Efficient Handling of Data Imbalance in Health Insurance Fraud Detection Using Meta-Reinforcement Learning by Supriya Seshagiri, K. V. Prema

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XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations by Mohammad Azad, Md Faraz Kabir Khan, Sameh Abd El-Ghany

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Diagnostic Performance of Deep Learning Applications in Hepatocellular Carcinoma Detection Using Computed Tomography Imaging by Enes Şahin, Ozan Can Tatar, Mehmet Eşref Ulutaş, Sertaç Ata Güler, Turgay Şimşek, Nihat Zafer Utkan, Nuh Zafer Cantürk

“…This study leverages the potential of deep learning (DL) technologies, employing the You Only Look Once (YOLO) architecture, to enhance the detection of HCC in computed tomography (CT) images, aiming to improve early diagnosis and thereby patient outcomes. …”
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Deep Transfer Learning for Lip Reading Based on NASNetMobile Pretrained Model in Wild Dataset by Ashwaq Waleed Abdul Ameer, Pedram Salehpour, Mohammad Asadpour

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ORGANIZATION OF STUDENTS’ INDEPENDENT WORK IN STUDYING PHYSICS BASED ON DISTANCE LEARNING TECHNOLOGY IN LMS MOODLE by Viktor Yu. Shurygin, Lyubov A. Krasnova

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BSG-WSL: BackScatter-guided weakly supervised learning for water mapping in SAR images by Kai Wang, Zhongle Ren, Biao Hou, Weibin Li, Licheng Jiao

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MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage by Xiong You, Yiting Shu, Xingcheng Ni, Hengmin Lv, Jian Luo, Jianping Tao, Guanghui Bai, Shusu Feng

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Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction by Mohammad Reza Faisal, Muhammad Thoriq Hidayat, Dwi Kartini, Fatma Indriani, Irwan Budiman, Triando Hamonangan Saragih

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Borofsky Robert et al., Yanomami: The Fierce Controversy and What We Can Learn From It by Philippe Erikson

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Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects by Gorynina, Olga, Legoll, Frédéric, Lelièvre, Tony, Perez, Danny

“…In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embedded-atom method potentials (EAM) as coarse potentials. …”
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Advanced Machine Learning Ensembles for Improved Precipitation Forecasting: The Modified Stacking Ensemble Strategy in China by Tiantian Tang, Yifan Wu, Yujie Li, Lexi Xu, Xinyi Shi, Haitao Zhao, Guan Gui

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The Mediating Effect of Social Emotional Learning between Relationship on Basic Psychological Needs and Technology Addiction by Murat Agirkan

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From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems by Xin Gu, Muralee Krish, Shaleeza Sohail, Sweta Thakur, Fariza Sabrina, Zongwen Fan

“…We explored the uses of machine learning algorithms and the hybrid solutions of combining the integer programming and machine learning algorithms in higher education timetabling solutions. …”
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Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data by Jian Liu, Xin Gu, Chao Shang

“…The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness, and strong implementation. …”
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Detection of Manipulations in Digital Images: A Review of Passive and Active Methods Utilizing Deep Learning by Paweł Duszejko, Tomasz Walczyna, Zbigniew Piotrowski

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Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study by Stanford Martinez, Carolina Ramirez-Tamayo, Syed Hasib Akhter Faruqui, Kal Clark, Adel Alaeddini, Nicholas Czarnek, Aarushi Aggarwal, Sahra Emamzadeh, Jeffrey R Mock, Edward J Golob

“…The encoded features of the eye-fixation data are used by machine learning classifiers to discriminate between faculty and trainee radiologists. …”
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An In-Depth Study of Personalized Anesthesia Management Models in Gastrointestinal Endoscopy Based on Multimodal Deep Learning by Hanqi Shi, Hongyu Wang, Xibing Ding, Zheng Dang

“…In response to the annual occurrence of over 10 million gastrointestinal endoscopic examinations in China, this study proposes a personalized anesthesia management model based on multimodal deep learning. This model was designed to enhance anesthesia management efficiency and disease detection rates. …”
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Invasive Pneumococcal Disease: Still Lots to Learn and a Need for Standardized Data Collection Instruments by T. J. Marrie, G. J. Tyrrell, Sumit R. Majumdar, Dean T. Eurich

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Stability Analysis of High-Order Iterative Learning Control for a Class of Nonlinear Switched Systems by Xuhui Bu, Fashan Yu, Ziyi Fu, Fuzhong Wang

“…It is shown that the proposed iterative learning control law can guarantee the asymptotic convergence of the tracking error for the entire time interval through the iterative learning process. …”
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Manual wheelchair training programs: a scoping review of educational approaches and intended learning outcomes by Kimberly Charlton, Carolyn Murray, Natasha Layton, Stacie Attrill

“…Abstract Background Training programs grounded in educational theory offer a systematic framework to facilitate learning and outcomes. This scoping review aims to map the educational approaches documented for manual wheelchair training and to record intended learning outcomes and any relationships between learning theories, instructional design and outcomes. …”
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