Learning about Inclusion Health in undergraduate medical education: a scoping review by Andrea E Williamson, Lindsey M Pope, Amy M Russell, Gemma Ashwell

“…Challenges included limited curricula time and faculty expertise, being an emotionally challenging subject, limitations of the biomedical model and informal learning perpetuating stigma. Key enablers included structured reflection, support, positive role models, interaction and co-production with people with lived experience, community partnerships and faculty commitment.Conclusions Developments in undergraduate medical education are required to produce doctors equipped to meet the needs of socially excluded groups. …”
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Recipe Based Anomaly Detection with Adaptable Learning: Implications on Sustainable Smart Manufacturing by Junhee Lee, Jaeseok Jang, Qing Tang, Hail Jung

“…The Kruskal-Wallis test is conducted to provide statistical evidence of differences in data based on varying settings, further justifying the necessity of Recipe-Based Learning. Then, Autoencoders for anomaly detection are trained with normal data from each recipe. …”
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Learning Rate Tuner with Relative Adaptation (LRT-RA): Road to Sustainable Computing by Saptarshi Biswas, Sumagna Dey, Subhrapratim Nath

“…A new technique called Learning Rate Tuner with Relative Adaptation (LRT-RA) is introduced to tackle these issues. …”
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Digital image representation by atomic functions: features for computer vision and machine learning by Viktor Makarichev, Vladimir Lukin, Sergii Kryvenko, Iryna Brysina

“…One of the key issues in this context is the development of compact image representations that retain essential features for further analysis. …”
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Data mining methods application in reflexive adaptation realization in e-learning systems by A. S. Bozhday, Y. I. Evseeva, A. A. Gudkov

“…In recent years, e-learning technologies are rapidly gaining momentum in their evolution. …”
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A Synergy Between Machine Learning and Formal Concept Analysis for Crowd Detection by Anas M. Al-Oraiqat, Oleksandr Drieiev, Sattam Almatarneh, Mohammadnoor Injadat, Karim A. Al-Oraiqat, Hanna Drieieva, Yassin M. Y. Hasan

“…Aiming to solve these issues, we take advantage of the prediction capabilities of machine learning models and the analysis and clustering strengths of Formal Concept Analysis (FCA) chosen for its strong mathematical foundation and superior clustering capabilities compared to traditional methods, as highlighted in recent works such as K-means or hierarchical clustering. …”
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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

“…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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An interpretable physics-informed deep learning model for estimating multiple air pollutants by Binjie Chen, Jiacong Hu, Yumiao Wang, Tian Feng, Weiwei Sun, Zunlei Feng, Gang Yang, Huiqiong Wang

“…Moreover, widely applied machine learning models have demonstrated a lack of interpretability and a weak physical basis. …”
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Compass-Canada's first child psychiatry access program: Implementation and lessons learned. by Vivian W L Tsang, Michelle C Q Lin, Natalie Huang, Sally Tsoi, Jack Hu, Sonja Sinclair, Priya Watson, Susan Baer, Roberto Sassi

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Looking into the History of Recent Police Reforms in the USA: Critical Insights, Learning and Implications by shaharia afrin

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AMNED: An Efficient Framework for Spiking Neuron Coding in AirComp Federated Learning by Juncheng Ji, Chan-Tong Lam, Ke Wang, Benjamin K. Ng

“…In advancing future ACFL technologies, Over-the-Air Computation (AirComp) has emerged as a groundbreaking innovation. AirComp Federated Learning (ACFL) integrates AirComp with federated learning, transforming distributed machine learning by enhancing data privacy and leveraging network device computation. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…This approach highlights the potential of integrating statistical methods with machine learning to improve suicide risk prediction and intervention strategies.…”
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Hybrid Optimization Machine Learning Framework for Enhancing Trust and Security in Cloud Network by Himani Saini, Gopal Singh, Amrinder Kaur, Sunil Saini, Niyaz Ahmad Wani, Vikram Chopra, Zahid Akhtar, Shahid Ahmad Bhat

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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

“…As a result, early identification of motor health issues enables professionals to offer more potent treatments, enhancing the patient’s general health. …”
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Born Too Soon: learning from the past to accelerate action in the next decade by Anna Gruending, Joy E. Lawn, Amy Reid, Etienne V. Langlois, Bo Jacobsson, Lori McDougall, Rajat Khosla, Zulfiqar A. Bhutta, Ashok K. Deorari, Lily Kak, Judith Robb-McCord, Nabila Zaka, Ditas Duque Decena, Helga Fogstad, Anshu Banerjee, Mary V. Kinney, Queen Dube

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Generating learning guides for medical education with LLMs and statistical analysis of test results by Iván Roselló Atanet, Mihaela Tomova, Miriam Sieg, Victoria Sehy, Patrick Mäder, Maren März

“…Abstract Background The Progress Test Medizin (PTM) is a formative test for medical students issued twice a year by the Charité-Universitätsmedizin Berlin. …”
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“Voice of Student” in the Analysis of Components of Digital Educational Environment during Online Learning by Olga I. Popova, Galina S. Timokhina, Natalya B. Izakova, Larisa M. Kapustina

“…In addition, analysis of student ratings of interaction with seven components of the digital learning environment in 2023 identified key issues in online learning. …”
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Features of Distance Learning in Higher Education Institutions in The Context of The Covid-19 Pandemic by Oksana N. Goncharova, Milera Yu. Halilova

“…The purpose of the research is to study the quality of distance learning. The paper identifies the main problems that arose during the transition to distance learning due to the epidemiological situation in 2020-2021 in the Russian Federation.Materials and methods. …”
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Image dehazing algorithm based on deep transfer learning and local mean adaptation by Dongyang Shi, Sheng Huang

“…Currently, it is challenging for dehazing models to simultaneously ensure effective dehazing in bright regions while maintaining strong noise suppression capabilities. To address both issues simultaneously, we propose an image dehazing algorithm based on deep transfer learning and local mean adaptation. …”
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Automated sleep staging model for older adults based on CWT and deep learning by Qunfeng Niu, Ranran Gui, Hengfang Liu, Liuyi Li, Lei Shi, Kunming Jia, Peng Li, Li Wang

“…Currently, research on automatic sleep staging models based on deep learning has made significant progress. However, the automatic sleep staging models proposed by researchers seldom distinguish between age groups. …”
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