Learning Rates for -Regularized Kernel Classifiers by Hongzhi Tong, Di-Rong Chen, Fenghong Yang

“…We consider a family of classification algorithms generated from a regularization kernel scheme associated with -regularizer and convex loss function. Our main purpose is to provide an explicit convergence rate for the excess misclassification error of the produced classifiers. …”
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KERATOCONUS DETECTION USING DEEP LEARNING by Younis Abbosh, Shatha Ali, Dia Ali, Iman Jasim

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Reinforcement learning for deep portfolio optimization by Ruyu Yan, Jiafei Jin, Kun Han

“…In addition, a novel risk-cost reward function was proposed, which realized optimal portfolio decision-making considering transaction cost and risk factors through reinforcement learning. …”
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Reinforcement learning in artificial intelligence and neurobiology by Tursun Alkam, Andrew H Van Benschoten, Ebrahim Tarshizi

“…Reinforcement learning (RL), a computational framework rooted in behavioral psychology, enables agents to learn optimal actions through trial and error. …”
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Unified differentiable learning of electric response by Stefano Falletta, Andrea Cepellotti, Anders Johansson, Chuin Wei Tan, Marc L. Descoteaux, Albert Musaelian, Cameron J. Owen, Boris Kozinsky

“…Here, we implement an equivariant machine-learning framework where response properties stem from exact differential relationships between a generalized potential function and applied external fields. …”
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SPATIAL UNIT FOR VISUAL ARTS AND LEARNING by Milica Jovanov

“…Participation of children and educators in play and exploration within a properly arranged, inspiring spatial unit for visual arts facilitates learning. The theoretical frameworks of this work focus on the requirements of preschool education, specifically on the arrangement of space as part of developing a realistic program—learning through play and exploration—which encourages students’ expression in alternative ways, the development of the ability to find appropriate solutions, application in new situations, and consequently functioning in the modern world. …”
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A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning by Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

“…Abstract Deep reinforcement learning (DRL) has been extensively used to address portfolio optimization problems. …”
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The Taxonomy Mobile Learning Applications in Higher Institutions of Learning in Ugandan Universities: A Case of Kabale University, Uganda. by Muhaise, Hussein, Businge,Phelix Mbabazi, Ssemaluulu, Paul, Muhoza, Gloria

“…Since the use of mobile devices outpaces that of laptops and desktop computers today, the usability of these mobile devices is an important consideration. When mobile learning (a new kind of electronic learning) takes shape, bringing an important feature of mobility, the trend expands deeper into teaching and learning. …”
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Knowledge- and Model-Driven Deep Reinforcement Learning for Efficient Federated Edge Learning: Single- and Multi-Agent Frameworks by Yangchen Li, Lingzhi Zhao, Tianle Wang, Lianghui Ding, Feng Yang

“…The resulting problem is a complicated stochastic sequential decision-making problem with an implicit objective function and unknown transition probabilities. To address these challenges, we propose knowledge/model-driven single-agent and multi-agent deep reinforcement learning (DRL) frameworks. …”
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Category-Based Sentiment Analysis of Sindhi News Headlines Using Machine Learning, Deep Learning, and Transformer Models by Safdar Ali Soomro, Siti Sophiayati Yuhaniz, Mazhar Ali Dootio, Ghulam Mujtaba, Jawaid Ahmed Siddiqui

“…To evaluate the effectiveness of different machine learning (ML), deep learning (DL), and transformer-based approaches, we conduct a comparative analysis of various models on SA and category classification tasks. …”
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Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data by Zhiqiang Wu, Yunquan Chen, Bingjian Zhang, Jingzheng Ren, Qinglin Chen, Huan Wang, Chang He

“…This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process. …”
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The mediating role of learning engagement in the relationship between master's students' learning motivation and scientific research and innovation capability by Ke Hu, Dongmei Tang, Huan He

“…However, few studies have systematically examined the mediating function of learning engagement in this association.MethodsThis study employed a questionnaire survey method, collecting data from 3,301 master's students across eight universities in Fujian Province, with a final sample size of 3,050 valid responses. …”
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Conceptual and theoretical foundations of personalised learning by E. F. Zeer, O. V. Krezhevskikh

“…The aim of the article is to describe the development and testing of a structural and functional model of personalised learning in education of future teachers.Methodology and research methods. …”
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The mathematical learning by Mathcad software use by Stasė Petraitienė, Liepa Bikulčienė, Violeta Kravčenkienė

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Electrochemical ohmic memristors for continual learning by Shaochuan Chen, Zhen Yang, Heinrich Hartmann, Astrid Besmehn, Yuchao Yang, Ilia Valov

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Learning Interactions between Rydberg Atoms by Olivier Simard, Anna Dawid, Joseph Tindall, Michel Ferrero, Anirvan M. Sengupta, Antoine Georges

“…We prove a theorem establishing a bijective correspondence between the correlation functions and the interaction parameters in the Hamiltonian, which provides a theoretical foundation for our learning algorithm. …”
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Learning the TEI in a Digital Environment by Stella Dee

“…This article provides a brief overview of currently-available digital resources for learning to understand and use the TEI Guidelines. …”
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The learning environment as a space for changes by Jolanta Nowak

“…This paper focuses on selected aspects of a new culture of learning: constructive, self-regulated, situated and collaborative, that prepares the student for smooth functioning in a world marked by change. …”
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Integrating Machine Learning and Educational Robotics by Charles Soares Pimentel, Fábio Ferrentini Sampaio

“…The scripted activities focused on the mathematical calculations involved in the algorithm's training and classification phases, enhancing students' understanding of machine learning (ML). The results showed that students grasped both the functioning of the WiSARD algorithm and the mathematical principles behind it. …”
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Interdisciplinary Distance Learning Workshop for IT Students by I. A. Malyj, V. V. Bulgakov, I. Yu. Sharabanova, O. I. Orlov

“…According to the investigation of the adaptation and usage of digital learning technologies to improve the professional competencies of graduates in the field of fire-fighting, the application of virtual reality technology was proposed that allows to simulate a professional environment and to organize it in both individual and group practical preparation of cadets to professional tasks on a variety of residential, social, industrial, transport and other functionalities in conditions of simulating various scenarios of the occurrence and development of fires. …”
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