Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…Here, we adapt k-means clustering for data clustering, kernel principal component analysis (kernel PCA), universal manifold approximation and projection (UMAP), and t-stochastic nearest neighbor (t-SNE) for dimensionality reduction. …”
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Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning by Haowen Zhong, Yilan Liu, Haibin Sun, Yuru Liu, Rentao Zhang, Baochen Li, Yi Yang, Yuqing Huang, Fei Yang, Frankie S. Mak, Klement Foo, Sen Lin, Tianshu Yu, Peng Wang, Xiaoxue Wang

“…Furthermore, our fine-grained uncertainty disentanglement enables efficient active learning, reducing 80% of data requirements. Additionally, our uncertainty analysis effectively identifies out-of-domain reactions and evaluates reaction robustness or reproducibility against environmental factors for scaling up, offering a practical framework for navigating chemical spaces and designing highly robust industrial processes.…”
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Multi-model learning for vessel ETA prediction in inland waterways using multi-attribute data by Abdullah Al Noman, Anton Zitnikov, Aaron Heuermann, Klaus-Dieter Thoben

“…Existing ETA prediction models largely rely on Automatic Identification System (AIS) data but often overlook additional factors. This study introduces a deep learning-based Multi-Model learning approach that fuses multi-attribute data from multiple sources to enhance ETA prediction accuracy. …”
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Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data by Silvia Elaine Cardozo Macedo, Marina de Borba Oliveira Freire, Oscar Schmitt Kremer, Ricardo Bica Noal, Fabiano Sandrini Moraes, Mauro André Barbosa Cunha

“…Epidemiological, clinical, and laboratory data were processed by machine learning algorithms in order to identify patterns. …”
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Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data by Gabriel Ferreira dos Santos Silva, Roberta Moreira Wichmann, Francisco Costa da Silva Junior, Alexandre Dias Porto Chiavegatto Filho

“…We compared three training approaches: a generalized model applicable across all countries, country-specific models tailored to local healthcare characteristics, and a model derived from the largest single-country dataset. Utilizing data from 2010 to 2016 for training and validation from 2017 to 2019, our analysis included 575,664 pregnancies and assessed five ML algorithms based on key neonatal health indicators recommended by the World Health Organization. …”
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How machine learning on real world clinical data improves adverse event recording for endoscopy by Stefan Wittlinger, Isabella C. Wiest, Mahboubeh Jannesari Ladani, Jakob Nikolas Kather, Matthias P. Ebert, Fabian Siegel, Sebastian Belle

“…This study evaluates a machine learning-based approach for systematically detecting endoscopic adverse events from real-world clinical metadata, including structured hospital data such as ICD-codes and procedure timings. …”
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Cropland Suitability Prediction Method Based on Biophysical Variables from Copernicus Data and Machine Learning by Dorijan Radočaj, Mateo Gašparović, Mladen Jurišić

“…The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (<i>Glycine max</i> L.) as a representative crop, aiming to provide an alternative to geographic information system (GIS)-based multicriteria analysis. …”
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Lost circulation intensity characterization in drilling operations: Leveraging machine learning and well log data by Ahmad Azadivash

“…After rigorous exploratory analysis and preprocessing of the data, seven machine learning methods are applied: Random Forest, Extra Trees, Decision Tree, XGBoost, k-Nearest Neighbors, Support Vector Machine, and Hard Voting. …”
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Implementing and Learning from a Summer Research Data Management Training Program for Student Researchers by Kevin B. Read, Sarah Rutley

“…Background This study explores a library-led research data management (RDM) training program at a Canadian post-secondary institution that targeted students participating in summer research assistantships as well as their faculty supervisors. …”
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Assessment of hydrological loading displacement from GNSS and GRACE data using deep learning algorithms by Changshou Wei, Maosheng Zhou, Zhixing Du, Lijing Han, Hao Gao

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A deep learning model to predict glioma recurrence using integrated genomic and clinical data by Jessica A. Patricoski-Chavez, Seema Nagpal, Ritambhara Singh, Jeremy L. Warner, Ece D. Gamsiz Uzun

“…Currently, no widely available models exist for reliably predicting early glioma recurrence, which is critical for optimizing patient outcomes. Machine learning (ML) and deep learning (DL) techniques have shown promise in predicting recurrence for various cancers, with those utilizing multimodal data sources showing increasing promise. …”
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Using Machine Learning and Nationwide Population-Based Data to Unravel Predictors of Treated Depression in Farmers by Pascal Petit, Vincent Bonneterre, Nicolas Vuillerme

“…To complement these traditional studies, big data and machine learning (ML) can advantageously be harnessed. …”
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iVAE: an interpretable representation learning framework enhances clustering performance for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

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Machine learning in big data: A performance benchmarking study of Flink-ML and Spark MLlib by Messaoud MEZATI, Ines AOURIA

“… Machine learning (ML) in big data frameworks plays a critical role in real-time analytics, decision making, and predictive modeling. …”
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Short-Term Building Electrical Load Prediction by Peak Data Clustering and Transfer Learning Strategy by Kangji Li, Shiyi Zhou, Mengtao Zhao, Borui Wei

“…First, a building’s electrical peak loads are clustered through peak/valley data analysis and K-nearest neighbors categorization method, thereby addressing the challenge of data clustering in data-sparse scenarios. …”
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Machine learning prediction of coal workers’ pneumoconiosis classification based on few-shot clinical data by Jiaqi Jia, Jingying Huang, Yuming Cui, Dekun Zhang, Haiquan Li, Songquan Wang, Wenlu Hang

“…Methods The study included a comparative analysis of clinical data from 202 healthy individuals and 81 CWP patients at general Hospital of Xuzhou Mining Group. …”
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Efficient Method of Road Outlier Recognition Using Deep Learning Coupled with Data Augmentation Approach by Sarfaraz Natha, Fareed Ahmed Jokhio, Muhammad Shafique, Naeem Ahmed, Danish Munir Arain

“…The proposed Real-time Road Outlier Recognition methodology is formulated as a classification task, involving the analysis and processing of real-time CCTV videos. The proposed study investigated different types of Convolutional Neural Network (CNN) pre-trained models with the Data Augmentation (DA) approach to address the frame variance problem in real-time videos. …”
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Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning by Muhammad Umar Nasir, Muhammad Zubair, Muhammad Tahir Naseem, Tariq Shahzad, Ahmed Saeed, Khan Muhammad Adnan, Amir H. Gandomi

“…The imported SVM model, slightly less accurate than XGBoost, still has strong performance, particularly on the HPLC data where the cumulative testing accuracy of the model stood at 99.4%. …”
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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection by Muhammad Shafiq, J. Kavitha, Dhruva R. Rinku, N. K. Senthil Kumar, Kamal Poon, Amar Y. Jaffar, V. Saravanan

“…It does most of the data analysis and decision-making. Feature Extraction (FE) is done through CAT-Swarm Optimization. …”
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Land Use and Land Cover Classification with Deep Learning-Based Fusion of SAR and Optical Data by Ayesha Irfan, Yu Li, Xinhua E, Guangmin Sun

“…., SAR’s coherent scattering versus optical’s reflectance properties) pose significant challenges in achieving effective multimodal fusion for LULC analysis. To address this gap, we propose a multimodal deep-learning framework that systematically integrates SAR and optical imagery. …”
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