAGDM) method. A case study was also studied to choose appropriate energy resources using our proposed methodology of the T-SFDWHM and T-SFDWDHM operators. To show the effectiveness and validity of our current methods, we compared the existing results with currently developed aggregation operators (AOs). Show more

Keywords: T-Spherical fuzzy values, aggregation operators, Dombi aggregation models, and multi-attribute decision-making method

DOI: 10.3233/JIFS-232505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9621-9641, 2023

Authors: Wang, Qingling | Zheng, Jian | Zhang, Wenjing

Article Type: Research Article

Abstract: Majority classes are easily to be found in imbalance datasets, instead, minority classes are hard to be paid attention to due to the number of is rare. However, most existing classifiers are better at exploring majority classes, resulting in that classification results are unfair. To address this issue of binary classification for imbalance data, this paper proposes a novel fuzzy support vector machine. The thought is that we trained two support vector machines to learn the majority class and the minority class, respectively. Then, the proposed fuzzy is used to estimate the assistance provided by instance points for the training …of the support vector machines. Finally, it can be judged for unknown instance points through evaluating that they provided the assistance to the training of the support vector machines. Results on the ten UCI datasets show that the class accuracy of the proposed method is 0.747 when the imbalanced ratio between the classes reaches 87.8. Compare with the competitors, the proposed method wins over them in classification performance. We find that aiming at the classification of imbalanced data, the complexity of data distribution has negative effects on classification results, while fuzzy can resist these negative effects. Moreover, fuzzy can assist those classifiers to gain superior classification boundaries. Show more

Keywords: Binary classification, fuzzy, imbalanced data, support vector machines

DOI: 10.3233/JIFS-232414

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9643-9653, 2023

Authors: Moghaddam Teymourlu, Sohrab Abdollahzadeh | Amini, Amir

Article Type: Research Article

Abstract: In the current study, a new approach to assess and select food suppliers in hospitals is presented using integrated group evaluation method of fuzzy best- worst method (FBWM) and fuzzy gray relational analysis (FGRA). Evaluation criteria are selected by experts and weighed by the fuzzy best-worst method. After that, suppliers are rated using FGRA method. The proposed approach was implemented with seven criteria in one of the Iranian hospitals, and the results showed that quality, delivery time and trust criteria had the highest and skilled manpower and lack of surplus production criteria had the lowest score. Using FGRA, existing suppliers …were ranked and the appropriate supplier was identified. In order to evaluate the reliability of the results, sensitivity analysis was performed on the criteria changes. The results showed that the supplier’s selection is greatly influenced by the criteria estimation values by the experts. Show more

Keywords: Food supply chain, supplier evaluation and selection, fuzzy best-worst method, fuzzy gray relational analysis, health and medical centers

DOI: 10.3233/JIFS-231845

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9655-9668, 2023

Authors: Xu, Dongsheng | Sun, Yuhuan | He, Xinyang

Article Type: Research Article

Abstract: This paper provides a novel target threat assessment model that utilizes TOPSIS and three-way decision-making under a single-valued neutrosophic environment. The presented model provides theoretical support for combat decision-making in complex battlefield environments with uncertain information. The model employs single-valued neutrosophic sets to handle uncertain data, which enhances the descriptive ability of information. The maximum deviation method is used to calculate attribute weight factors, which highlights the importance of each attribute. The final target threat ranking is obtained based on the relative closeness coefficient of each target. Furthermore, the proposed model constructs a multi-attribute aggregation loss function matrix for each …target, sets the risk avoidance coefficient under the knowledge of the battlefield condition, and calculates the decision threshold of each target using three-way decision theory. This method produces the classification of the target choice. The numerical examples and comparison analysis demonstrate that the suggested model can handle ambiguous scenario information effectively and reasonably, transform traditional decision-making ranking results into three-way classification findings, and provide a rationale for choosing an attacking target. Show more

Keywords: Threat assessment, three-way decision, TOPSIS, single-valued neutrosophic

DOI: 10.3233/JIFS-232267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9669-9680, 2023

