DREAMS: A python framework for training deep learning models on EEG data with model card reporting for medical applications by Rabindra Khadka, Pedro G. Lind, Anis Yazidi, Asma Belhadi

“…However, most existing frameworks for EEG data analysis are either focused on preprocessing techniques or deep learning model development, often overlooking the crucial need for structured documentation and model interpretability. …”
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Evaluating and Diagnosing Urban Function and Perceived Quality Based on Multi-Source Data and Deep Learning Using Dalian as an Example by Yumeng Meng, Mei Lyu, Dong Sun, Jiaxuan Shi, Hiroatsu Fukuda

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AN EFFECTIVE MOOC MODEL TO SUPPORT FREEDOM TO LEARN PROGRAM by Ahmad Chafid Alwi, Siti Irene Astuti Dwiningrum, Suyanto Suyanto, Sunaryo Sunarto, Surono Surono

“…The data obtained were analysed by identifying the differences and the similarities of each MOOC. …”
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Features of the use of big data in the financial sector by F. O. Chernenkov

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A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction by Md. Alamin Talukder, Majdi Khalid, Nasrin Sultana

“…This study presents a novel hybrid machine learning (ML) model that integrates KMeans-SMOTE (KMS) for data balancing and principal component analysis (PCA) for dimensionality reduction, evaluated using the WSN-DS and TON-IoT datasets. …”
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Deep learning-driven medical image analysis for computational material science applications by Li Lu, Mingpei Liang

“…IntroductionDeep learning has significantly advanced medical image analysis, enabling precise feature extraction and pattern recognition. …”
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Big data-driven corporate financial forecasting and decision support: a study of CNN-LSTM machine learning models by Aixiang Yang

“…With the rapid advancement of information technology, particularly the widespread adoption of big data and machine learning, corporate financial management is undergoing unprecedented transformation. …”
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The possibilities of machine learning in determining the variants of the course of viral pneumonia associated with COVID-19 based on computed tomography data by I. M. Skorobogach, L. T. Khamidova, R. S. Muslimov, N. V. Rybalko, S. S. Petrikov

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Abnormality detection of sliding surface and exploration suitable sensor data for condition monitoring by calculating contribution using machine learning by Ryo NAKASHIMA, Tomomi HONDA, Tomohiko KON

“…As a result, it was found that SHAP is useful as a method to give interpretation to the analysis results of machine learning for monitoring the condition of sliding surfaces, because the condition of sliding surfaces over time, which cannot be understood by unsupervised learning, can be understood in more detail than before based on the contribution of each sensor data. …”
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Machine Learning or Human Education? Thoughts and Data on Artificial Intelligence and the Labor Market: Economic Sociological Aspects of Career Orientation. by Keszi, Roland

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Height estimation in children and adolescents using body composition big data: Machine-learning and explainable artificial intelligence approach by Dohyun Chun, Taesung Chung, Jongho Kang, Taehoon Ko, Young-Jun Rhie, Jihun Kim

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Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids by Yanfei Li, Juejing Liu, Xiaodong Zhao, Wenjun Liu, Tong Geng, Ang Li, Xin Zhang

“…Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.…”
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Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing data by Ruyi Pan, Jie Zhang, Wentao Xue, Qianqian Xia, Jiangxiang Liu, Junjie Yan, Hongbo Ling, Xufan Jin

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Going Deeper With Deep Learning: Automatically Tracing Internal Reflection Horizons in Ice Sheets—Methodology and Benchmark Data Set by Hameed Moqadam, Daniel Steinhage, Adalbert Wilhelm, Olaf Eisen

“…Here, our goal is to present a complete pipeline for automatic tracing of internal reflection horizons (IRH) of intermediate to large depths in the ice sheet from radargrams using deep learning. We introduce IRHMapNet, which is a deep learning framework that uses a U‐Net‐based architecture to trace IRHs, based on airborne RES data with preprocessing steps such as noise removal and data augmentation, and postprocessing techniques such as morphological filtering and skeletonization. …”
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Investigating Catching Hotspots of Fishing Boats: A Framework Using BeiDou Big Data and Deep Learning Algorithms by Fen Wang, Xingyu Liu, Tanxue Chen, Hongxiang Feng, Qin Lin

“…This contribution presents the efficacy of China’s summer fishing moratorium using BeiDou vessel monitoring system (VMS) data from 2805 fishing vessels in the East China Sea and Yellow Sea, integrated with a deep learning framework for spatiotemporal analysis. …”
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Forecasting the daily evaporation by coupling the ensemble deep learning models with meta-heuristic algorithms and data pre-processing in dryland by Tonglin Fu, Dong Wang, Jing Jin

“…However, developing highly accurate and universal data- driven models using time-series analysis methods to achieve precise evaporation estimation remains a challenging. …”
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Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples by Eloy Pérez-Gómez, José Gómez, José Gómez, Jennifer Gonzalo, Sergio Salgüero, Daniel Riado, María Luisa Casas, María Luisa Gutiérrez, Elena Jaime, Enrique Pérez-Martínez, Rafael García-Carretero, Javier Ramos, Conrado Fernández-Rodríguez, Conrado Fernández-Rodríguez, Myriam Catalá, Myriam Catalá, Luca Martino, Óscar Barquero-Pérez

“…Despite remarkable therapeutic advancements for the treatment of HCV, several challenges remain, such as improved fast diagnostic procedures allowing universal screening.ObjectiveWe propose a novel approach that combines Near-Infrared Spectroscopy (NIRS) and clinical data with machine learning (ML) to improve Hepatitis C Virus (HCV) detection in serum samples.MethodsNIRS offers a fast, non-destructive, and residue-free alternative to traditional diagnostic methods, while ML models enable feature selection and predictive analysis. …”
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Improving Data Entry Quality in Enterprise Applications With NLP Methods: A Model Proposal Based on BERT and Deep Learning by H. Canli

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FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting by He Zhu, Jun Bai, Na Li, Xiaoxiao Li, Dianbo Liu, David L. Buckeridge, Yue Li

“…Abstract Federated learning (FL) enables collaborative analysis of decentralized medical data while preserving patient privacy. …”
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Machine-Learned Codes from EHR Data Predict Hard Outcomes Better than Human-Assigned ICD Codes by Ying Yin, Yijun Shao, Phillip Ma, Qing Zeng-Treitler, Stuart J. Nelson

“…We used machine learning (ML) to characterize 894,154 medical records of outpatient visits from the Veterans Administration Central Data Warehouse (VA CDW) by the likelihood of assignment of 200 International Classification of Diseases (ICD) code blocks. …”
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