Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

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Generation of Multiple‐Depth 3D Computer‐Generated Holograms from 2D‐Image‐Datasets Trained CNN by Xingpeng Yan, Jiaqi Li, Yanan Zhang, Hebin Chang, Hairong Hu, Tao Jing, Hanyu Li, Yang Zhang, Jinhong Xue, Xunbo Yu, Xiaoyu Jiang

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Applying SSVEP BCI on Dynamic Background by Junkai Li, Boxun Fu, Fu Li, Wenkai Gu, Youshuo Ji, Yang Li, Tiejun Liu, Guangming Shi

“…Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. …”
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Methods of security situation prediction for industrial internet fused attention mechanism and BSRU by Xiangdong HU, Zhengguo TIAN

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Spectrum sensing based on adversarial transfer learning by Jiawu Miao, Yuebo Li, Xiaojun Jing, Fangpei Zhang, Junsheng Mu

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A Fog Computing-Based Cost-Effective Smart Health Monitoring Device for Infectious Disease Applications by Saranya Govindakumar, Vijayalakshmi Sankaran, Paramasivam Alagumariappan, Bhaskar Kosuru Bojji Raju, Daniel Ford

“…Also, three different sensor modules were integrated with the Raspberry PI microcontroller and individuals’ physiological parameters, such as oxygen saturation (SPO2), heartbeat rate, and cough sounds, were recorded by the computing device. Additionally, a convolutional neural network (CNN)-based deep learning algorithm was coded inside the Raspberry PI and was trained with normal and COVID-19 cough sounds from the KAGGLE database. …”
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Can AI-Based ChatGPT Models Accurately Analyze Hand–Wrist Radiographs? A Comparative Study by Ahmet Yıldırım, Orhan Cicek, Yavuz Selim Genç

“…<b>Background/Aims:</b> The aim of this study was to evaluate the effectiveness of large language model (LLM)-based chatbot systems in predicting bone age and identifying growth stages, and to explore their potential as practical, infrastructure-independent alternatives to conventional methods and convolutional neural network (CNN)-based deep learning models. …”
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COMPARISON OF POROSITY PREDICTION FROM SEISMIC DATA IN THE F3 BLOCK, NETHERLANDS USING MACHINE LEARNING by Urip Nurwijayanto Prabowo, Sudarmaji Sudarmaji, Jarot Setyowiyoto, Sismanto Sismanto

“…Both generators utilize a convolutional neural network-gated recurrent unit network (CNN-GRU). …”
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Machine learning-based coalbed methane well production prediction and fracturing parameter optimization by HU Qiujia, LIU Chunchun, ZHANG Jianguo, CUI Xinrui, WANG Qian, WANG Qi, LI Jun, HE Shan

“…Furthermore, the absence of tailored fracturing designs has caused substantial variations in post-fracturing production performance among adjacent wells. …”
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The Robust Vessel Segmentation and Centerline Extraction: One-Stage Deep Learning Approach by Rostislav Epifanov, Yana Fedotova, Savely Dyachuk, Alexandr Gostev, Andrei Karpenko, Rustam Mullyadzhanov

“…We designed a hybrid architecture that integrates convolutional and graph layers, along with a task-specific loss function, to effectively capture the topological relationships between segmentation and centerline extraction, leveraging their complementary features. …”
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CrossModalSync: joint temporal-spatial fusion for semantic scene segmentation in large-scale scenes by Shuyi Tan, Yi Zhang, Yan Li, Byeong-Seok Shin

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Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning by Umile Giuseppe Longo, Sergio De Salvatore, Alice Piccolomini, Nathan Samuel Ullman, Giuseppe Salvatore, Margaux D'Hooghe, Maristella Saccomanno, Kristian Samuelsson, Rocco Papalia, Ayoosh Pareek

“…Out of the various ML algorithms, four models have proven to be particularly significant and were used in almost 20% of the studies, including elastic net penalized logistic regression, artificial neural network, convolutional neural network (CNN) and multiple linear regression. …”
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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review by Ikram Bagri, Karim Tahiry, Aziz Hraiba, Achraf Touil, Ahmed Mousrij

“…In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. …”
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Deep Learning-Based Intelligent Detection Algorithm for Surface Disease in Concrete Buildings by Jing Gu, Yijuan Pan, Jingjing Zhang

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High-Performance YOLOv5s: Traffic Sign Detection Algorithm for Small Target by Mengxia Hu, Shengwen Pi, Jinying Zhou, Xinming Wan, Qiaoshou Liu

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TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

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Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques by Miguel Diaz, Luis Lopez, Michel Amancio, Italo Inocente, Jhianpiere Salinas, Sergio Isuhuaylas, Erika Flores, Edisson Moscoso

“…A high-accuracy crack measurement technique was developed, combining a convolutional neural network to generate a binary crack mask and a binary search algorithm to extract polylines and convert them into length measurements, achieving a detection accuracy of 78%. …”
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Complex-Valued CNN Nonlinear Equalization Enabled 36-Tbit&#x002F;s (45&#x00D7;800-Gbit&#x002F;s) WDM Transmission Over 3150 Km Using Silicon-Based IC-TROSA by Yuhan Gong, Xiaoshuo Jia, Ying Zhu, Kailai Liu, Ming Luo, Jin Tao, Zhixue He, Chao Li, Zichen Liu, Yan Li, Jian Wu, Chao Yang

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ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks by Lin Zhu, Yi Fang, Shuting Liu, Hong-Bin Shen, Wesley De Neve, Xiaoyong Pan

“…ToxDL 2.0 consists of three key modules: (1) a Graph Convolutional Network (GCN) module for generating protein graph embeddings based on AlphaFold2-predicted structures, (2) a domain embedding module for capturing protein domain representations, and (3) a dense module that combines these embeddings to predict the toxicity. …”
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Predicting troponin biomarker elevation from electrocardiograms using a deep neural network by Truls Råmunddal, Erik Andersson, Zacharias Mandalenakis, Kristofer Skoglund, Araz Rawshani, Elmir Omerovic, Jan Borén, Petur Petursson, Aidin Rawshani, Peter Lundgren, Christian Dworeck, Lukas Hilgendorf, Vibha Gupta, Charlotta Ljungman, Gustav Smith

“…On this dataset, a residual convolutional neural network (ResNet) was trained 10 times, each on a unique split of the data. …”
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