Authors: Ramyasree, Kummari | Kumar, Chennupati Sumanth

Article Type: Research Article

Abstract: At present, the local appearance-based texture descriptors in Facial Expression Recognition have limited accuracy due to the inability to encode the discriminative edges. The major cause is the presence of distorted and weak edges due to noise. Hence, this paper proposes new Expression Descriptor called as Weighted Edge Local Directional Pattern (WELDP) which can discriminate the weak and strong edges. Unlike the conventional local descriptors, WELDP searches for the support of neighbor pixels in the determination of Facial expression attributes such as Edges, Corners, Lines, and Curved Edges. WELDP encodes only Strong edge responses and discards weaker edge responses after …extracting them through edge detection masks. This work adapted two masks for edge detection: they are Robinson Compass Mask and Kirsch Compass Mask. Moreover, the WELDP aims at code redundancy and encode each pixel only with seven bits (one sign bit and six directional bits). Then the WELDP image is described by a histogram and then processed through SVM (Support Vector Machine) for expression identification. From the simulation experiments, the proposed WELDP is found as better than several existing methods. Show more

Keywords: Face expression recognition, edge detection, gaussian weight, compass mask, directional encoding, and accuracy

DOI: 10.3233/JIFS-232985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9681-9696, 2023

Authors: Priya Varshini, A.G. | Anitha Kumari, K.

Article Type: Research Article

Abstract: As the size and complexity of projects grows, estimates are increasingly used, especially in the agile community. Software development cannot begin without first conducting thorough planning and estimation. Estimating how much work a project will take is a common first step in the software development life cycle. By employing ensemble techniques, we integrate multiple learning algorithms to build a more accurate predictive model. The core elements of our proposed stacked ensemble strategy include Decision Tree, Principal Components Regression, Random Forest, NeuralNet, GLMNET, XGBoost, Earth, and Support Vector Machine. Moreover, we augment the model’s performance by incorporating a blend of these …foundational algorithms with other ensemble regression methods. Extensive testing in the suggested research work with a number of Super Learners demonstrates that Regression is the best technique for judging effort. The evaluation of the different estimators involved the use of various metrics, including Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Percentage of Close Approximations within 25% of the True Values (PRED (25)), R-Squared Coefficients, Precision, Recall, and F1-Score. The proposed method yields more trustworthy predicted performance than either single-model approaches or stacked ensembles. Effort estimation serves as the foundation for the rest of the project management process. Show more

Keywords: Software effort estimations, stacked ensemble method, super learner, principal components regression

DOI: 10.3233/JIFS-230676

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9697-9713, 2023

Authors: Patidar, Neelam | Makrariya, Akshara

Article Type: Research Article

Abstract: The human body is a complex system that can be disrupted by various types of infections and viruses, and body temperature is a major contributor to these problems. To prevent this, doctors recommend comfortable clothing made from good fabric. This paper proposes a model that can be used to analyze how different types of fabric impact the thermal profile of skin layers during and after physical activity. The information gained from this model could be useful in designing exercise apparel for different climates and in generating thermal stress protocols for treating infections and providing physical activity guidelines for healthy living. …The model uses Pennes’ bio-heat equation and finite difference method to examine the temperature distribution in skin layers while accounting for both physiological and clothing parameters. The numerical findings were compared to existing studies, and the model’s accuracy was found to be in good agreement with previous research. The proposed model can be used to predict how much rest and acclimation are needed to cope with thermal stress and could also be modified to obtain thermal information for patients with skin diseases. Additionally, the thermal profile obtained from this model could be helpful in designing exercise clothes for patients with skin diseases. Show more

Keywords: Finite difference method (FDM), exercise, skin layers, clothing, temperature distribution, mathematical modeling, one dimensional (1D)

DOI: 10.3233/JIFS-231524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9715-9728, 2023

Authors: Naveen Kmuar, M. | Godfrey Winster, S.

