YOLOX-LS: Strong Gravitational Lenses Detection in the DECaLS with Deep Learning by Yangyang Liu, Liangping Tu, Jianxi Li, Jiawei Miao, Gengqi Lin, Chenying Zhao

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Application of Deep Learning to the Classification of Stokes Profiles: From the Quiet Sun to Sunspots by Ryan J. Campbell, M. Mathioudakis, Carlos Quintero Noda, P. H. Keys, D. Orozco Suárez

“…Previous studies used manual methods or unsupervised machine learning (ML) to classify the shapes of circular polarization profiles. …”
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Deep Learning−Driven Exophthalmometry through Facial Photographs in Thyroid Eye Disease by Joonhyeon Park, PhD, Jin Sook Yoon, MD, PhD, Namju Kim, MD, PhD, Kyubo Shin, PhD, Hyun Young Park, MD, PhD, Jongchan Kim, MS, Jaemin Park, MS, Jae Hoon Moon, MD, PhD, JaeSang Ko, MD, PhD

“…Objective: To develop and evaluate a deep learning (DL)-assisted system for proptosis measurement using facial photographs in thyroid eye disease (TED). …”
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Multispectral Sensors and Machine Learning as Modern Tools for Nutrient Content Prediction in Soil by Rafael Felippe Ratke, Paulo Roberto Nunes Viana, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro, Dthenifer Cordeiro Santana, Carlos Eduardo da Silva Santos, Alan Mario Zuffo, Jorge González Aguilera

“…The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. …”
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Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort by Jose Lozano-Montoya, Ana Jimenez-Pastor, Almudena Fuster-Matanzo, Glen J. Weiss, Leonor Cerda-Alberich, Diana Veiga-Canuto, Blanca Martínez-de-Las-Heras, Blanca Martínez-de-Las-Heras, Adela Cañete-Nieto, Sabine Taschner-Mandl, Barbara Hero, Thorsten Simon, Ruth Ladenstein, Luis Marti-Bonmati, Luis Marti-Bonmati, Angel Alberich-Bayarri

“…IntroductionNeuroblastoma, the most prevalent solid cancer in children, presents significant biological and clinical heterogeneity. …”
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Novel Blended Learning on Artificial Intelligence for Medical Students: Qualitative Interview Study by Zoe S Oftring, Kim Deutsch, Daniel Tolks, Florian Jungmann, Sebastian Kuhn

“…ObjectiveThis paper first introduces a novel blended learning curriculum including one module on AI for medical students in Germany. …”
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Deep Learning Approaches for Continuous Sign Language Recognition: A Comprehensive Review by Asma Khan, Seyong Jin, Geon-Hee Lee, Gul E. Arzu, L. Minh Dang, Tan N. Nguyen, Woong Choi, Hyeonjoon Moon

“…The study highlights the effectiveness of deep learning systems in capturing different sign language signals. …”
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AI’s role in transforming learning environments: a review of collaborative approaches and innovations by Dwi Mariyono, Akmal Nur Alif Hd

“…Purpose – This study aims to examine how artificial intelligence (AI) is transforming education globally by personalizing learning, fostering inclusivity and enhancing human–machine collaboration. …”
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Evaluation of an Interdisciplinary Educational Program to Foster Learning Health Systems: Education Evaluation by Sathana Dushyanthen, Nadia Izzati Zamri, Wendy Chapman, Daniel Capurro, Kayley Lyons

“…In a flipped classroom model, participants (n=400) undertook 2 hours of preclass learning online and then attended 2.5 hours of live synchronous learning in interactive weekly Zoom workshops for 13 weeks. …”
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Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging by Saurabh Bhattacharya, Sashikanta Prusty, Sanjay P. Pande, Monali Gulhane, Santosh H. Lavate, Nitin Rakesh, Saravanan Veerasamy

“…Current approaches are less helpful for forecasting, however, as they do not always blend spatial and temporal patterns from different sources properly. This work presents a novel mixed deep learning (DL) method combining data from many sources using CNN, GRU, and attention techniques. …”
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Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview by Imran Ul Haq, Mustafa Mhamed, Mohammed Al-Harbi, Hamid Osman, Zuhal Y. Hamd, Zhe Liu

