SVM action recognition model based on skeletal key point analysis with posture sensors to help sports training by Yixuan Cao, Tie Li

“…Therefore, the study proposes an SVM-based action recognition model utilizing skeletal key point analysis with posture sensors, aiming to provide an accurate sports training analysis tool. …”
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Real-Time Analysis of Athletes’ Physical Condition in Training Based on Video Monitoring Technology of Optical Flow Equation by Cuijuan Wang

“…This article is dedicated to the research of video motion segmentation algorithms based on optical flow equations. First, some mainstream segmentation algorithms are studied, and on this basis, a segmentation algorithm for spectral clustering analysis of athletes’ physical condition in training is proposed. …”
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A Proposed Method for Deep Learning-Based Automatic Tracking with Minimal Training Data for Sports Biomechanics Research by Daichi Yamashita, Minoru Matsumoto, Takeo Matsubayashi

“…Transfer learning is implemented by freezing the backbone weights and training only the head network. By restricting the training data generation to regions surrounding the manually annotated points and training specifically for each video, this approach minimizes training time while maintaining high precision. …”
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AIF: Infrared and Visible Image Fusion Based on Ascending–Descending Mechanism and Illumination Perception Subnetwork by Ying Liu, Xinyue Mi, Zhaofu Liu, Yu Yao

“…The image fusion model is trained in an unsupervised manner with a customized loss function. …”
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A Video-Based Cognitive Emotion Recognition Method Using an Active Learning Algorithm Based on Complexity and Uncertainty by Hongduo Wu, Dong Zhou, Ziyue Guo, Zicheng Song, Yu Li, Xingzheng Wei, Qidi Zhou

“…However, due to the high cost of marking and training video samples, feature extraction is inefficient and ineffective, which leads to a low accuracy and poor real-time performance. …”
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RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings by Fengwei Yuan, Guoning Ma, Qinghao Zeng, Jinghong Liu, Zhang Xiao, Zhenhong Zou, Xiangjiang Wang

“…Finally, a classification model is trained using these extracted features to establish a grading system. …”
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Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection by Onur Polat, Ali Ayid Ahmad, Saadin Oyucu, Enes Algul, Ferdi Dogan, Ahmet Aksoz

“…The study revealed the superior successes of deep learning-based approaches for IoT security.…”
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Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network by Caixia TAO, Xu WANG, Fengyang GAO

“…According to the characteristics of the DBN, the impacts of training sets, training periods and restricted boltzmann machine (RBM) layers on the model performance are analyzed through recognition experiments. …”
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Encrypted traffic classification method based on convolutional neural network by Rongna XIE, Zhuhong MA, Zongyu LI, Ye TIAN

“…Aiming at the problems of low accuracy, weak generality, and easy privacy violation of traditional encrypted network traffic classification methods, an encrypted traffic classification method based on convolutional neural network was proposed, which avoided relying on original traffic data and prevented overfitting of specific byte structure of the application.According to the data packet size and arrival time information of network traffic, a method to convert the original traffic into a two-dimensional picture was designed.Each cell in the histogram represented the number of packets with corresponding size that arrive at the corresponding time interval, avoiding reliance on packet payloads and privacy violations.The LeNet-5 convolutional neural network model was optimized to improve the classification accuracy.The inception module was embedded for multi-dimensional feature extraction and feature fusion.And the 1*1 convolution was used to control the feature dimension of the output.Besides, the average pooling layer and the convolutional layer were used to replace the fully connected layer to increase the calculation speed and avoid overfitting.The sliding window method was used in the object detection task, and each network unidirectional flow was divided into equal-sized blocks, ensuring that the blocks in the training set and the blocks in the test set in a single session do not overlap and expanding the dataset samples.The classification experiment results on the ISCX dataset show that for the application traffic classification task, the average accuracy rate reaches more than 95%.The comparative experimental results show that the traditional classification method has a significant decrease in accuracy or even fails when the types of training set and test set are different.However, the accuracy rate of the proposed method still reaches 89.2%, which proves that the method is universally suitable for encrypted traffic and non-encrypted traffic.All experiments are based on imbalanced datasets, and the experimental results may be further improved if balanced processing is performed.…”
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Multi-channel based edge-learning graph convolutional network by Shuai YANG, Ruiqin WANG, Hui MA