Article Type: Research Article

Abstract: Investigation of human face images forms an important facet in affective analysis. The work, a DL-based ensemble is proposed for this purpose. Seven pre-trained models namely Facenet, Facenet2018, VGG16, Resnet-50, Senet-50, Arcface and Openface that have been developed for face verification have been exploited and customized for emotion identification. To each of these models, each all over interaction with softmax method to classification groups are augmented and entire network is then trained completely for emotion recognition. After training all the models individually, the probabilities for each of the class by each of the model are summed to derive at the …final value. The class that holds the highest of this value is finalized as the predicted emotion. Thus, the proposed methodology involves image collection, image pre-processing comprising of contrast enhancement, face detection and extraction, face alignment, image augmentation facilitating rotation, shifting, flipping and zooming transformations and appropriate resizing and rescaling, feature extraction and classification through ensemble of customized afore-mentioned pre-trained convolutional neural networks, evaluation and evolving of best weights for emotion recognition from face images with enhanced accuracy. The proposed methodology is evaluated on the well-established FER-2013 dataset. The methodology achieves a validation accuracy of 74.67% and test accuracy of 76.23%. Further, similar images of another dataset (Face Expression Recogniton dataset) are included for training the models and the impact of extra training is assessed to see if there is improvement in performance. The experiments reveal marked improvement in face emotion identification performance reaching values of 94.98% for both validation and test set of FER-2013 dataset and 94.99% on validation set of Face Expression Recognition dataset. Show more

Keywords: Emotion identification, transfer learning, ensemble, pre-trained models, CNN, DNN, DL, multi-class classification, image classification, human faces

DOI: 10.3233/JIFS-231199

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9729-9752, 2023

Authors: Ge, Yilin | Sun, Liping | Wang, Di

Article Type: Research Article

Abstract: Veneer is the critical raw material for manufacturing man-made board products, therefore the quality of the veneer determines the level of the man-made board. However, defects in the veneer may significantly lower its grade. Currently, identifying veneer defects requires manual inspection and subsequent inpainting using a veneer digging machine. Unfortunately, this method only removes the defects of the veneer but ignore the consistency of its texture. To address this issue, we propose a feasible veneer defect reconstruction method that utilizes a texture-aware-multiscale-GAN architecture. Our method performs texture reconstruction of veneer defects to increase the texture information of the reconstructed image …while improving the model efficiency, so that generates natural-looking textures in the reconstructed defect areas. The model is trained by end-to-end updating of four cascades of efficient generators and discriminators. We also employed a loss function based on local binary patterns (LBP) to ensure that the restored images contain sufficient texture information. Finally, region normalization is used in the model to enhance the accuracy of the model. Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) are used to evaluate the effectiveness of image restoration, the results show that PSNR of the method reacheds 35.32 and SSIM reaches 0.971. By minimizing the difference between the generated texture and that of the original image, our model produces high-quality results. Show more

Keywords: Image reconstruction, deep learning, veneer defect, LBP, texture aware multiscale

DOI: 10.3233/JIFS-231692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9753-9769, 2023

Authors: Chen, Wei | Tang, Hong | Yan, Tingting

Article Type: Research Article

Abstract: The energy consumption of mechanical products in China is enormous, and the energy utilization rate is low, which is increasingly receiving people’s attention. Conducting product design for energy optimization is of great significance for improving energy utilization efficiency. The scheme design of a product is the key to achieving innovation in product design, and the evaluation and decision-making of the design scheme directly affect the results of the later stage of the design. Therefore, correctly evaluating and making reliable decisions on product design schemes that are oriented towards fuzzy decision optimization is an important aspect of product innovation conceptual design. …The product modeling design quality evaluation is a multiple attribute group decision making (MAGDM) problems. Recently, the Combined Compromise Solution (CoCoSo) method and information entropy method has been employed to cope with MAGDM issues. The interval neutrosophic sets (INSs) are employed as a tool for portraying uncertain information during the product modeling design quality evaluation. In this paper, the CoCoSo method is designed for MAGDM under INSs. Then, the interval neutrosophic numbers CoCoSo (INN-CoCoSo) method based on the Hamming distance and Euclidean distance is built for MAGDM. The information Entropy method is employed to produce the weight information based on the Hamming distance and Euclidean distance under INNSs. Finally, a practical numerical example for product modeling design quality evaluation is supplied to show the INN-CoCoSo method. The main contributions of this paper are constructed: (1) This paper builds the novel MAGDM based on CoCoSo model under INSs; (2) The information Entropy method is employed to produce the weight information based on the Hamming distance and Euclidean distance under INNSs; (3) The new MAGDM method is proposed for product modeling design quality evaluation based on INN-CoCoSo. Show more

