Perturbation-theory machine learning for mood disorders: virtual design of dual inhibitors of NET and SERT proteins by Valeria V. Kleandrova, M. Natália D. S. Cordeiro, Alejandro Speck-Planche

“…From a chemical point of view, the PTML-MLP model could accurately identify both single- and dual-target inhibitors present in the dataset used to build it. Through the application of the fragment-based topological design (FBTD) approach, the molecular descriptors (multi-label graph-based indices) present in the PTML-MLP model were physicochemically and structurally interpreted. …”
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3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology by Isaac Y. Tian, Jason Liu, Michael C. Wong, Nisa N. Kelly, Yong E. Liu, Andrea K. Garber, Steven B. Heymsfield, Brian Curless, John A. Shepherd

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Machine learning for classifying affective valence from fMRI: a systematic review and meta-analysis by Charith Chitraranjan, Ruwan Dayananda, Dakshitha Suriyaaratchie, Nuwan Abeynayake, Svetlana Shinkareva

“…However, we suggest that future studies also explore deep learning architectures such as convolutional and graph neural networks, which have not yet been applied to classify valence from fMRI data.…”
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A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes by Husnain Ali, Rizwan Safdar, Yuanqiang Zhou, Yuan Yao, Le Yao, Zheng Zhang, Weilong Ding, Furong Gao

“…Traditional monitoring techniques for automatic anomaly detection, identifying the potential variables, and root cause analysis for fault information are not intelligent enough to tackle the intricate problems of real-time practices in the industrial and chemical sectors. This study presents a novel dynamic machine learning based explainable fusion approach to address the issues of process monitoring in industrial and chemical process systems. …”
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Key Frame Detection in Badminton Swings and Its Application to Physical Education by Jen-Hao Hsu, Chi-Chuan Lee, Jing-Yuan Chang, Duan-Shin Lee

“…The use of video analysis in sports training has revolutionized the way coaches and players evaluate performance and develop strategies. This paper presents a machine learning based approach for key frame detection in badminton swings aimed at improving the learning experience for beginners through visualization and real-time feedback. …”
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Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework. by Xiaoyi Zhang, Yakang Zhang, Angelina Lilac Chen, Manning Yu, Lihao Zhang

“…The GNN-TINet utilizes InceptionNet, transformer architectures, and graph neural networks (GNN) to improve precision in multi-label student performance forecasting. …”
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Deep knowledge tracing and cognitive load estimation for personalized learning path generation using neural network architecture by Chunyan Tong, Changhong Ren

“…Abstract This paper presents a novel approach for personalized learning path generation by integrating deep knowledge tracing and cognitive load estimation within a unified framework. …”
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B-learning y aplicaciones de calculadoras gráficas en instituciones de educación básica y media. by Dina Luz Rojas Jovel, Willian Alejandro Aristizabal Bossa, Fabián Moreno Rodríguez, Sebastián Gustavo Moreno Barón

“…El presente estudio tuvo como objetivo evaluar la incidencia de dos estrategias educativas para la resolución de problemas de aplicación de derivadas de funciones y sistemas de ecuaciones lineales, la primera con la modalidad Blended Learning (b-learning) y la segunda usando aplicaciones de calculadoras gráficas. …”
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Chemical space-informed machine learning models for rapid predictions of x-ray photoelectron spectra of organic molecules by Susmita Tripathy, Surajit Das, Shweta Jindal, Raghunathan Ramakrishnan

“…We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. …”
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Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges by Flaviu-Ioan Gheorghita, Vlad-Ioan Bocanet, Laszlo Barna Iantovics

“…This review examines recent advances in DDIp. It presents an in-depth review of the state-of-the-art studies relating to semi-supervised, supervised, self-supervised learning, and other techniques such as graph-based learning and matrix factorization methods for predicting DDIs. …”
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PassAI: An Explainable Machine Learning Framework for Predicting Soccer Pass Outcomes Using Multimodal Match Data by Ryota Takamido, Jun Ota, Hiroki Nakamoto

“…As sports data, increasingly shaped by recent advances in in-game data acquisition technologies, become more complex and high-dimensional, analyzing such multimodal datasets presents challenges in both predictive performance and interpretability. …”
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Cost-Efficient Fall Risk Assessment With Attention Augmented Vision Machine Learning on Sit-to-Stand Test Videos by Chunhua Pan, Boting Qu, Rui Miao, Xin Wang

“…To tackle this challenge, this paper presents a novel machine learning-based fall risk assessment approach called <sc>FRAVM</sc>, which operates on Five times Sit-To-Stand (FSTS) test videos captured with standard, widely available cameras to identify individuals requiring fall prevention interventions. …”
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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging by Arwa Alzughaibi, Adwan A. Alanazi, Mohammed Alshahrani, Ines Hilali Jaghdam, Abaker A. Hassaballa

“…This study presents a Leveraging Flying Foxes Optimization with an Ensemble of Deep Learning for Accurate Kidney Stone Detection (LFFOEDL-AKSD) technique in CT scans. …”
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Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models by Mehmet Tahir Ucar, Asim Kaygusuz

“…The R²_score indexes graphs are presented. Finally, the 10 most successful applications are listed.…”
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Optimizing base fluid composition for PEMFC cooling: A machine learning approach to balance thermal and rheological performance by Praveen Kumar Kanti, Prashantha Kumar H. G, Nejla Mahjoub Said, V. Vicki Wanatasanappan, Prabhu Paramasivam, Leliso Hobicho Dabelo

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Multimodal Deep Learning for Cardiovascular Risk Stratification: Integrating Retinal Biomarkers and Cardiovascular Signals for Enhanced Heart Attack Prediction by K. Sathya, G. Magesh

“…Conventional measures for risk prediction among most heterogeneous patients, such as the Framingham Risk Score and ASCVD calculator, are often less precise and poorly generalizable as they fail to capture individual variations in the mechanisms. This study seeks to present a new multimodal deep learning model developed for cardiovascular risk stratification by fusing retinal microvascular features with cardiovascular physiological signals. …”
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Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito, Massimo Celino

“…We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. …”
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Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning. by Hasitha Kuruwita Arachchige, Shu Kay Ng, Alan Wee-Chung Liew, Brent Richards, Luke Haseler, Kuldeep Kumar, Kelvin Ross, Ping Zhang

“…This study aims to uncover these subtle interconnections and identify the key ICU markers for the timely care of TBI patients using advanced machine-learning techniques. We combined correlation-based network analysis and graph neural network (GNN) techniques to explore relationships among electrocardiography (ECG) features, vital signs, pathology test results, Glasgow Coma Scale (GCS) scores, and demographics from 29 TBI patients admitted to the Gold Coast University Hospital (GCUH). …”
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Attention-Enhanced Hybrid Automatic Modulation Classification for Advanced Wireless Communication Systems: A Deep Learning-Transformer Framework by Sam Ansari, Khawla A. Alnajjar, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny, Abir Hussain, Soliman Mahmoud

“…In contrast to existing hybrid or purely sequential architectures, this design attains high classification fidelity without the need for complex graph-based structures or stacked attention mechanisms, thereby enhancing both model interpretability and practical deployment feasibility. …”
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LoD2 Building Reconstruction from Stereo Satellite Imagery using Deep Learning and Model-Driven Approach by Rojgar Qarani Ismael, Haval Sadeq

“… This study presents a Level of Detail 2 building reconstruction approach for open and occluded areas from stereo-satellite imagery. …”
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