Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma by Zhengyu Wei, Guoli Wang, Yanghao Hu, Chongchang Zhou, Yuna Zhang, Yi Shen, Yaowen Wang

“…Abstract Purpose Exosomes are recognized as essential mediators in the intercellular communication between tumor cells, serving a pivotal function in tumor development. Nevertheless, the patterns of expression and medical relevance of exosome-related genes (ERGs) in nasopharyngeal carcinoma (NPC) remain insufficiently characterized. …”
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Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning by ShengHui Miao, YiJing Liu, Min Li, Jing Yan

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Trust-driven approach to enhance early forest fire detection using machine learning by Tayyab Khan, Karan Singh, Bhoopesh Singh Bhati, Khaleel Ahmad, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…The detrimental impacts of forest fires, such as the exacerbation of the greenhouse effect, the hastening of global warming, and the modification of climatic patterns, underscore the urgent necessity for the creation of efficient detection systems. …”
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Time Series Analysis of Solar Power Generation Based on Machine Learning for Efficient Monitoring by Umer Farooq, Muhammad Faheem Mushtaq, Zahid Ullah, Muhammad Talha Ejaz, Urooj Akram, Sheraz Aslam

“…However, meteorological factors, such as solar irradiation, weather patterns, precipitation, and overall climate conditions, pose challenges to the seamless integration of energy production into the power grid. …”
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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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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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A comprehensive review of research on surface defect detection of PCBs based on machine vision by Zihan He, Yudong Lian, Yulei Wang, Zhiwei Lu

“…We believe that the Transformer architecture has transformative potential in handling global defect patterns, while emphasizing the need for lightweight model architectures through techniques like knowledge distillation. …”
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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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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

“…These suggest disruptive flight patterns, indicating insecticidal irritancy. While IS mosquitoes displayed rapid, disordered trajectories and mostly died within 30 min, IR mosquitoes persisted throughout the 2-h experiments but exhibited similarly disturbed behaviour, suggesting resistance does not fully mitigate disruption. …”
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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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Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Ahmed M. Ebid, Shadi Hanandeh, Susana Monserrat Zurita Polo, Rolando Fabián Zabala Vizuete, Rodney Orlando Santillán Murillo, Rolando Marcel Torres Castillo, Siva Avudaiappan

“…The records were divided into training set (900 records = 80%) and validation set (235 records = 20%) following standard partitioning pattern reported in the literature. The kNN and AdaBoost, with SSE of 1408.5 MPa2 and 1397 MPa2 respectively and a tie Accuracy of 95.5% and R2 of 0.985 showed the best models suggesting excellent model performance while GMDH-NN showed the worst. …”
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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

“…The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). …”
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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

“…The XGBoost model can also effectively capture the impact of drought treatments on SR.DiscussionThe XGBoost model’s tree-based structure allows it to effectively capture complex interactions and nonlinear patterns within variables, while its high sensitivity to changes in SR rates under drought conditions makes it more reliable for modeling SR across different growth stages compared to the linear-based MLR model. …”
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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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