A Few-Shot Learning for Predicting Radar Receiver Interference Response Based on Distillation Meta-Learning by Lingyun Zhang, Hui Tan, Mingliang Huang

“…Therefore, this paper proposes a few-shot modeling method based on distillation meta-learning, integrating meta-learning and distillation learning. …”
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Comparative Analysis of Voting and Stacking Ensemble Learning for Heart Disease Prediction: A Machine Learning Approach by Gregorius Airlangga

“…Using a dataset containing clinical and diagnostic attributes, preprocessing steps such as label encoding and standardization were applied to ensure compatibility with machine learning models. …”
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Optimized Fake News Classification: Leveraging Ensembles Learning and Parameter Tuning in Machine and Deep Learning Methods by Abubaker A. Alguttar, Osama A. Shaaban, Remzi Yildirim

“…With its ability to analyze vast datasets, machine learning emerges as a powerful tool. This research aims to explore fake news detection and emphasize the crucial role of preprocessing techniques. …”
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The Effect of Visual Auditory, Kinesthetic Learning Styles on Students' Learning Interest at Christian Junior High School by Nani Setiawati, Susmita Irene, Orbit Thomas, Rinto Alexandro, Kharisma Nugraha Putra

“…Individuals who are familiar with their learning styles can take essential steps to learn faster and more easily, resulting in desired learning outcomes. …”
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Efficient optimal power flow learning: A deep reinforcement learning with physics-driven critic model by Ahmed Sayed, Khaled Al Jaafari, Xian Zhang, Hatem Zeineldin, Ahmed Al-Durra, Guibin Wang, Ehab Elsaadany

“…This approach enables accurate policy gradient computation through a differentiable loss function based on system states of realized uncertainties, simplifying both the replay buffer and the learning process. By leveraging continuation power flow principles, D-HELM ensures operable, feasible solutions while accelerating gradient steps through simple matrix operations. …”
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Principles to award learning achievements for lifelong learning in health using micro-credentials: an international Delphi study by Sharon Mitchell, John Hart, Muntaha Gharaibeh, Graham T. McMahon, Anthea Rhoda, Siobhan Fitzpatrick, Tana Wuliji, Janusz Janczukowicz

“…Consensus was achieved on the core principles to recognise learning achievements. The expert panel agreed on the need for standards that are competency-based, and require evidence of learning indicating what a learner can do. …”
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AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING by Muhammad Anas, Muhammad Atif Imtiaz, Saad Khan, Arshad Ali, Noor Fatima Naghman, Hamayun Khan, Sami Albouq

“…The next steps will consist in enlarging a database and implementing these models in supporting clinical practice with realtime diagnostic potential. …”
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Trade Ease With Machine Learning and AWS by Kamurthi Ravi Teja, Chuan-Ming Liu

“…The roadmap provides a step-by-step overview of the entire process from strategy development to execution. …”
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Machine Learning for Wind Speed Estimation by Ilker Karadag, Miray Gür

“…One of the primary challenges addressed is the integration of visual outputs from ML models with quantitative data, a necessary step to enhance model reliability and applicability. …”
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MLHOps: Machine Learning Health Operations by Faiza Khan Khattak, Vallijah Subasri, Amrit Krishnan, Chloe Pou-Prom, Sedef Akinli-Kocak, Elham Dolatabadi, Deval Pandya, Laleh Seyyed-Kalantari, Frank Rudzicz

“…Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. …”
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Improving scholarship accessibility with reinforcement learning by Haining Wang, Jason Clark, Hannah McKelvey, Leila Sterman, Gao Zheng, Zuoyu Tian, Xiaozhong Liu

“…We introduce a reinforcement learning approach that fine-tunes a language model to rewrite scholarly abstracts into more comprehensible versions. …”
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Steps to Facilitate the Use of Clinical Gait Analysis in Stroke Patients: The Validation of a Single 2D RGB Smartphone Video-Based System for Gait Analysis by Philipp Barzyk, Alina-Sophie Boden, Justin Howaldt, Jana Stürner, Philip Zimmermann, Daniel Seebacher, Joachim Liepert, Manuel Stein, Markus Gruber, Michael Schwenk

