Machine learning enabled measurements of astrophysical (p,n) reactions with the SECAR recoil separator by P. Tsintari, N. Dimitrakopoulos, R. Garg, K. Hermansen, C. Marshall, F. Montes, G. Perdikakis, H. Schatz, K. Setoodehnia, H. Arora, G. P. A. Berg, R. Bhandari, J. C. Blackmon, C. R. Brune, K. A. Chipps, M. Couder, C. Deibel, A. Hood, M. Horana Gamage, R. Jain, C. Maher, S. Miskovich, J. Pereira, T. Ruland, M. S. Smith, M. Smith, I. Sultana, C. Tinson, A. Tsantiri, A. Villari, L. Wagner, R. G. T. Zegers

“…We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of (p,n) reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. …”
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Listen, Learn, Like! Dorsolateral Prefrontal Cortex Involved in the Mere Exposure Effect in Music by Anders C. Green, Klaus B. Bærentsen, Hans Stødkilde-Jørgensen, Andreas Roepstorff, Peter Vuust

“…Participants showed learning effects, better recognising melodies heard more often. …”
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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

“…Notably, this surpasses the capabilities of other models that rely on amalgamations of machine learning algorithms and feature dimensionality reduction methods. …”
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LINGUISTIC ANALYSIS FOR THE BELARUSIAN CORPUS WITH THE APPLICATION OF NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING TECHNIQUES by Yu. S. Hetsevich, I. V. Reentovich

“…The natural language processing of Belarusian corpus with the help of developed algorithm using machine learning was carried out. The precision of developed models of machine learning has been 80–90 %. …”
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Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images by Lei Song, Xuesong Zhang, Jian Zhang, Jie Wu, Jinkai Wang, Feng Wang

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Machine learning-driven identification of critical gene programs and key transcription factors in migraine by Lei Zhang, Yujie Li, Yunhao Xu, Wei Wang, Guangyu Guo

“…This study applies machine learning techniques to explore region-specific gene expression profiles and identify critical gene programs and transcription factors linked to migraine pathogenesis. …”
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Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models by Lei Tianxing

“…This study explores the application of machine learning algorithms in assessing diabetes risk, with a particular focus on Decision Trees (DT) and Ensemble Learning techniques. …”
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Motion Route Planning and Obstacle Avoidance Method for Mobile Robot Based on Deep Learning by Jichao Cui, Guanghua Nie

“…In order to improve the motion route planning effect and obstacle avoidance effect of mobile robots, this paper combines the deep learning theory to analyze the motion route planning and obstacle avoidance process of mobile robots. …”
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Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions by Lingyun Feng, Danyang Ma, Min Xie, Mengzhu Xi

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PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection by Mehdi Khalil, Chi Zhang, Zhiwei Ye, Peng Zhang

“…Traditional machine learning approaches have limitations in handling the ever-increasing volume and complexity of data. …”
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Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages by Sijin Lee, Hyun Ji Park, Jumi Hwang, Sung Woo Lee, Kap Su Han, Won Young Kim, Jinwoo Jeong, Hyunggoo Kang, Armi Kim, Chulung Lee, Su Jin Kim

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A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning by Weitong Liu, Khairunnisa Hasikin, Anis Salwa Mohd Khairuddin, Meizhen Liu, Xuechen Zhao

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Development of Embedded Boot Software for a Satellite Instrument Control Unit: Lessons Learned by Jesús Fernández-Conde, Jaime Gómez-Saez-de-Tejada, David Pérez-Lizán, Rafael Toledo-Moreo

“…This paper presents the lessons learned in the development and HW/SW integration phases of a satellite ICU BSW designed for a European Space Agency mission.…”
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Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms by Sahar K. Hussin, Salah M. Abdelmageid, Adel Alkhalil, Yasser M. Omar, Mahmoud I. Marie, Rabie A. Ramadan

“…Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. …”
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Retracted: Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern by null Complexity

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Modulation by NPY/NPF-like receptor underlies experience-dependent, sexually dimorphic learning by Sonu Peedikayil-Kurien, Rizwanul Haque, Asaf Gat, Meital Oren-Suissa

“…Here, we reveal sexual dimorphism in learning: C. elegans males do not learn to avoid the pathogenic bacteria PA14 as efficiently and rapidly as hermaphrodites. …”
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Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization by Xue-Bo Jin, Hong-Xing Wang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, Jian-Lei Kong

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A pan-European perspective of third space professionals located in learning and teaching centres by Graham William Scott, Manuel Joao Costa, Mike Ewen, Catherine Lillie, Helene Peterbauer, Oliver Vettori, Carina Weiß, Johanna Warm, Aune Valk, Mari Karm, Hanna-Britt Soots, Guðrún Geirsdóttir, Francesco Magni, Laura Sara Agrati, Nikos Basbas, Daniela Căprioară, Mihai Girtu, Gunvor Larsson Torstensdotter, Peter Dalenius, Josefine Kilborn, Berrin Yanıkkaya, Mehmet Korman, Denizalp Şimşek, Toprak Cem Savaş

Subjects: “…Learning and Teaching Centres…”
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Predicting mechanical properties of bainitic rail steels based on microstructure digitalization and machine learning by L.Q. Bai, Z.Y. Ding, J.L. Wang, Z.J. Xie, Z.N. Yang, C.J. Shang

Subjects: “…Machine learning…”
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The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning by Chaodong Tan, Song Wang, Hanwen Deng, Guoqing Han, Guanghao Du, Wenrong Song, Xiongying Zhang

“…Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. …”
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