Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel) by C. Katsavrias, S. Aminalragia‐Giamini, C. Papadimitriou, I. A. Daglis, I. Sandberg, P. Jiggens

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MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage by Xiong You, Yiting Shu, Xingcheng Ni, Hengmin Lv, Jian Luo, Jianping Tao, Guanghui Bai, Shusu Feng

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Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks by Reza Bayat Rizi, Amir R. Forouzan, Farshad Miramirkhani, Mohamad F. Sabahi

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AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery by Ahmad Almadhor, Hafiz Tayyab Rauf, Muhammad Ikram Ullah Lali, Robertas Damaševičius, Bader Alouffi, Abdullah Alharbi

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Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks by Alireza Pourkeyvan, Ramin Safa, Ali Sorourkhah

Subjects: “…Machine learning…”
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Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review by Francesco De Micco, Francesco De Micco, Gianmarco Di Palma, Gianmarco Di Palma, Davide Ferorelli, Anna De Benedictis, Anna De Benedictis, Luca Tomassini, Vittoradolfo Tambone, Mariano Cingolani, Roberto Scendoni, Roberto Scendoni

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A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data by Vasileios Skaramagkas, Ioannis Kyprakis, Georgia S. Karanasiou, Dimitris I. Fotiadis, Manolis Tsiknakis

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Determination of the melanin and anthocyanin content in barley grains by digital image analysis using machine learning methods by E. G. Komyshev, M. A. Genaev, I. D. Busov, M. V. Kozhekin, N. V. Artemenko, A.  Y. Glagoleva, V. S. Koval, D. A. Afonnikov

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Unveiling Cuproptosis-Driven Molecular Clusters and Immune Dysregulation in Ankylosing Spondylitis by Wei B, Wang S, Li S, Gu Q, Yue Q, Tang Z, Zhang J, Liu W

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Interpretable Detection of Malicious Behavior in Windows Portable Executables Using Multi-Head 2D Transformers by Sohail Khan, Mohammad Nauman

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Machine Learning Enabled High‐Throughput Screening of 2D Ultrawide Bandgap Semiconductors for Flexible Resistive Materials by Chi Chen, Hao Wang, Houzhao Wan, Dan Sun

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Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target by Hao Wu, Yuan Li, Yuxuan Jiang, Xinran Li, Shenglan Wang, Changle Zhao, Changle Zhao, Ximiao Yang, Baocheng Chang, Juhong Yang, Juhong Yang, Jianjun Qiao, Jianjun Qiao

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A machine learning based algorithm accurately stages liver disease by quantification of arteries by Zhengxin Li, Xin Sun, Zhimin Zhao, Qiang Yang, Yayun Ren, Xiao Teng, Dean C. S. Tai, Ian R. Wanless, Jörn M. Schattenberg, Chenghai Liu

Subjects: “…Machine learning…”
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Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python by Polina Lemenkova

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Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning by Zeynep Kucukakcali, Ipek Balikci Cicek

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Fluorescent identification of axons, dendrites and soma of neuronal retinal ganglion cells with a genetic marker as a tool for facilitating the study of neurodegeneration by Puttipong Sripinun, Wennan Lu, Sergei Nikonov, Suhani Patel, Sarah Hennessy, Tianyuan Yao, Qi N. Cui, Brent A. Bell, Claire H. Mitchell

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Interpretable machine learning model of effective mass in perovskite oxides with cross-scale features by Changjiao Li, Zhengtao Huang, Hua Hao, Zhonghui Shen, Guanghui Zhao, Ben Xu, Hanxing Liu

Subjects: “…Machine learning…”
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Development of Risk Activity Detection System for Forklifts Based on Inertial Sensors by Luciano Radrigan, Sebastian E. Godoy

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Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets by Moamen Zaher, Amr S. Ghoneim, Laila Abdelhamid, Ayman Atia

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A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning by Fei Si, Qian Liu, Jing Yu

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