SorpVision: A Comprehensive Dataset for Cementitious Sorptivity Analysis Powered by Computer Vision by Hossein Kabir, Jordan Wu, Sunav Dahal, Tony Joo, Nishant Garg

“…The framework, trained using 1,440 data points from pastes with water-to-cement (w/c) ratios of 0.4–0.8 and curing durations of 1–7 days, achieves high predictive accuracy for initial and secondary sorptivities (R2 > 0.9 for cement pastes). …”
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Hybrid Paddy disease classification using optimized statistical feature based transformation technique with explainable AI by M Amudha, Brindha K

“…This research proposes a machine learning strategy based on Explainable AI that can assist farmers quickly diagnose and manage rice leaf diseases. To enhance predictive performance by transforming features, an improved Owl Search Optimization (IOSO) algorithm is used. …”
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Advanced hybrid computational analysis of febuxostat solubility using machine learning in supercritical processing method by Turki Al Hagbani, Rami M. Alzhrani, Majed Ahmed Algarni

“…With the R2 criterion, the GPR predictive model has a score of 0.819 and the KNN model has a score of 0.854, but the voting model has a score of 0.980, which shows that the combined voting method using the other two models has a better result than both of them. …”
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The role of artificial intelligence in enhancing disaster preparedness and trauma care by Mohamad Sorani

“…Furthermore, ethical considerations such as data privacy, algorithmic bias, and equitable access are crucial to ensuring that AI-driven solutions are implemented responsibly and inclusively.This review also explores future directions for AI, including the development of sophisticated predictive models, autonomous systems for high-risk tasks, immersive training programs utilizing virtual and augmented reality, and global collaboration aimed at standardizing AI tools and practices. …”
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Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network by Bin Luo, Susan Halabi

“…Simulation studies demonstrate that the proposed methods yield superior variable selection and predictive performance compared to traditional and outcome-specific approaches, while remaining robust to violations of the common predictor assumption. …”
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Development of a battery free, solar powered, and energy aware fixed wing unmanned aerial vehicle by Jackson Liller, Rishabh Goel, Abdul Aziz, Josiah Hester, Phuc Nguyen

“…We also derive the Greedy Energy-Aware Control (GEAC) and Predictive Energy-Aware Control (PEAC) solar control algorithm that overcomes power system brownouts and total-loss-of-thrust events, enabling the prototype to perform maneuvers without a battery. …”
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Location allocation approach based on influence of points of dispensing by Nusaybah Alghanmi, Reem Alotaibi, Sultanah Alshammari, Arif Mahmood

“…It also obtained the closest results to the case study in terms of number, location and predictive performance.…”
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Privacy, ethics, transparency, and accountability in AI systems for wearable devices by Petar Radanliev, Petar Radanliev

“…The integration of artificial intelligence (AI) and machine learning (ML) into wearable sensor technologies has substantially advanced health data science, enabling continuous monitoring, personalised interventions, and predictive analytics. However, the fast advancement of these technologies has raised critical ethical and regulatory concerns, particularly around data privacy, algorithmic bias, informed consent, and the opacity of automated decision-making. …”
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Enhancing Power Allocation in DAS: A Hybrid Machine Learning and Reinforcement Learning Model by S. Gnanasekar, K. C. Sriharipriya

“…The proposed method combines Machine Learning (ML) for predictive modeling with Multi-Agent Reinforcement Learning (MARL) for real-time coordination among RAUs. …”
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Multi-exit Kolmogorov–Arnold networks: enhancing accuracy and parsimony by James Bagrow, Josh Bongard

“…Here we introduce multi-exit KANs, where each layer includes its own prediction branch, enabling the network to make accurate predictions at multiple depths simultaneously. …”
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Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke by Danis Rifa Nurqotimah, Ahsanun Naseh Khudori, Risqy Siwi Pradini

“…Classification is one of a few methods in predicting stroke symptoms with the aim of obtaining accurate prediction of disease. …”
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IGWO-MALSTM: An Improved Grey Wolf-Optimized Hybrid LSTM with Multi-Head Attention for Financial Time Series Forecasting by Mingfu Zhu, Haoran Qi, Panke Qin

“…The present study investigates the potential of time series forecasting (TSF) in financial application scenarios, aiming to predict future spreads and inform investment decisions more effectively. …”
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Spectroscopic Quantification of Metallic Element Concentrations in Liquid-Propellant Rocket Exhaust Plumes by Siyang Tan, Song Yan, Xiang Li, Tong Su, Qingchun Lei, Wei Fan

“…Finally, the trained network is employed to predict the concentration of metallic elements from the measured atomic emission spectra. …”
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Computational selection of transcriptomics experiments improves Guilt-by-Association analyses. by Prajwal Bhat, Haixuan Yang, László Bögre, Alessandra Devoto, Alberto Paccanaro

“…We demonstrate that: using the selected experiments there is a statistically significant improvement in correlation between genes in the functional category of interest; the selected experiments improve GBA-based gene function prediction; the effectiveness of the selected experiments increases with annotation specificity; our algorithm can be successfully applied to GBA-based pathway reconstruction. …”
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Ultra-Short-Term Forecasting-Based Optimization for Proactive Home Energy Management by Siqi Liu, Zhiyuan Xie, Zhengwei Hu, Kaisa Zhang, Weidong Gao, Xuewen Liu

“…This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. …”
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Surrogate model for calculating spatial magnetic track forces in permanent magnet levitation transportation by LIU Haodong, FENG Yang, ZHAO Chunfa, LUO Shihui, SONG Xiaolin

“…Furthermore, the predicted overall resultant and corresponding torque for the 600 mm long vehicle-mounted PML module also demonstrate high accuracy. …”
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Designing diverse and high-performance proteins with a large language model in the loop. by Carlos A Gomez-Uribe, Japheth Gado, Meiirbek Islamov

“…We present a protein engineering approach to directed evolution with machine learning that integrates a new semi-supervised neural network fitness prediction model, Seq2Fitness, and an innovative optimization algorithm, biphasic annealing for diverse and adaptive sequence sampling (BADASS) to design sequences. …”
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Microgrid Resilience Enhancement with Sensor Network-Based Monitoring and Risk Assessment Involving Uncertain Data by Tangxiao Yuan, Kossigan Roland Assilevi, Kondo Hloindo Adjallah, Ayité Sénah A. Ajavon, Huifen Wang

“…At the application level, this framework is successfully applied to two critical decision-making scenarios: the first is to optimize the power allocation strategy between solar energy and the auxiliary grid, aiming to maximize cost efficiency and minimize power outage losses; the second is to develop low-risk maintenance plans based on the predicted failure probabilities of microgrid components with uncertain information. …”
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The Gut Microbiota and Colorectal Cancer: Understanding the Link and Exploring Therapeutic Interventions by Imen Zalila-Kolsi, Dhoha Dhieb, Hussam A. Osman, Hadjer Mekideche

“…The study of intratumoral microbiota offers new diagnostic and prognostic tools that, combined with artificial intelligence algorithms, could predict treatment responses and assess the risk of adverse effects. …”
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A Novel Mutation of the CYP11B2 in a Saudi Infant with Primary Hypoaldosteronism by Lama Alfaraidi, Abrar Alfaifi, Rawan Alquaiz, Faten Almijmaj, Horia Mawlawi

“…Given the position of the transition at a highly conserved nucleotide and the predictions of different bioinformatic algorithms, it is likely that the mutation is the pathogenic cause of this condition. …”
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