Application of the Method of Problem Learning in the Study of the Discipline “Information Security” by V. A. Sizov, D. M. Malinichev, Kh. K. Kuchmezov, V. V. Mochalov

“…Thus, the paper substantiates the method of problem learning in the study of the discipline “Information Security” and presents an example of its use in solving the problem of information protection in state information systems with terminal access devices. …”
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Exploring the Landscape of Programming Language Identification With Machine Learning Approaches by Amandeep Verma, Rahul Saha, Gulshan Kumar, Alessandro Brighente, Mauro Conti, Tai-Hoon Kim

“…In this paper, we present the first survey for ML-based PLI methods providing insights into the domain’s present status and guiding towards a futuristic tool development for PLI. …”
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Evaluating Success of E-Learning in Different Faculties of a University by Mohammad Javad Shayegan, Amir Ashoori

“…The success of e-learning is still a challenging issue. This study presents a model for the evaluation of the success of e-learning in three different faculties. …”
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Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning by Retantyo Wardoyo, Wenty Dwi Yuniarti

“…The study results show that although the use of fuzzy logic requires more complicated procedures and tools, it can present various kinds of assessment as an option for educators to assess students in e-Learning.…”
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Picking Regional Seismic Phase Arrival Times with Deep Learning by Albert Leonardo Aguilar Suarez, Gregory Beroza

“…Here we present deep learning models designed and trained to be effective at picking the arrival times of earthquake phases at distances up to 20 degrees. …”
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Exploring the Intersection of Machine Learning and Big Data: A Survey by Elias Dritsas, Maria Trigka

“…The integration of machine learning (ML) with big data has revolutionized industries by enabling the extraction of valuable insights from vast and complex datasets. …”
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Interdisciplinarity: a Current Need to Improve the Teaching-Learning Process by Lizgrace Llano Arana, Miriam Gutiérrez Escobar, Addys Stable Rodríguez, María Cristina Núñez Martínez, Rosa María Masó Rivero, Bárbara Rojas Rivero

“…It also presents different experiences from the implementation of this approach, which have enabled ratifying the need for interdisciplinary relationships in the teaching-learning process, the difficulties encountered when designing a proposal using this approach, and the benefits of its application.…”
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Learning the Collection: a New Librarian's Experience in Collection Management. by Heather Lewin

“… A personal narrative is presented which explores the author's experience of having to learn collection management as a librarian at the Iowa State University. …”
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Machine Learning in Intelligent Networks: Architectures, Techniques, and Use Cases by Elias Dritsas, Maria Trigka

“…Integrating machine learning (ML) into intelligent networks (INs) has redefined the capabilities of modern communication systems by enabling real-time decision-making, adaptive optimization, and enhanced security. …”
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Learning to Drive in the NGSIM Simulator Using Proximal Policy Optimization by Yang Zhou, Yunxing Chen

“…The PPO model achieved an 84% success rate in the test which is comparable to the related studies. The present study proved that the public driving dataset and reinforcement learning can provide a useful tool to achieve autonomous driving.…”
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Enabling All In-Edge Deep Learning: A Literature Review by Praveen Joshi, Mohammed Hasanuzzaman, Chandra Thapa, Haithem Afli, Ted Scully

“…Secondly, this paper presents enabling technologies, such as model parallelism, data parallelism, and split learning, which facilitates DL training and deployment at edge servers. …”
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An explainable and federated deep learning framework for skin cancer diagnosis. by Shuvo Biswas, Sajeeb Saha, Muhammad Shahin Uddin, Rafid Mostafiz

“…Therefore, this study presents a smart framework for classifying SC leveraging deep learning (DL), federated learning (FL) and explainable AI (XAI). …”
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Drone Landing and Reinforcement Learning: State-of-Art, Challenges and Opportunities by Jose Amendola, Linga Reddy Cenkeramaddi, Ajit Jha

“…This work aims to present a systematic literature review on works employing Deep Reinforcement Learning for multirotor drone landing in both static and dynamic platforms. …”
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Learning From Imbalanced Data Using Triplet Adversarial Samples by Jaesub Yun, Jong-Seok Lee

“…The imbalance of classes in real-world datasets poses a major challenge in machine learning and classification, and traditional synthetic data generation methods often fail to address this problem effectively. …”
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Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery by Maria Zavadskaya, Anastasia Orlova, Andrei Dmitrenko, Vladimir Vinogradov

“…Scientific contribution The study presents the first application of QSAR-guided reinforcement learning for Syk inhibitor discovery, yielding structurally novel candidates with predicted high potency. …”
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Application of machine learning to stress corrosion cracking risk assessment by Aeshah H. Alamri

“…The fundamentals of machine learning algorithms and stress corrosion cracking were presented. …”
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E-learning future trends in higher education in the 2020s and beyond by Shahid Bashir, Alexander L. Lapshun

“…This research assesses worldwide e-learning trends in higher education, offering insights into its present condition and influence by evaluating recent advancements, practices, and problems. …”
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Distance learning in cardiology: the use of multimedia clinical diagnostic tasks by S. I. Karas, E. V. Grakova, M. V. Balakhonova, M. B. Arzhanik, E. E. Kara-Sal

“…To create a methodological base for distance learning of cardiology healthcare professionals — multimedia clinical diagnostic tasks.Material and methods. …”
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Synthesis of Fault-Tolerant Reliable Broadcast Algorithms With Reinforcement Learning by Diogo Vaz, David R. Matos, Miguel L. Pardal, Miguel Correia

“…To automate the process of developing such algorithms, this work presents a new approach that uses Reinforcement Learning to synthesize correct and efficient fault-tolerant distributed algorithms. …”
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Algebraic Expressions for Complex Deep Learning Systems Modeling and Analysis by Rhandall Valdez-Yepez, Bryan V. Piguave, Jennifer C. Pita, Andres G. Abad

“…Many modern Deep Learning (DL) systems have achieved impressive state-of-the-art results by combining individual sub-systems, including foundation models, to form increasingly more complex DL systems. …”
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