A Dual-Stream Deep Learning Architecture With Adaptive Random Vector Functional Link for Multi-Center Ischemic Stroke Classification by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Raja Rizal Azman Bin Raja Aman, Nadia Fareeda Muhammad Gowdh, Izzah Amirah Binti Mohd Ahir, Mohd Salahuddin Bin Kamaruddin, Khairul Azmi Abdul Kadir, Filippo Molinari, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

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Pansharpening Techniques: Optimizing the Loss Function for Convolutional Neural Networks by Rocco Restaino

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Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning by Morteza Fattahi, Milad Esmaeil-Zadeh, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Reza Rostami, Jamil Mansouri, Gholam-Ali Hossein-Zadeh, Gholam-Ali Hossein-Zadeh

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Mapping variants in thyroid hormone transporter MCT8 to disease severity by genomic, phenotypic, functional, structural and deep learning integration by Stefan Groeneweg, Ferdy S. van Geest, Mariano Martín, Mafalda Dias, Jonathan Frazer, Carolina Medina-Gomez, Rosalie B. T. M. Sterenborg, Hao Wang, Anna Dolcetta-Capuzzo, Linda J. de Rooij, Alexander Teumer, Ayhan Abaci, Erica L. T. van den Akker, Gautam P. Ambegaonkar, Christine M. Armour, Iiuliu Bacos, Priyanka Bakhtiani, Diana Barca, Andrew J. Bauer, Sjoerd A. A. van den Berg, Amanda van den Berge, Enrico Bertini, Ingrid M. van Beynum, Nicola Brunetti-Pierri, Doris Brunner, Marco Cappa, Gerarda Cappuccio, Barbara Castellotti, Claudia Castiglioni, Krishna Chatterjee, Alexander Chesover, Peter Christian, Jet Coenen-van der Spek, Irenaeus F. M. de Coo, Regis Coutant, Dana Craiu, Patricia Crock, Christian DeGoede, Korcan Demir, Cheyenne Dewey, Alice Dica, Paul Dimitri, Marjolein H. G. Dremmen, Rachana Dubey, Anina Enderli, Jan Fairchild, Jonathan Gallichan, Luigi Garibaldi, Belinda George, Evelien F. Gevers, Erin Greenup, Annette Hackenberg, Zita Halász, Bianka Heinrich, Anna C. Hurst, Tony Huynh, Amber R. Isaza, Anna Klosowska, Marieke M. van der Knoop, Daniel Konrad, David A. Koolen, Heiko Krude, Abhishek Kulkarni, Alexander Laemmle, Stephen H. LaFranchi, Amy Lawson-Yuen, Jan Lebl, Selmar Leeuwenburgh, Michaela Linder-Lucht, Anna López Martí, Cláudia F. Lorea, Charles M. Lourenço, Roelineke J. Lunsing, Greta Lyons, Jana Krenek Malikova, Edna E. Mancilla, Kenneth L. McCormick, Anne McGowan, Veronica Mericq, Felipe Monti Lora, Carla Moran, Katalin E. Muller, Lindsey E. Nicol, Isabelle Oliver-Petit, Laura Paone, Praveen G. Paul, Michel Polak, Francesco Porta, Fabiano O. Poswar, Christina Reinauer, Klara Rozenkova, Rowen Seckold, Tuba Seven Menevse, Peter Simm, Anna Simon, Yogen Singh, Marco Spada, Milou A. M. Stals, Merel T. Stegenga, Athanasia Stoupa, Gopinath M. Subramanian, Lilla Szeifert, Davide Tonduti, Serap Turan, Joel Vanderniet, Adri van der Walt, Jean-Louis Wémeau, Anne-Marie van Wermeskerken, Jolanta Wierzba, Marie-Claire Y. de Wit, Nicole I. Wolf, Michael Wurm, Federica Zibordi, Amnon Zung, Nitash Zwaveling-Soonawala, Fernando Rivadeneira, Marcel E. Meima, Debora S. Marks, Juan P. Nicola, Chi-Hua Chen, Marco Medici, W. Edward Visser

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Machine learning-based prediction of 6-month functional recovery in hypertensive cerebral hemorrhage: insights from XGBoost and SHAP analysis by Menghui He, Zhongsheng Lu, Yiwei Lv, Zihai Cheng, Qiang Zhang, Xiaoqing Jin, Pei Han

