A Collection of Machine Learning Models for Improved Airport Operations Amidst Adverse Weather Conditions by Ramon Dalmau, Jonathan Attia

Subjects: “…Machine Learning…”
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Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning by Zhaoxiang Cai, Sofia Apolinário, Ana R. Baião, Clare Pacini, Miguel D. Sousa, Susana Vinga, Roger R. Reddel, Phillip J. Robinson, Mathew J. Garnett, Qing Zhong, Emanuel Gonçalves

“…Addressing this, our study introduces an unsupervised deep learning model, MOSA (Multi-Omic Synthetic Augmentation), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). …”
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Creating a Successful Early Learning Environment for Children Who Have Autistic Spectrum Disorders by Heidi Liss Radunovich, Jessica L. Kochert

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Neurocognitive & Ecological Motor Learning Considerations for the 11+ ACL Injury Prevention Program: A Commentary by Dustin R. Grooms, Mario Bizzini, Holly Silvers-Granelli, Anne Benjaminse

“…Updates to the 11+ considering practical implications of neurocognitive and ecological motor learning may be warranted for coaches and practitioners. …”
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Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete by Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba

“…Given the nonlinear characteristics of supplementary cement material concrete (SCMC) mixtures, researchers are increasingly turning to machine learning methods. This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). …”
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Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements by Brady Metherall, Anna K. Berryman, Georgia S. Brennan

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Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications by Georgios Kougioumtzidis, Vladimir K. Poulkov, Pavlos I. Lazaridis, Zaharias D. Zaharis

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A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR by Yuqi Zhang, Sijin Li, Peibiao Mai, Yanqi Yang, Niansang Luo, Chao Tong, Kuan Zeng, Kun Zhang

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Efficient Handling of Data Imbalance in Health Insurance Fraud Detection Using Meta-Reinforcement Learning by Supriya Seshagiri, K. V. Prema

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Diagnostic Performance of Deep Learning Applications in Hepatocellular Carcinoma Detection Using Computed Tomography Imaging by Enes Şahin, Ozan Can Tatar, Mehmet Eşref Ulutaş, Sertaç Ata Güler, Turgay Şimşek, Nihat Zafer Utkan, Nuh Zafer Cantürk

“…This study leverages the potential of deep learning (DL) technologies, employing the You Only Look Once (YOLO) architecture, to enhance the detection of HCC in computed tomography (CT) images, aiming to improve early diagnosis and thereby patient outcomes. …”
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Borofsky Robert et al., Yanomami: The Fierce Controversy and What We Can Learn From It by Philippe Erikson

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Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects by Gorynina, Olga, Legoll, Frédéric, Lelièvre, Tony, Perez, Danny

“…In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embedded-atom method potentials (EAM) as coarse potentials. …”
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INTEGRATION OF COLLABORATIVE LEARNING STRATEGIES INTO VOCATIONAL EDUCATION IN FEDERAL COLLEGE OF EDUCATION (TECHNICAL) GUSAU, NIGERIA by APITYAN TUME

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Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study by Maik JM Beuken, Melanie Kleynen, Susy Braun, Kees Van Berkel, Carla van der Kallen, Annemarie Koster, Hans Bosma, Tos TJM Berendschot, Alfons JHM Houben, Nicole Dukers-Muijrers, Joop P van den Bergh, Abraham A Kroon, Iris M Kanera

“…Particularly personalized interventions have been shown to be effective for long-term behavior change. Machine learning can be used to uncover insights without predefined hypotheses, revealing complex relationships and distinct population clusters. …”
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The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review by Thippa Reddy Gadekallu, Gokul Yenduri, Rajesh Kaluri, Dharmendra Singh Rajput, Kuruva Lakshmanna, Kai Fang, Junxin Chen, Wei Wang

“…GPT can provide personalized and diverse solutions that successfully cater to the distinct requirements of students with learning disabilities. This motivated us to conduct an extensive review to assess the effectiveness of GPT in enhancing accessibility and inclusivity in higher education for students with learning disabilities. …”
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Learning and intergenerational communication through digital storytelling in the first grades of primary school: Yesteryear Jobs by Mouchtari Efthalia, Meimaris Michalis, Gouscos Dimitris, Sfyroera Maria

“…The research reported in this paper examines how two different groups, primary schoolchildren and elderly people, could close the generation gap through a digital storytelling-based interaction framework that can result in learning for the younger and intergenerational communication. …”
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How the General Public and Local Officials Prefer to Learn about Agricultural Water Use in Florida by Courtney T. Owens, Alexa J. Lamm, Ricky W. Telg

“…AEC585/WC247: How the General Public and Local Officials Prefer to Learn about Agricultural Water Use in Florida (ufl.edu) …”
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Implementation of Differentiated Learning in Improving Problem-solving Skills in Collaborative Projects Based on Science and Technology by Desy Aprima, Sasmita Sari

Subjects: “…Differentiated Learning…”
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Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…The article explores the integration of adaptive learning algorithms to optimize low-dose imaging in coronary artery angiography. …”
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Prediction of digestible energy requirement in growing finishing stage of pigs using machine learning models by Nibas Chandra Deb, Jayanta Kumar Basak, Sijan Karki, Elanchezhian Arulmozhi, Dae Yeong Kang, Niraj Tamrakar, Eun Wan Seo, Junghoo Kook, Myeong Yong Kang, Hyeon Tae Kim

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