Predicting drug protein interactions based on improved support vector data description in unbalanced data by Alireza Khorramfard, Jamshid Pirgazi, Ali Ghanbari Sorkhi

“…To address the challenge of unbalanced datasets, a Support Vector Data Description (SVDD) approach is employed, outperforming standard techniques like SMOTE and ENN in balancing data. …”
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Data-Driven Energy Consumption Analysis and Prediction of Real-World Electric Vehicles at Low Temperatures: A Case Study Under Dynamic Driving Cycles by Yifei Zhao, Hang Liu, Jinsong Li, Hongli Liu, Bin Li

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Artificial intelligence in environmental monitoring: in-depth analysis by Emran Alotaibi, Nadia Nassif

“…Abstract This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. …”
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Supporting offshore wind growth: Automating data analysis in digital aerial surveys to enhance wildlife protection and survey efficiency by Ben Bartlett, Matheus Santos, Petar Trslic, Gerard Dooly

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Urban road travel speed prediction based on multi-feature data fusion by HUO Jianan, CHENG Wei, LI Bing

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Ripen banana dataset: A comprehensive resource for carbide detection and ripening stage analysis to enhance food quality and agricultural efficiencyMendeley Data by Elman Alam, Md Tarequl Islam, Ishrat Zahan Raka, Onamika Sarkar Ritu, Md Shakhawat Hossain, Wahidur Rahman, Rahat Khan

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A Method to Detect Concealed Damage in Concrete Tunnels Using a Radar Feature Vector and Bayesian Analysis of Ground-Penetrating Radar Data by Junfang Wang, Heng Chen, Jianfu Lin, Xiangxiong Li

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Computational analysis of the functional impact of MHC-II-expressing triple-negative breast cancer by Yang Cui, Weihang Zhang, Xin Zeng, Yitao Yang, Sung-Joon Park, Kenta Nakai, Kenta Nakai

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Federated learning with joint server-client momentum by Boyuan Li, Shaohui Zhang, Qiuying Han

“…In this study, we introduce a novel Federated Learning algorithm called Federated Joint Server-Client Momentum (FedJSCM) to address data heterogeneity in real-world Federated Learning applications. …”
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Sentiment Analysis of Online Vehicle Tax Renewal Application Users Using Support Vector Machine Algorithm by Muhamad Ilham Fauzy, Ferian Fauzi Abdulloh

Subjects: “…sentimen analysis…”
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A Hybrid Regression–Kriging–Machine Learning Framework for Imputing Missing TROPOMI NO₂ Data over Taiwan by Alyssa Valerio, Yi-Chun Chen, Chian-Yi Liu, Yi-Ying Chen, Chuan-Yao Lin

“…This study presents a novel application of a hybrid regression–kriging (RK) and machine learning (ML) framework to impute missing tropospheric NO₂ data from the TROPOMI satellite over Taiwan during the winter months of January, February, and December 2022. …”
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Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System by Xiongwei Zhang, Hager Saleh, Eman M. G. Younis, Radhya Sahal, Abdelmgeid A. Ali

“…It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. …”
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Second-generation downscaled earth system model data using generative machine learningOEDI by Grant Buster, Brandon N. Benton, Deeksha Rastogi, Shih-Chieh Kao, Guilherme Castelao, Jordan Eisenman

“…This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). …”
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Transformer-Driven Inverse Learning for AI-Powered Ceramic Material Innovation With Advanced Data Preprocessing by Murad Ali Khan, Syed Shehryar Ali Naqvi, Muhammad Faseeh, Do-Hyeun Kim

“…The Transformer model, leveraging this enhanced data, demonstrated strong predictive performance, achieving an R2 score of 0.966 for component analysis and an outstanding R2 score of 0.982 for process analysis in Barium Titanate (BaTiO3) material data. …”
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Multimodal data integration with machine learning for predicting PARP inhibitor efficacy and prognosis in ovarian cancer by Xi’an Xiong, Li Cai, Li Cai, Zhen Yang, Zhongping Cao, Nayiyuan Wu, Nayiyuan Wu, Qianxi Ni

“…The integration of multimodal data with machine learning holds significant potential for enhancing prognosis prediction in PARPi treatment for ovarian cancer.…”
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Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data. by Dominik Schaack, Markus A Weigand, Florian Uhle

“…We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). …”
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Clustered embedding using deep learning to analyze urban mobility based on complex transportation data. by Sung-Bae Cho, Jin-Young Kim

“…In particular, based on its exceptional ability to extract patterns from complex large-scale data, embedding based on deep learning is a promising method for analyzing the mobility patterns of urban residents. …”
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Supervised Learning-Based Fault Classification in Industrial Rotating Equipment Using Multi-Sensor Data by Aziz Kubilay Ovacıklı, Mert Yagcioglu, Sevgi Demircioglu, Tugberk Kocatekin, Sibel Birtane

“…The presented system focuses on multi-modal data analysis, such as vibration analysis, temperature monitoring, and ultrasound, for more effective fault diagnosis. …”
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Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique by Shafi Muhammad Pathan, Abdul Ghani Pathan, Muhammad Saad Memon

“…This study investigates shovel excavation performance across various rockmass conditions by integrating real-time performance assessments, rockmass property analysis, and machine learning techniques. Correlation analysis revealed significant positive relationships between Total Loading Time (TLT) and selected rock properties, specifically uniaxial compressive strength (UCS), tensile strength (TS) cohesion (C), and moisture content (M), while a negative correlation was observed with wet bulk density (WBD). …”
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MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning by Yu Wang, Zaiyi Liu, Xiaoke Ma

“…Here, we present a flexible multi-modal contrastive learning for the integration of SRT data (MuCST), which joins denoising, heterogeneity elimination, and compatible feature learning. …”
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