Remote Sensing of River Discharge From Medium‐Resolution Satellite Imagery Based on Deep Learning by Zhen Hao, Naier Xiang, Xiaobin Cai, Ming Zhong, Jin Jin, Yun Du, Feng Ling

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A novel paradigm in cleft lip education: integration of 3D-printed simulator and problem-based learning by Xiao-Le Wang, Ying Xie, Ye-Xin Yue, Xue-Wen Yang, Jian Li

“…Consequently, it is also a pivotal and difficult subject in clinical education. Problem-based learning (PBL) is a student-centered teaching methodology that facilitates students in solving complex, practical, or real-world problems collaboratively. …”
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Estimating Self-Confidence in Video-Based Learning Using Eye-Tracking and Deep Neural Networks by Ankur Bhatt, Ko Watanabe, Jayasankar Santhosh, Andreas Dengel, Shoya Ishimaru

“…Our results underscore the superior performance of the deep-learning model in estimating self-confidence in video-based learning contexts compared to hand-crafted feature-based methods. …”
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MLRec: A Machine Learning-Based Recommendation System for High School Students Context of Bangladesh by Momotaz Begum, Mehedi Hasan Shuvo, Jia Uddin

“…Here, we propose a comparative model named the MLRec model, where we assess how well different machine learning methods predict the dynamics of student life and provide a recommendation to society, parents, and academic advisors. …”
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A Spider Wasp Optimizer-Based Deep Learning Framework for Efficient Citrus Disease Detection by Abisola Olayiwola, Ajibola Oyedeji, Dare Olayiwola, Olufemi Awodoye, Olukunle Oyebode

“…Performance evaluations based on sensitivity, specificity, false positive rate, accuracy, and identification time show that the SWO-DCNN outperforms the conventional DCNN in every disease category. …”
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Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization. by Honghua Liu, Mingwei Zhao, Chang She, Han Peng, Mailan Liu, Bo Li

“…This paper proposes a low false positive rate disease detection method based on COVID-19 lung images and establishes a two-stage optimization model. …”
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More Accurate Constraints for Self-Supervised Learning in Remote Sensing Images-Based Object Detection by Shangdong Zheng, Zebin Wu, Yang Xu, Qian Liu, Zhihui Wei

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Randomized trial on the impact of card Game-Based teaching on learning and memory retention of neurological syndromes by Xi Yu, Jiafu Wu, Yuhuan Zhang, Zhixin Di, Wanling Nie, Mingyu Wang, Xingyu Zhu, Yunkai Zhang, Yimeng Wu, Yan Ma, Yuxi Han, Miao Yu

“…Conclusion The NSCG-based teaching method significantly enhances students’ learning and memory retention of neurological syndromes, reduces cognitive load, and increases learning interest. …”
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Experiences Using Media Health Claims to Teach Evidence-Based Practice to Healthcare Students: A Mixed Methods Study [version 2; peer review: 1 approved, 2 approved with reservatio... by Astrid Dahlgren, Marianne Molin, Jürgen Kasper, Lisa Garnweidner-Holme, Hilde Tinderholt Myrhaug, Ida-Kristin Orjasaeter Elvsaas

“…Synthesizing the results, we found that students viewed the inclusion of health claim assessment as a useful entry point for learning evidence-based practice. In addition, the students identified both the blended learning design and the group exam as contributors to a positive perception of learning outcomes from the course. …”
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AAGP integrates physicochemical and compositional features for machine learning-based prediction of anti-aging peptides by Saptashwa Datta, Jen-Chieh Yu, Yi-Hsiang Lin, Yun-Chen Cheng, Ching-Tai Chen

“…Peptide therapies have emerged as a promising approach in aging studies because of their excellent tolerability, low immunogenicity, and high specificity. Computational methods can significantly expedite wet lab-based anti-aging peptide discovery by predicting potential candidates with high specificity and efficacy. …”
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Robust Representation Learning Based on Deep Mutual Information for Scene Classification Against Adversarial Perturbations by Linjuan Li, Gang Xie, Haoxue Zhang, Xinlin Xie, Heng Li

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MACHINE LEARNING-BASED CLASSIFICATION OF HBV AND HCV-RELATED HEPATOCELLULAR CARCINOMA USING GENOMIC BIOMARKERS by Sami Akbulut, Zeynep Küçükakçalı, Cemil Çolak

“…Conclusion: As a result of the study, two different etiological factors (HBV and HCV) causing HCC were classified using a machine learning-based prediction approach, and genes that could be biomarkers for HBV-related HCC were identified. …”
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Design and evaluation of a problem-based learning VR module for apparel fit correction training. by Aditi Galada, Fatma Baytar

“…However, existing studies spanning from engineering to design education indicate that students feel incompetent in understanding 3D digital prototypes and navigating the software, so there is a need to find effective training methods. In the current study, training modules were developed to teach participants fit correction skills through an iterative problem-based learning (PBL) approach. …”
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A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments by Abdullah AlHayan, Jalal Al-Muhtadi

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MGACL: Prediction Drug–Protein Interaction Based on Meta-Graph Association-Aware Contrastive Learning by Pinglu Zhang, Peng Lin, Dehai Li, Wanchun Wang, Xin Qi, Jing Li, Jianshe Xiong

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Federated stochastic gradient averaging ring homomorphism based learning for secure data aggregation in WSN by Saravanakumar Pichumani, T. V. P. Sundararajan, S. M. Ramesh

“…As a consequence, data aggregation leads the ways for new confrontations to WSN security. In this work a method called Federated Stochastic Gradient Averaging Ring Homomorphism-based Learning (FSGARH-L) for secure data aggregation in WSN is proposed. …”
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Patch-Wise-Based Self-Supervised Learning for Anomaly Detection on Multivariate Time Series Data by Seungmin Oh, Le Hoang Anh, Dang Thanh Vu, Gwang Hyun Yu, Minsoo Hahn, Jinsul Kim

“…The proposed approach comprises four key components: (i) maintaining continuous features through patching, (ii) incorporating various temporal information by learning channel dependencies and adding relative positional bias, (iii) achieving feature representation learning through self-supervised learning, and (iv) supervised learning based on anomaly augmentation for downstream tasks. …”
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Efficient wildlife monitoring: Deep learning-based detection and counting of green turtles in coastal areas by Naoya Noguchi, Hideaki Nishizawa, Taro Shimizu, Junichi Okuyama, Shohei Kobayashi, Kazuyuki Tokuda, Hideyuki Tanaka, Satomi Kondo

“…In this study, deep-learning-based You Look Only Once, Version 7 (YOLOv7) models were developed to automatically detect green turtles (Chelonia mydas) in Japanese coastal areas featuring coral reefs and seagrass beds. …”
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A Transfer Learning-Based Framework for Classifying Lymph Node Metastasis in Prostate Cancer Patients by Suryadipto Sarkar, Teresa Wu, Matthew Harwood, Alvin C. Silva

“…An emerging field is the development of artificial intelligence (AI) models, including machine learning and deep learning, for medical imaging to assist in diagnostic tasks. …”
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A Clinical Risk Prediction Model for Depressive Disorders Based on Seven Machine Learning Algorithms by Jin W, Chen S, Wang M, Lin P

“…Weifeng Jin,* Shuzi Chen,* Mengxia Wang,* Ping Lin Department of Medical Laboratory, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ping Lin, Email Linpingsun20000@aliyun.comObjective: To develop a clinical risk prediction model for depressive disorders using seven machine learning algorithms based on routine blood test indicators.Methods: A retrospective study was conducted, involving 284 patients with depressive disorders and 214 healthy controls recruited between January and October 2024. …”
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