How STEM content is presented in mathematics textbooks from the U.S. and China: a comparative study by Shuhui Li, Lianghuo Fan, Jietong Luo

“…However, despite the critical role that textbooks play in mathematics teaching and learning, existing research on mathematics textbooks has largely overlooked examining STEM content and their characteristics. …”
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Enhancing leaf disease classification using GAT-GCN hybrid model by Shyam Sundhar, Riya Sharma, Priyansh Maheshwari, Suvidha Rupesh Kumar, T. Sunil Kumar

“…This requires accurate, efficient, and timely disease detection methods. The research presented in this paper addresses this need by analyzing a hybrid model built using Graph Attention Network (GAT) and Graph Convolution Network (GCN) models. …”
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Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques by Waleed Bin Tahir, Shah Khalid, Sulaiman Almutairi, Mohammed Abohashrh, Sufyan Ali Memon, Jawad Khan

“…While this review highlights advancements in social media-based depression detection, it excludes alternative approaches like graph-based systems and reinforcement learning, and its focus on social media may limit its applicability to other domains.…”
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Predicting spread through air space of lung adenocarcinoma based on deep learning and machine learning models by Zengming Wang, Lingxin Kong, Bin Li, Qingtao Zhao, Xiaopeng Zhang, Huanfen Zhao, Wenfei Xue, Wei Li, Shun Xu, Guochen Duan

“…Results Imaging histology features showed good model efficacy in both the training set (LR AUC = 0.764) and the test set (LR AUC = 0.776), and we combined the imaging histology and clinical features to jointly build a nomogram graph (AUC = 0.878), extracted the deep learning features, and built a machine learning model based on the ResNET50 algorithm, where the LR AUC = 0.918. …”
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Effect of Occupation Performance Coaching with Four-Quadrant Model of Facilitated Learning on Children with Specific Learning Disorder by Amin Ghaffari, Akram Azad, Mehdi Alizadeh Zarei, Mehdi Rassafiani, Hamid Sharif Nia

“…Objective. The present study is aimed at investigating the effectiveness of Occupational Performance Coaching (OPC) and the Four-Quadrant Model of Facilitated Learning (4QM) interventions on the participation in occupational performance and executive function skills in children with SLD. …”
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Role of topological indices in predictive modeling and ranking of drugs treating eye disorders by Nazeran Idrees, Esha Noor, Saima Rashid, Fekadu Tesgera Agama

“…Abstract Topological indices (TIs) of chemical graphs of drugs hold the potential to compute important properties and biological activities leading to more thoughtful drug design. …”
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Development of the Physics Practicum Apparatus based on Microcontroller: A Prototype Constructed from Misconceptions of Basic Kinematics Concepts by Endah Nur Syamsiah, Muhammad Rizka Taufani, Adam Hadiana Aminudin, Rahadian Sri Pamungkas, Reno Muhammad Fadilla, Fatih Najah Nabilah

“…The prototype we created can present scientific facts from two misconceptions in basic kinematics material, in addition, our prototype can be used in learning that focuses on conceptual change.…”
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A novel approach for detecting malicious hosts based on RE-GCN in intranet by Haochen Xu, Xiaoyu Geng, Junrong Liu, Zhigang Lu, Bo Jiang, Yuling Liu

“…Lastly, the traditional graph neural network model has limitations in processing edge information and is unable to directly learn the information in netflow. …”
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Deep Learning Scheduling on a Field-Programmable Gate Array Cluster Using Configurable Deep Learning Accelerators by Tianyang Fang, Alejandro Perez-Vicente, Hans Johnson, Jafar Saniie

“…This paper presents the development and evaluation of a distributed system employing low-latency embedded field-programmable gate arrays (FPGAs) to optimize scheduling for deep learning (DL) workloads and to configure multiple deep learning accelerator (DLA) architectures. …”
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ProstaNet: A Novel Geometric Vector Perceptrons–Graph Neural Network Algorithm for Protein Stability Prediction in Single- and Multiple-Point Mutations with Experimental Validation... by Tianjian Liang, Ze-Yu Sun, Rieko Ishima, Xiang-Qun Xie, Ying Xue, Wei Li, Zhiwei Feng

