ECG Signal Ｒecognition Based on Deep Stacked Network by ZHANG Ｒiu, WANG Ｒu, HUANG Jun, ZENG Xin

“…The feature extraction of ECG signals was completed by stacking three sparse autoencoders，and the high-dimensional features of ECG signals were depicted layer by layer，and the ECG signals were identified by Softmax classifier. …”
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Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model by A. M. Mutawa

“…This study investigated the healthcare process during a pandemic, facilitating ML-based decision-making solutions to confront healthcare problems.…”
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Research on laser point cloud classification based on Light-BotNet by Lei Genhua, Wang Lei, Zhang Zhiyong

“…Aiming at the problems of low classification efficiency and low classification accuracy of the existing deep learning Network framework for laser point cloud data, a CNN Transform framework based on laser point cloud feature image and Light-BotNet is proposed. …”
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Diagnosis of depression based on facial multimodal data by Nani Jin, Renjia Ye, Peng Li

“…We use spatiotemporal attention module to enhance the extraction of visual features and combine the Graph Convolutional Network (GCN) and the Long and Short Term Memory (LSTM) to analyze the audio features. …”
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Hierarchical Knowledge Transfer: Cross-Layer Distillation for Industrial Anomaly Detection by Junning Xu, Sanxin Jiang

“…There are two problems with traditional knowledge distillation methods in industrial anomaly detection: first, traditional methods mostly use feature alignment between the same layers. …”
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Improved YOLOv8 Object Detection Method for Drone Aerial Images by Zhong Shuai, Wang Liping

“…A new improved YOLOv8 drone aerial image object detection method, referred to as the BDI-YOLO model, is proposed to address the problems of small target object size and blurry feature information in drone aerial images, which can lead to missed and false detections. …”
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Research on Lightweight Small Object Detection Algorithm Based on Context Representation by Li Qiang, Cui Jianghui

“…This framework model consists of three parts: a backbone network, a multi-scale feature representation network, and a detection head. …”
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A novel adaptive safe semi-supervised learning framework for pattern extraction and classification by Jun Ma, Junjie Li, Jiachen Sun

“…In response to the above problems, this paper first proposed an adaptive safety semi-supervised learning framework. …”
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A Novel Lightweight Algorithm for Sonar Image Recognition by Gang Wan, Qi He, Qianqian Zhang, Hanren Wang, Huanru Sun, Xinnan Fan, Pengfei Shi

“…Utilizing CNNs would lead to problems such as inadequate target recognition accuracy. …”
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Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model by Beevi Fathima, N Santhi Dr, N Ramasamy Dr

“…Furthermore, the Focus View Algorithm is suggested, which incorporates features from infarcted regions to improve early detection of emerging lesions. …”
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Improving small object detection via cross-layer attention by Ru Peng, Guoran Tan, Xingyu Chen, Xuguang Lan

“…To address these problems, we propose a cross-layer attention (CLA) block as a generic block for capturing long-range dependencies and reducing noise from high-level features. …”
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DAHD-YOLO: A New High Robustness and Real-Time Method for Smoking Detection by Jianfei Zhang, Chengwei Jiang

“…We introduce the ECA-FPN, an improved version of the feature pyramid network, designed to refine the extraction of hierarchical information and enhance cross-scale feature interactions. …”
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Using near-infrared spectroscopy to estimate soil water retention curves with the van Genuchten model by Youssef Fouad, Inès Soltani, Christophe Cudennec, Didier Michot

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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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CF-YOLO for small target detection in drone imagery based on YOLOv11 algorithm by Chengcheng Wang, Yuqi Han, Chenggui Yang, Mingjie Wu, Zaiqing Chen, Lijun Yun, Xuesong Jin

“…Firstly, addressing the issue of small target information loss that may arise from hierarchical convolutional structures, we conduct in-depth research on the Path Aggregation Network (PAN) and innovatively propose a Cross-Scale Feature Pyramid Network (CS-FPN). Secondly, to overcome the problems of positional information deviation and feature redundancy during multi-scale feature fusion, we design a Feature Recalibration Module (FRM) and a Sandwich Fusion Module. …”
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Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance by M. Elhoseny, Deepak Dasaratha Rao, Bala Dhandayuthapani Veerasamy, Noha Alduaiji, J. Shreyas, Piyush Kumar Shukla

“…Here, the major objective is to locate problems in detection by analysing previous data or sequential patterns of data that cause failure. …”
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Speech emotion recognition algorithm of intelligent robot based on ACO-SVM by Xueliang Kang

“…In the feature selection stage, ACO algorithm is introduced to explore the optimal combination of emotion features, aiming at improving the efficiency and robustness of emotion recognition. …”
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Psychological Network Analysis for Risk and Protective Factors of Problematic Social Media Use by Suzan M. Doornwaard, Vladimir Hazeleger, Ina M. Koning, Albert Ali Salah, Sven Vos, Regina J. J. M. van den Eijnden

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A Temporal-Spectral Fused and Attention-Based Deep Model for Automatic Sleep Staging by Guidan Fu, Yueying Zhou, Peiliang Gong, Pengpai Wang, Wei Shao, Daoqiang Zhang

“…The TSA-Net is composed of a two-stream feature extractor, feature context learning, and conditional random field (CRF). …”
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Clinical Features of Cutaneous Acute Graft-versus-Host Disease after Allogeneic Hematopoietic Stem Cells Transplantation in Children with Hemato-Oncological Diseases by Tatiana S. Belysheva, Timur Z. Aliev, Timur T. Valiev, Elena B. Machneva, Natalia V. Sidorova, Nikolay N. Murashkin, Kirill I. Kirgizov, Svetlana R. Varfolomeeva

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