Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers by Víctor Pérez-Cano, Francisco Jurado

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Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples by Yuanxin Tong, Hongxia Luo, Zili Qin, Hua Xia, Xinyao Zhou

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Indonesian Government Revenue Prediction Using Long Short-Term Memory by Mahmud, Windu Gata, Hafifah Bella Novitasari, Sigit Kurniawan, Dedi Dwi Saputra

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Improving Airport Flight Prediction System Based on Optimized Regression Vector Machine Algorithm by Baraa Yousif Salman, Jaber Parchami

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SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models by Gazi Husain, Daniel Nasef, Rejath Jose, Jonathan Mayer, Molly Bekbolatova, Timothy Devine, Milan Toma

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Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen by Mark Hutchinson, Jeffrey A. Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J. Gray, Gilad Kaplan

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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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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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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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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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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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A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

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A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning by Lina Pang, Qingxia Sun, Wenyue Wang, Mingjie Song, Ying Wu, Xin Shi, Xiaonan Shi

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