Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-making by Mohd Mobasshir, Praveen Pachauri, Pratibha Kumari, Faisal Khan, Azhar Equbal, Osama Khan, Mohd Parvez, Taufique Ahamad, Shadab Ahmad

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Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran by Somayeh Kianian Bigdeli, Marjan Ghazisaedi, Seyed Mohammad Ayyoubzadeh, Sedigheh Hantoushzadeh, Marjan Ahmadi

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Machine learning identification of a novel vasculogenic mimicry-related signature and FOXM1’s role in promoting vasculogenic mimicry in clear cell renal cell carcinoma by Chao Xu, Sujing Zhang, Jingwei Lv, Yilong Cao, Yao Chen, Hao Sun, Shengtao Dai, Bowei Zhang, Meng Zhu, Yuepeng Liu, Junfei Gu

Subjects: “…Clear cell renal cell carcinoma;Vasculogenic mimicry;Machine learning;FOXM1;Tumor microenvironment;Prognosis;Therapeutic targets…”
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[Translated article] Analysis of machine learning algorithmic models for the prediction of vital status at six months after hip fracture in patients older than 74 years by I. Calvo Lorenzo, I. Uriarte Llano, M.R. Mateo Citores, Y. Rojo Maza, U. Agirregoitia Enzunza

“…However, we believe that the method used for the generation of algorithms based on machine learning can serve as a reference for future works. …”
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Flood hazard monitoring and modeling systems for improving climate risk management using machine learning and geospatial models in the Hennops River catchment, Centurion, South Africa by Paidamwoyo Mhangara, Eskinder Gidey, Matilda Mbazo

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Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach by Yongchuang Shi, Lei Yan, Shengmao Zhang, Fenghua Tang, Shenglong Yang, Wei Fan, Haibin Han, Yang Dai

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Predicting Factors Affecting Survival Rate in Patients Undergoing On‐Pump Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review by Alireza Jafarkhani, Behzad Imani, Soheila Saeedi, Amir Shams

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A comprehensive dataset of above-ground forest biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir HimalayaZenodo by Syed Danish Rafiq Kashani, Faisal Zahoor Jan, Imtiyaz Ahmad Bhat, Nadeem Ahmad Najar, Irfan Rashid

“…This dataset provides a manually delineated multi-temporal forest inventory and a comprehensive record of above-ground biomass (AGB) across the Kashmir Himalaya, generated from field observations, advanced remote sensing and machine learning. Data were collected and generated through remote sensing techniques and extensive in-situ measurements of 6220 trees (n=275 plots), including tree diameter at breast height, species composition, and tree density to map forest area and model AGB across varied terrain. …”
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Accurate Modeling of GaN HEMTs Oriented to Analysis of Kink Effects in S₂₂ and h₂₁: An Effective Machine Learning Approach by Zegen Zhu, Gianni Bosi, Antonio Raffo, Giovanni Crupi, Jialin Cai

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The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population–Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis by Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Refilwe Nancy Phaswana-Mafuya

“…Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. …”
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Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study by Niteesh K Choudhry, Shweta Priyadarshini, Jaganath Swamy, Mridul Mehta

“…Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with T2D. …”
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LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment by Firas Al-Hindawi, Peter Serhan, Yonas E. Geda, Francis Tsow, Teresa Wu, Erica Forzani

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Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction by Shota Kunimatsu, Hiroyuki Kurota, Soyoka Muko, Seiji Ohshimo, Takeshi Tomiyama

“…We developed a spatiotemporal machine learning approach to predict the CPUE values while taking into consideration environmental variables and changes in fish distribution. …”
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Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approach by Farid Hamzeh Aghdam, Aleksandr Zavodovski, Adeleye Adetunji, Mehdi Rasti, Eva Pongracz, Mohammad Sadegh Javadi, João P.S. Catalão

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Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study by Wivica Kauppi, Henrik Imberg, Johan Herlitz, Oskar Molin, Christer Axelsson, Carl Magnusson

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Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning by Fei Wang, Yuanxin Lin, Lian Qin, Xiangtai Zeng, Hancheng Jiang, Yanlan Liang, Shifeng Wen, Xiangzhi Li, Shiping Huang, Chunxiang Li, Xiaoyu Luo, Xiaobo Yang

“…PFHxA and PFDoA exposure associated with increased TC risk, while PFHxS and PFOA associated with decreased TC risk in single compound models (all P < 0.05). Machine learning algorithms identified PFHxA, PFDoA, PFHxS, PFOA, and PFHpA as the key PFAS influencing the development of TC, and mixed exposures have an overall positive effect on TC risk, with PFHxA making the primary contribution. …”
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Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms by Susan Thapa, Lori A. Fischbach, Robert Delongchamp, Mohammed F. Faramawi, Mohammed S. Orloff

“…Morbidity and mortality from gastric cancer may be decreased by identification of those that are at high risk for progression in the gastric precancerous process so that they can be monitored over time for early detection and implementation of preventive strategies. Method. Using machine learning, we developed prediction models for gastric precancerous progression in a population from a developing country with a high rate of gastric cancer who underwent gastroscopies for dyspeptic symptoms. …”
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RiskTree: Decision trees for asset and process risk assessment quantification in big data platforms by Zhan Haomou, Yang Jiawei, Guo Zhenyang, Cao Jin, Zhang Dong, Zhao Xingwen, You Wei, Li Hui

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Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid‐Latitude Stations by Michael Coughlan, Amy Keesee, Victor Pinto, Raman Mukundan, José Paulo Marchezi, Jeremiah Johnson, Hyunju Connor, Don Hampton

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Neuromorphic, physics-informed spiking neural network for molecular dynamics by Vuong Van Pham, Temoor Muther, Amirmasoud Kalantari Dahaghi

Subjects: “…physics-informed machine learning…”
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