Investigating the performance of random oversampling and genetic algorithm integration in meteorological drought forecasting with machine learning by Tahsin Baykal, Özlem Terzi, Gülsün Yıldırım, Emine Dilek Taylan

“…However, traditional drought monitoring approaches are limited in dealing with data imbalances and capturing complex temporal patterns. Therefore, this study aims to evaluate the effectiveness of machine learning methods for meteorological drought estimation and to integrate Random Oversampling (ROS) and Genetic Algorithm (GA) methods to improve estimation accuracy. …”
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Machine learning-based prediction of scale formation in produced water as a tool for environmental monitoring by Arash Tayyebi, Ali Alshami, Erfan Tayyebi, Ademola Owoade, MusabbirJahan Talukder, Nadhem Ismail, Zeinab Rabiei, Xue Yu, Glavic Tikeri

“…This is primarily due to the continuous variation in salt concentrations, temperature and pressure affecting inorganic scale composition. Machine learning (ML) as a data-driven method is a powerful tool for uncovering hidden patterns in experimental data necessary for decision-making on scale formation predictions by analyzing the complex relationships between mainly the water chemistry and the pH. …”
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Analyzing High-Speed Rail’s Transformative Impact on Public Transport in Thailand Using Machine Learning by Chinnakrit Banyong, Natthaporn Hantanong, Panuwat Wisutwattanasak, Thanapong Champahom, Kestsirin Theerathitichaipa, Rattanaporn Kasemsri, Manlika Seefong, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…This study investigates the impact of high-speed rail (HSR) on Thailand’s public transportation market and evaluates the effectiveness of machine learning techniques in predicting travel mode choices. …”
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Applying in machine learning and deep learning in finance industry: A case study on repayment prediction by Nguyễn Phát Đạt, Hồ Mai Minh Nhật, Trương Công Vinh, Lê Quang Chấn Phong, Lê Hoành Sử

“…The present inquiry advocates for the adoption of sophisticated computational methodologies, including machine learning and deep learning, to analyze borrowers’ behavioral patterns, demographic profiles, and credit histories, thus facilitating the prognostication of loan repayment likelihood. …”
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Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification by Lei Wang, Lei Wang, Xueying Jin, Yuchen Wu, Runing Qiu, Jianfang Wang

“…This study aims to construct a multi-omics framework combining ferroptosis-related signatures, immune infiltration patterns, and machine-learning approaches to stratify melanoma patients and guide therapeutic decision-making.MethodsWe developed a multi-omics framework integrating bulk transcriptomics (TCGA/GEO), single-cell RNA sequencing, and machine learning to decode melanoma's ferroptosis-immune axis. …”
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Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning by Jungpil Shin, Abu Saleh Musa Miah, Koki Hirooka, Md. Al Mehedi Hasan, Md. Maniruzzaman

“…While this method can capture broad patterns, it has several limitations, including a lack of focus on dynamic change, oversimplified feature representation, a lack of directional information, and missing micro-movements or subtle variations. …”
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Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery by Linming Huang, Fen Zhao, Guozheng Hu, Hasbagan Ganjurjav, Rihan Wu, Qingzhu Gao

“…These data, combined with field-measured pasture yields, were employed to construct models using four machine learning algorithms: elastic net regression (Enet), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). …”
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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…A systematic analysis will be conducted using bioinformatics and machine learning methods.MethodsGene expression data of GSE60993, GSE61144, GSE66360 and GSE48060 from four datasets were collected from the Gene Expression Omnibus (GEO) database. …”
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Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females by Sara A. Heyn, Taylor J. Keding, Josh Cisler, Katie McLaughlin, Ryan J. Herringa

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Micro-electrical Discharge Machining of Micro-holes Based on Integrated Orthogonal Experiments and CNN Methods by Yuandong MO, Yazhi WANG, Shuqi HUANG, Jiajun ZHONG

“…The meticulous analysis of the impact patterns and optimal parameters for micro-EDM of H62 brass micro-holes offers a comprehensive understanding of the intricate relationships between various machining factors. …”
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Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma by Hehe Wang, Junge Zhang, Peng Cheng, Lujie Yu, Chunlin Li, Yaowen Wang

“…Immune infiltration patterns and functional enrichment were analyzed using CIBERSORT and GSEA/GSVA, respectively. …”
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Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep by Fernando Amarilho-Silveira, Ignacio De Barbieri, Elly A. Navajas, Jaime Araujo Cobuci, Gabriel Ciappesoni

“…This indicates that support vector machines effectively captures the underlying patterns of feed intake distribution. …”
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Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning by Tiantian Yu, Guiying Wang, Xia Xu, Jianying Yan

“…The five genes that ranked highest in the RF machine learning model were considered to be predictor genes. …”
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Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang, Zhenqing Wang

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Identification and validation of pyroptosis-related genes in Alzheimer’s disease based on multi-transcriptome and machine learning by Yuntai Wang, Yuntai Wang, Yilin Li, Lu Zhou, Yihuan Yuan, Chuanfei Liu, Zimeng Zeng, Yuanqi Chen, Qi He, Zhuoze Wu

“…By application of the protein–protein interaction and machine learning algorithms, seven pyroptosis feature genes (CHMP2A, EGFR, FOXP3, HSP90B1, MDH1, METTL3, and PKN2) were identified. …”
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Machine Learning-Based Intrusion Detection Systems for the Internet of Drones: A Systematic Literature Review by Mostafa Ogab, Sofiane Zaidi, Abdelhabib Bourouis, Carlos T. Calafate

“…Existing Intrusion Detection Systems (IDS) for IoD face several limitations, including high false positive rates, resource constraints of drones, limited adaptability to evolving attack patterns, and a lack of standardized datasets for benchmarking, despite ongoing research efforts. …”
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Performance of unmarked abundance models with data from machine‐learning classification of passive acoustic recordings by Cameron J. Fiss, Samuel Lapp, Jonathan B. Cohen, Halie A. Parker, Jeffery T. Larkin, Jeffery L. Larkin, Justin Kitzes

“…Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine‐learning classifiers to sample significantly more time for focal species at any given point. …”
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Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…Furthermore, an Adjusted R 2 value of 0.965 further confirmed its ability to capture complex patterns in water quality data with remarkable accuracy. …”
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CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space by Maria Laura Zoffoli, Vittorio Brando, Gianluca Volpe, Luis González Vilas, Bede Ffinian Rowe Davies, Robert Frouin, Jaime Pitarch, Simon Oiry, Jing Tan, Simone Colella, Christian Marchese

“…To improve these results, we developed CIAO (Chlorophyll In the Arctic Ocean), a machine learning-based algorithm specifically designed for AO waters and trained with satellite Rrs data. …”
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Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis by Daniil Lisik, Saliha Selin Özuygur Ermis, Gregorio Paolo Milani, Giulia Carla Immacolata Spolidoro, Selin Ercan, Michael Salisu, Faozyat Odetola, Daniele Giovanni Ghiglioni, Danylo Pylov, Emma Goksör, Rani Basna, Göran Wennergren, Hannu Kankaanranta, Bright I. Nwaru

“…Introduction Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. …”
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