A systematic review on sleep stage classification and sleep disorder detection using artificial intelligence by Tayab Uddin Wara, Ababil Hossain Fahad, Adri Shankar Das, Md Mehedi Hasan Shawon

“…The Neural Network algorithms are the most popular, having 47 % of the total usage. …”
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Method for estimating non-measurable parameters of gas turbine engines considering variations in technical condition by Sergiy Yepifanov, Oleksii Bondarenko

“…The practical value of the method lies in its applicability to both direct control algorithms based on non-measurable parameters, and onboard estimation and diagnostic algorithms that operate effectively with limited measurement data and under constrained computational resources.…”
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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…Integrating spatial and contextual factors with BER establishes a new standard for predictive accuracy in urban energy research.…”
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Estimating Energy Consumption During Soil Cultivation Using Geophysical Scanning and Machine Learning Methods by Jasper Tembeck Mbah, Katarzyna Pentoś, Krzysztof S. Pieczarka, Tomasz Wojciechowski

“…Three machine learning algorithms were tested: support vector machines (SVMs), multilayer perceptron (MLP), and radial basis function (RBF) neural networks. …”
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Target Detection and Characterization of Multi-Platform Remote Sensing Data by Koushikey Chhapariya, Emmett Ientilucci, Krishna Mohan Buddhiraju, Anil Kumar

“…We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. This multi-platform analysis provides critical insights into the challenges of hyperspectral target detection in complex, real-world landscapes, demonstrating the influence of platform variability on detection performance and the necessity for robust algorithmic approaches in multi-source data integration.…”
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A hybrid approach to enhance HbA1c prediction accuracy while minimizing the number of associated predictors: A case-control study in Saudi Arabia. by Faten Al-Hussein, Mali Abdollahian, Laleh Tafakori, Khalid Al-Shali

“…Despite this high rate, the country lacks focused research on T2D. This study aims to develop hybrid prediction models that integrate the strengths of multiple algorithms to enhance HbA1c prediction accuracy while minimising the number of significant Key Performance Indicators (KPIs). …”
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Unveiling Aducanumab's safety profile: A comprehensive pharmacovigilance analysis. by Huanhuan Ji, Zongren Zhao, Chuanwei Zhao

“…<h4>Strengths and limitations of this study</h4>Strength: Utilized multiple signal detection algorithms (ROR, PRR, BCPNN, MGPS) to enhance robustness of pharmacovigilance findings. …”
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State of affairs of tuberculosis in prison facilities: a systematic review of screening practices and recommendations for best TB control. by Natalie V S Vinkeles Melchers, Sabine L van Elsland, Joep M A Lange, Martien W Borgdorff, Jan van den Hombergh

“…<h4>Discussion</h4>TB screening algorithms differ by income area and should be adapted to local contexts. …”
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Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

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Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving by Ambati Pravallika, Mohammad Farukh Hashmi, Aditya Gupta

“…The progression of reference datasets, such as KITTI, Waymo, and NuScenes, was examined, emphasizing their crucial role in propelling detection algorithms forward and enabling comparative studies across diverse methodologies. …”
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Intelligent Assessment Systems in Medical Education: A Systematic Review by MAJID ALIZADEH, MARYAM JAFAR SAMERI

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Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities by Paul Iacobescu, Virginia Marina, Catalin Anghel, Aurelian-Dumitrache Anghele

“…Machine learning methods, particularly classification algorithms, have demonstrated their potential to accurately predict the risk of cardiovascular disease (CVD) by analyzing patient data. …”
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Enhancing Security for Resource-Constrained Smart Cities IoT Applications: Optimizing Cryptographic Techniques with Effective Field Multipliers by Atef Ibrahim, Fayez Gebali

“…Consequently, the streamlined execution of this operation is pivotal, as it will facilitate the effective deployment of encryption algorithms on these resource-limited devices. Therefore, the presented research concentrates on the formulation of a spatially and energetically efficient hardware implementation for the finite-field multiplication operation. …”
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Artificial intelligence in STEM education: a transdisciplinary framework for engagement and innovation by Cristo Leon, James Lipuma, Xavier Oviedo-Torres

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Reinforcement Learning for Mitigating Malware Propagation in Wireless Radar Sensor Networks with Channel Modeling by Guiyun Liu, Hao Li, Lihao Xiong, Yiduan Chen, Aojing Wang, Dongze Shen

“…With the rapid development of research on Wireless Radar Sensor Networks (WRSNs), security issues have become a major challenge. …”
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Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data by Aleksandra Szymura

“…This action enables the resulting dataset to be of the highest quality. In further research, it is proposed that other algorithms are tested (e.g. …”
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Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning by Jun Tan, Raoof Mohammed Radhi, Kimia Shirini, Sina Samadi Gharehveran, Zamen Parisooz, Mohsen Khosravi, Hossein Azarinfar

“…We developed a hybrid approach integrating a Digital Twin model of the hydropower system with advanced Deep Learning algorithms for real-time monitoring and predictive analysis. …”
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Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories by John T. Delaney, M. Van Appledorn, N. R. De Jager, K. L. Bouska, J. J. Rohweder

“…This information can be used to better understand the extent of invasion, prioritize restoration efforts, and develop further research.…”
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Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies by Vahid Asadi, Akram Hasani Zonoozi, Hosein Haghi, Fatemeh Abedini, Atousa Kalantari, Marziye Jafariyazani, Nima Chartab

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Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former by Zhiwei SONG, Xinbo HUANG, Chao JI, Fan ZHANG, Ye ZHANG

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