Optimizing droplet coalescence dynamics in microchannels: A comprehensive study using response surface methodology and machine learning algorithms by Seyed Morteza Javadpour, Erfan Kadivar, Zienab Heidary Zarneh, Ebrahim Kadivar, Mohammad Gheibi

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Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach by Yifeng Yang, Liangyun Hu, Yang Chen, Weidong Gu, Guangwu Lin, YuanZhong Xie, Shengdong Nie

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Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia by Wudneh Ketema Moges, Awoke Seyoum Tegegne, Aweke A. Mitku, Esubalew Tesfahun, Solomon Hailemeskel

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Statistical Characteristics of Nighttime Medium‐Scale Traveling Ionospheric Disturbances From 10‐Years of Airglow Observation by the Machine Learning Method by Chang Lai, Jiyao Xu, Zhishuang Lin, Kun Wu, Donghe Zhang, Qinzeng Li, Longchang Sun, Wei Yuan, Yajun Zhu

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Machine learning predicted fast progression after initiation of immune checkpoint inhibitors in advanced non-small cell lung cancer by Martin Stockler, Yu Yang Soon, Chee Khoon Lee, Teng Hwee Tan

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Exploring Mortality and Prognostic Factors of Heart Failure with In-Hospital and Emergency Patients by Electronic Medical Records: A Machine Learning Approach by Yu CS, Wu JL, Shih CM, Chiu KL, Chen YD, Chang TH

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The state-of-the-art review on biochar as green additives in cementitious composites: performance, applications, machine learning predictions, and environmental and economic implications by Ping Ye, Binglin Guo, Huyong Qin, Cheng Wang, Yang Liu, Yuyang Chen, Pengfei Bian, Di Lu, Lei Wang, Weiping Zhao, Yonggan Yang, Li Hong, Peng Gao, Peiyong Ma, Binggen Zhan, Qijun Yu

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Circadian rhythm related genes signature in glioma for drug resistance prediction: a comprehensive analysis integrating transcriptomics and machine learning by Junbo Liao, Yingxing Duan, Xiangwang Xu, Yaxue Liu, Chaohong Zhan, Gelei Xiao

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Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and fatty acid metabolism-associated gene by Hong Li, Yongyun Xu, Aiting Wang, Chuanxin Zhao, Man Zheng, Chunyan Xiang

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Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning by Morteza Fattahi, Milad Esmaeil-Zadeh, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Reza Rostami, Jamil Mansouri, Gholam-Ali Hossein-Zadeh, Gholam-Ali Hossein-Zadeh

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Investigation of alpha-glucosidase inhibition activity of Artabotrys sumatranus leaf extract using metabolomics, machine learning and molecular docking analysis. by Dela Rosa, Berna Elya, Muhammad Hanafi, Alfi Khatib, Eka Budiarto, Syamsu Nur, Muhammad Imam Surya

“…Both multivariate statistical analysis and machine learning approaches were used to improve the confidence of the predictions. …”
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Estimation of the Effect of Oblique Positioned Obstacle Placement on Thermal Performance of a Horizontal Mantle Hot Water Tank with Machine Learning by Aslı Durmuşoğlu, Buket Turgut, Yusuf Tekin, Burak Turgut

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A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model by Meng He, Hui Wang, Myo Thwin

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Predicting cardiovascular disease and all-cause mortality using the lymphocyte-to-monocyte ratio: Insights from explainable machine learning models by Jichao Wu, Die Huang, Jiefang Li, Jingxing Yi, Yu Lei, Jun Yin

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Mapping groundwater potential zone by robust machine learning algorithms & remote sensing techniques in agriculture dominated area, Bangladesh by M. M. Shah Porun Rana, Muhammad Tauhidur Rahman, Md Fuad Hassan

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Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea by Hyunji Sang, Jaeyu Park, Soeun Kim, Myeongcheol Lee, Hojae Lee, Sun-Ho Lee, Dong Keon Yon, Sang Youl Rhee

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Advancing privacy-aware machine learning on sensitive data via edge-based continual µ-training for personalized large models by Zhaojing Huang, Leping Yu, Luis Fernando Herbozo Contreras, Kamran Eshraghian, Nhan Duy Truong, Armin Nikpour, Omid Kavehei

“…Machine Learning: Science and Technology…”
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Author Correction: Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations by Mingfeng Liu, Jiantao Wang, Junwei Hu, Peitao Liu, Haiyang Niu, Xuexi Yan, Jiangxu Li, Haile Yan, Bo Yang, Yan Sun, Chunlin Chen, Georg Kresse, Liang Zuo, Xing-Qiu Chen

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Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation by Manash Sarma, Subarna Chatterjee

“…Both of these samples, obtained from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), were independently analyzed utilizing machine learning (ML)-based multiclassifiers. This study applied novel machine learning-based data augmentation techniques to gene expression profile data that are high-dimensional, low-sample-size (HDLSS) and inherently highly imbalanced. …”
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Early detection of canine hemangiosarcoma via cfDNA fragmentation and copy number alterations in liquid biopsies using machine learning by Soohyun Ko, Jinhee Jang, Sun Shin Yi, Sun Shin Yi, ChangHyuk Kwon

“…Additionally, we identified seven novel genomic gains and nine losses in the hemangiosarcoma samples. Applying machine learning to the cfDNA fragment size distribution, we achieved an impressive average Area Under the Curve (AUC) of 0.93 in 10-fold cross-validation, underscoring the potential of this approach for precise early-stage cancer classification. …”
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