EDGE-BASED VIDEO RECOGNITION: ADVANCING DEEP LEARNING FOR EFFICIENT VISUAL by Sai Babu Veesam, Aravapalli Rama Satish

“…Additionally, we employ GAN for image denoising to improve feature selection. Thorough evaluations validate our system's exceptional F1 score, accuracy, and precision, establishing its utility for edge-based HAR applications. …”
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Altered brain network centrality in patients with cervical spondylotic myelopathy: insights from resting-state fMRI by Kaifu Wu, Hui Zheng, Yan Jiang, Shutong Zhang, Xiang Wang

“…The DC method was utilized to evaluate the changed spontaneous brain activities. …”
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Modeling visual working memory using recurrent on-center off-surround neural network with distance dependent inhibition by Rakesh Sengupta

“…We conduct a detailed stability analysis to demonstrate non-divergence through energy function evaluations, highlighting the robustness of the network under varying input conditions. …”
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Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms by Vedant Jaiswal, Narendiranath Babu T, Pandiyan Murugan, Rama Prabha D

“…The input parameters are represented by using 14 features in the evaluation. Next, a feature ranking method is established to classify the bearing fault and contribution of each of the features is used as input conditions. …”
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Data-Driven Health Status Assessment of Fire Protection IoT Devices in Converter Stations by Yubiao Huang, Tao Sun, Yifeng Cheng, Jiaqing Zhang, Zhibing Yang, Tan Yang

“…Compared with conventional distance-based approaches, the proposed method better captures differences between features and more effectively evaluates the reliability of fire protection systems. …”
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Prediction of Head Injury Criteria in Pedestrian Crashes Using Frequency Response Function-Based Deep Neural Networks by Seon-Hong Kim, Seounghyun Lee, Taewung Kim, Je-Heon Han

“…To overcome these physical differences, a deep neural network model incorporating various features was designed to improve HIC prediction accuracy. …”
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The Application of Lite-GRU Embedding and VAE-Augmented Heterogeneous Graph Attention Network in Friend Link Prediction for LBSNs by Ziteng Yang, Boyu Li, Yong Wang, Aoxue Liu

“…Friend link prediction is an important issue in recommendation systems and social network analysis. In Location-Based Social Networks (LBSNs), predicting potential friend relationships faces significant challenges due to the diversity of user behaviors, along with the high dimensionality, sparsity, and complex noise in the data. …”
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Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning by Johannes Pauli, Maximilian Hoffmann, Ralph Bergmann

“…Previous work tackles this problem by using Graph Neural Networks (GNNs) to learn pairwise graph similarities. …”
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Modified Whale Optimization Algorithm for Multiclass Skin Cancer Classification by Abdul Majid, Masad A. Alrasheedi, Abdulmajeed Atiah Alharbi, Jeza Allohibi, Seung-Won Lee

“…To address these challenges, this paper proposes an innovative deep learning-based framework that integrates an ensemble of two pre-trained convolutional neural networks (CNNs), SqueezeNet and InceptionResNet-V2, combined with an improved Whale Optimization Algorithm (WOA) for feature selection. …”
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Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network by Chen Zhou, Fan Zhang, Edric John Cruz Nacpil, Zheng Wang, Fei-Xiang Xu

“…The convolutional neural network (CNN) layer extracts features from the stochastic driver behavior, which is input to the gated-recurrent-unit (GRU) layer. …”
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Highly accurate anomaly based intrusion detection through integration of the local outlier factor and convolutional neural network by Rahimullah Rabih, Hamed Vahdat-Nejad, Wathiq Mansoor, Javad Hassannataj Joloudari

“…CNN’s strength lies in its ability to automatically extract relevant features from network traffic data through convolutional layers, thereby enhancing classification performance. …”
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WTL-CNN: a news text classification method of convolutional neural network based on weighted word embedding by Weidong Zhao, Lin Zhu, Ming Wang, Xiliang Zhang, Jinming Zhang

“…Thirdly, according to the location distribution law of important features of news texts, the location information is converted into weights and integrated into the pooling process convolutional neural network to further improve the accuracy of classification. …”
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A dual-branch deep learning model based on fNIRS for assessing 3D visual fatigue by Yan Wu, Yan Wu, Yan Wu, TianQi Mu, SongNan Qu, XiuJun Li, XiuJun Li, XiuJun Li, Qi Li, Qi Li, Qi Li

“…A transformer was integrated into the convolutional network to enhance long-range feature extraction. …”
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Integrating deformable CNN and attention mechanism into multi-scale graph neural network for few-shot image classification by Yongmin Liu, Fengjiao Xiao, Xinying Zheng, Weihao Deng, Haizhi Ma, Xinyao Su, Lei Wu

“…The feature extraction module of graph neural networks has always been designed as a fixed convolutional neural network (CNN), but due to the intrinsic properties of convolution operations, its receiving domain is limited. …”
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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

“…The proposed approach is evaluated using four publicly available heart disease datasets, each representing diverse clinical and demographic features. …”
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DS4NN: Direct training of deep spiking neural networks with single spike-based temporal coding by Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh

“…These features together improve the cost and the speed of network computation. …”
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Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption. by Abrar Ahmed, Safdar Ali, Ali Raza, Ibrar Hussain, Ahmad Bilal, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

“…The proposed model is capable of capturing complex temporal and spatial features to predict short-term and long-term energy consumption. …”
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AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. by Gregor Donabauer, Anca Rath, Aila Caplunik-Pratsch, Anja Eichner, Jürgen Fritsch, Martin Kieninger, Susanne Gaube, Wulf Schneider-Brachert, Udo Kruschwitz, Bärbel Kieninger

“…We used data from 8,372 patients, combining more than 125,000 movements within our hospital with patient-related information to create time-dependent graph sequences and applied graph neural networks (GNNs) to classify patients as VRE carriers or noncarriers. …”
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Modeling Higher-Order Interactions in Graphs Through Combinatorial Arc-Transitive Structure Using Graph Convolutional Network by Qingwei Wen

“…Traditional Graph Convolutional Networks (GCNs) have demonstrated success in graph representation learning, particularly in homophilic networks where nodes share similar features. …”
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MPLS Network Actions: Technological Overview and P4-Based Implementation on a High-Speed Switching ASIC by Fabian Ihle, Michael Menth

“…Scalability and performance of P4-MNA are evaluated, showing negligible impact on processing delay caused by network actions. …”
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