Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children by Satrio Agung Wicaksono, Satrio Hadi Wijoyo, Fatmawati Fatmawati, Tri Afirianto, Diva Kurnianingtyas, Mochammad Chandra Saputra

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Student’s Digital Intentions Prediction Using CatBoost by Zulwisli, Ambiyar, Muhammad Anwar, Andhika Herayono

“…A recent study developed a predictive model using the CatBoost algorithm to understand better the factors influencing digital entrepreneurship. …”
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An Interpretable Method for Asphalt Pavement Skid Resistance Performance Evaluation Under Sand-Accumulated Conditions Based on Multi-Scale Fractals by Yuhan Weng, Zhaoyun Sun, Huiying Liu, Yingbin Gu

“…The performance of mainstream machine learning models is compared, and the eXtreme Gradient Boosting (XGBoost) model is optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. …”
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Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation by Yuksel Rudy Alkarem, Kimberly Huguenard, Richard W. Kimball, Stephan T. Grilli

“…We introduce the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>τ</mi></semantics></math></inline-formula>-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. …”
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Construction and Application of Feature Recommendation Model for Remote Sensing Interpretation of Rock Strata Based on Knowledge Graph by Liufeng Tao, Qirui Wu, Miao Tian, Zhong Xie, Jianguo Chen, Yueyu Wu, Qinjun Qiu

“…This study proposes a geological remote sensing interpretation framework that integrates textual geological data, which enhances lithological identification accuracy by systematically combining multi-source geological knowledge with machine learning algorithms. Using a dataset of 2591 geological survey reports and scientific literature, a remote sensing interpretation ontology model was established, featuring four core entities (rock type, stratigraphic unit, spectral feature, and geomorphological indicator). …”
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Impact of anthropogenic disturbance and climate on bamboo distribution in shifting cultivation landscapes of Northeast India by Muna Tamang, Subrata Nandy, Ritika Srinet, Yamini Bhat, Hitendra Padalia, Arun Jyoti Nath, Ashesh Kumar Das, R. P. Singh

“…The influence of climatic drivers on bamboo distribution was analyzed using the RF algorithm, and vapour pressure deficit was identified as the most influential factor. …”
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Deep learning based automation of mean linear intercept quantification in COPD research by Lars Leyendecker, Anna Louisa Weltin, Florian Nienhaus, Michaela Matthey, Bastian Nießing, Daniela Wenzel, Daniela Wenzel, Robert H. Schmitt, Robert H. Schmitt

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Immunohistochemical Subtype and Parameters of International Prognostic Index in the New Prognostic Model of Diffuse Large B-Cell Lymphoma by SV Samarina, AS Luchinin, NV Minaeva, IV Paramonov, DA D’yakonov, EV Vaneeva, VA Rosin, VA Rosin

“…A new integrated system of DLBCL prognosis is suggested which includes IPI risk parameters and immunohistochemical subtype based on Hans algorithm. This prognostic system can be used in clinical practice for DLBCL patient stratification and risk-adapted therapy.…”
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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis by Fernanda Pereira Leite Aguiar, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…This study introduces a novel preprocessing algorithm to bridge the gap between computational efficiency and segmentation fidelity in object-based image analysis for machine learning applications. …”
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The dual-edged potential of AI autonomously defining loss functions by Abbas Ghori

“…They predominantly act as guiding principles for any optimization algorithm, thereby influencing both the convergence characteristics as well as the generalization of the model. …”
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A Novel Transformer-Based Approach for Reliability Evaluation of Composite Systems With Renewables and Plug-in Hybrid Electric Vehicles by Chiranjeevi Yarramsetty, Tukaram Moger, Debashisha Jena, Veeranki Srinivasa Rao

“…To address these issues, this research presents an advanced mixed-integer linear programming (MILP)-based algorithm for optimizing EV charging. The algorithm prioritizes clean energy utilization through intelligent power allocation strategies while considering cost-revenue trade-offs. …”
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An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning by Abdourahman Djamal Djama

“…Method. information-theoretical analysis of methods for detecting fraud with bank cards, machine learning algorithms in improving the accuracy of fraud detection. …”
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Electric Vehicle Sentiment Analysis Using Large Language Models by Hemlata Sharma, Faiz Ud Din, Bayode Ogunleye

“…In the field of natural language processing (NLP), transformer models have shown great performance compared to traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. …”
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Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation by Karthik Raman, Rukmini Kumar, Cynthia J. Musante, Subha Madhavan

“…Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. …”
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ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

“…Anomaly detection, crucial for identifying issues such as financial fraud or medical malfunctions, has advanced significantly with machine learning (ML) and deep learning (DL). However, a major problem in the field is that no single model works best with diverse datasets and problem domains. …”
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Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra by Dong Xing, Penghui Sun, Yulin Wang, Mei Jiang, Siyu Miao, Wei Liu, Huahong Huang, Erpei Lin

“…The spectral data of the canopy area of the seedlings were extracted and preprocessed using the Savitzky-Golay smoothing (SG) algorithm. Subsequently, the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) methods were employed to extract the most informative wavelengths. …”
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An intelligent identification for pest and disease detection in wheat leaf based on environmental data using multimodal data fusion by SHENG-HE XU, Sai Wang

“…First, deep - learning algorithms in image analysis detect early - stage pests and diseases on wheat leaves. …”
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Enhancing Medical Image Classification with Unified Model Agnostic Computation and Explainable AI by Elie Neghawi, Yan Liu

“…By unifying and standardizing computational models and algorithms, UMAC ensures adaptability across different data types and computational environments while incorporating state-of-the-art methodologies. …”
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Harnessing quantum power: Revolutionizing materials design through advanced quantum computation by Zikang Guo, Rui Li, Xianfeng He, Jiang Guo, Shenghong Ju

“…This review introduces a comprehensive methodology for materials design using cutting‐edge quantum computing, with a particular focus on quadratic unconstrained binary optimization (QUBO) and quantum machine learning (QML). We introduce the loop framework for QUBO‐empowered materials design, including constructing high‐quality datasets that capture critical material properties, employing tailored computational methods for precise material modeling, developing advanced figures of merit to evaluate performance metrics, and utilizing quantum optimization algorithms to discover optimal materials. …”
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Crop Yield Prediction: Data Structure and Ai-Powered Methods by V. K. Kalichkin, K. Yu. Maksimovich, O. A. Aleshchenko, V. V. Aleshchenko

“…(Research purpose) The aim of this study is to analyze data structures and compare machine learning and deep learning algorithms used in used in crop yield prediction. …”
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