Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach by Tanmoy Mazumder, Md. Mustafa Saroar

“…By analyzing spatiotemporal patterns of lightning and casualties, and incorporating meteorological, geographical, and socio-economic factors into ML models (Random Forest, Multinomial Logistic Regression, Support Vector Machine, and Artificial Neural Networks), this research provides a nuanced understanding of lightning impacts. …”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and capturing complex nonlinear transmission patterns. …”
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Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…By integrating wavelet-based feature extraction with machine learning-driven analysis, this approach enhances the ability to detect complex wave propagation patterns, leading to more precise subsurface modeling. …”
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Machine learning-driven identification of critical gene programs and key transcription factors in migraine by Lei Zhang, Yujie Li, Yunhao Xu, Wei Wang, Guangyu Guo

“…Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associated brain regions, remain unclear. …”
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Vibration Characteristics of Double-Shield TBM Cutterhead Under Rock–Machine Interaction Excitation by Guang Zhang, Qing Song, Qiuming Gong, Dongxing Liu, Dongwei Li, Minghao Sun

“…During the tunneling process of a double-shield TBM, vibrations generated by rock–machine interaction can affect its safe, efficient, and stable operation. …”
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A comprehensive review of research on surface defect detection of PCBs based on machine vision by Zihan He, Yudong Lian, Yulei Wang, Zhiwei Lu

“…This paper presents a comprehensive review of machine vision-based surface defect detection methods for PCBs, addressing the transition from traditional image processing to advanced deep learning techniques. …”
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A blood test-based machine learning model for predicting lung cancer risk by Lihi Schwartz, Naor Matania, Matanel Levi, Teddy Lazebnik, Teddy Lazebnik, Shiri Kushnir, Noga Yosef, Assaf Hoogi, Dekel Shlomi, Dekel Shlomi

“…For lung cancer (LC), age and smoking history are the primary criteria for annual low-dose CT screening, leaving other populations at risk of being overlooked. Machine learning (ML) is a promising method to identify complex patterns in the data that can reveal personalized disease predictors.MethodsAn ML-based model was used on blood test data collected before the diagnosis of LC, and sociodemographic factors such as age and gender among LC patients and controls were incorporated to predict the risk for future LC diagnosis.ResultsIn addition to age and gender, we identified 22 blood tests that contributed to the model. …”
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Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning by Yating Zhan, Min Weng, Yangyang Guo, Dingfeng Lv, Feng Zhao, Zejun Yan, Junhui Jiang, Yanyi Xiao, Lili Yao

“…Prognostic differentially expressed genes (DEGs) were filtered via differentially expression analysis and univariate Cox regression analysis. Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. …”
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Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices by Gavriela Asiminari, Lefteris Benos, Dimitrios Kateris, Patrizia Busato, Charisios Achillas, Claus Grøn Sørensen, Simon Pearson, Dionysis Bochtis

“…This study aimed to simplify field efficiency estimation by training machine learning regression algorithms on data generated from a farm management information system covering a combination of different field areas and shapes, working patterns, and machine-related parameters. …”
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Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset nets by Yasser M. Qureshi, Vitaly Voloshin, Amy Guy, Hilary Ranson, Philip J. McCall, James A. Covington, Catherine E. Towers, David P. Towers

“…This study examined the flight behaviour of insecticide-resistant (IR) and insecticide-susceptible (IS) Anopheles gambiae strains around an Olyset net (OL), a permethrin-impregnated ITN, versus an untreated net (UT). Using machine learning (ML) models, we classified mosquito flight trajectories with high balanced accuracy (0.838) and ROC AUC (0.925). …”
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Scalability analysis of heavy-duty gas turbines using data-driven machine learning by Shubhasmita Pati, Julian D. Osorio, Mayank Panwar, Rob Hovsapian

“…In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. …”
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A synthetic data-driven machine learning approach for athlete performance attenuation prediction by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Lorcan Daly, Lorcan Daly, David T. Kelly, David T. Kelly, Thiago B. Rodrigues

