Hybrid data driven approach based on ANNs-PCA for wastewater treatment plant performance assessment by Redouane Elharbili, Tawfik El Moussaoui, Khalid El Ass, Mohamed Oussama Belloulid, Abdelhafid El Alaoui El Fels, Mohamed Yassine Samiri

“…The proposed hybrid approach consists of a combination of artificial neural networks (ANNs) and principal component analysis (PCA). Measurement results of a municipal activated sludge WWTP operation of 1.3 million inhabitant equivalents are presented and discussed. …”
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Optimal graph representations and neural networks for multichannel time series data in seizure phase classification by Alan A. Díaz-Montiel, Richard Zhang, Milad Lankarany

“…Moreover, we conducted an analysis of graph neural networks (GNN), a paradigm of artificial neural networks optimized to operate on graph-structured data, as a framework to classify seizure phases (preictal vs. ictal vs. postictal) from intracranial electroencephalographic (iEEG) data. …”
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Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer by Ting Yang, Hongyi Yu, Danhong Lu, Shengkui Bai, Yan Li, Wenyao Fan, Ketian Liu

“…The explosive growth of power load data has led to a substantial presence of abnormal data, which significantly reduce the accuracy of power system operation planning, load forecasting, and energy usage analysis. …”
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Suprasubduction geochemical dataset of ultramafic minerals in Southern Iran: The Ab-Bid complexMendeley Data by Mahdieh Mohammadi, Hamid Ahmadipour, Abbas Moradian, Daniele Brunelli, Reza Derakhshani

“…The data facilitate applications in melt–rock interaction modeling, mineral thermometry, and suprasubduction zone geochemical comparison. …”
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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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Prediction of Corrosion Rate for Carbon Steel Using Regression Model with Commercial LPR Sensor Data by Kwang-Hu Jung, Jung-Hyung Lee

“…A regression model trained on 54 MPY data points demonstrated high accuracy, achieving a coefficient of determination (R²) of 0.9733. …”
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Duty of care, data science, and gambling harm: A scoping review of risk assessment models by Virve Marionneau, Kim Ristolainen, Tomi Roukka

“…The final sample consists of 31 academic studies published between 2015 and 2025, and 11 commercial solutions. Our analysis focuses on three critical stages of model development: 1) selection of estimation data; 2) decisions related to the model estimation process; and 3) assessment and interpretation of prediction model results. …”
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Research on High-Speed Modeling Method of Modular Multilevel Converter Valve Based on Data Derivation by Jun Zhang, Shuhong Wang, Youpeng Huangfu, Ruting Tang

“…Due to the highly demanding operating conditions of the Modular Multilevel Converter (MMC), as well as its inherently complex structure, electric field analysis of the MMC valve is crucial for the safe and stable operation of MMC-HVDC (MMC high voltage direct current) transmission systems. …”
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Turning Trash Into Treasure: Extracting Meaning From Discordant Data via a Dedicated Application by Lucy M. Mathieson, C. L. Kirkland, M. L. Daggitt

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On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review by Biwei Yan, Kun Li, Minghui Xu, Yueyan Dong, Yue Zhang, Zhaochun Ren, Xiuzhen Cheng

“…Agents built on LLMs (LLM agents) further extend these capabilities, allowing them to process user interactions and perform complex operations in diverse task environments. However, during the processing and generation of massive data, LLMs and LLM agents pose a risk of sensitive information leakage, potentially threatening data privacy. …”
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A novel small-scale wind-turbine blade failure detection according to monitored-data by A. Aranizadeh, H. Shad, B. Vahidi, A. Khorsandi

“…This paper introduces a method for estimating blade damage in small-scale wind turbines, using real data. According to this method, data filtering is done initially to remove the outlier data. …”
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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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Under-representation of Women as Proceduralists and Patients in TAVR and TMVr Procedures: Data, Implications and Proposed Solutions by Sonya Burgess, F Aaysha Cader, Elizabeth Shaw, Shrilla Banerjee, Julia Stehli, Roopa Krishnamorthy, Lynn Khor, Homa Forotan, Alexandra Bastiany, Sarita Rao, Jaya Chandrasekhar, Sarah Zaman, Mirvat Alasnag, Alaide Chieffo, Megan Coylewright

“…This under-representation has the potential to affect the recruitment of women to randomised trials, subsequent guideline recommendations, selection for treatment, patient outcomes and sex-specific data analysis.…”
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Harmonizing and integrating the NCI Genomic Data Commons through accessible, interactive, and cloud-enabled workflows. by Ling-Hong Hung, Bryce Fukuda, Robert Schmitz, Varik Hoang, Wes Lloyd, Ka Yee Yeung

“…Although the executables themselves are usually available in repositories, the GDC pipelines that describe that entire analysis workflow are currently published as text-based standard operating procedures (SOPs). …”
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Geothermal resource potentials estimation from the interpretation of aeromagnetic data over parts of Southwestern Nigeria by Emmanuel Ike, Adetola Sunday Oniku, Sabastine Chinedu Ezike, Maxwell Obia Kanu, Rodney Ewusi-Wilson

“…Regional-residual anomaly separation was conducted, and spectral analysis was applied on the residual anomaly component of 14 aeromagnetic data sheets. …”
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Dynamic Optimization of Bus Line Schedule in Commuter Corridor Based on Bus IC Card Data by Zhihong Li, Han Xu, Shiyao Qiu, Jun Liu, Kairan Yang, Jiahao Wu

“…The results show that the model in this paper provides a new idea for the information mining of bus routes in the research based on the bus IC Card data and provides an effective tool for the management of different operation decision preferences.…”
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Imaging the main molecular biological subtypes of breast cancer: comparison of mammographic data and histological findings by O. S. Khodorovich, L. B. Kanakhina, T. V. Sherstneva, A. A. Kalinina-Masri, Sh. M. Dibirova

“…To explore such a method of visualization of BC as mammography and the correlation of its results with the data of histological and immunohistochemical studies, and their significance in planning organ-conserving operations.Materials and methods. …”
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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

“…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. Methods We developed a DL-based predictive model with attention mechanisms, gLioma recUrreNce Attention-based classifieR (LUNAR), to predict early vs. late glioma recurrence using clinical, mutation, and mRNA-expression data from patients with primary grade II-IV gliomas from The Cancer Genome Atlas (TCGA) and, as an external validation set, the Glioma Longitudinal Analysis Consortium (GLASS). …”
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A Model for Building Student Physical Health Information Management in a Big Data Environment by Yan Luo, Zhibin Nie

“…In view of the problem of low validity of students' physical health information assessment results and difficulty in timely improvement of physical health status, this article constructed a visual, real-time, and comprehensive student physical health IM model using big data. Students' physical health was evaluated using multiple Gaussian distributions, ensuring data reliability, systematic management, comprehensive analysis, and real-time feedback, thereby effectively improving the effectiveness and practical guidance of students’ physical health management results. …”
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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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