Keywords: Multiple attribute group decision making (MAGDM), interval neutrosophic sets (INSs), CoCoSo method, information entropy, informationization teaching ability evaluation

DOI: 10.3233/JIFS-233825

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9771-9783, 2023

Authors: Gupta, Shivani | Patel, Nileshkumar | Kumar, Ajay | Jain, Neelesh Kumar | Dass, Pranav | Hegde, Rajalaxmi | Rajaram, A.

Article Type: Research Article

Abstract: Due to resource constraints and the diverse nature of the devices involved, energy efficiency and scalability enhancement are important challenges in the Internet of Things (IoT) ecosystem. It is difficult to manage the edge resources in a consistent way that promotes cooperation and sharing of resources across the devices because of the heterogeneity of the Internet of Things devices and the dynamic nature of the surroundings in which edge computing takes place. In this research, we offer Intelligent techniques for resource optimization for Internet of Things devices. This is a full-stack system architecture to support across heterogeneous Internet of Things …devices that have limited resources. The paradigm that is being suggested is made up of several edge servers, and Internet of Things (IoT) devices have the qualities of being heterogeneity-compatible, high performing, and intelligently adaptable. In order to do this, a clustered environment is generated in heterogeneous Internet of Things devices, and a routing method called Search and Rescue Optimization is used to set up connections between the CH nodes. After that, the edge nodes that are closest to the source of the data are chosen for transmission. Overall, what was suggested Multi-Edge-IoT accomplishes superior efficiency in terms of consumption of energy, latency, communication overhead, and packet loss rate than existing approaches to attaining energy efficiency in the Internet of Things. Show more

Keywords: Multi-edge-IoT, EDGE load balancing, heterogeneous network, Bi-fuzzy vikor, search & rescue optimization algorithm

DOI: 10.3233/JIFS-233819

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9785-9801, 2023

Authors: Xia, Jing | Zhang, Shiya

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-234976

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9803-9813, 2023

Authors: Niu, Guocheng | Hu, Dongmei | Zhao, Yang | Eladdad, M.E.

Article Type: Research Article

Abstract: To solve the problem that the operation state of transformer is difficult to quantify, a method of quantitative evaluation and prediction of transformer operating state is proposed, which combines the information entropy of matter element and Support Vector Machine. In the evaluation, various hydrogen gases in the transformer operation are taken as the evaluation indexes and the three-dimensional cross compound element is constructed. The analytic hierarchy process (AHP) is used to determine the theoretical weight of the evaluation index, and the entropy method is used to determine the objective weight of the evaluation index, and the final weight is the …joint weight of the theoretical weight and the objective weight. Transformer Health index is calculated by using complex element correlation entropy. In prediction, the grid search, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize the parameters of Support Vector Machine. and the prediction model of Health index is established by SVM. Experiment results show that the Support Vector Machine based on Gauss kernel function and genetic algorithm has a prominent effect on the prediction of health index. Show more

Keywords: Transformer, health index, analytic hierarchy process (AHP), matter element information entropy, support vector machine (SVM)

DOI: 10.3233/JIFS-182785

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9815-9825, 2023

Authors: Majumder, Saibal | Kutum, Rintu | Khatua, Debnarayan | Sekh, Arif Ahmed | Kar, Samarjit | Mukerji, Mitali | Prasher, Bhavana

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-220990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9827-9844, 2023

