Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce Readmissions by Odai Mohammad Al-Jbour, Mohammad Alshraideh, Bahaaldeen Alshraideh

“…The study introduces a novel method that employs machine learning to automatically extract features from patient data, eliminating labor-intensive manual feature engineering. …”
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Prospective preschool teachers learning to use picturebooks for philosophical inquiries by Sofia Nikolidaki

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Advances in AI-based rainfall forecasting: a comprehensive review of past, present, and future directions with intelligent data fusion and climate change models by Farhan Amir Fardush Sham, Ahmed El-Shafie, Wan Zurina Wan Jaafar, Adarsh S, Mohsen Sherif, Ali Najah Ahmed

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Interpretable machine learning models for evaluating strength of ternary geopolymers by Junfei Zhang, Huisheng Cheng, Ninghui Sun, Zehui Huo, Junlin Chen

“…However, the mechanisms governing strength development and the design of optimal mixtures are not fully understood due to the complexity of their components. This study presents the development of four machine learning models—Artificial Neural Network (ANN), Support Vector Regression (SVR), Extremely Randomized Tree (ERT), and Gradient Boosting Regression (GBR)—for predicting the unconfined compressive strength (UCS) of ternary geopolymers. …”
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Distorted learning from local metacognition supports transdiagnostic underconfidence by Sucharit Katyal, Quentin JM Huys, Raymond J. Dolan, Stephen M. Fleming

“…The origin of such metacognitive biases presents a puzzle, given that individuals should be able to learn appropriate levels of confidence from observing their own performance. …”
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Detection of OSA Through the Application of Deep Learning on Polysomnography Data by Hasan Ulutas, Recep Sinan Arslan, Muhammet Emin Sahin, Halil Ibrahim Cosar, Cagri Arisoy, Ahmet Sertol Koksal, Mehmet Bakir, Bulent Ciftci

“…This paper presents a comprehensive study on the application of deep learning techniques to accurately detect sleep apnea. …”
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Applications of Machine Learning and Remote Sensing in Soil and Water Conservation by Ye Inn Kim, Woo Hyeon Park, Yongchul Shin, Jin-Woo Park, Bernie Engel, Young-Jo Yun, Won Seok Jang

“…The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. …”
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Reinforcement Learning Solutions for Microgrid Control and Management: A Survey by Pedro I. N. Barbalho, Anderson L. Moraes, Vinicius A. Lacerda, Pedro H. A. Barra, Ricardo A. S. Fernandes, Denis V. Coury

“…Thus, MG management and control has become a broad area of research due to its complex operation. Reinforcement learning (RL) offers adaptive solutions for handling MG complex dynamics and nonlinearity. …”
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Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare by Kaidong Wang, Bing Tan, Xinfei Wang, Shicheng Qiu, Qiuping Zhang, Shaolei Wang, Ying‐Tzu Yen, Nan Jing, Changming Liu, Xuxu Chen, Shichang Liu, Yan Yu

“…Despite these barriers, the translational potential of machine learning‐assisted POC devices presents significant opportunities for advancement in CVDs healthcare.…”
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A data-to-forecast machine learning system for global weather by Xiuyu Sun, Xiaohui Zhong, Xiaoze Xu, Yuanqing Huang, Hao Li, J. David Neelin, Deliang Chen, Jie Feng, Wei Han, Libo Wu, Yuan Qi

“…Recent advances in machine learning present a promising alternative, but still depend on the initial conditions generated by NWP systems. …”
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Powder Bed Fusion via Machine Learning-Enabled Approaches by Utkarsh Chadha, Senthil Kumaran Selvaraj, Abel Saji Abraham, Mayank Khanna, Anirudh Mishra, Isha Sachdeva, Swati Kashyap, S. Jithin Dev, R. Srii Swatish, Ayushma Joshi, Simar Kaur Anand, Addisalem Adefris, R. Lokesh Kumar, Jayakumar Kaliappan, S. Dhanalakshmi

“…For PBF to reach its maximum potential, machine learning (ML) algorithms are used with suitable materials to achieve goals cost-effectively. …”
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Advanced multiscale machine learning for nerve conduction velocity analysis by Hossein Sadeghi

“…Abstract This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocity (NCV) analysis, integrating multiscale signal processing with physiologically-constrained deep learning. …”
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Multilabel Classification of Radiology Image Concepts Using Deep Learning by Vito Santamato, Agostino Marengo

“…In this study, we developed a deep learning-based system capable of automatically assigning multiple standardized medical concepts to radiology images, leveraging deep learning models. …”
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Distance learning as a factor in increasing the efficiency of the educational process by Tatiana Nikolaevna Poddubnaya

“…The purpose of the research is to present the experience of distance learning as a factor in increasing the efficiency of the educational process. …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…Machine learning (ML) and Internet of Things (IoT) are bolting down the agriculture in the area of plant disease management, especially. …”
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Benchmarking Spiking Neural Network Learning Methods With Varying Locality by Jiaqi Lin, Sen Lu, Malyaban Bal, Abhronil Sengupta

“…We also investigate the performance of local learning methods under gradient and non-gradient-based adversarial attacks.…”
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TYPOLOGY OF STUDENTS ACCORDING TO LEARNING STYLES: INSIGHTS FOR ECONOMIC INNOVATION by Rumyana Angelova Jeleva

“…This paper presents and justifies a typology of students in the fields of Economics and Administration and Management based on their learning styles, using a sample of undergraduate and graduate students at the University of National and World Economy, Sofia. …”
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Reflections on reflection: Supporting employability learning in the higher education context by Anna Richards, Andrea Reid

“…Our study found value in the use of a formalised, stepwise approach to reflective thinking that helps students make and express meaning from learning opportunities. The findings suggest, however, that attempting to shoehorn the untidy business of reflective thinking into a standard linear approach does present some challenges. …”
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Multimodal deep learning for cephalometric landmark detection and treatment prediction by Fei Gao, Yulong Tang

“…Traditional approaches rely on manual landmark identification, which is time-consuming and subject to inter-observer variability, while existing automated methods typically utilize single imaging modalities with limited accuracy. This paper presents DeepFuse, a novel multi-modal deep learning framework that integrates information from lateral cephalograms, CBCT volumes, and digital dental models to simultaneously perform landmark detection and treatment outcome prediction. …”
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Space Trajectory Planning with a General Reinforcement-Learning Algorithm by Andrea Forestieri, Lorenzo Casalino

“…Traditional optimization methods struggle with the increasing computational complexity as the number of possible targets grows. This paper presents a novel reinforcement-learning algorithm, inspired by AlphaZero, designed to handle hybrid discrete–continuous action spaces without relying on discretization. …”
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