An Efficient Random Forest Classifier for Detecting Malicious Docker Images in Docker Hub Repository by Maram Aldiabat, Qussai M. Yaseen, Qusai Abu Ein

“…The paper develops a machine learning model for detecting malicious Docker images by using a newly created dataset containing Docker images associated with 14 corresponding features that were specifically chosen as they are critical indicators of potential security risks in Docker images, and the dataset was published for research purposes. Moreover, the paper developed and tested several machine learning algorithms using Docker image features: Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting, and Neural Network. …”
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Software Methodology for Data Control and Collection for Autonomous Monitoring Systems with a Large Amount of Generated Information on the Example of Software for a Hydrological Ra... by I. S. Serdiukov, V. I. Veremyev, Van Tuan Nguyen

“…In some applications, such a flow is redundant and can be optimized through the use of various algorithms for the primary processing of information. …”
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A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease by Ji Seung Ryu, Jang Hoon Ru, Yunkoo Kang, Sejung Yang

“…However, there is a lack of research focusing specifically on patients with GSD. …”
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Internet of Things in healthcare: An adaptive ethical framework for IoT in digital health by Abubakar Wakili, Sara Bakkali

“…The research reveals pressing issues related to data privacy, security, and user autonomy and highlights the imperative need for an increased focus on algorithmic transparency and the integration of ethical considerations in the design and development of IoT applications. …”
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Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach by Vasileios Xezonakis, Olusegun David Samuel, Christopher Chintua Enweremadu

“…The dataset used in this research is a 9568-unit full combined cycle power plant basis load dataset, accessible through the public UCI Machine Learning Repository. …”
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Development of the Model of Diagnostics of Design-Technological Thinking in Secondary School Students in the Subject of Labor (Technology) in the Module “Robotics and Automated Sys... by Yakov M. Mashukov

“…Currently, there is a significant amount of research papers devoted to design and technological styles of thinking. …”
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The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials by Vera Malheiro, Beatriz Santos, Ana Figueiras, Filipa Mascarenhas-Melo

“…AI systems work through algorithms based on rules or instructions that enable the machine to make decisions. …”
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Energy-efficient distributed heterogeneous clustered spectrum-aware cognitive radio sensor network for guaranteed quality of service in smart grid by Emmanuel Ogbodo, David Dorrell, Adnan Abu-Mahfouz

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Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques by Hepseeba Kode, Khaled Elleithy, Laiali Almazaydeh

“…This research presents a novel approach to detecting epileptic seizures leveraging the strengths of Machine Learning (ML) and Deep Learning (DL) algorithms in EEG signals. …”
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Innovative Solutions for Oil Well Monitoring: Data-Driven Multiphase Virtual Flow Metering Using Ensemble Machine Learning and Historical Field Data by Wael A. Farag, Hussein H. Ismail, Muhammad Nadeem

“…By combining data-driven ensemble machine-learning algorithms and historical oil field portable test reports, this paper proposes a Data-Drive Multiphase Virtual Flow Meter (DD-MVFM) that estimates oil, gas, and water flow rates and provides real-time monitoring, and predicts future production with appropriate accuracy. …”
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Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis by Muawiyah A. Bahhah, Eyad Talal Attar

“…<b>Conclusions:</b> The present research extends the epilepsy diagnosis with clues on brain activity during seizures and further demonstrates the effectiveness of advanced preprocessing techniques. …”
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Deep Reinforcement Learning: A Chronological Overview and Methods by Juan Terven

“…The survey extends to policy gradient methods, actor–critic architectures, and state-of-the-art algorithms such as proximal policy optimization, soft actor–critic, and emerging model-based approaches. …”
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From information exposure to legal consciousness: The chain mediating role of political trust and social equality in Chinese college students by Liu Peng, Min Tian, Wan Li

“…The cultivation of college students' legal consciousness in the digital media environment is crucial for advancing social responsibility, moral awareness, and rule-of-law societies. However, prior research often assumes a linear positive relationship between information exposure and legal consciousness, overlooking potential negative effects in which algorithmic biases and emotional framing in digital media may weaken legal consciousness. …”
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Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods by Qi Sang, Chen Chen, Zeguo Shao

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Computer-Aided Facial Soft Tissue Reconstruction with Computer Vision: A Modern Approach to Identifying Unknown Individuals by Svenja Preuß, Sven Becker, Jasmin Rosenfelder, Dirk Labudde

“…The models did not indicate significant similarity, highlighting a gap between human perception and algorithmic assessment. These findings suggest that current face recognition algorithms may not yet be fully suited to evaluating reconstructions, which tend to deviate in subtle but critical facial features. …”
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Digital technologies for signal radio vision and radio monitoring by M. S. Kostin, K. A. Boikov

“…The results of fundamental research on electrodynamic effects of vector-wave deformation of nonstationary fields of sub-nanosecond configuration are presented as a means of identifying and authenticating signal radio images. …”
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Development of Engineering Models of Nanosatellites for Student Training by V. Е. Evchik, A. A. Spiridonov, D. V. Ushakov, V. S. Baranova, I. A. Shalatonin, V. A. Saechnikov

“…It has reduced development costs, flexible equipment reconfiguration, and easier access to the simulator's internal architecture for demonstration purposes.The developed complexes allow students to practically study the ultra-small satellite components design and ground stations, methods for receiving and processing telemetry and scientific information, attitude determination and control algorithms. The complexes allow to conduct of research in the development of individual onboard systems and special-purpose equipment of the nanosatellite and their testing in the loop. …”
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SYNTHESIS METHODS OF ALGEBRAIC NORMAL FORM OF MANY-VALUED LOGIC FUNCTIONS by A. V. Sokolov, O. N. Zhdanov, O. A. Ayvazian

“…The results could be the basis for further theoretical research and practical applications such as: the development of new cryptographic primitives, error-correcting codes, algorithms of data compression, signal structures, and algorithms of block and stream encryption, all based on the perspective principles of many-valued logic. …”
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The quantum circuit implementation and feasibility analysis of quantum public-key cryptosystem based on the $$QSCD_{ff}$$ Q S C D ff problem by Anyi Li, Qiqing Xia, Qianru Zhu, Li Yang

“…Our contribution provides a theoretical foundation for the physical feasibility analysis of quantum cryptographic algorithms, offering insights into the challenges and prospects of implementing these algorithms in quantum computation environments.…”
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Analysis of disease severity and mortality prediction using machine learning during COVID-19 by Hodjat (Hojatollah) Hamidi, Mostafa Moradi

“…This paper focuses on how machine learning (ML) algorithms and applications have been used to analyze disease severity and mortality prediction in COVID-19 research. …”
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