Analysing Bibliometrics Trends: The Flipped Classroom Approach in Foreign Language Learning by Nuraeni Nuraeni, Nurul Fachrunnisa

“…This included especially recognizing the keywords that are in use nowadays, the articles and journals that are most cited, and the countries and institutions that are most influential. The result presented some interesting issues as follows.1) the countries with the highest link of collaborations were Malaysia, Iran, China, and Indonesia, while the most cited countries were Indonesia, Iran, China, Turkey and Malaysia. 2) The most used keywords in research on FC in EFL learning formed into four clusters. …”
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Identification of high-risk COVID-19 patients using machine learning. by Mario A Quiroz-Juárez, Armando Torres-Gómez, Irma Hoyo-Ulloa, Roberto de J León-Montiel, Alfred B U'Ren

“…The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. …”
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General-purpose machine-learned potential for 16 elemental metals and their alloys by Keke Song, Rui Zhao, Jiahui Liu, Yanzhou Wang, Eric Lindgren, Yong Wang, Shunda Chen, Ke Xu, Ting Liang, Penghua Ying, Nan Xu, Zhiqiang Zhao, Jiuyang Shi, Junjie Wang, Shuang Lyu, Zezhu Zeng, Shirong Liang, Haikuan Dong, Ligang Sun, Yue Chen, Zhuhua Zhang, Wanlin Guo, Ping Qian, Jian Sun, Paul Erhart, Tapio Ala-Nissila, Yanjing Su, Zheyong Fan

“…Abstract Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. …”
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Eecs-fl: energy-efficient client selection for federated learning in AIoT by Yiyang Zhang, Yiming Luo, Tao Yang, Xiaofeng Wu, Bo Hu

“…Abstract The Artificial Intelligence of Things (AIoT) ecosystem faces significant challenges related to limited client energy budgets and resource heterogeneity, particularly when employing the Federated Learning (FL) framework. This paper presents a novel energy-efficient client selection algorithm for FL, designed to address these challenges by integrating Wireless Power Transfer (WPT), where WPT involves in the client selection optimization, based on real-time energy availability and resource heterogeneity. …”
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Comparing Domain Expert and Machine Learning Data Enrichment of Building Registry by Ants Torim, Elisa Iliste, Ergo Pikas, Innar Liiv, Tarmo Robal, Targo Kalamees

“…However, poor open-data quality presents a tenacious challenge, especially for automatic calculations or decision-making. …”
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Impact of data bias on machine learning for crystal compound synthesizability predictions by Ali Davariashtiyani, Busheng Wang, Samad Hajinazar, Eva Zurek, Sara Kadkhodaei

“…Machine learning models are susceptible to being misled by biases in training data that emphasize incidental correlations over the intended learning task. …”
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Enhancing wound healing through deep reinforcement learning for optimal therapeutics by Fan Lu, Ksenia Zlobina, Nicholas A. Rondoni, Sam Teymoori, Marcella Gomez

“…We propose an adaptive closed-loop control framework that incorporates deep learning, optimal control and reinforcement learning to accelerate wound healing. …”
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Topology Prediction of Branched Deformable Linear Objects Using Deep Learning by Shengzhe Ouyang, Manuel Zurn, Lukas Zeh, Armin Lechler, Alexander Verl

“…Therefore, this paper presents a novel deep learning model to predict the configuration of a wire harness using artificially generated datasets mixed with real annotated data. …”
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Machine learning-assisted development of polypyrrole-grafted yarns for e-textiles by Matteo Iannacchero, Joakim Löfgren, Mithila Mohan, Patrick Rinke, Jaana Vapaavuori

“…In particular, the process of testing and optimizing new candidate materials is both time-consuming and resource intensive.To address these challenges, we present a machine learning-assisted approach to the design of fully-textile based conductive e-textile prototypes. …”
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Online Behavior Analysis-Based Student Profile for Intelligent E-Learning by Kun Liang, Yiying Zhang, Yeshen He, Yilin Zhou, Wei Tan, Xiaoxia Li

“…According to the E-Learning resources and learner behaviors, we also present the intelligent guide model to guide both E-Learning platform and learners to improve learning things. …”
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Physics-constrained machine learning for reduced composition space chemical kinetics by Anuj Kumar, Tarek Echekki

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A neuromorphic processor with on-chip learning for beyond-CMOS device integration by Hugh Greatorex, Ole Richter, Michele Mastella, Madison Cotteret, Philipp Klein, Maxime Fabre, Arianna Rubino, Willian Soares Girão, Junren Chen, Martin Ziegler, Laura Bégon-Lours, Giacomo Indiveri, Elisabetta Chicca

“…One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with complementary metal-oxide-semiconductor circuitry. To address this, we present a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. …”
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Evaluating Task Optimization and Reinforcement Learning Models in Robotic Task Parameterization by Michele Delledonne, Enrico Villagrossi, Manuel Beschi, Alireza Rastegarpanah

“…A comparative analysis of the traditional algorithm and RL models is presented, highlighting efficiency, flexibility, and usability. …”
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Parents’ attitudes towards distance learning during the COVID-19 pandemic by Ante Kolak, Ivan Markić, Zoran Horvat

“…We believe that due to these limitations, distance learning has some of the characteristics of home-schooling. …”
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Enhancing Registration Offices’ Communication Through Interpretable Machine-Learning Techniques by Danilo Augusto Sarti, Tommaso Bardelli, Pier Giacomo Bianchi, Anna Pia Maria Giulini

“…This study presents a protocol for applying Interpretable Machine Learning (IML) to enhance communication within Variety Registration Offices (VROs). …”
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Quantitative Assessment of Data Volume Requirements for Reliable Machine Learning Analysis by Xukuan Xu, Jinghou Bi, Michael Moeckel, Hajo Wiemer, Steffen Ihlenfeldt

“…Applying machine learning (ML) techniques in the context of limited data remains a challenge of practical importance. …”
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Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning by Ji-Ah Choi, Ji-Seong Jang, Sang-Won Ji

“…This study presents a method for predicting nozzle surface temperature and the timing of frost formation during hydrogen refueling using machine learning. …”
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Applications of density functional theory and machine learning in nanomaterials: A review by Nangamso Nathaniel Nyangiwe

“…The high degree of complexity and diversity in nanomaterials presents a real challenge in their theoretical and experimental studies. …”
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WindDragon: automated deep learning for regional wind power forecasting by Julie Keisler, Etienne Le Naour

“…However, the inherent variability and uncertainty of wind energy present significant challenges for grid operators, particularly in maintaining system stability and balance. …”
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Enhanced‐Resolution Learning‐Based Direction of Arrival Estimation by Programmable Metasurface by Nawel Meftah, Badreddine Ratni, Mohammed Nabil El Korso, Shah Nawaz Burokur

“…While traditional DOA estimation methods rely on antenna arrays and complex algorithms, recent progress achieved in the design and implementation of metasurfaces has proved their effectiveness as promising alternatives. This study presents a distinct approach for DOA estimation that combines the use of a programmable metasurface with deep learning. …”
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