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AI-Based Breast Cancer Detection System: Deep Learning and Machine Learning Approaches for Ultrasound Image Analysis by Amro Moursi, Abdulrahman Aboumadi, Uvais Qidwai

“…Using a combination of advanced deep learning and machine learning techniques, we offer a comprehensive solution to enhance breast cancer detection accuracy. …”
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Machine learning-driven nanoparticle toxicity by Zied Hosni, Sofiene Achour, Fatma Saadi, Yangfan Chen, Mohammed Al Qaraghuli

“…This work lays the groundwork for future research aimed at refining NP design for safer biomedical applications and consumer products, marking a significant step towards responsible nanotechnology development.…”
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Implementing best practice for peripheral intravenous cannula use in Australian emergency departments: a stepped-wedge cluster-controlled trial and health economic analysis protoco... by Gerben Keijzers, Cathrine Mihalopoulos, Peter Cameron, Emily Callander, Louise Cullen, Gerard O’Reilly, Diana Egerton-Warburton, Helena J Teede, Joanne Enticott, Carolyn Hullick, Daniel Fatovich, Claire Rickard, Lisa Kuhn, Julia Morphet, Paul Buntine, Long Khanh-Dao Le, Sundy Ni-Yen Yang, Leah Heiss, Bibesh Pokhrel, Viet Tran

“…Interventions will be phased across three steps (three sites per step), and each site will collect control and postintervention data using mainly routinely collected clinical data. …”
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SIBILA: Automated Machine-Learning-Based Development of Interpretable Machine-Learning Models on High-Performance Computing Platforms by Antonio Jesús Banegas-Luna, Horacio Pérez-Sánchez

“…As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. …”
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Success in Science: The effect of Bybee\'s 5E Model on Academic Self-Efficacy and Academic Engagement for Bushehr 6th Grade Students by Mehdi Farazja, Eshrat Nargesi, Mitra Razavi Klishadi, Effat Bahrani

“…Objective: he objective of this study was to investigate the impact of the Bybee five-step model (5E) teaching method on the academic engagement and academic self-efficacy of 6th grade students in experimental sciences in Bushehr. …”
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Knowledge- and Model-Driven Deep Reinforcement Learning for Efficient Federated Edge Learning: Single- and Multi-Agent Frameworks by Yangchen Li, Lingzhi Zhao, Tianle Wang, Lianghui Ding, Feng Yang

“…We consider a general FL algorithm with configurable parameters, including the number of local iterations, mini-batch sizes, step sizes, aggregation weights, and quantization parameters, and provide a rigorous convergence analysis. …”
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Examining the psychosocial model of sense of presence in the learning management system of e-learning center’s Shahid Beheshti university by soraya khazaei, abbas mohammadi, Mahboobeh Arefi, Esmail Jafari

“…At first, the data were analyzed through the one-sample t-test and then, used the AMOS software in the second step. Results: The results showed the average variables of sense of presence (2.416), educational presence (2.663), emotional presence (2.256), cognitive presence (2.263), learning presence (2.472), physical presence (2.340), autonomous presence (2.444) and aesthetic presence (2.319) obtained from the expected average (3) are lower; Therefore, according to the obtained t value and according to the significance level (p< 0.05), the average state of the sense of presence and its dimensions were significantly different from the expected average and were lower than expected. …”
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Implementation strategies to integrate HIV and hypertension care in Kampala and Wakiso districts, Uganda: study protocol for a stepped wedge cluster randomized trial (PULESA-Uganda... by Chris T. Longenecker, John Baptist Kiggundu, Florence Ayebare, Martin Muddu, James Kayima, Gerald Mutungi, Isaac Ssinabulya, Jeremy I. Schwartz, Donna Spiegelman, Guangyu Tong, Rachel Nugent, Angela Aifah, Faith Kagoya, Drew B. Cameron, Brian Hutchinson, Moses R. Kamya, Anne R. Katahoire, Fred C. Semitala

“…At two pre-specified time points, we are conducting a Learn-As-You-Go optimization analysis and will adapt the HTN-PLUS strategy accordingly. …”
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