“…IntroductionAthlete performance monitoring is effective for optimizing training strategies and preventing injuries. However, applying machine learning (ML) frameworks to this domain remains challenging due to data scarcity limitations. …”
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Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning by Fanchao Zeng, Fanchao Zeng, Jinwei Sun, Huihui Zhang, Lizhen Yang, Xiaoxue Zhao, Jing Zhao, Xiaodong Bo, Yuxin Cao, Fuqi Yao, Fenghui Yuan, Fenghui Yuan

“…However, it remains uncertain whether hyperspectral remote sensing can provide an accurate and efficient method for estimating SR rate in croplands, particularly across different maize growth stages of under varying drought conditions.MethodsIn the study, we investigated the potential of combining hyperspectral remote sensing data with machine learning model (ML) to quantify SR rate in croplands. …”
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Early-stage detection of maize seed germination based on RGB image and machine vision by Xiaohang Liu, Zhao Zhang, Yunxia Li, C. Igathinathane, Jiangfan Yu, Zhaoyu Rui, Afshin Azizi, Xiqing Wang, Alireza Pourreza, Man Zhang

“…Collected color (RGB) images of germination trays planted with maize seeds sown in preset patterns were preprocessed as regions of interest (RoI) for each seed for analysis. …”
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Exploratory development of human–machine interaction strategies for post-stroke upper-limb rehabilitation by Kang Xia, Xue-Dong Chang, Chong-Shuai Liu, Yu-Hang Yan, Han Sun, Yi-Min Wang, Xin-Wei Wang

“…To fulfill the rehabilitation needs, human–machine interaction (HMI) technology strives continuously. …”
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Immune Microenvironment Characterization and Machine Learning-Guided Identification of Diagnostic Biomarkers for Ulcerative Colitis by Zheng Q, Wang L, Zhang Y, Peng J, Hou J, Wang H, Ma Y, Tang P, Li Y, Li H, Chen Y, Li J, Chen Y

“…It highlights potential diagnostic biomarkers and therapeutic targets, aiding in the development of precision medicine approaches for UC management.Keywords: ulcerative colitis, immune microenvironment, machine learning, diagnostic biomarkers…”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…These results have the potential to transform operational risk management in banks, leading to significant reductions in associated costs and losses.A key insight from this study is that leveraging large and diverse datasets can substantially enhance prediction accuracy. Machine learning models can process complex datasets, identify hidden patterns, and facilitate early risk detection, enabling banks to implement preventive measures before risks materialize. …”
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A review of the critical conditions required for effective hole cleaning while horizontal drilling by Amir Shokry Youssef, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Talal Al Shafloot

“…It investigates the mechanics of cuttings transport within horizontal wells, analyzing the forces at play and various flow patterns. It discusses different methodologies, including empirical correlations, experimental studies, machine learning models, and modeling techniques, used to assess hole cleaning efficiency. …”
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Integrating non-target analysis and machine learning: a framework for contaminant source identification by Peng Liu, Ding Pan, Xin-Yi Jiao, Ji-Ning Liu, Peng-Hui Du, Peng-Cheng Li, Meng-Zhu Xue, Yan-Chao Jin, Cai-Shan Wang, Xue-Rong Wang, Ying-Zhi Ding, Guang-Ning Zhu, Jing-Hao Yang, Wen-Ze Wu, Lu-Feng Liang, Xin-Hui Liu, Li-Ping Li

“…Abstract Machine learning-based non-target analysis (ML-based NTA) faces the critical challenge of linking complex chemical signals to contamination sources. …”
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Reconciling Global Terrestrial Evapotranspiration Estimates From Multi‐Product Intercomparison and Evaluation by Yaoting Cai, Qingchen Xu, Fan Bai, Xueqi Cao, Zhongwang Wei, Xingjie Lu, Nan Wei, Hua Yuan, Shupeng Zhang, Shaofeng Liu, Yonggen Zhang, Xueyan Li, Yongjiu Dai

“…These products were obtained from various sources or methods and were grouped into six categories: remote sensing, reanalysis, land surface models, climate models, machine learning methods, and ensemble estimates. …”
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