“…Accurate early prediction of 6-month functional outcomes is critical for optimizing therapeutic strategies. …”
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Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability by Xiaoling Qin, Shifu Wang, Meng Meng, Haiyan Long, Huilan Zhang, Haochen Shi

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Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods by Weinan Zhou, Diyang Fu, Zhiyu Duan, Jiping Wang, Linfu Zhou, Liquan Guo

“…Wearable inertial measurement units (IMUs) and deep learning approaches have been effectively employed for motor function prediction. …”
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Machine Learning‐Based Identification of Children With Intermittent Exotropia Using Multiple Resting‐State Functional Magnetic Resonance Imaging Features by Mengdi Zhou, Huixin Li, Xiaoxia Qu, Lirong Zhang, Xueying He, Xiwen Wang, Jie Hong, Jing Fu, Zhaohui Liu

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Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation by Banafshe Parizad, Ali Jamali, Hamid Khayyam

“…Sustainable forecasting of home energy demand (SFHED) is crucial for promoting energy efficiency, minimizing environmental impact, and optimizing resource allocation. Machine learning (ML) supports SFHED by identifying patterns and forecasting demand. …”
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Spatial proximity effects on cognitive processing of multimedia learning among college students: evidence from functional near-infrared spectroscopy by Yan Ma, Yan Ma, Qiuyue Peng, Qiuyue Peng, Bowen Long, Bowen Long

“…However, most previous studies mainly relied on behavioral experiments.MethodsTo investigate the neural mechanisms underlying the spatial proximity effect, this study employed functional near-infrared spectroscopic imaging (fNIRS) to monitor and analyze the neural activity in the prefrontal cortex of 36 college students while they engaged in learning tasks with different graphic formats (proximity vs. separation). …”
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The impact of electronic device use on learning quality in young children: the mediating role of executive function and the moderating role of parental mediation by Fangbing Qu, Shiqi He, Feifei Yu, Changwei Gu

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Dexmedetomidine Improves Learning Functions in Male Rats Modeling Cognitive Impairment by Modulating the BDNF/TrkB/CREB Signaling Pathway by Sinan Saral, Tolga Mercantepe, Atilla Topçu, Ali Koray Kaya, Aykut Öztürk

“…In this study, we investigated the potential effects of DEX on learning and memory functions in rats with experimental cognitive impairment. …”
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Evaluating the role of AI-simulated patients compared with peer-to-peer learning models in the enhancement of medical education: is it beyond theoretical functionality? by Pegin Poulose

“…Introduction: This research study evaluated the effectiveness of utilising patients simulated through artificial intelligence (AI) for medical education, and the role of AI as a learning tool compared with traditional peer-to-peer formats. …”
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Exploring biomarkers of MAPK pathway co-expression in lung adenocarcinoma and their functions based on machine learning algorithms and single-cell analysis by Mingkai Lin, Ruoyi Zheng, Peixian Liang, Jiayang Huang, Xintong Ke, Wenjing Zhang, Pei Shang

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Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics by Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun

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A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning by Congyang Li, Chenggang Wang, Jiru Zhang, Wenjun Zheng, Jing Shi, Li Li, Xuezhi Shi

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Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors by Alexandru Bogdan Ilies, Cornel Cheregi, Hassan Hassan Thowayeb, Jan Reinald Wendt, Maur Sebastian Horgos, Liviu Lazar

“…Conclusions: Lokomat-assisted robotic rehabilitation demonstrates high functional symmetry and biomechanical consistency in SCI patients. …”
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Deep Reinforcement Learning-Based Routing Method for Low Earth Orbit Mega-Constellation Satellite Networks with Service Function Constraints by Yan Chen, Huan Cao, Longhe Wang, Daojin Chen, Zifan Liu, Yiqing Zhou, Jinglin Shi

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Review of zeolitic-based sorbents for CO2 capture: Insights into functional modifications, economic feasibility and machine learning-enhanced strategies by Saheed A. Ganiyu

“…Advances in zeolitic sorbents, such as pristine zeolites, metal-modified zeolites, amine-functionalized zeolites, and silica-modified zeolites, are reviewed in detail, highlighting their enhanced adsorption performance and selectivity. …”
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Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder by Cyrus Su Hui Ho, Jinyuan Wang, Gabrielle Wann Nii Tay, Roger Ho, Hai Lin, Zhifei Li, Nanguang Chen

“…This study investigates whether functional near-infrared spectroscopy (fNIRS) and clinical assessment information can predict treatment response in major depressive disorder (MDD) through machine-learning techniques. …”
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