Emotion-Aware Ensemble Learning (EAEL): Revolutionizing Mental Health Diagnosis of Corporate Professionals via Intelligent Integration of Multi-Modal Data Sources and Ensemble Tech... by Gaurav Yadav, Mohammad Ubaidullah Bokhari, Saleh I. Alzahrani, Shadab Alam, Mohammed Shuaib

“…EAEL integrates machine learning and deep learning paradigms to process multimodal data, including facial expression analysis and typing pattern recognition, offering a holistic evaluation of emotional well-being. …”
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Student behavior patterns in vocational education big data based on clustering algorithm by Jing Jin

“…Abstract In vocational education, big data analysis of student behaviour has problems such as single data dimension, failure to effectively identify differences between student groups, and overly simple data analysis methods. …”
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Organ-system-based subclassification of preeclampsia using machine learning predicts pregnancy outcomes by Yanhong Xu, Yizheng Zu, Xiaosi Lu, Yiping Wang, Jiaying Zheng, Xia Xu, Jianying Yan

“…It remains unclear whether machine learning can identify organ-system-based subclasses of PE. …”
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Cluster analysis of social determinants of health and HIV/AIDS knowledge among Peruvian youths using Kohonen’s self-organized maps: a data-exploration study based on a Demographic... by Alejandro Aybar-Flores, Alvaro Talavera, Elizabeth Espinoza-Portilla

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An Assessment of Land Use Land Cover Using Machine Learning Technique by V. Pushpalatha, H. N. Mahendra, A. M. Prasad, N. Sharmila, D. Mahesh Kumar, N. M. Basavaraju, G. S. Pavithra and S. Mallikarjunaswamy

“…Remote sensing imagery, Geographic Information System (GIS) tools, and machine learning algorithms are leveraged to process and interpret satellite data for accurate land-cover classification. …”
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PhyloFunc: phylogeny-informed functional distance as a new ecological metric for metaproteomic data analysis by Luman Wang, Caitlin M. A. Simopoulos, Joeselle M. Serrana, Zhibin Ning, Yutong Li, Boyan Sun, Jinhui Yuan, Daniel Figeys, Leyuan Li

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A Comprehensive Review of Transformer Winding Diagnostics: Integrating Frequency Response Analysis with Machine Learning Approaches by Meysam Beheshti Asl, Issouf Fofana, Fethi Meghnefi, Youssouf Brahami, Joao Pedro Da Costa Souza

“…Integrating Machine Learning (ML) with FRA can significantly enhance fault diagnosis by automatically identifying complex patterns within the data that are difficult to detect using through human analysis. …”
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Leveraging Cross-Project Similarity for Data Augmentation and Security Bug Report Prediction by Jinfeng Ji, Geunseok Yang

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Effective defense against physically embedded backdoor attacks via clustering-based filtering by Mohammed Kutbi

“…Abstract Backdoor attacks pose a severe threat to the integrity of machine learning models, especially in real-world image classification tasks. …”
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Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study by Xiaolei Lu, Chenye Qiao, Hujun Wang, Yingqi Li, Jingxuan Wang, Congxiao Wang, Yingpeng Wang, Shuyan Qie

“…Conclusions: This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. …”
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Detection of Economic Crises With Language Models and Comparative Analysis of Simple Time Series Analysis Models and Machine Learning Algorithms on the Stock Market by Kurban Kotan, Bayram Kotan, Serdar Kirisoglu

“…This study investigates the use of natural language processing language representation models as an early warning system for economic crises, and compares the performance of time series analysis and machine learning models in financial markets before and during the economic crises in order to select the best model. …”
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Machine learning assists regulated cell death crucial biomarker selection in adenocarcinoma of the lung: biological data testing and cell assay determination by Han Ning, Ying Jiang, Mengli Zheng, Gao Yang, Lianjun Ma, Yachao Zhao

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A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification by Amoakoh Gyasi-Agyei

“…A publicly available raw osteosarcoma dataset was explored and then preprocessed using different combinations of data denoising techniques (including principal component analysis, mutual information gain, analysis of variance and Kendall’s rank correlation analysis) and data augmentation to derive seven different datasets. …”
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Deep Learning Autoencoders for Fast Fourier Transform-Based Clustering and Temporal Damage Evolution in Acoustic Emission Data from Composite Materials by Serafeim Moustakidis, Konstantinos Stergiou, Matthew Gee, Sanaz Roshanmanesh, Farzad Hayati, Patrik Karlsson, Mayorkinos Papaelias

“…The present study introduces a novel approach that integrates frequency-domain analysis using the fast Fourier transform (FFT) with deep learning techniques for more accurate and proactive damage detection. …”
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Assessment of environmental impacts of armed conflict in Mozambique using remotely sensed data by Focas Francisco Bacar, Hilário Biché Faque

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An information dissemination strategy in social networks based on graph and content analysis by Jing Huang

“…By leveraging advanced computational techniques and data analysis, we can strive towards a more informed and trustworthy digital environment, where users can navigate through the sea of information with confidence and make well-informed decisions.…”
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Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach by David Agustriawan, Adithama Mulia, Marlinda Vasty Overbeek, Vincent Kurniawan, Jheno Syechlo, Moeljono Widjaja, Muhammad Imran Ahmad

“… Abstract BackgroundPrevious machine learning approaches for prostate cancer detection using gene expression data have shown remarkable classification accuracies. …”
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Diagnostic potential of the B9D2 gene in colorectal cancer based on whole blood gene expression data and machine learning by Zhaorui Wang, Yongcheng Fu, Haozhe Zhang, Na Liu, Ningjing Lei

“…The biological functions of these genes were investigated through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), the Human Protein Atlas (HPA), and Gene Set Enrichment Analysis (GSEA). Additionally, three machine learning methods—Random Forest (RF), LASSO regression, and Support Vector Machine Recursive Feature Elimination (SVM-RFE)—were employed for feature selection and to evaluate the robustness and predictive power of the selected features, with diagnostic efficacy evaluated through Receiver Operating Characteristic (ROC) curves. …”
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From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers by Heather Shaw, Pinkie Chambers, Matthew Watson, Luke Steventon, James Harmsworth King, Angelo Ercia, Noura Al Moubayed

“…We propose gold-standard bias mitigation steps for ML models: evaluation on multisite data, thorough patient population analysis, and both formalised bias measures and model performance comparisons on patient subgroups. …”
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Lessons Learned From European Health Data Projects With Cancer Use Cases: Implementation of Health Standards and Internet of Things Semantic Interoperability by Amelie Gyrard, Somayeh Abedian, Philip Gribbon, George Manias, Rick van Nuland, Kurt Zatloukal, Irina Emilia Nicolae, Gabriel Danciu, Septimiu Nechifor, Luis Marti-Bonmati, Pedro Mallol, Stefano Dalmiani, Serge Autexier, Mario Jendrossek, Ioannis Avramidis, Eva Garcia Alvarez, Petr Holub, Ignacio Blanquer, Anna Boden, Rada Hussein

“…These initiatives aim to enhance the analysis of heterogeneous health data while aligning with EHDS implementation, specifically for the EHDS for the secondary use of data (EHDS2). …”
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