Process optimisation: spinal versus general anaesthesia for endourological surgery. A randomised, controlled trial and machine-learning approach by Kornel Skitek, Gregor A. Schittek, Jens Soukup

“…G3, NS vs. bupivacaine). Machine-learning models were trained and demonstrated satisfactory performance in predicting the time spent in recovery. …”
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Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data by Haytam Elyoussfi, Abdelghani Boudhar, Salwa Belaqziz, Mostafa Bousbaa, Karima Nifa, Bouchra Bargam, Abdelghani Chehbouni

“…Study focus: The research integrates remote sensing data, particularly the Normalized-Difference Snow Index (NDSI) from the MODIS Sensor, with machine learning (ML) and deep learning (DL) models to predict daily snow depth (DSD) at a local scale. …”
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Estimation of microbial load in Ganoderma lucidum using a solar-electric hybrid dryer enhanced by machine learning and IoT by Pinit Nuangpirom, Siwasit Pitjamit, Weerin Pheerathamrongrat, Wasawat Nakkiew, Parida Jewpanya

“…This study demonstrates the hybrid dryer's efficiency and the potential of ML models to optimize the drying process, contributing to energy efficiency and product quality control. …”
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Impedance-Driven Decoupling Water–Nitrogen Stress in Wheat: A Parallel Machine Learning Framework Leveraging Leaf Electrophysiology by Shuang Zhang, Xintong Du, Bo Zhang, Yanyou Wu, Xinyi Yang, Xinkang Hu, Chundu Wu

“…A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. …”
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TiAlNb alloy interatomic potentials: comparing passive and active machine learning techniques with MTP and DeePMD by Anju Chandran, Archa Santhosh, Claudio Pistidda, Paul Jerabek, Roland C. Aydin, Roland C. Aydin, Christian J. Cyron, Christian J. Cyron

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Leveraging Machine Learning to Analyze Influencer Credibility’s Impact on Brand Admiration and Consumer Purchase Intent in Social Media Marketing by Karam Zaki, Abrar Alhomaid, Hany Shared

“…This research underscores the transformative potential of machine learning in decoding consumer behavior, offering fresh insights for marketers aiming to optimize influencer-driven campaigns in the ever-evolving digital landscape.…”
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Comparison of Support Vector Machine and Decision Tree Algorithm Performance with Undersampling Approach in Predicting Heart Disease Based on Lifestyle by Gusti Ayu Putu Febriyanti, Anna Baita

“…The K-fold cross-validation method with K=10 and hyperparameter tuning were applied to obtain the optimal performance of both models. The results showed that SVM without undersampling achieved 92% accuracy, while with undersampling the accuracy decreased to 76%. …”
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Factors Associated with COVID-19 Mortality in Mexico: A Machine Learning Approach Using Clinical, Socioeconomic, and Environmental Data by Lorena Díaz-González, Yael Sharim Toribio-Colin, Julio César Pérez-Sansalvador, Noureddine Lakouari

“…This study aimed to identify factors associated with death in COVID-19 patients by considering clinical, demographic, environmental, and socioeconomic conditions, using machine learning models and a national dataset from Mexico covering all pandemic waves. …”
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Application of numerical analysis and machine learning techniques to improve drying performance and energy consumption of microwave-assisted convective dryer by Hany S. El-Mesery, Ahmed H. ElMesiry, Mohammad Kaveh, Zicheng Hu, Abdallah Elshawadfy Elwakeel, Sara Elhadad

“…Thermodynamic parameters, including energy consumption, drying efficiency, and thermal efficiency, were calculated. Machine learning models, specifically Feedforward Backpropagation (FFBP) and Cascade Forward Backpropagation (CFBP) artificial neural networks, were developed to predict drying outcomes based on input variables. …”
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Regression machine learning methods for isolation prediction and massive gain broadband MIMO antenna design for 28 GHz applications by Md.Ashraful Haque, Redwan A. Ananta, Jun-Jiat Tiang, Mouaaz Nahas, Md Afzalur Rahman, Narinderjit Singh Sawaran Singh

