Dataset and machine learning-based computer-aided tools for modeling working sorption isotherms in dried parchment and green coffee beansMendeley Data by Gentil A. Collazos-Escobar, Andrés F. Bahamón-Monje, Nelson Gutiérrez-Guzmán

“…This script provides a latent-variable-based tool for analyzing spectral patterns associated with different coffee types, allowing for robust model-based differentiation of coffee samples using their infrared properties. …”
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Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) by Daniel Adane, Assefa Andargie Kassa, Berhanu Abebaw Mekonnen, Rahel Mulatie Anteneh, Chalachew Yenew, Meron Asmamaw Alemayehu, Tilahun Degu Tsega, Sintayehu Simie Tsega, Almaw Genet Yeshiwas, Abathun Temesegen, Habitamu Mekonen, Amare Genetu Ejigu, Abraham Teym, Gashaw Melkie Bayeh, Getaneh Atikilit, Tesfaneh Shimels, Wolde Melese Ayele, Ahmed Fentaw Ahmed, Birhanemaskal Malkamu, Wondimnew Desalegn Addis, Getasew Yirdaw, Chalachew Abiyu Ayalew, Kalaab Esubalew, Zeamanuel Anteneh Yigzaw

“…Meanwhile, the average births per woman showed an increasing trend but fluctuated over time, influenced by demographic shifts such as changes in fertility preferences, age structure and migration patterns.Conclusions This study demonstrates the effectiveness of combining time-series models and machine learning, with GLMNET and Prophet XGBoost emerging as the most effective. …”
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Short-Term Water Demand Forecasting Using Machine Learning Approaches in a Case Study of a Water Distribution Network Located in Italy by Qidong Que, Jinliang Gao, Wenyan Wu, Huizhe Cao, Kunyi Li, Hanshu Zhang, Yi He, Rui Shen

“…Machine learning’s application in short-term water demand forecasting remains a pivotal area of research in water distribution system studies. …”
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Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study by Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

“…ObjectiveWe aimed to create a novel machine learning model for delirium prediction in ICU patients using only continuous physiological data. …”
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A scoping review and quality assessment of machine learning techniques in identifying maternal risk factors during the peripartum phase for adverse child development. by Hsing-Fen Tu, Larissa Zierow, Mattias Lennartsson, Sascha Schweitzer

“…After removing duplicates, the searches yielded 10,336 studies, of which 60 studies were included in the final report. Among these 60 machine learning studies, a majority were pattern-focused, using machine learning primarily as a tool to more accurately describe associations between variables, while 16 studies were prediction-focused (26.7%), exploring the predictive performance of their models. …”
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Mapping global yields of four major crops at 5-minute resolution from 1982 to 2015 using multi-source data and machine learning by Juan Cao, Zhao Zhang, Xiangzhong Luo, Yuchuan Luo, Jialu Xu, Jun Xie, Jichong Han, Fulu Tao

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Detecting Black-Box Model Probing Attacks Through Probability Scores by Yongzhi Wang, Ahsan Habib, Likhitha Reddy Kesara, Brahmarshi Jasti, Renjie Hu, Tejasv Singh

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Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques by Mohammed Raffic Noor Mohamed, Ganesh Babu Karuppiah, Dharani Kumar Selvan, Rajasekaran Saminathan, Shubham Sharma, Shashi Prakash Dwivedi, Sandeep Kumar, Mohamed Abbas, Dražan Kozak, Jasmina Lozanovic

“…The main goals of this research are to identify the significant input parameters using supervised machine learning methods and investigate the relationship between the process, structure, and properties of components created using fused deposition modeling utilizing nylon aramid composite filaments. …”
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Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis by Lizhong Liang, Tianci Liu, William Ollier, Yonghong Peng, Yao Lu, Chao Che

“…ObjectiveThe main aim of this study was to use machine learning models to model and analyze the complex interplay between mental health disorders and chronic physical illnesses. …”
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Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System by Michael Olumuyiwa Adio, Ogunmakinde Jimoh Ogunwuyi, Mayowa Oyedepo Oyediran, Adebimpe Omolayo Esan, Olufikayo Adepoju Adedapo

“…With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. …”
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Machine Learning-Enhanced Model-Based Optical Proximity Correction Framework With Convolutional Neural Network-Based Variable Threshold Method Near the Diffraction Limit by Jinhao Zhu, Liwan Yue, Ying Li, Xianhe Liu, Qiang Wu, Qi Wang, Yanli Li

“…In CD simulations for typical patterns, the hybrid model reduces error medians and confines the statistical upper and lower limits of the distribution ranges to ±5 nm. …”
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Enhancement of Chest X-Ray Images to Improve Screening Accuracy Rate Using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier by Chia-Hung Lin, Jian-Xing Wu, Chien-Ming Li, Pi-Yun Chen, Neng-Sheng Pai, Ying-Che Kuo

“…The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. …”
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Spatiotemporal Urban Evolution Along the China–Laos Railway in Laos Determined Using Multiple Sources of Remote-Sensed Landscape Indicators and Interpretable Machine Learning by Dongxue Li, Jin Tang, Qiao Hu, Mingjuan Dong, Soukanh Chithpanya

“…These spillover effects have exhibited a distance attenuation pattern, reflecting obvious development in 2D rather than in 3D urban space. …”
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Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier by Hong Zheng MS, Wei Chen MS, Jun Liu MD, Lian Jian MD, Tao Luo BS, Xiaoping Yu MD

“…Subsequently, radiomics refinement and deep learning features were employed using a machine learning algorithm to construct the RRDLC-Classifier, which aims to predict high-grade patterns in clinical stage I solid LADC. …”
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Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibri... by Peter Ruppersberg, Steven Castellano, Philip Haeusser, Kostiantyn Ahapov, Melissa H. Kong, Stefan G. Spitzer, Stefan G. Spitzer, Georg Nölker, Andreas Rillig, Tamas Szili-Torok

“…Our machine learning approach established an activity threshold, above which divergent wavefront patterns—termed “significant sources”—predicted AF recurrence. …”
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The Comprehensive Analysis of Weighted Gene Co-Expression Network Analysis and Machine Learning Revealed Diagnostic Biomarkers for Breast Implant Illness Complicated with Breast Ca... by Huang Z, Wang H, Pang H, Zeng M, Zhang G, Liu F

“…After constructing the PPI network, 17 key genes were selected, and three potential hub genes include KRT14, KIT, ALB were chosen for nomogram creation and diagnostic assessment through machine learning. The validation of these results was conducted by examining gene expression patterns in the validation dataset, breast cancer cell lines, and BII-BC patients. …”
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E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances by Ali Agheli, Kayvan Aghabayk, Matin Sadeghi, Subasish Das

“…We employed a random parameters logit model and investigated several machine learning algorithms, with XGBoost performing best. …”
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Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach by Seyyed Hossein Mousavie Anijdan, Daryush Moslemi, Reza Reiazi, Hamid Fallah Tafti, Ali Akbar Moghadamnia, Reza Paydar

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Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las... by Jingyi Liu, Yuxuan Cai, Xiwei Shen

“…This study leverages recent advances in machine learning to examine how environmental, economic, and demographic factors contribute to ethnic segregation, using Las Vegas as a case study with broader urban relevance. …”
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Detecting the Distribution of Callery Pear (<i>Pyrus calleryana</i>) in an Urban U.S. Landscape Using High Spatial Resolution Satellite Imagery and Machine Learning by Justin Krohn, Hong He, Timothy C. Matisziw, Lauren S. Pile Knapp, Jacob S. Fraser, Michael Sunde

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