Deep Feature Learning for Intrinsic Signature Based Camera Discrimination by Chaity Banerjee, Tharun Kumar Doppalapudi, Eduardo Pasiliao, Tathagata Mukherjee

Subjects: “…deep learning…”
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Primary sources in online history learning: enhancing engagement and retention by Fahruddin Fahruddin, Arif Saefudin

Subjects: “…Online history learning…”
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Pronunciation learning attitudes: The case of EFL undergraduate students in Indonesia by Toni Indrayadi, Herri Mulyono, Sri Kusuma Ningsih, Amirul Mukminin

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The Impacts of the Analysis, Debate, and Finding Models on Learning Natural Sciences by Apdoludin Apdoludin, Megawati Megawati, Randi Eka Putra, Dodi Harianto, Wiwik Pudjaningsih, Elfa Eriyani

“… Problem solving of low critical thinking skills, difficulty in practicing science skills, analyzing students' concepts in depth and low learning outcomes are serious problems that must be solved. …”
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Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning by Hosna Tavakoli, Reza Rostami, Reza Shalbaf, Mohammad‐Reza Nazem‐Zadeh

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Deep Learning Based on Residual Networks for Automatic Sorting of Bananas by Abdulkader Helwan, Mohammad Khaleel Sallam Ma’aitah, Rahib H. Abiyev, Selin Uzelaltinbulat, Bengi Sonyel

“…This study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residual learning-based network “ResNet-50” is designed to sort out fruits, particularly bananas into healthy or defective classes. …”
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Machine Learning Techniques for Quantification of Knee Segmentation from MRI by Sujeet More, Jimmy Singla, Ahed Abugabah, Ahmad Ali AlZubi

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Evaluation of the Use of Artificial Intelligence in Teaching Learning Evaluation Courses by Muhamad Raffi Fadliansyah, Rian Dwiyanto, Nur Syamsiyah Nur Syamsiyah

“… This study aims to evaluate the use of Artificial Intelligence (AI) in teaching the Learning Evaluation course for students. By utilizing AI technology, such as chatbots, learning recommendation systems, and data analysis tools, teaching this course is expected to increase the effectiveness, efficiency, and engagement of students in the learning process. …”
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Machine learning configuration-dependent friction tensors in Langevin heatbaths by Matthias Sachs, Wojciech G Stark, Reinhard J Maurer, Christoph Ortner

“…Machine Learning: Science and Technology…”
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WEBRESOURCE FOR SELFREGULATED FOREIGN LANGUAGE ACQUISITION: CONTENT AND LEARNING POSSIBILITIES by Anna K. Kuptsova

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Risk Assessment of Government Debt Based on Machine Learning Algorithm by Dan Chen

“…This paper builds an effective government debt risk assessment system based on machine learning algorithm. According to forming the performance of local government debt risk and its internal and external influencing factors, this study applies the analytic hierarchy process, entropy method, and BP neural network method to construct the local government risk assessment index system, which includes the primary and secondary indexes including the explicit debt risk, the contingent implicit debt risk, and the financial and economic operation risk. …”
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Gender and learning attitudes in using Web-based science lessons by Siew Chee Leong, Suliman Al-Hawamdeh

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Bio-primed machine learning to enhance discovery of relevant biomarkers by David M. Henke, Alexander Renwick, Joseph R. Zoeller, Jitendra K. Meena, Nicholas J. Neill, Elizabeth A. Bowling, Kristen L. Meerbrey, Thomas F. Westbrook, Lukas M. Simon

“…Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. …”
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Interpretable Machine Learning Techniques for an Advanced Crop Recommendation Model by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…Our research addresses this critical imperative by introducing an innovative predictive model that refines crop recommendation systems through advanced machine learning techniques, specifically random forest and SHapley Additive exPlanations (SHAP). …”
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Machine learning-powered, high-affinity modification strategies for aptamers by Gubu Amu, Xin Yang, Hang Luo, Sifan Yu, Huarui Zhang, Yuan Tian, Yuanyuan Yu, Shijian Ding, Yufei Pan, Zefeng Chen, Yixin He, Yuan Ma, Baoting Zhang, Ge Zhang

“…This approach harnessed the power of machine learning to predict the most promising high-affinity modification strategy for aptamers.…”
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An efficient modified HS conjugate gradient algorithm in machine learning by Gonglin Yuan, Minjie Huang

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Muscarinic ACh Receptors Contribute to Aversive Olfactory Learning in Drosophila by Bryon Silva, Claudia Molina-Fernández, María Beatriz Ugalde, Eduardo I. Tognarelli, Cristian Angel, Jorge M. Campusano

“…The most studied form of associative learning in Drosophila consists in pairing an odorant, the conditioned stimulus (CS), with an unconditioned stimulus (US). …”
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Prospective de novo drug design with deep interactome learning by Kenneth Atz, Leandro Cotos, Clemens Isert, Maria Håkansson, Dorota Focht, Mattis Hilleke, David F. Nippa, Michael Iff, Jann Ledergerber, Carl C. G. Schiebroek, Valentina Romeo, Jan A. Hiss, Daniel Merk, Petra Schneider, Bernd Kuhn, Uwe Grether, Gisbert Schneider

“…We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. …”
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First Aid Education Guidelines for Peer Learning: The Approach in Practice

Subjects: “…peer learning…”
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Predicting metabolite response to dietary intervention using deep learning by Tong Wang, Hannah D. Holscher, Sergei Maslov, Frank B. Hu, Scott T. Weiss, Yang-Yu Liu

“…Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. …”
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