“…Potential future research directions are also identified. This review presents an overview of multimodal machine learning (MMML) in radiology, a field at the cutting edge of integrating artificial intelligence into medical imaging. …”
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Evaluating the slope behavior for geophysical flow prediction with advanced machine learning combinations by Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh, Viroon Kamchoom

“…This is the development of a closed-form equation that allows its model to be applied manually in the design of slope stability problems. Overall, the present research models outperformed the eleven (11) models of the previous work due to sorting and elimination of unrealistic data entries deposited in the literature, the application of dimensionless combination of the studied slope stability parameters and the superiority of the selected machine learning techniques.…”
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Modeling the compressive strength of concrete at different curing regimes using machine learning by Kennedy C. Onyelowe, Ahmed M. Ebid, Rosa Belén Ramos Jiménez, Angel Geovanny Lozano Guamán, Diego Ramiro Ñacato Estrella, Juan Carlos Cayán Martínez, Eduardo Francisco García Cabezas, Paul Awoyera

“…Abstract In this research work, a representative data of 334 collected from literature of concrete admixed with highly pozzolanic industrial waste materials, which are nano-silica (nSi), nano-alumina (nAl) and micro-silica (mSi) has been deployed to the learning abilities of the artificial neural network (ANN), genetic programming (GP), and the evolutionary polynomial regression (EPR) under the influence of the variable structure neuron interface in the hidden layer and the levels complexity of the techniques. …”
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ORGANIZING SELF-EDUCATION ACTIVITIES FOR MEDICAL COLLEGE STUDENTS IN THE CONTEXT OF BLENDED LEARNING by Pysmenna Oleksandra

“…The blended learning system is presented as an integration of face-to-face classroom and clinical sessions, independent student work, and online learning. …”
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Weed instance segmentation from UAV Orthomosaic Images based on Deep Learning by Chenghao Lu, Klaus Gehring, Stefan Kopfinger, Heinz Bernhardt, Michael Beck, Simon Walther, Thomas Ebertseder, Mirjana Minceva, Yuncai Hu, Kang Yu

“…This study aimed to develop deep learning-based segmentation models in identifying weeds in potato fields captured by Unmanned Aerial Vehicle (UAV) orthophotos and to explore the effects of weeds on potato yield. …”
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Weed detection based on deep learning from UAV imagery: A review by Lucía Sandoval-Pillajo, Iván García-Santillán, Marco Pusdá-Chulde, Adriana Giret

“…These systems aim to improve the localized application of chemicals in weed control by using advanced image analysis techniques, computer vision, deep learning (DL), and geo-positioning (GPS) to detect and recognize weeds. …”
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Mobile learning in the implementation of educational programs for training at the tertiary level: The national aspect by Tetyana Sobchenko, Svitlana Dotsenko, Natalia Smolianiuk

“…The study substantiates the need to take into account the technical and psychological readiness, and the competence in the use of mobile devices of first-year bachelor's degree students in the use of mobile devices for mobile learning. A roadmap for the organisation of mobile learning in higher pedagogical education institutions has been developed and presented.…”
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Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area by Girma Tariku, Isabella Ghiglieno, Andres Sanchez Morchio, Luca Facciano, Celine Birolleau, Anna Simonetto, Ivan Serina, Gianni Gilioli

“…This study addresses these gaps by presenting a methodology for semantic segmentation of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. …”
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An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

“…This study presents a novel dual approach for diabetes risk prediction in women, combining machine learning classification with fractional-order physiological modeling. …”
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Image dehazing algorithm based on deep transfer learning and local mean adaptation by Dongyang Shi, Sheng Huang

“…To address both issues simultaneously, we propose an image dehazing algorithm based on deep transfer learning and local mean adaptation. The framework consists of several key modules: an atmospheric light estimation module based on deep transfer learning, a transmission map estimation module utilizing local mean adaptation, a haze-free image reconstruction module, an image enhancement module, and a noise reduction module. …”
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