Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data by Ibrahim Olalekan Alabi, Ibrahim Olalekan Alabi, Hans-Peter Marshall, Jodi Mead, Ernesto Trujillo

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Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case Study by Rabia Javed, Tahir Abbas, Tariq Shahzad, Khadija Kanwal, Sadaqat Ali Ramay, Muhammad Adnan Khan, Khmaies Ouahada

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Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial by Marcus Ong, Hairil Rizal Abdullah, Ecosse Lamoureux, Elaine Lum, Gek Hsiang Lim, Tan Pei Yi Brenda, Celestine Loh

“…In Singapore General Hospital (SGH), we introduced the Combined Assessment of Risk Encountered in Surgery-Machine Learning (CARES-ML) in June 2023, focusing on predicting 30-day postoperative mortality and the need for post-surgery intensive care unit (ICU) stays. …”
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Arquitectura de IoT para el Monitoreo de Emisiones de Gases Contaminantes de Vehículos y su Validación a través de Machine Learning by Washington Torres Guin, José Sánchez Aquino, Samuel Bustos Gaibor, Marjorie Coronel Suarez

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A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes by Magboul M. Sulieman, Fuat Kaya, Abdullah S. Al-Farraj, Eric C. Brevik

Subjects: “…Establish soil heavy metals background concentrations and spatial variability using machine learning algorithms coupled with remote sensing and digital elevation model derivatives…”
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Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management by Liwen Chen, Boting Hu, Jingxuan Sun, Y. Jun Xu, Guangxin Zhang, Hongbo Ma, Jingquan Ren

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Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS) by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

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MEMPSEP‐III. A Machine Learning‐Oriented Multivariate Data Set for Forecasting the Occurrence and Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach by Kimberly Moreland, Maher A. Dayeh, Hazel M. Bain, Subhamoy Chatterjee, Andrés Muñoz‐Jaramillo, Samuel T. Hart

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3D highly isolated 6-port tri-band MIMO antenna system with 360° coverage for 5G IoT applications based machine learning verification by Md Afzalur Rahman, Samir Salem Al-Bawri, Sultan S. Alharbi, Wazie M. Abdulkawi, Noorlindawaty Md Jizat, Mohammad Tariqul Islam, Abdel-Fattah A. Sheta

“…In addition, the machine learning prediction is used to verify the single element realized gain, and the results demonstrate that it performs admirably with an accuracy of more than 89% using the random forest regression model throughout the entire frequency spectrum. …”
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Health Care Professionals and Data Scientists’ Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study by Joana Seringa, Anna Hirata, Ana Rita Pedro, Rui Santana, Teresa Magalhães

“…ObjectiveThis study aimed to explore the perspectives of health care professionals and data scientists regarding the relevance, challenges, and potential benefits of using machine learning (ML) models to predict decompensation from patients with HF. …”
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The triglyceride–glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis by Chenyang Li, Zixi Zhang, Xiaoqin Luo, Yichao Xiao, Tao Tu, Chan Liu, Qiming Liu, Cancan Wang, Yongguo Dai, Zeying Zhang, Cheng Zheng, Jiafeng Lin

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Identification and Characterization of Genes Associated with Intestinal Ischemia-Reperfusion Injury and Oxidative Stress: A Bioinformatics and Experimental Approach Integrating High-Throughput Sequencing, Machine Learning, and Validation by Xie Y, Yang M, Huang J, Jiang Z

“…The least absolute shrinkage and selection operator, as well as the support vector machine with random forest algorithm, were utilized for machine learning. Subsequently, the PPI network was established, and the Degree and MNC algorithms of the Cytohuba plugin were integrated with the genes obtained through the machine learning algorithms to identify hub IOSRGs (HIOSRGs). …”
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Extracellular domain of TREM2 possess two distinct ligand recognition sites: Insights from machine-learning guided docking and all-atoms molecular dynamics simulations by Sarbani Mishra, Preety Sthutika Swain, Sanghamitra Pati, Budheswar Dehury

“…Herein, we undertook a systematic investigation for exploring the mode of ligand recognition in immunoglobulin-like ectodomain by employing both knowledge-based and machine-learning guided molecular docking approach followed by the state-of-the-art all atoms molecular dynamics (MD) simulations. …”
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Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing by Mehdi Tlija, Muhammad Sana, Anamta Khan, Sana Hassan, Muhammad Umar Farooq

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UAS-Guided Analysis of Electric and Magnetic Field Distribution in High-Voltage Transmission Lines (Tx) and Multi-Stage Hybrid Machine Learning Models for Battery Drain Estimation by Tanzim Jim Hassan, Jamison Jangula, Akshay Ram Ramchandra, Niroop Sugunaraj, Barathwaja Subash Chandar, Prashanth Rajagopalan, Farishta Rahman, Prakash Ranganathan, Ryan Adams

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Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples by Annika Wiebe, Benjamin Selaskowski, Martha Paskin, Laura Asché, Julian Pakos, Behrem Aslan, Silke Lux, Alexandra Philipsen, Niclas Braun

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Prediction of clinical efficacy of acupuncture intervention on upper limb dysfunction after ischemic stroke based on machine learning: a study driven by DSA diagnostic reports data by Yaning Liu, Yuqi Tang, Zechen Li, Pei Yu, Jing Yuan, Lichuan Zeng, Can Wang, Su Li, Ling Zhao, Ling Zhao, Ling Zhao

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A Novel Approach to Cognitive Load Measurement in N-Back Tasks Using Wearable Sensors, Empirical Mode Decomposition With Machine Learning, and Explainable AI for Feature Importance by Aakanksha Dayal, Ziaullah Khan, Kiramat Ullah, Hee-Cheol Kim

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Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas by Osareni C. Ogiesoba, Fritz C. Palacios

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A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identification by Nourelhouda Groun, María Villalba-Orero, Lucía Casado-Martín, Enrique Lara-Pezzi, Eusebio Valero, Jesús Garicano-Mena, Soledad Le Clainche

“…In a way, the database dimension was augmented, hence HODMD has been used, for the first time to the authors knowledge, for data augmentation in the machine learning framework. Six sets of the original echocardiography databases were hold out to be used as unseen data to test the performance of the CNN. …”
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