Machine learning approach for identifying and forecasting streamflow droughts in data limited basins of South Korea using threshold levels by Young-Ho Seo, Jang Hyun Sung, Byung-Sik Kim, Junehyeong Park

“…We utilized the XGBoost algorithm, integrating comprehensive meteorological data to enhance the accuracy and reliability of the drought predictions. …”
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Artificial intelligence and machine learning in modern cardiology: Advancements in diagnosis, treatment and patient monitoring by Szymon Kopciał, Dawid Piecuch, Edyta Hańczyk, Karolina Kornatowska, Natalia Pawelec, Weronika Mazur

“…These technologies facilitate early detection of conditions such as coronary artery disease, atrial fibrillation, and hypertrophic cardiomyopathy, while also enabling risk stratification for heart failure, myocardial infarction, and sudden cardiac death. Additionally, AI-driven algorithms support personalized treatment strategies, real-time remote monitoring, and precision-guided coronary interventions, reducing procedural complications. …”
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Study on the Lightweighting Strategy of Target Detection Model with Deep Learning by Junli Hu

“…Experiments on the public data set AI-TOD to show that the target detection lightweight model of deep learning has stronger detection ability and higher average detection accuracy than other algorithms, which proves the applicability and effectiveness of this algorithm.…”
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Artificial Intelligence in Healthcare: Medical Students' Perspectives on Balancing Innovation, Ethics, and Patient-Centered Care by Eleanor Roy, Sara Malafa, Lina M. Adwer, Houda Tabache, Tanishqa Sheth, Vasudha Mishra, Moaz Elsayed Abouelmagd, Andrea Cushieri, Sajjad Ahmed Khan, Mihnea-Alexandru Gaman, Juan C. Puyana, Francisco Javier Bonilla-Escobar

“…Algorithmic bias further threatens equity in care, as many AI systems are trained on unrepresentative datasets, leading to disparities in diagnosis and treatment. …”
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Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches by Guohua Li, Qian Shi, Qibiao Wu, Xinbing Sui

“…AI-driven models, especially machine learning algorithms, have proven effective in processing complex proteomic data and predicting potential NP-protein interactions. …”
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Applications of Artificial Intelligence in Vasculitides: A Systematic Review by Mahmud Omar, Reem Agbareia, Mohammad E. Naffaa, Abdulla Watad, Benjamin S. Glicksberg, Girish N. Nadkarni, Eyal Klang

“…Conclusion The current literature shows that AI algorithms can enhance vasculitis diagnosis and prediction, with deep‐ and machine‐learning models showing promise in Kawasaki disease. …”
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Nav2Scene: Navigation-driven fine-tuning for robot-friendly scene generation by Bowei Jiang, Tongyuan Bai, Peng Zheng, Tieru Wu, Rui Ma

“…The integration of embodied intelligence in indoor scene synthesis holds significant potential for future interior design applications. …”
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Improving distributed systems failure prediction via multi-objective feature selection and deep forest by Zhidan Yuan, Yikai Zhang, Yingqi Yu, Taizhi Lv, Tao Huang

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The utilization of generative artificial intelligence in the professional development of teachers by Katia Cristian Puente Muniz, Caterine Fagundes Vila

“… This study focused on the integration of generative artificial intelligence, represented by ChatGPT, as an educational tool in teacher training courses in Brazil and Spain. …”
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Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System by Nu Wen, Ying Zhou, Yang Wang, Ye Zheng, Yong Fan, Yang Liu, Yankun Wang, Minmin Li

“…To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems that leverage edge computing. …”
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The geriatric 5Ms, artificial intelligence, and Hannah Arendt’s critique: ethical reflections within contemporary gerontology by Virgílio Garcia Moreira, Andréia Pain, Ivan Aprahamian

“…The Geriatric 5Ms framework — Mind, Mobility, Medications, Multicomplexity, and Matters Most — guides current clinical practice in the approach to older patients. The integration of AI into geriatrics has the potential to improve diagnostic accuracy, optimize therapies, and individualize interventions. …”
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Artificial Intelligence and MCDA in Circular Economy: Governance Strategies and Optimization for Reverse Supply Chains of Solid Waste by Joel Joaquim de Santana Filho, Arminda do Paço, Pedro Dinis Gaspar

“…The integration of multi-criteria decision analysis (MCDA) and Artificial Intelligence (AI) is revolutionizing the governance of reverse supply chains for solid waste (RSCSW) within a circular economy framework. …”
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Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare by Feilong Zhang, Deming Zhai, Guo Bai, Junjun Jiang, Qixiang Ye, Xiangyang Ji, Xianming Liu

“…Abstract The widespread integration of AI algorithms in healthcare has sparked ethical concerns, particularly regarding privacy and fairness. …”
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Automated Detection and Classification of Sleep Spindles using Machine Learning and Signal Processing Techniques by Sindhu Faiza, Rehman Hafiz Zia Ur, Khan Zeashan, Moshayedi Ata Jahangir

“…Cross-validation and precision, recall, and F1 scores helped identify the best classification algorithm. This research can be integrated with the wearable technology for real-time monitoring and intervention to the next level, providing important insights into sleep spindle classification and analysis.…”
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Review on the Innovation of Investment Banks’ Credit Risk Assessment System in a Highly Volatile Market by Wei Yakun

“…Challenges such as model interpretability and data quality persist, necessitating future research on explainable AI and ESG integration. The findings provide actionable insights for modernizing risk management practices, offering a robust pathway to bolster financial stability in increasingly unpredictable markets.…”
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<italic>WhiteDwarf</italic>: A Holistic Co-Design Approach to Ultra-Compact Neural Inference Acceleration by Angel Lopez Garcia-Arias, Yasuyuki Okoshi, Jaehoon Yu, Junnosuke Suzuki, Hikari Otsuka, Kazushi Kawamura, Thiem Van Chu, Daichi Fujiki, Masato Motomura

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Agenda setting for health equity assessment through the lenses of social determinants of health using machine learning approach: a framework and preliminary pilot study by Maryam Ramezani, Mohammadreza Mobinizadeh, Ahad Bakhtiari, Hamid R. Rabiee, Maryam Ramezani, Hakimeh Mostafavi, Alireza Olyaeemanesh, Ali Akbar Fazaeli, Alireza Atashi, Saharnaz Sazgarnejad, Efat Mohamadi, Amirhossein Takian

“…Abstract Introduction The integration of Artificial Intelligence (AI) and Machine Learning (ML) is transforming public health by enhancing the assessment and mitigation of health inequities. …”
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Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review by Mahmoud Saleh Al-Khafaji, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, Dhiya Al‑Jumeily

“…By synthesizing 1,032 studies (2011–2025), we demonstrate that AI-driven systems achieve 94% accuracy in prediction and reduce field sampling costs by 60% through Landsat 8 satellite integration. …”
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Research Progress and Applications of Artificial Intelligence in Agricultural Equipment by Yong Zhu, Shida Zhang, Shengnan Tang, Qiang Gao

“…The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. …”
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Cervical cancer demystified: exploring epidemiology, risk factors, screening, treatment modalities, preventive measures, and the role of artificial intelligence by N. Mohammad, M. Khan, M. Maqsood, A. H. K. Naseeb

“…However, disparities persist due to limited healthcare infrastructure and access to routine screening. AI-driven technologies, including deep learning algorithms and machine learning models, are emerging as valuable tools in cervical cancer detection, risk assessment, and treatment planning. …”
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