Authors: Cheng, Yafei | Zhao, Bin

Article Type: Research Article

Abstract: In recent years, there are many works about conditional distributivity for aggregation functions, which is closely related to integration theory and utility theory. In this paper, our main idea is to solve conditional distributivity equations from left and right for semi-t-operators over uninorms. One part focuses on these equations involving semi-t-operators over t-norms and obtains some complete characterizations. The other part gives the necessary and sufficient conditions of conditional distributivity for semi-t-operators over uninorms in U max and U min under the condition 0 < U  (x , y ) <1, …which transforms it into the conditional distributivity between t-norms and t-conorms (semi-t-norms and t-conorms, semi-t-conorms and t-norms). Show more

Keywords: Semi-t-operators, t-norms, uninorms, conditional distributivity

DOI: 10.3233/JIFS-230966

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9845-9860, 2023

Authors: Ragu, G. | Ramamoorthy, S.

Article Type: Research Article

Abstract: When a digital adversary or an insider compromised a framework, cloud Forensic examiners can simply lay out the scene of the crime and reconstruct how the event took place using scientific evidence to determine when, why, and how it happened. Be that as it may, computerized proof procurement in a cloud environment is confounded and demonstrated troublesome, Despite modern scientific securing tool compartments. Multi-occupancy, Geo-area, and Administration Level Understanding have added another layer of complexity to obtaining computerized proof from a cloud environment. To moderate these intricacies of proof procurement in the cloud environment, we want a system that can …forensically keep up with the reliability and respectability of proof. In this review, we plan and execute a Blockchain-based Forensic in Cloud (BBFC) structure, utilizing a Cloud Forensic methodology (CFA). The outcomes from our single contextual analysis will exhibit that BBFC will alleviate the difficulties and intricacies looked at by computerized forensic specialists in getting acceptable advanced proof from the cloud biological system. Moreover, a quick exhibition observing the proposed Blockchain based measurable in cloud structure was assessed. BBFC will guarantee dependability, respectability, validness, and non-renouncement of the proof in the cloud. The proposed BBFC framework was also subjected to performance evaluation, considering factors such as latency, bandwidth, energy and resource utilization, and failure points. This evaluation provides insights into the efficiency and effectiveness of the framework in real-world cloud forensic scenarios. Show more

Keywords: Blockchain, cloud computing, forensic data

DOI: 10.3233/JIFS-231072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9861-9874, 2023

Authors: Chen, Xinquan | Ma, Jianbo | Qiu, Yirou | Liu, Sanming | Xu, Xiaofeng | Bao, Xianglin

Article Type: Research Article

Abstract: The purpose of clustering is to identify distributions and patterns within unlabelled datasets. Since the proposal of the original synchronization clustering (SynC) algorithm in 2010, synchronization clustering has become a significant research direction. This paper proposes a shrinking synchronization clustering (SSynC) algorithm utilizing a linear weighted Vicsek model. SSynC algorithm is developed from SynC algorithm and a more effective synchronization clustering (ESynC) algorithm. Through analysis and comparison, we find that SSynC algorithm demonstrates superior synchronization effect compared to SynC algorithm, which is based on an extensive Kuramoto model. Additionally, it exhibits similar effect to ESynC algorithm, based on a linear …version of Vicsek model. In the simulations, a comparison is conducted between several synchronization clustering algorithms and classical clustering algorithms. Through experiments using some artificial datasets, eight real datasets and three picture datasets, we observe that compared to SynC algorithm, SSynC algorithm not only achieves a better local synchronization effect but also requires fewer iterations and incurs lower time costs. Furthermore, when compared to ESynC algorithm, SSynC algorithm obtains reduced time costs while achieving nearly the same local synchronization effect and the same number of iterations. Extensive comparison experiments with some class clustering algorithms demonstrate the effectiveness of SSynC algorithm. Show more

Keywords: SynC algorithm, Kuramoto model, shrinking synchronization, a linear weighted Vicsek model, near neighbor points

