Identifying Suitability for Data Reduction in Imbalanced Time-Series Datasets by Dominic Sanderson, Tatiana Kalganova

“…Occupancy detection for large buildings enables optimised control of indoor systems based on occupant presence, reducing the energy costs of heating and cooling. Through machine learning models, occupancy detection is achieved with an accuracy of over 95%. …”
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Toward improving precision and complexity of transformer-based cost-sensitive learning models for plant disease detection by Manh-Tuan Do, Manh-Hung Ha, Duc-Chinh Nguyen, Oscal Tzyh-Chiang Chen, Oscal Tzyh-Chiang Chen

“…This study introduces an automated system for early disease detection in plants that enhanced a lightweight model based on the robust machine learning algorithm. …”
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Global trends and health workforce analysis of breast cancer burden from high red meat consumption 1990–2050 using machine learning approach by Yuzhou Cai, Jingxian Qian

“…Age-period-cohort analysis, decomposition analysis, health inequality assessment, frontier analysis, and correlation analysis with healthcare workforce density were employed. Machine learning models (ARIMA, Prophet) provided projections to 2050.ResultsDespite declining global age-standardized mortality rates (APC: −0.772%), absolute breast cancer deaths increased from 45,074 (1990) to 81,506 (2021), with DALYs rising from 1.4 to 2.5 million. …”
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A novel method for quantifying the contribution of regional transport to PM_2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory... by K. Hu, H. Liao, D. Liu, J. Jin, L. Chen, S. Li, Y. Wu, C. Wu, S. Zhao, X. Jiang, P. Tian, P. Tian, K. Bi, K. Bi, Y. Wang, Y. Wang, Y. Wang, D. Zhao, D. Zhao

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Learning-Based Energy Management System for Scheduling of Appliances inside Smart Homes by Nastaran Gholizadeh, Mehrdad Abedi, Hamed Nafisi, Mousa Marzband

“…The current structure uses machine learning techniques to design the best demand response programs for heating, ventilation and cooling devices of each user based on his/her behavior and desired comfort level. …”
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Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models by Sami Naouali, Oussama El Othmani

“…This study introduces a novel framework leveraging Rough Set Theory (RST)-based feature selection—MLReduct, MLSpecialReduct, and MLFuzzyRoughSet—to enhance machine learning performance on uncertain data. …”
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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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Long Term Optimal DG Placement Considering Transmission System Reliability and Load Uncertainty by Ali Badri, Mahdy Norouzi

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Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow by Vinicius L.S. Silva, Pablo Salinas, Claire E. Heaney, Matthew D. Jackson, Christopher C. Pain

“…Furthermore, this work performs a sensitivity study in the dimensionless parameters (machine learning features), assess the efficacy of various machine learning models, demonstrate a decrease in nonlinear iterations using our method in more intricate, realistic three-dimensional models, and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85% reduction in computational time.…”
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Cooperative control method for multi-agent ground fracturing truck group based on offline reinforcement learning by RuYi Wang, HuiShen Jiao, YingCheng Tian, Yi Zhao, SiQi Wang, Ke Zhang, Bo Huang, QinRui Sun, DanDan Zhu

“…Furthermore, the proposed method has been experimentally compared with both classical and cutting-edge models of machine learning and reinforcement learning, resulting in 37.5% to 48.6% reductions in pump speed deviation. …”
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Different Approaches to Artificial Intelligence–Based Predictive Maintenance on an Axle Test Bench with Highly Varying Tests by Markus Siebert, Michael Fister, Christian Spieker, Daniel Stengler

“…Predictive assessments of system conditions ensure greater reliability and cost reductions through longer service life. The implementation of a machine learning and a deep learning algorithm for predictive maintenance through early damage detection on an electric rear axle test bench is presented in this paper. …”
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Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning by Jumanah Ahmed Darwish

“…This study offers a comprehensive evaluation of six machine learning algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine One-vs-One (SVM OVO), Support Vector Machine One-vs-All (SVM OVA), and Multi-Layer Perceptron (MLP)—in the context of corporate credit rating classification. …”
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MetaStackD A robust meta learning based deep ensemble model for prediction of sensors battery life in IoE environment by D. Gayathri, S. P. Shantharajah

“…Abstract Advancements in Artificial Intelligence, Machine Learning, and Deep Learning have paved the way for ample applications in real-time. …”
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Integrated bioinformatics analysis of biomarkers and pathways to explore the mechanisms and molecular targets related to allergic rhinitis and pyroptosis by Mingai You, Fangrui Liu, Youshijie Cai, Guodong Yu

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Optical OTFS waveform PAPR analysis for high order modulation employing CNN, DNN, and AE machine learning algorithms under a variety of channel scenarios by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…This paper proposes an algorithm for machine-learning (ML)-based PAPR reduction dedicated to optical OTFS under varying channel conditions, such as turbulence and multipath fading. …”
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Impact of national e-commerce demonstration city pilot policy on urban carbon emission efficiency: From the perspective of soft and hard environments by YUE Li, WANG Xinran

“…[Methods] Based on prior research, this study employed multiple-period difference-in-differences and double machine learning models to rigorously identify the causal relationship between the NEDC pilot policy and urban CEE, using panel data of 282 prefecture-level administrative units in China from 2006 to 2022. …”
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Tropical Cyclone Surface Winds From Aircraft With a Neural Network by Alexander J. DesRosiers, Michael M. Bell, Jennifer C. DeHart, Jonathan L. Vigh, Christopher M. Rozoff, Eric A. Hendricks

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Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study by Niteesh K Choudhry, Shweta Priyadarshini, Jaganath Swamy, Mridul Mehta

“…Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with T2D. …”
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Integrating radiomic texture analysis and deep learning for automated myocardial infarction detection in cine-MRI by Wang Xu, Xiangjiang Shi

“…A three-stage feature selection pipeline was employed, followed by classification using multiple machine learning models. Early and intermediate fusion strategies were integrated into the hybrid architecture. …”
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Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms by Fude Duan, Mahdiyeh Eslami, Mohammad Khajehzadeh, Ali Basem, Dheyaa J. Jasim, Sivaprakasam Palani

“…In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. …”
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