Survey of video behavior recognition by Huilan LUO, Chanjuan WANG, Fei LU

“…Behavior recognition is developing rapidly,and a number of behavior recognition algorithms based on deep network automatic learning features have been proposed.The deep learning method requires a large number of data to train,and requires higher computer storage and computing power.After a brief review of the current popular behavior recognition method based on deep network,it focused on the traditional behavior recognition methods.Traditional behavior recognition methods usually followed the processes of video feature extraction,modeling of features and classification.Following the basic process,the recognition process was overviewed according to the following steps,feature sampling,feature descriptors,feature processing,descriptor aggregation and vector coding.At the same time,the benchmark data set commonly used for evaluating the algorithm performance was also summarized.…”
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CRGAN: A Context-Aware Clothing Design and Recommendation System for Young Sri Lankan Females Using Generative Adversarial Networks by Nethmi Pathirana, Azma Imtiaz, Shakir Saheel, Kasun Karunanayaka, Adrian David Cheok

“…The proposed system leverages a Conditional Generative Adversarial Network (GAN) model, trained on three predefined parameters–attire type, temperature conditions, and the user’s skin tone. …”
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Enhanced Hybrid U-Net Framework for Sophisticated Building Automation Extraction Utilizing Decay Matrix by Ting Wang, Zhuyi Gong, Anqi Tang, Qian Zhang, Yun Ge

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The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…To address this, this paper proposed a novel deep neural network (DNN) model designed to predict soft power based on educational factors. …”
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STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent by James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju, Rajagopal Maheswar

“…The rapid evolution of IoT environment in medical and industrial applications has led to an increase in network vulnerabilities, making an intrusion detection system a critical requirement. …”
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V³IP Model: A Multiview Vegetation Information Perception Network for Wetland Mapping With Hyperspectral Imagery by Chunyan Yu, Feihong Zhou, Chi Yu, Enyu Zhao, Yulei Wang, Yabin Hu, Haoyang Yu

“…In this article, we present a novel multiview vegetation information perception (V³IP) network for HSI wetland mapping, which significantly enhances the discriminative representation of feature extraction by incorporating three presented vegetation perception components. …”
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Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression by Jinli Gao, Qinglang Wang, Jie Liu, Siqian Zheng, Jiahong Liu, Zhiyong Gao, Cheng Zhu

“…Depression-related gene modules were identified and subjected to feature selection using the random forest model. The biological relevance of selected genes was further assessed, and model accuracy was validated through performance evaluation.ResultsOur findings revealed significant differential expression of genes such as Oprm1, BDNF, Tph2, and Zfp769 in the depression mouse model (p < 0.05). …”
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A Hybrid Deep Learning Model for Network Intrusion Detection System Using Seq2Seq and ConvLSTM-Subnets by S. Hariharan, Y. Annie Jerusha, G. Suganeshwari, S. P. Syed Ibrahim, Uday Tupakula, Vijay Varadharajan

“…This enables the model to leverage the spatial feature extraction capabilities of Convolutional Neural Networks (CNN) alongside the sequential learning strengths of LSTM networks. …”
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A Novel Approach Based on Hypergraph Convolutional Neural Networks for Cartilage Shape Description and Longitudinal Prediction of Knee Osteoarthritis Progression by John B. Theocharis, Christos G. Chadoulos, Andreas L. Symeonidis

“…We propose two novel models, namely, the <i>C_Shape.Net</i> and the predictor network. The <i>C_Shape.Net</i> operates on a hypergraph of volumetric nodes, especially designed to represent the surface and volumetric features of the cartilage. …”
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HSF: A Hybrid SVM-RF Machine Learning Framework for Dual-Plane DDoS Detection and Mitigation in Software-Defined Networks by Abdinasir Hirsi, Lukman Audah, Mohammed A. Alhartomi, Adeb Salh, Godwin Okon Ansa, Mustafa Maad Hamdi, Diani Galih Saputri, Salman Ahmed, Abdullahi Farah

“…Software-defined networking (SDN) has revolutionized network management by centralizing control through software, thereby enabling dynamic traffic adjustments that are independent of the data plane. …”
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Alterations of white matter functional networks in pediatric drug-resistant temporal lobe epilepsy: A graph theory analysis study by Kexin Huang, Yuxin Xie, Haifeng Ran, Jie Hu, Yulun He, Gaoqiang Xu, Guiqin Chen, Qiane Yu, Xuhong Li, Junwei Liu, Heng Liu, Tijiang Zhang

“…Therefore, we combine graph theory with resting-state functional magnetic resonance imaging (rs-fMRI) and T1-weighted imaging (T1WI) to investigate the topological features of the WM network in children with DRTLE, discover potential biomarkers, and understand the underlying neurological mechanisms. …”
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Detecting All-to-One Backdoor Attacks in Black-Box DNNs via Differential Robustness to Noise by Hao Fu, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami

“…The necessity for black-box A2O backdoor defenses arises, particularly in scenarios where only the network&#x2019;s input and output are accessible. …”
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Construction of a CURE Community to Empower Faculty and Accelerate Pedagogical Change by Laura M. Christian, Kristin M. Fox, Anthony J. Bell Jr., Sue DeChenne-Peters, Joseph Provost PhD, Jessica K. Bell, J. Ellis Bell

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Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation by Junxian Li, Mingxing Li, Shucheng Huang, Gang Wang, Xinjing Zhao

“…<i>SACD</i> comprises two pivotal components: (1) a reverse distillation (RD) paradigm whereby a pre-trained teacher network extracts hierarchically structured representations, subsequently guiding the student network with inverse architectural configuration to establish hierarchical feature alignment; (2) a group of feature calibration (<i>FeaCali</i>) modules designed to refine the student’s outputs by eliminating anomalous feature responses. …”
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Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation by Chuangan Zhou, Siqi Wang, Meiyi Wu, Wei Lai, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…PurposeThis study develops a particle swarm optimization (PSO)-based assessment system for evaluating upper extremity and shoulder joint muscle strength with potential application to stroke rehabilitation. …”
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An artificial intelligence approach to palaeogeographic studies: a case study of the Late Ordovician brachiopods of Laurentia by Akbar Sohrabi

“…The neural network tries to relate these morphometric data to their location in order to predict their initial location by providing new morphometric measures to the neural network model. …”
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DMCCT: Dual-Branch Multi-Granularity Convolutional Cross-Substitution Transformer for Hyperspectral Image Classification by Laiying Fu, Xiaoyong Chen, Yanan Xu, Xiao Li

“…In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress. However, convolutional neural network models encounter challenges in hyperspectral image classification due to limitations in their receptive fields. …”
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Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model by Maofa Wang, Bingcheng Yan, Yibo Zhang, Lu Zhang, Pengcheng Wang, Jingjing Huang, Weifeng Shan, Haijun Liu, Chengcheng Wang, Yimin Wen

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Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals by Chengfa Sun, Changchun Liu, Xinpei Wang, Yuanyuan Liu, Shilong Zhao

“…To remove irrelevant information while preserving discriminative features, we add an autoencoder network to compress feature dimension. …”
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Enhanced Cleft Lip and Palate Classification Using SigLIP 2: A Comparative Study with Vision Transformers and Siamese Networks by Oraphan Nantha, Benjaporn Sathanarugsawait, Prasong Praneetpolgrang

“…A comparative analysis is conducted, evaluating the performance of our original ViT-Siamese network model (using BiomedCLIP) against a new model leveraging SigLIP 2 for feature extraction. …”
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