Efficient modelling of anharmonicity and quantum effects in PdCuH2 with machine learning potentials by Francesco Belli, Eva Zurek

“…We propose a protocol that pairs machine learning interatomic potentials, which can be tailored for the system at hand via active learning, with the SSCHA. …”
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Optimisation of Ensemble Learning Algorithms for Geotechnical Applications: A Mathematical Approach to Relative Density Prediction by Mahdy Khari, Ali Dehghanbanadaki, Danial Jahed Armaghani, Manoj Khandelwal

“…The challenge of predicting relative dry density (Dr) in granular materials is addressed through advanced mathematical modelling and machine learning (ML) techniques. A novel approach to optimise ensemble learning algorithms is presented, with a focus placed on the mathematical foundations of these methods. …”
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New Deep Learning-Based Approach for Source Code Generation: Application to Computer Vision Systems by Wafa Alshehri, Salma Kammoun Jarraya, Arwa Allinjawi

“…Deep learning has enabled significant progress in source code generation, aiming to reduce the manual, error-prone, and time-consuming aspects of software development. …”
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Deep learning based dual stage model for accurate nasogastric tube positioning in chest radiographs by Inseo Park, Gwiseong Moon, Ji Young Hong, Jeongwon Heo, Hongseok Ko, Doohee Lee, Yoon Kim, Woo Jin Kim, Hyun-Soo Choi, Kyoung Min Moon

“…This study introduces a deep-learning model that enhances the detection and analysis of NGT positioning in chest radiographs. …”
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Dynamic reconfiguration of brain coactivation states associated with active and lecture-based learning of university physics by Donisha D. Smith, Jessica E. Bartley, Julio A. Peraza, Katherine L. Bottenhorn, Jason S. Nomi, Lucina Q. Uddin, Michael C. Riedel, Taylor Salo, Robert W. Laird, Shannon M. Pruden, Matthew T. Sutherland, Eric Brewe, Angela R. Laird

“…Abstract Academic institutions are increasingly adopting active learning methods to enhance educational outcomes. Using functional magnetic resonance imaging (fMRI), we investigated neurobiological differences between active learning and traditional lecture-based approaches in university physics education. …”
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An enhanced machine learning-based prognostic prediction model for patients with AECOPD on invasive mechanical ventilation by Yujie Fu, Yining Liu, Chuyue Zhong, Ali Asghar Heidari, Lei Liu, Sudan Yu, Huiling Chen, Peiliang Wu

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Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review by Paolo Visconti, Giuseppe Rausa, Carolina Del-Valle-Soto, Ramiro Velázquez, Donato Cafagna, Roberto De Fazio

“…Finally, the presented work outlines the features and functionalities of future IoT-based systems for smart industry applications.…”
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Reconstructing a Manufacturing Laboratory to a Learning Factory: a TTK UAS Case Study by Tavo Kangru, Madis Moor, Andrei Rudz, Aleksander Jänes

“…Using the case-study methodology, factory automation simulation software, and a new pedagogical approach, the TTK UAS industrial engineering laboratories are functioning as a cluster, achieving higher learning and R&D efficiency.…”
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Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning by Huibo Zhang, Lulu Chen, Lan Li, Yang Liu, Barnali Das, Shuang Zhai, Juan Tan, Yan Jiang, Simona Turco, Yi Yao, Dmitrij Frishman

“…We introduce TILScout, a pan-cancer deep-learning approach to compute patch-level TIL scores from whole slide images (WSIs). …”
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Efficient Deep Learning-Based Device-Free Indoor Localization Using Passive Infrared Sensors by Sira Yongchareon, Jian Yu, Jing Ma

“…As a result, device-free indoor localization functions have become a crucial part of application areas such as healthcare, safety, and energy management. …”
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Applying Cognitive Linguistics to Enhance Vietnamese English-Major Undergraduates’ Learning of English Modal Verbs by Anh Kim Hoang, Nguyet Minh Thi Le

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Evaluasi Heuristik E-Learning Institut Agama Hindu Negeri Tampung Penyang Palangka Raya by Ni Wayan Purnawati, I Nyoman Alit Arsana, I Putu Janardana Febrian Agus

“…Institute of Hindu Religion (IAHN) Tampung Penyang Palangka Raya has e-learning in an e-campus with complex features. Many e-learning features become ineffective, and make users unable to maximize their use. …”
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Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS by Polina Lemenkova

“…The methodology includes ML modules of GRASS GIS ‘r.learn.train’, ‘r.learn.predict’, and ‘r.random’ with algorithms of supervised classification implemented from the Scikit-Learn libraries of Python. …”
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Young beginning learners’ vocabulary learning via input and output tasks: The role of working memory by Mark Feng Teng

“…The results demonstrated that: (1) both input and output tasks significantly influenced the learning and retention of new words, (2) complex WM did not substantially predict task effects on the learning and retention of new words, and (3) phonological WM had a notable impact on learning and retaining new words under the input and output task conditions. …”
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Genetic Animal Models of Idiopathic Generalized Epilepsies: What Can We Learn from Them? by Bernard Lakaye, Laurent Nguyen

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Research on cultivating students’ creative thinking ability in art design teaching based on machine learning by Ke Xue

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Energy management of microgrid coalitions considering renewable energy prediction based on machine learning algorithms by Negar Dehghani Mahmoudabadi, Mehran Khalaj, Davood Jafari, Ali Taghizadeh Herat, Parisa Mousavi Ahranjani

“…First, a systematic management framework is established by defining the functions of various management units throughout the multi-microgrid system. …”
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Future Smart Grids Control and Optimization: A Reinforcement Learning Tool for Optimal Operation Planning by Federico Rossi, Giancarlo Storti Gajani, Samuele Grillo, Giambattista Gruosso

“…This study presents a methodology based on reinforcement learning (RL) for evaluating optimal power flow with respect to various cost functions. …”
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Wireless Sensor Networks Focusing on Predicting Average Localization Error through Machine Learning Applications by Ioanna Gounari, Mattheos Kanzilieris

“…This study proposes using machine learning (ML) techniques to predict ALE in WSNs and compares the performance of different models. …”
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A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning by Kailai Li, Junyi Liang, Nan Li, Jianbo Fang, Xinyi Zhou, Jian Zhang, Anqi Lin, Peng Luo, Hui Meng

“…By evaluating 113 machine learning algorithm combinations, the glmBoost+NaiveBayes model was selected to construct the NPC-RSS based on 18 key genes, which demonstrated good predictive performance in both public and in-house datasets. …”
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