“…The results obtained from CST and ADS modeling, alongside actual and expected outcomes from machine learning, indicate that the proposed antenna is a strong candidate for 5G applications.…”
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Public Sentiment Analysis of Nadiem Makarim as Minister of Education, Culture, Research, and Technology using Support Vector Machine (SVM) by Shasha Ramadhani Putri, Muhammad Arifin, Supriyono Supriyono

“…This study aims to analyze public sentiment toward Nadiem Makarim’s performance and optimize sentiment classification models in handling data imbalance. …”
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Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM by Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir, Mohammed Gamal Ragab, Alawi Alqushaibi, Ebrahim Hamid Sumiea

“…Methods: This study introduces an ensemble stacked bidirectional Long Short-Term Memory model enhanced by a proposed Adaptive Aquila Optimizer (AAO). …”
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Implementation of Adaptive Short Time Fourier Transform and Sigmoid based Kernel Support Vector Machine for Radar Signal Identification by AHMAD Ashraf Adam, MUHAMMAD Farouk Isah

“…The research investigates the effectiveness of this combined approach for radar signal classification. Mathematical models for various radar signals and design of ASTFT optimized for accurate time-frequency analysis are presented. …”
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Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data by Jan Tiede, Joshua Leon Lovell, Christian Jordan, Armin Moghimi, Torsten Schlurmann

“…This study examines the development of a large-scale sand nourishment (600,000 m³) in the southwestern Baltic Sea over 25 months (October 2021–November 2023) using UAV-derived digital surface models (DSMs) and machine learning (ML). High-frequency, multi-temporal UAV surveys enabled detailed analyses of the development of the nourished beach and dune. …”
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Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution by Mood Mohan, Nikhitha Gugulothu, Sreelekha Guggilam, T. Rajitha Rajeshwar, Michelle K. Kidder, Jeremy C. Smith

“…The ML models developed in the present study showed accurate predictions with high determination coefficient (R2) and low root mean square error (RMSE) values. …”
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Quinary Classification of Human Gait Phases Using Machine Learning: Investigating the Potential of Different Training Methods and Scaling Techniques by Amal Mekni, Jyotindra Narayan, Hassène Gritli

“…Preprocessing methods such as Min–Max Scaling (MMS), Standard Scaling (SS), and Principal Component Analysis (PCA) were applied to the dataset to ensure optimal performance of the machine learning models. …”
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Transcriptome signature for multiple biotic and abiotic stress in barley (Hordeum vulgare L.) identifies using machine learning approach by Bahman Panahi

“…Feature selection was performed using five weighting algorithms, resulting in the prioritization of 400 core genes. Machine learning models, specifically Random Forest and C4.5, were optimized and evaluated using a 10-fold cross-validation approach. …”
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Machine learning‐accelerated computational screening of CrNiCu ternary alloy as superior cocatalyst for photocatalytic hydrogen evolution by Shouwei Sang, Kangyu Zhang, Lichang Yin, Gang Liu

“…Combining with density functional theory calculations, XGBoost regression models were trained to predict hydrogen adsorption energies and water dissociation energy barriers on CrNiCu alloy surfaces. …”
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Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights by V. S. Shaisundaram, P. V. Elumalai, S. Padmanabhan, U. Nalini Ramachandran, Abhishek Kumar Tripathi, Cui Yaping, B. Nagaraj Goud, S. Prabhakar

“…These results highlight the potential of ML models in optimizing engine performance for sustainable energy systems across various engine types and fuel sources.…”
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Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004–2023) by Jian-cheng Lou, Xiao-fen Yu, Jian-jun Ying, Da-qiao Song, Wen-hua Xiong

“…The most notable research hotspots currently are the optimal selection of neural imaging markers and the most suitable machine learning algorithm models.…”
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