Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample by Mahdieh Arian, Talat Khadivzadeh, Mahla Shafeei, Sedigheh Abdollahpour

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Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

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Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence by Xiaoyin Wu, Xiaoyin Wu, Buyu Guo, Buyu Guo, Xingyu Chang, Xingyu Chang, Yuxuan Yang, Yuxuan Yang, Qianqian Liu, Qianqian Liu, Jiahui Liu, Jiahui Liu, Yichen Yang, Yichen Yang, Kang Zhang, Yumei Ma, Songbo Fu, Songbo Fu, Songbo Fu

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The COVID-19 Pandemic Heightens Interest in Cytokine Storm Disease and Advances in Machine Learning Diagnosis, Telemedicine, and Primordial Prevention of Rheumatic Diseases by Tomohiro Koga, Shin-Ya Kawashiri, Fumiaki Nonaka, Yoshika Tsuji, Mami Tamai, Atsushi Kawakami

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Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes by B. S. Dhruva Darshan, Niranjana Sampathila, G. Muralidhar Bairy, Srikanth Prabhu, Sushma Belurkar, Krishnaraj Chadaga, S. Nandish

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Crisis Text Line and Loris.ai Controversy Highlights the Complexity of Informed Consent on the Internet and Data-Sharing Ethics for Machine Learning and Research by Gunther Eysenbach

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Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning by Kang Huang, Jianjun Wu, Xin Yang, Ziyou Gao, Feng Liu, Yuting Zhu

“…To fill this gap, according to the actual speed and energy data collected from the real-world urban rail system, this paper proposes a data-driven model and integrated heuristic algorithm based on machine learning to determine the optimal speed profile with minimum energy consumption. …”
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Deciphering the Immune Subtypes and Signature Genes: A Novel Approach Towards Diagnosing and Prognosticating Severe Asthma Through Interpretable Machine Learning by Yue Hu, Yating Lin, Bo Peng, Chunyan Xiang, Wei Tang

“…We employ single-sample gene set enrichment analysis (ssGSEA) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms to identify differentially expressed immune cells and utilize machine learning techniques, including Extreme Gradient Boosting (XGBoost) and random forest, to predict severe asthma outcomes and identify key genes associated with immune cells. …”
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Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems by Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, Pradeep Kumar Singh, Komal Parashar, Ali E. Anqi, Husam Rajab, Pancham Cajla, H. Maleki

Subjects: “…Machine learning…”
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An interpretable machine learning tool for in-home monitoring of agitation episodes in people living with dementia: a proof-of-concept studyResearch in context by Marirena Bafaloukou, Ann-Kathrin Schalkamp, Nan Fletcher-Lloyd, Alex Capstick, Chloe Walsh, Cynthia Sandor, Samaneh Kouchaki, Ramin Nilforooshan, Payam Barnaghi

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Landslide susceptibility assessment for Uttarakhand, a Himalayan state of India, using multi-criteria decision making, bivariate, and machine learning models by Vipin Chauhan, Laxmi Gupta, Jagabandhu Dixit

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Novel Optimization Algorithms Usage to Model the Compressive Strength of Ultra-High-Performance Concrete in Machine Learning Technique: Support Vector Regression by Tianhua Zhou, Dorota Mozyrska

“…This study suggested a machine learning method for predicting the CS of UHPC including support vector regression (SVR). …”
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Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An In Vitro Comparative Study by Taseef Hasan Farook, Saif Ahmed, Jamal Giri, Farah Rashid, Toby Hughes, James Dudley

“…It also evaluated the validation accuracy of an unsupervised machine-learning model trained with these scans. Methods. …”
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Application Of ArtifiCial Intelligence in E-Governance: A Comparative Study of Supervised Machine Learning and Ensemble Learning Algorithms on Crime Prediction. by Niyonzima, Ivan, Muhaise, Hussein, Akankwasa, Aureri

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MLOps Components, Tools, Process, and Metrics: A Systematic Literature Review by Adrian P. Wozniak, Mateusz Milczarek, Joanna Wozniak

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Boosting any learning algorithm with Statistically Enhanced Learning by Florian Felice, Christophe Ley, Stéphane P. A. Bordas, Andreas Groll

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Ionospheric TEC Prediction Based on Ensemble Learning Models by Yang Zhou, Jing Liu, Shuhan Li, Qiaoling Li

Subjects: “…machine learning…”
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Predicting phage-host interaction via hyperbolic Poincaré graph embedding and large-scale protein language technique by Jie Pan, Rui Wang, Wenjing Liu, Li Wang, Zhuhong You, Yuechao Li, Zhemeng Duan, Qinghua Huang, Jie Feng, Yanmei Sun, Shiwei Wang

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Characterisation of precursory seismic activity towards early warning of landslides via semi-supervised learning by David Murray, Lina Stankovic, Vladimir Stankovic, Stella Pytharouli, Adrian White, Ben Dashwood, Jonathan Chambers

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Book Review by Shiva Singh

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