“…Usually the edges of the graph contain important information of the graph.However, most of deep learning models for graph learning, such as graph convolutional network (GCN) and graph attention network (GAT), do not fully utilize the characteristics of multi-dimensional edge features.Another problem is that there may be noise in the graph that affects the performance of graph learning.Multilayer perceptron (MLP) was used to denoise and optimize the graph data, and a multi-channel learning edge feature method was introduced on the basis of GCN.The multi-dimensional edge attributes of the graph were encoded, and the attributes contained in the original graph were modeled as multi-channel.Each channel corresponds to an edge feature attribute to constrain the training of graph nodes, which allows the algorithm to learn multi-dimensional edge features in the graph more reasonably.Experiments based on Cora, Tox21, Freesolv and other datasets had proved the effectiveness of denoising methods and multi-channel methods.…”
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THE INTELLIGENT AUTOMATED SYSTEM OF EDUCATION BASED ON INFORMATION AND INTERNET-TECHNOLOGIES by Andrey V. Dementienko, Alexander F. Egorov, Lina A. Zapasnaya, Sergey A. Nikitin, Tatiana V. Savitskaya

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Multi-Modal MR Image Segmentation Strategy for Brain Tumors Based on Domain Adaptation by Qihong Yang, Ruijun Jing, Jiliang Mu

“…Based on the aforementioned reasons, a new multimodal brain tumor MR segmentation strategy based on domain adaptation is proposed in this study. …”
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A YOLO-Based Method for Head Detection in Complex Scenes by Ming Xie, Xiaobing Yang, Boxu Li, Yingjie Fan

“…For this purpose, two new modules have been constructed: one is a feature fusion module based on context enhancement with scale adjustment, and the other is an attention-based convolutional module. …”
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Spatio-temporal Matrix Factorization Based Air Quality Inference by Keyong HU, Xiaolan GUO, Guoxiao LIU, Xin YANG, Xupeng WANG

“…Such integration allows for improved inference performance through the collaborative training and supervision of different tasks. In this model, spatial and temporal feature matrices and the air quality matrix are constructed and collaboratively factorized into spatial and temporal feature representations. …”
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Feature Selection based on Genetic Algorithm for Classification of Mammogram Using K-means, k-NN and Euclidean Distance by Kameran Adil Ibrahim

“…Out of these 115, 60% were used for training and 40% for testing. Therefore from 64 benign cases 39 images were used for training and rest for testing, and out of 51 malignant cases 31 images were used for training and rest for testing., the classifications was done on the bases of the features selected using genetic algorithm. …”
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An Optimized Cascaded CNN Approach for Feature Extraction From Brain MRIs for Tumor Classification by Santosh Kumar Chhotray, Debahuti Mishra, Sarada Prasanna Pati, Sashikala Mishra

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Research on operation optimization of heavy-haul combined trains in long and steep downhill sections based on reinforcement learning by WANG Jianhua, WANG Chunyi, ZENG Zhou, WANG Cong, WANG Qingyuan, YANG Hang

“…To mitigate longitudinal impulse and address challenge posed by continuous air braking operations in long and steep downhill sections for 20 000-ton heavy haul combined trains, this paper proposes an approach for operation optimization of such trains featuring a long formation in such sections based on a data-driven algorithm. …”
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Physical education and sport activity assessment tool-based machine learning predictive analysis for planification of training sessions by Mohamed Rebbouj, Said Lotfi

“…The obtained predictive model provides an explication of the most impacting features on students’ performance allowing any training planification to relay on their importance respectively based on their density that affects prediction.…”
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Research on scenario recognition for THz channels based on mRMR-GA by HAO Xinyu, LIAO Xi, WANG Yang, LIN Feng, LUO Jiao, ZHANG Jie

“…To validate the method, a dataset containing 12 channel features was constructed with 1 745 groups of terahertz channel simulation data collected from indoor scenarios, and the model was trained and rigorously validated based on this dataset. …”
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Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification. by Daniel R Ripoll, Sidhartha Chaudhury, Anders Wallqvist

“…We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. …”
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