“…In the present work, we introduced ProstaNet, a deep learning framework that predicts stability changes resulting from single- and multiple-point mutations using geometric vector perceptrons–graph neural network for 3-dimensional feature processing. …”
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Computational analysis of learning in young and ageing brains by Jayani Hewavitharana, Kathleen Steinhofel, Karl Peter Giese, Carolina Moretti Ierardi, Amida Anand

“…In this paper, we present a computational analysis focusing on the differences in learning between young and old brains. …”
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Primer on machine learning applications in brain immunology by Niklas Binder, Ashkan Khavaran, Roman Sankowski

“…We explore how machine learning, particularly deep learning methods like autoencoders and graph neural networks, is addressing these challenges by enhancing dimensionality reduction, data integration, and feature extraction. …”
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Multimodal fusion with relational learning for molecular property prediction by Zhengyang Zhou, Yunrui Li, Pengyu Hong, Hao Xu

“…Abstract Graph-based molecular representation learning is essential for predicting molecular properties in drug discovery and materials science. …”
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Edge Cloud Resource Scheduling with Deep Reinforcement Learning by Y. Feng, M. Li, J. Li, Y. Yu

“…We utilize a transformer architecture to capture resource states on directed acyclic graphs (DAGs), accelerating the aggregation speed of the Graph Neural Network (GNN). …”
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Hierarchical contrastive learning for multi-label text classification by Wei Zhang, Yun Jiang, Yun Fang, Shuai Pan

“…Our approach leverages the contrastive knowledge embedded within label relationships by constructing a graph representation that explicitly models the hierarchical dependencies among labels. …”
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Optimal Poisson Cognitive System with Markov Learning Model by A. A. Solodov

“…These probabilities are calculated for an interesting case in which the discreteness of the appearance of stimuli in time is clearly manifested and the corresponding graphs are given. Stationary probabilities are also calculated, i.e. for an infinite number of training steps, the probabilities of the system staying in each of the states and the corresponding graph is presented.In conclusion, it is noted that the presented graphs of the behavior of the trained system correspond to an intuitive idea of the reaction of the cognitive system to the appearance of stimuli. …”
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Adaptation of mathematical educational content in e-learning resources by Yuliya V. Vainshtein, Victoria A. Shershneva, Roman V. Esin, Tatyana V. Zykova

“…For each stage of the proposed system, mathematical algorithms for educational content adaptation in adaptive e-learning resources are presented.Due to the high level of abstraction and complexity perception of mathematical disciplines, educational content is represented in the various editions of presentation that correspond to the levels of assimilation of the course material. …”
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Disentangled diffusion dodels for probabilistic spatio-temporal traffic forecasting by Wenyu Zhang, Kaidong Zheng

“…Our model aims to capture the inherent uncertainty and complex spatio-temporal dynamics of traffic data by quantifying uncertainty through a diffusion model and learning spatio-temporal dependencies via a spatio-temporal graph neural network. …”
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A fine-grained course session recommendation method based on knowledge point pruning by Yiwen Zhang, Xiaolan Cao, Wangjian Li, Li Zhang

“…Abstract Course recommendation represents a significant research avenue within the educational domain. Presently, it predominantly employs collaborative filtering techniques to generate recommendations based on users’ historical learning behaviors, such as their past rating information. …”
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Spatiotemporal Wind Energy Forecasting: A Comprehensive Survey and a Deep Equilibrium-Based Case Study With StemGNN by Luiza Scapinello Aquino, Laio Oriel Seman, Viviana Cocco Mariani, Leandro Dos Santos Coelho, Stefano Frizzo Stefenon, Gabriel Villarrubia Gonzalez

“…This paper addresses the challenge of modeling the complex spatial and temporal dependencies inherent in wind power generation by presenting a comprehensive survey of existing spatiotemporal forecasting methods and introducing an innovative deep learning approach. …”
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