Obesity Biomarkers: Exploring Factors, Ramification, Machine Learning, and AI‐Unveiling Insights in Health Research by Ankita Awari, Deepika Kaushik, Ashwani Kumar, Emel Oz, Kenan Çadırcı, Charles Brennan, Charalampos Proestos, Mukul Kumar, Fatih Oz

“…Recent advances in biosciences, including next‐generation sequencing, multi‐omics analysis, high‐resolution imaging, and smart sensors, have revolutionized data generation. …”
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Aerodynamic Optimization of Morphing Airfoil by PCA and Optimization-Guided Data Augmentation by Ao Guo, Jing Wang, Miao Zhang, Han Wang

“…This study proposes an aerodynamic optimization framework for morphing airfoils by integrating Principal Component Analysis (PCA) for geometric dimensionality reduction and deep learning (DL) for surrogate modeling, alongside an optimization-guided data augmentation strategy. …”
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Unsupervised machine learning-based stratification and immune deconvolution of liver hepatocellular carcinoma by Mae Montserrat Reierson, Animesh Acharjee

“…Methods The unsupervised machine learning methods— agglomerative hierarchical clustering, Multi-Omics Factor Analysis with K-means++, and an autoencoder with K-means++ — stratified patients using microarray data from HCC samples. …”
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A Machine Learning Model for Predicting Breast Cancer Recurrence and Supporting Personalized Treatment Decisions Through Comprehensive Feature Selection and Explainable Ensemble Le... by Lee TF, Shiau JP, Chen CH, Yun WP, Wuu CS, Huang YJ, Yeh SA, Chen HC, Chao PJ

“…By identifying individualized recurrence risks through SHAP analysis, the model supports more precise, data-driven clinical decision-making. …”
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Predicting Scattering From Complex Nano-Structures via Deep Learning by Yongzhong Li, Yinpeng Wang, Shutong Qi, Qiang Ren, Lei Kang, Sawyer D. Campbell, Pingjuan L. Werner, Douglas H. Werner

“…Furthermore, the proposed DL framework has demonstrated robustness to changes in design variables which govern the nano-structure geometry and material selection as well as properties of the incident wave, shedding light on universal scattering predictions at the nano scale via deep learning techniques. This framework increases the viability of the design and analysis of complex nanostructures, offering great potential for applications pertaining to complex light-matter interaction between electromagnetic fields and nanomaterials.…”
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Heart rate variability in soccer players and the application of unsupervised machine learning by Wollner Materko, Sávio Andrei Medeiros Miranda, Thiago Henrique Lobato Bezerra, Carlos Alberto Machado de Oliveira Figueira

“…Factor analysis was then performed using principal component (PC) extraction and varimax rotation. …”
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Indonesian Nursing Educators’ Experiences with Developing Student-Centered Learning Methods by Vigdis Abrahamsen Grøndahl, Kirsti Lauvli Andersen, Ann Karin Helgesen, Asniar Asniar, Randi Martinsen, Riza Septiani, Dharina Baharuddin, Signe Rasch Woersaa, Anna Willman, Liv Berit Olsen

“…The current study used a qualitative, descriptive design with a questionnaire containing four open-ended questions focusing on the educators’ experiences with developing student-active methods and conditions affecting this process. Data were collected between January and November 2022 and analyzed using qualitative content analysis. …”
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Key factors in predictive analysis of cardiovascular risks in public health by Ghazi I. Al Jowf, Manjur Kolhar

“…Conventional ML models like Random Forest and Gradient Boosting Machines were effective in identifying patients at risk achieving up to 74% accuracy and 72% recall. On the hand, deep learning models like Multilayer Perceptron excelled in handling data with an impressive ROC AUC score of approximately 80%. …”
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Survey on differential privacy and its progress by Zhi-qiang GAO, Yu-tao WANG

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Survey on differential privacy and its progress by Zhi-qiang GAO, Yu-tao WANG

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Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters by Luca Quagliato, Jiin Seo, Jiheun Hong, Taeyong Lee, Yoon-Sok Chung

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MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENT by Coralia TANASUICA (ZOTIC), Mihai Daniel ROMAN

“…The methodology is based on the analysis of data gathered from sensors installed on the vehicle's Controller Area Network (CAN), collected over a span of five months. …”
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Causal discovery and fault diagnosis based on mixed data types for system reliability modeling by Xiaokang Wang, Siqi Jiang, Xinghan Li, Mozhu Wang

“…However, due to limitations in existing frameworks regarding model representations and learning algorithms, only a few studies have explored causal discovery on non-Euclidean data. …”
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The Design of the Problem Analysis-Based E-Teaching Materials for the Tahsin Al-Quran Course by Fauza Masyhudi, Ilpi Zukdi, Rendy Nugraha Frasandy, Alfurqan Alfurqan, Annisaul Khairat

“…This research is development research using the ADDIE model at the analysis and design stage. Data on The Tahsin Al-Quran lecture problems have been obtained from questionnaires, interviews and observations carried out at the problem analysis stage. …”
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Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO₂: A Data-Driven Approach by Man Fang, Yutong Yao, Chao Pang, Xiehang Chen, Yutao Wei, Fan Zhou, Xiaokun Zhang, Yong Xiang

“…First, we conducted a feature election to reduce model overfitting through a combined approach of mechanistic analysis and Pearson correlation analysis. Second, the experimental results revealed that RF and XGBoost are the two best-performing models for data fitting. …”
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Data-driven machine learning algorithm model for pneumonia prediction and determinant factor stratification among children aged 6–23 months in Ethiopia by Addisalem Workie Demsash, Rediet Abebe, Wubishet Gezimu, Gemeda Wakgari Kitil, Michael Amera Tizazu, Abera Lambebo, Firomsa Bekele, Solomon Seyife Alemu, Mohammedamin Hajure Jarso, Geleta Nenko Dube, Lema Fikadu Wedajo, Sanju Purohit, Mulugeta Hayelom Kalayou

“…Jupyter Notebook from Anaconda Navigators was used for data management and analysis. Important libraries such as Pandas, Seaborn, and Numpy were imported from Python. …”
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On the Application of a Sparse Data Observers (SDOs) Outlier Detection Algorithm to Mitigate Poisoning Attacks in UltraWideBand (UWB) Line-of-Sight (LOS)/Non-Line-of-Sight (NLOS) C... by Gianmarco Baldini

“…In particular, the study described in this paper proposes a recent outlier detection algorithm, which has low computing complexity: the sparse data observers (SDOs) algorithm. The study proposes a comprehensive analysis of both conventional and novel types of attacks and related mitigation techniques based on outlier detection algorithms for UltraWideBand (UWB) channel classification. …”
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A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing by Yuchen Liang, Yuqi Wang, Anping Li, Chengyi Gu, Jie Tang, Xianjuan Pang

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Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning by Jakub Horvath, Pavel Jedlicka, Marie Kratka, Zdenek Kubat, Eduard Kejnovsky, Matej Lexa

“…Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.…”
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Correction: Learning the therapeutic targets of acute myeloid leukemia through multiscale human interactome network and community analysis by Suruthy Sivanathan, Ting Hu

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