Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors by Ran Zhang, Guo Chen, Shasha Gao, Lu Chen, Yongchao Cheng, Xiuquan Gu, Yue Wang

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Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability by Mohammad Z. Shanti, Chan Yeob Yeun, Chung-Suk Cho, Ernesto Damiani, Tae-Yeon Kim

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Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses by Masashi Hayakawa, Shinji Hirooka, Koichiro Michimoto, Stelios M. Potirakis, Yasuhide Hobara

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Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation Prediction by Theofilos Gofas, Argyros Maris

“…It underscores the importance of accurate prognosis and therapeutic efficacy and advocates for the use of machine learning (ML) as a crucial diagnostic tool. …”
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Advanced Hybrid Machine Learning Model for Accurate Detection of Cardiovascular Disease by Navita, Pooja Mittal, Yogesh Kumar Sharma, Umesh Kumar Lilhore, Sarita Simaiya, Kashif Saleem, Ehab Seif Ghith

“…Thus, there is an urgent need for a detection model comprising intelligent technologies, including Machine Learning (ML) and deep learning, to predict the future state of an individual suffering from cardiovascular disease by effectively analyzing patient data. …”
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Modeling women cyclists' perceived security: A comparison of machine learning techniques by Peyman Noorbakhsh, Navid Khademi, Phromphat Thansirichaisree

“…While prior studies have explored certain aspects of perceived security —mainly for pedestrians—the application of Machine Learning (ML) models to predict cyclists’ perceived security remains a relatively developing research area. …”
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Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations by Ahmad J. Obaidullah, Wael A. Mahdi

“…Abstract This study introduces a sophisticated predictive framework for determining drug solubility and activity values in formulations via machine learning. The framework utilizes a comprehensive dataset consisting of more than 12,000 data rows and 24 input features containing a wide range of parameters to estimate drug solubility in formulation. …”
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PREDICTING TREATMENT UNFAVOURABLE IN PULMONARY TUBERCULOSIS PATIENTS USING STACKING ENSEMBLE MACHINE LEARNING APPROACH by Fayaz Ahamed Shaik, Lakshmanan Babu, Palaniyandi Paramasivam, Selvam Nagarajan, Sundarakumar Karuppasamy, Ponnuraja Chinnaiyan

“…In healthcare, the application of machine learning (ML) that helps in diagnosis is on the rise. …”
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Identifying novel biomarkers for biliary tract cancer based on volatile organic compounds analysis and machine learning by Jingrong Qian, Qi Liu, Jue Wang, Xuewei Zhuang, Jun Fang

“…In BTC and BBD patients, the diagnostic model was constructed based on six machine learning method. Among them, RF had the highest diagnostic performance (AUC = 0.935, p < 0.001), with a sensitivity of 76.2% and a specificity of 96.3%. …”
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Machine Learning-Driven Optimization of Transport Layers in MAPbI₃ Perovskite Solar Cells for Enhanced Performance by Velpuri Leela Devi, Piyush Kuchhal, Debasis de, Abhinav Sharma, Neeraj Kumar Shukla, Mona Aggarwal

“…This study aims to analyse the performance of MAPbI3-based perovskite solar cells (PSCs) by integrating machine learning (ML) models with the SCAPS-1D simulator. …”
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Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records by Dérick G. F. Borges, Eluã R. Coutinho, Thiago Cerqueira-Silva, Malú Grave, Adriano O. Vasconcelos, Luiz Landau, Alvaro L. G. A. Coutinho, Pablo Ivan P. Ramos, Manoel Barral-Netto, Suani T. R. Pinho, Marcos E. Barreto, Roberto F. S. Andrade

“…Methods Unsupervised machine learning methods and dynamical systems concepts were combined into the Mixed Model of Artificial Intelligence and Next-Generation (MMAING) ensemble, which aims to detect early signs of outbreaks based on primary healthcare encounters. …”
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Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N₂O Emissions in China by Yuan Zeng, Sujuan Chen, Yunpeng Li, Li Xiong, Cheng Liu, Muhammad Azeem, Xiaoting Jie, Mei Chen, Longjiang Zhang, Jianfei Sun

“…This study uses a global dataset of BBF field experiments to build predictive models with three machine learning algorithms for crop yields and N₂O emissions, and to assess BBFs’ potential to increase yields and mitigate emissions in China’s major crops. …”
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Landslide Susceptibility Mapping Based on Ensemble Learning in the Jiuzhaigou Region, Sichuan, China by Bangsheng An, Zhijie Zhang, Shenqing Xiong, Wanchang Zhang, Yaning Yi, Zhixin Liu, Chuanqi Liu

“…Results demonstrate that integrating machine learning with ensemble learning and SHAP yields more reliable landslide susceptibility mapping and enhances model interpretability. …”
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BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes by Thanadol Tuntiwongwat, Sippawit Thammawiset, Thongchai Rohitatisha Srinophakun, Chawalit Ngamcharussrivichai, Somboon Sukpancharoen

“…This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. …”
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Data-driven machine learning algorithm model for pneumonia prediction and determinant factor stratification among children aged 6–23 months in Ethiopia by Addisalem Workie Demsash, Rediet Abebe, Wubishet Gezimu, Gemeda Wakgari Kitil, Michael Amera Tizazu, Abera Lambebo, Firomsa Bekele, Solomon Seyife Alemu, Mohammedamin Hajure Jarso, Geleta Nenko Dube, Lema Fikadu Wedajo, Sanju Purohit, Mulugeta Hayelom Kalayou

“…Therefore, this study aimed to develop data-driven predictive model using machine learning algorithms to predict pneumonia and stratify the determinant factors among children aged 6–23 months in Ethiopia. …”
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Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns by Merve Gonca, Mehmet Birol Özel

“…Traditional skeletal classifications, such as the ANB angle, may oversimplify complex relationships among malocclusion types. Machine learning-based unsupervised methods may allow for more nuanced sub-phenotypic classification. …”
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Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study by Gaoyang Li, Shrikant S. Kolan, Franco Grimolizzi, Joseph Sexton, Giulia Malachin, Guro Goll, Tore K. Kvien, Tore K. Kvien, Nina Paulshus Sundlisæter, Manuela Zucknick, Siri Lillegraven, Espen A. Haavardsholm, Espen A. Haavardsholm, Bjørn Steen Skålhegg

“…The model employed a super learner algorithm that combine three base algorithms of elastic net, random forest and support vector machine. …”
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Energy storage efficiency modeling of high-entropy dielectric capacitors using extreme learning machine and swarm-based hybrid support vector regression computational methods by Yas Al-Hadeethi, Taoreed O. Owolabi, Mouftahou B. Latif, Bahaaudin M. Raffah, Ahmad H. Milyani, Saheed A. Tijani

“…This work employs single hidden layer extreme learning machine (ELM) algorithm and hybrid particle swarm optimization-based support vector regression (PS-SVR) for determining energy storage efficiency of high-entropy ceramics. …”
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Machine-Learning-Based Integrated Mining Big Data and Multi-Dimensional Ore-Forming Prediction: A Case Study of Yanshan Iron Mine, Hebei, China by Yuhao Chen, Gongwen Wang, Nini Mou, Leilei Huang, Rong Mei, Mingyuan Zhang

“…Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. …”
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U-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking data by Solomon Asghar, Ran Ni, Giorgio Volpe

“…Here, we introduce a novel machine-learning framework, U-net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME), that applies a U-Net 3+ based neural network alongside Gaussian mixture models to enable highly accurate characterisation of single-particle tracking data. …”
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