Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles by Wasana Yuwattana, Thanit Saeliw, Marlieke Lisanne van Erp, Chayanit Poolcharoen, Songphon Kanlayaprasit, Pon Trairatvorakul, Weerasak Chonchaiya, Valerie W. Hu, Tewarit Sarachana

“…Clustering analyses revealed three distinct subgroups identifiable through both clinical symptoms and gene expression patterns. When ASD were grouped based on clinical features, stronger associations emerged between symptoms and underlying molecular profiles compared to grouping based on gene expression alone. …”
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Investigation of brain response to acquisition and learning the second languages based on EEG signals and machine learning techniques by Talal A. Aldhaheri, Sonali B. Kulkarni, Pratibha R. Bhise, Baraq Ghaleb

“…To validate the approach and demonstrate cognitive and functional differences in brain activity during second language acquisition, various machine learning classification models were applied: Random Forest, Support Vector Machine, Decision Tree, Xgboost, and Catboost. …”
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Identifying heterogeneous air pollution exposure using machine learning models with dynamic traffic and population data by Ruoxi Wu, Yifan Wen, Xiaomeng Wu, Cheng Huang, Qingyan Fu, Qingyao Hu, Hongli Wang, Ye Wu, Shaojun Zhang

“…Crucially, dynamic population-weighted exposure assessments show 4.1%–10.9% higher NO _2 and PM _2.5 exposure versus conventional static estimates on weekdays, with weekend O _3 exposure 7.1% lower, which highlight how data-driven traffic patterns and mobility data reshape social risk distributions. …”
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Topological Data Analysis and Wavelet- Unsupervised Machine Learning Approaches to Identifying the Flooding and Non-Flooding Zones by Md Raqibul Hasan, Md. Jamal Hossain, Md. Waliullah, Abdul Hannan, Md. Mijanoor Rahman

“…The intertwined weather patterns known as the atmospheric river (AR) of Bangladesh make use of topological data analysis (TDA) in connection with wavelet decomposition and unsupervised machine learning (k-means clustering) methods to pave the way for enhanced flood detection. …”
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Leveraging Artificial Intelligence for Smart Healthcare Management: Predicting and Reducing Patient Waiting Times with Machine Learning by Kristijan CINCAR, Todor IVAŞCU

“… The paper focuses on a machine-learning-based methodology for predictive modelling and simulation enhancement of hospital resource management. …”
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Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media by Fatih Tarlak

“…Machine learning models provided superior accuracy over traditional approaches, with R²_adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. …”
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Improving ICESat-2 photon classification and tree height estimation using Moran's I and machine learning by Mei-Kuei Lu, Sorin Popescu, Lonesome Malambo

“…Random Forest models were developed and compared, with one model incorporating Moran's I to capture spatial patterns. The study covered 12 diverse ecoregions across the United States, including conifer forests, broadleaf forests, and savannas. …”
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Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

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Machine Learning and SHAP-Based Analysis of Deforestation and Forest Degradation Dynamics Along the Iraq–Turkey Border by Milat Hasan Abdullah, Yaseen T. Mustafa

“…This study explores the spatiotemporal patterns and drivers of deforestation and forest degradation along the politically sensitive Iraq–Turkey border within the Duhok Governorate between 2015 and 2024. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…The joint interpretation of these statistics aids in comprehending model limitations and facilitates discussions on the environmental mechanisms shaping observed patterns. We propose two analytical workflows that not only enable the exploration and enhancement of model accuracy but also facilitate the investigation of potential cause-and-effect relationships inherent in the  data. …”
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A Spatial Long-Term Load Forecast Using a Multiple Delineated Machine Learning Approach by Terence Kibula Lukong, Derick Nganyu Tanyu, Yannick Nkongtchou, Thomas Tamo Tatietse, Detlef Schulz

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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…The joint interpretation of these statistics aids in comprehending model limitations and facilitates discussions on the environmental mechanisms shaping observed patterns. We propose two analytical workflows that not only enable the exploration and enhancement of model accuracy but also facilitate the investigation of potential cause-and-effect relationships inherent in the  data. …”
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Machine learning supported ground beef freshness monitoring based on near‐infrared and paper chromogenic array by Yihang Feng, Yi Wang, Burcu Beykal, Zhenlei Xiao, Yangchao Luo

“…Changes in ground beef volatile organic compounds during storage were captured in the shifts of PCA color patterns. Nippy, an open‐source Python module, was used for automated NIR spectra preprocessing. …”
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Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach by Gagandeep Sharma, Tejbir Kaur, Sanjay Kumar Mangal, Amit Kohli

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A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence by Hatef Dastour, Quazi K. Hassan

“…This study is divided into two primary parts: the first part investigates the predictive performance of a machine learning framework based on CatBoost and XGBoost models in estimating LST across different land cover classes in Alberta, Canada. …”
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State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

“…Hybrid models combining machine learning with optimization have made detection more accurate. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

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A two-step machine learning approach for predictive maintenance and anomaly detection in environmental sensor systems by Saiprasad Potharaju, Ravi Kumar Tirandasu, Swapnali N. Tambe, Devyani Bhamare Jadhav, Dudla Anil Kumar, Shanmuk Srinivas Amiripalli

“…Using Environmental Sensor Telemetry Data, this study introduces a novel methodology that combines unsupervised and supervised machine learning approaches to detect anomalies and predict sensor failures. …”
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Machine learning-based prediction and classification of seawater intrusion in the hyper-arid coastal aquifer of Fujairah, UAE by Assaad Kassem, Ahmed Sefelnasr, Abdel Azim Ebraheem, Luqman Ali, Faisal Baig, Mohsen Sherif

“…Future work should address temporal dynamics and salinity vertical distribution patterns.…”
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