Recent advances in explainable Machine Learning models for wildfire prediction by Abira Sengupta, Brendon J. Woodford

“…However, understanding what factors lead to generating models that exhibit optimal performance and providing insight into the importance of features on model outcomes is the subject of ongoing research. …”
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Migraine triggers, phases, and classification using machine learning models by Anusha Reddy, Ajit Reddy

“…These models are run with the dataset without optimal tuning across the entire dataset for different migraine types; which is further improved with selective sampling and optimal tuning. …”
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Ensemble Machine Learning Model for Classification of Spam Product Reviews by Muhammad Fayaz, Atif Khan, Javid Ur Rahman, Abdullah Alharbi, M. Irfan Uddin, Bader Alouffi

“…Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy. …”
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Predicting Crime Time Intervals Using Machine Learning Models by Arief Deswandi, Widi Hastomo

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Why Machine Learning Models Systematically Underestimate Extreme Values by Yuan-Sen Ting

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Benchmarking machine learning models for predicting lithium ion migration by Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, Dmitry A. Aksyonov, Stanislav S. Fedotov, Semen A. Budennyy

“…Abstract The development of fast ionic conductors to improve the performance of electrochemical devices relies on expensive high-throughput (HT) density functional theory (DFT) calculations of transport properties. Machine learning (ML) can accelerate HT workflows but requires high-quality data to ensure accurate predictions from trained models. …”
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Machine Learning-Based Cost Estimation Models for Office Buildings by Guolong Chen, Simin Zheng, Xiaorui He, Xian Liang, Xiaohui Liao

“…This paper explores the application of algorithm-optimized back propagation neural networks and support vector machines in predicting the costs of office buildings. …”
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Interpretable Machine Learning Techniques for an Advanced Crop Recommendation Model by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…Our research addresses this critical imperative by introducing an innovative predictive model that refines crop recommendation systems through advanced machine learning techniques, specifically random forest and SHapley Additive exPlanations (SHAP). …”
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Fault Diagnosis of Oil Pumping Machine Retarder Based on Sound Texture-Vibration Entropy Characteristics and Gray Wolf Optimization-Support Vector Machine by Shutao Zhao, Ke Chang, Erxu Wang, Bo Li, Kedeng Wang, Qingquan Wu

“…Finally, joint eigenvectors were constructed and fed into SVM for learning. The gray wolf optimization (GWO) algorithm was used to optimize the parameters of the SVM model based on mixed kernel function, which reduces the impact of sensor frequency response, environmental noise, and load fluctuation disturbance on the accuracy of retarder fault diagnosis. …”
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Auto regressive neural network-driven reliability optimization in base-isolated building design by John Thedy, Kuo-Wei Liao, Taeyong Kim

Subjects: “…Optimal isolation…”
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Advanced Handover Optimization (AHO) using deep reinforcement learning in 5G Networks by T. Senthil Kumar, Mardeni Roslee, J. Jayapradha, Yasir Ullah, Chilakala Sudhamani, Sufian Mousa Ibrahim Mitani, Anwar Faizd Osman, Fatimah Zaharah Ali

Subjects: “…Machine learning (ML)…”
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Explainability enhanced liver disease diagnosis technique using tree selection and stacking ensemble-based random forest model by Mohammad Mamun, Safiul Haque Chowdhury, Muhammad Minoar Hossain, M.R. Khatun, Sadiq Iqbal

“…Findings: The analysis reveals TSRF as the most effective model, achieving a peak accuracy of 99.92 % on Dataset-1 without feature optimization and 88.88 % on Dataset-2 with RFE optimization. …”
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Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users by Christopher Meaney, Xuesong Wang, Jun Guan, Therese A. Stukel

Subjects: “…Supervised machine learning…”
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Total Tardiness Minimization in a Single-Machine with Periodical Resource Constraints by Bruno Prata, Levi Ribeiro de Abreu, Marcelo Seido Nagano

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Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai by Mohammad H. Mehraban, Aljawharah A. Alnaser, Samad M. E. Sepasgozar

Subjects: “…Building Information Modeling (BIM)…”
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An Optimized Multi-Stage Framework for Soil Organic Carbon Estimation in Citrus Orchards Based on FTIR Spectroscopy and Hybrid Machine Learning Integration by Yingying Wei, Xiaoxiang Mo, Shengxin Yu, Saisai Wu, He Chen, Yuanyuan Qin, Zhikang Zeng

“…The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. …”
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Development of a sustainable strategy model for predicting optimal container stacking locations in container yards using artificial intelligence and cubic data by Mohammed Ahmed Moqbel Saleh, Attariuas Hicham, M.L. Ben Maâti, Hatem Taha, Yahya M.A. Mohammed

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Predicting Student Performance through Machine Learning Methods: Naive Bayesian Classifier by Chenyang Li, Qianzhi Zhou, Luming Du, Sihan Zhang

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Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting by Abdul Wadood, Hani Albalawi, Aadel Mohammed Alatwi, Hafeez Anwar, Tariq Ali

Subjects: “…machine learning…”
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A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability Improvement by Mehrdad Ebrahimi, Mohammad Rastegar

“…In this paper, a scalable model is proposed based on machine learning methods to determine the optimal number and location of switching devices according to system conditions. …”
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