DOI: 10.3233/JIFS-231817

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9875-9897, 2023

Authors: Rida, Zakaria | Boukachour, Hadhoum | Ennaji, Mourad | Machkour, Mustapha

Article Type: Research Article

Abstract: The articulation between machine tutoring and human tutoring remains a productive research within in the context of Intelligent Tutoring Systems (ITS), particularly in the context of e-learning where the dropout rate is high. We explore an innovative approach, the automation of tutoring as it is done in the classroom to respond to the difficulties of the learner. We propose a generic Intelligent Multi-Tutoring System (IMTS) architecture composed of two modules COMES and MAT. The Communication Entry Service (COMES) module manages communications between the IMTS and a Learning Management System (LMS). The module Multi-Agent Tutoring (MAT) is the multi-agent system developed …with JADE, which allows the dynamic adaptation of tutoring (Machine, Peer, Teacher) according to the profile of the learner. We offer a configurable system to customize tutoring to the individual needs of each learner. It can be grafted onto any learning platform, making it multidisciplinary and easy to integrate into existing learning environments. The teacher will be able to devote more time to learners which need greater his intervention.The peer will develop human and relational qualities linked to their know-how, transversal skills sought by recruiters. To validate this architecture, we provide an application and results that integrate the elements of the described model. The results of the experiment prove the feasibility and reliability of our approach. Show more

Keywords: Intelligent tutoring system, multi-agent system, adaptive system, markov, complex system

DOI: 10.3233/JIFS-232319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9899-9913, 2023

Authors: Liu, Yiyang | Li, Changxian | Cui, Yunxian | Song, Xudong

Article Type: Research Article

Abstract: Intelligent bearing fault diagnosis plays an important role in improving equipment safety and reducing equipment maintenance costs. Noise in the signal can seriously reduce the accuracy of fault diagnosis. To improve the accuracy of fault diagnosis, a novel noise reduction method based on weighted multi-scale morphological filter (WMMF) is proposed. Firstly, Teager energy operator (TEO) is used to amplify the morphological information of the signal. Then, a scale filtering operator using envelope entropy (SFOEE) is proposed to select appropriate scales. At these scales, the noise in the signal can be adequately suppressed. A new weighting method is proposed to integrate …the selected scales to construct the WMMF. Finally, multi-headed self-attention capsule restricted boltzmann network (MSCRBN) is proposed to diagnose bearing faults.The performance of the TEO-SFOEE-WMMF-MSCRBN fault diagnosis method is verified on the CWRU dataset. Compared with existing fault diagnosis methods, this approach achieves 100% identification accuracy. The experimental results indicate that the proposed diagnosis method can effectively resist noise and precisely diagnose bearing faults. Show more

Keywords: Bearing fault diagnosis, mathematical morphological filter, restricted boltzmann machine, capsule network

DOI: 10.3233/JIFS-232737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9915-9928, 2023

Authors: Cheng, Gang | You, Qinliang | Shi, Lei | Wang, Zhenxue | Luo, Jia | Li, Tianbin

Article Type: Research Article

Abstract: With the rapid development of information science and social networks, the Internet has accumulated various data containing valuable information and topics. The topic model has become one of the primary semantic modeling and classification methods. It has been widely studied in academia and industry. However, most topic models only focus on long texts and often suffer from semantic sparsity problems. The sparse, short text content and irregular data have brought major challenges to the application of topic models in semantic modeling and topic discovery. To overcome these challenges, researchers have explored topic models and achieved excellent results. However, most of …the current topic models are applicable to a specific model task. The majority of current reviews ignore the whole-cycle perspective and framework. It brings great challenges for novices to learn topic models. To deal with the above challenges, we investigate more than a hundred papers on topic models and summarize the research progress on the entire topic model process, including theory, method, datasets, and evaluation indicator. In addition, we also analyzed the statistical data results of the topic model through experiments and introduced its applications in different fields. The paper provides a whole-cycle learning path for novices. It encourages researchers to give more attention to the topic model algorithm and the theory itself without paying extra attention to understanding the relevant datasets, evaluation methods and latest progress. Show more

