Multi-View Collaborative Training and Self-Supervised Learning for Group Recommendation by Feng Wei, Shuyu Chen

“…By incorporating both group and individual recommendation tasks, MCSS leverages graph convolution and attention mechanisms to generate three sets of embeddings, enhancing the model’s representational power. …”
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Accelerometry and the Capacity–Performance Gap: Case Series Report in Upper-Extremity Motor Impairment Assessment Post-Stroke by Estevan M. Nieto, Edaena Lujan, Crystal A. Mendoza, Yazbel Arriaga, Cecilia Fierro, Tan Tran, Lin-Ching Chang, Alvaro N. Gurovich, Peter S. Lum, Shashwati Geed

“…This case series investigates whether traditional machine learning (ML) and convolutional neural network (CNN) models trained on wrist-worn accelerometry data collected in a laboratory setting can accurately predict real-world functional hand use in individuals with chronic stroke. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. …”
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Meta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction pieces by Kai Hu, Chu-he Lin, Xing Jin, Hangjuan Lin

“…We propose Meta-YOLOv8, a novel few-shot object detection network based on YOLOv8. To effectively integrate YOLOv8 with meta-learning, we introduce three key modules: (i) Multi-Scale Class Feature Extraction Module (CFEM), (ii) Heterogeneous Graph Convolutional Networks (HGCN), and (iii) Multi-Scale Classification Auxiliary Module (CAM). …”
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Enhanced credit risk prediction using deep learning and SMOTE-ENN resampling by Idowu Aruleba, Yanxia Sun

“…The study compares the performance of various DL architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), Gated Recurrent Units (GRU), and Graph Neural Networks (GNN), on two real-world datasets: the Australian and German credit datasets. …”
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Research on the Application of Reinforcement Learning in Traffic Flow Prediction by Hu Yiquan

“…Additionally, the article discusses the application of RL-based Long Short-Term Memory Networks, Graph Convolutional Networks (GCN), and Dynamic GCN in TFP. …”
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Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review by Shixiao Tan, Zhen Tang, Qiang He, Ying Li, Yuliang Cai, Jiawei Zhang, Di Fan, Zhenkai Guo

“…In recent years, nonlinear dynamics methods such as chaos theory, fractal analysis, and entropy computation have provided new perspectives for EEG signal analysis, while deep learning approaches like convolutional neural networks and long short-term memory networks further enhance the robustness of dynamical pattern recognition through end-to-end nonlinear feature extraction. …”
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CrysMTM: a multiphase, temperature-resolved, multimodal dataset for crystalline materials by Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban

“…Baseline benchmarking across 18 models–including graph neural networks (GNNs), convolutional neural networks, and foundation models–reveals significant property-specific challenges. …”
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Heart failure prognosis risk assessment model based on multimodal data fusion and IoT device monitoring by Zhe Zhang, Dengao Li, Jumin Zhao, Huiting Ma, Fei Wang, Qinglian Hao

“…This deep learning framework combines graph neural networks (GNN) and convolutional neural networks (CNN) to extract comprehensive features from diverse data types, thereby improving risk predictions. …”
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Intelligent Interior Design Systems: Optimizing Layouts and Aesthetics Using AI and User Data by Zhe Ji, Yan Yu

“…Our computational framework leverages convolutional neural networks (CNNs) for layout parsing, graph neural networks (GNNs) for modeling spatial relationships, and Transformer-based architectures for context-aware reasoning. …”
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Deterministic reservoir computing for chaotic time series prediction by Johannes Viehweg, Constanze Poll, Patrick Mäder

“…Abstract Reservoir Computing was shown in recent years to be useful as efficient to learn networks in the field of time series tasks. Their randomized initialization, a computational benefit, results in drawbacks in theoretical analysis of large random graphs, because of which deterministic variations are still an open field of research. …”
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Interface-aware molecular generative framework for protein–protein interaction modulators by Jianmin Wang, Jiashun Mao, Chunyan Li, Hongxin Xiang, Xun Wang, Shuang Wang, Zixu Wang, Yangyang Chen, Yuquan Li, Kyoung Tai No, Tao Song, Xiangxiang Zeng

“…Subsequently, Convolutional Neural Networks extract compound representations in voxel and electron density spaces. …”
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Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach by Arefin Ittesafun Abian, Mohaimenul Azam Khan Raiaan, Asif Karim, Sami Azam, Nur Mohammad Fahad, Niusha Shafiabady, Kheng Cher Yeo, Friso De Boer

“…The objective of the study is to improve the quality of video frames, boost the diversity of the dataset, maintain the sequence of frames, and create a hybrid 3D model [Three-Dimensional Time Distributed Convolutional Neural Network-Long short-term memory (TD-CNNLSTM-LungNet)] for precise classification. …”
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Task Offloading with LLM-Enhanced Multi-Agent Reinforcement Learning in UAV-Assisted Edge Computing by Feifan Zhu, Fei Huang, Yantao Yu, Guojin Liu, Tiancong Huang

“…This framework integrates the QTRAN algorithm with a large language model (LLM) for efficient region decomposition and employs graph convolutional networks (GCNs) combined with self-attention mechanisms to adeptly manage inter-subregion relationships. …”
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UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review by Zhen Zhang, Lehao Huang, Qingwang Wang, Linhuan Jiang, Yemao Qi, Shunyuan Wang, Tao Shen, Bo-Hui Tang, Yanfeng Gu

“…This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. …”
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Harnessing artificial intelligence for brain disease: advances in diagnosis, drug discovery, and closed-loop therapeutics by Su-jun Fang, Su-jun Fang, Zhao-di Yin, Qi Cai, Li-fan Li, Peng-fei Zheng, Li-zhen Chen, Li-zhen Chen

“…Recent advancements in artificial intelligence (AI), especially deep learning models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), offer powerful new tools for analysis. …”
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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…Their approach combines advanced techniques, including convolutional neural networks (CNNs) for feature extraction, long short-term memory networks (LSTMs) for temporal patterns, transformer-based attention mechanisms for critical feature identification, and graph neural networks (GNNs) for modeling sensor-machine relationships. …”
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Spatial–Temporal Transformer for Optimizing Human Health Through Skeleton-Based Body Sports Action Recognition by Faze Liang, Lejia Ou, Zujun Lei, Xiaohong Tu, Kai Xin

“…Despite progress in skeleton-based recognition using Graph Convolutional Networks (GCNs) and Transformers, existing methods often fail to effectively model complex spatial-temporal dependencies, especially in dynamic or subtle actions. …”
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Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection by Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, the CMS-HCAL Collaboration

“…In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. …”
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Real-Time Player Engagement Measurement Using Nonintrusive Game Telemetry by Ammar Rashed, Shervin Shirmohammadi, Mohamed Hefeeda

“…Our approach combines graph convolutional networks for modeling player interactions with Transformer networks for temporal processing, enabling indirect measurement of both player skill and game challenge, which in turn are used to classify player engagement. …”
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