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

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