Keywords: Topic model, text mining, semantic understanding, whole-cycle, topic detection

DOI: 10.3233/JIFS-233551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9929-9953, 2023

Authors: Lalitha, K. | Murugavalli, S. | Roseline, A. Ameelia

Article Type: Research Article

Abstract: For retrieving the relevant images from the internet, CBIRs (content based image retrievals) techniques are most globally utilized. However, the traditional image retrieval techniques are unable to represent the image features semantically. The CNNs (convolutional neural networks) and DL has made the retrieval task simpler. But, it is not adequate to consider only the finalized aspect vectors from the completely linked layers to fill the semantic gap. In order to alleviate this problem, a novel Hash Based Feature Descriptors (HBFD) method is proposed. In this method, the most significant feature vectors from each block are considered. To reduce the number …of descriptors, pyramid pooling is used. To improve the performance in huge databases, the hash code like function is introduced in each block to represent the descriptors. The proposed method has been evaluated in Oxford 5k, Paris 6k, and UKBench datasets with the accuracy level of 80.6%, 83.9% and 92.14% respectively and demonstrated better recall value than the existing methods. Show more

Keywords: Content-based image retrieval, CNNs, hash based feature descriptor (HBFD), pyramid pooling and hash code

DOI: 10.3233/JIFS-233891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9955-9964, 2023

Authors: Liao, Tao | Sun, Haojie | Zhang, Shunxiang

Article Type: Research Article

Abstract: The entity-relationship extraction model has a significant influence in relation extraction. The existing model cannot effectively identify the entity-relationship triples in overlapping relationships. It also has the problem of long-distance dependencies between entities. In this paper, an inter span learning for document-level relation extraction model is proposed. Firstly, the model converts input of the BERT pre-training model into word vectors. Secondly, it divides the word vectors to form span sequences by random initial span and uses convolutional neural networks to extract entity information in the span sequences. Dividing the word vector into span sequences can divide the entity pairs that …may have overlapping relationships into the same span sequence, partially solving the overlapping relationship problem. Thirdly, the model uses inter span learning to obtain entity information in different span sequences. It fuses entity type features and uses Softmax regression to achieve entity recognition. Aiming at solving the problem of long-distance dependence between entities, inter span learning can fuse the information in different span sequences. Finally, it fuses text information and relationship type features, and uses Linear Layer to classify relationships. Experiments demonstrate that the model improves the F1-score of the DocRED dataset by 2.74% when compared to the baseline model. Show more

Keywords: Joint extraction, entity relation extraction, span, document-level extraction, neural network

DOI: 10.3233/JIFS-234202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9965-9977, 2023

Authors: Bai, Shi | Li, Pujie

Article Type: Research Article

Abstract: This paper presents an in-depth study and analysis of oil painting classification and simulation using an improved embedded learning fusion vision perception algorithm. This paper analyzes and models the image quality evaluation problem by simulating the human visual system and extracting quality perception features as the main entry point to improve the prediction accuracy of the overall algorithm. This paper proposes a multi-classification method of CCNN, which uses the similarity measure based on information first to achieve multi-classification of artwork styles and artists, and this part is the main part of this paper. This paper uses the wiki art repository …to construct a dataset of oil paintings, including over 2000 works by 20 artists in 13 styles. CNN achieves an accuracy of 85.75% on the artist classification task, which is far more effective than traditional deep learning networks such as Resnet. Finally, we use the network model of this paper and other network models to train the classification of 3, 4, and 6 categories of art images. The accuracy of art image classification by this paper’s algorithm is higher than that of the current mainstream convolutional neural network models, and the extracted features are more comprehensive and more accurate than traditional art image feature extraction methods, which do not rely on researchers to extract image features. Experiments show that the proposed method can achieve excellent prediction accuracy for both synthetic distorted images and distorted images. Show more

Keywords: Visual perception, embedded learning, oil painting classification, algorithm simulation

DOI: 10.3233/JIFS-234545

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9979-9989, 2023