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2024 International Conference on Smart Devices, ICSD 2024

作者： Sinha, Aashna Sharma, Nishesh Kumar, Shivam Lande, Ayapal Iqbal, Mohammed Ismail Uttaranchal University School of Applied and Life Science Division of Research & Innovation Dehradun India Uttaranchal University Aerospace Engineering Uttaranchal Institute of Technology Dehradun India SR University School of Computer Science & Artificial Intelligence Warangal India University of Technology and Sciences College of Engineering and Technology Nizwa Oman

ISBN: (纸本)9798350394412

An important area of attention is energy efficiency since artificial intelligence (AI) algorithms may control power use by identifying occupant habits and modifying systems accordingly. This not only lessens the impact on the environment but also generates significant financial savings over time. A new age of ease, effectiveness, and security for homeowners has arrived with the integration of AI in smart homes. Machine learning algorithms are used in AI-powered smart homes to automate and improve a variety of daily tasks. These solutions, which range from voice-activated virtual assistants to intelligent lighting and climate management, adapt and react to the particular tastes and demands of residents. AI-driven surveillance substantially improves security by utilizing tools like face recognition, predictive analytics, and remote monitoring to provide users piece of mind. Smart homes powered by AI provide advantages in terms of healthcare and wellbeing in addition to convenience and efficiency. Modern home-based health monitoring devices may measure vital signs and give early warnings of potential health problems. Reminders for taking medications and assistance for seniors who want to age in place can improve quality of life. A unified environment that adjusts and anticipates inhabitant demands is created via interconnected gadgets and central control centers. Strong data protection and secure network protocols are necessary since cybersecurity and privacy are urgent issues. The key developments and future ramifications of AI-driven smart homes are summarized in this study. © 2024 IEEE.

关键词： Atificial Intelligence Machine learning Smart home

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Robustness of community structure under edge addition

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Physical Review E 2023年第5期108卷 054302-054302页

作者： Moyi Tian Pablo Moriano Division of Applied Mathematics Brown University Providence Rhode Island 02912 USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge Tennessee 37830 USA

Communities often represent key structural and functional clusters in networks. To preserve such communities, it is important to understand their robustness under network perturbations. Previous work in community robustness analysis has focused on studying changes in the community structure as a response of edge rewiring and node or edge removal. However, the impact of increasing connectivity on the robustness of communities in networked systems is relatively unexplored. Studying the limits of community robustness under edge addition is crucial to better understanding the cases in which density expands or false edges erroneously appear. In this paper, we analyze the effect of edge addition on community robustness in synthetic and empirical temporal networks. We study two scenarios of edge addition: random and targeted. We use four community detection algorithms, Infomap, Label Propagation, Leiden, and Louvain, and demonstrate the results in community similarity metrics. The experiments on synthetic networks show that communities are more robust when the initial partition is stronger or the edge addition is random, and the experiments on empirical data also indicate that robustness performance can be affected by the community similarity metric. Overall, our results suggest that the communities identified by the different types of community detection algorithms exhibit different levels of robustness, and so the robustness of communities depends strongly on the choice of detection method.

关键词： Community structure Network evolution Robustness

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Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method 27th

Finite Basis Physics-Informed Neural Networks as a Schwarz D...

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27th International Conference on Domain Decomposition Methods in science and Engineering, DD 2022

作者： Dolean, Victorita Heinlein, Alexander Mishra, Siddhartha Moseley, Ben University of Strathclyde 26 Richmond Street GlasgowG1 1XH United Kingdom Faculty of Electrical Engineering Mathematics & Computer Science Delft University of Technology Delft Institute of Applied Mathematics Mekelweg 4 Delft2628 CD Netherlands Computational and Applied Mathematics Laboratory/ETH AI Center ETH Zurich Ramistrasse 101 Zurich8092 Switzerland

The success and advancement of machine learning (ML) in fields such as image recognition and natural language processing has lead to the development of novel methods for the solution of problems in physics and enginee... 详细信息

ISBN: (纸本)9783031507687

关键词： Domain decomposition methods

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An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks

An Intelligent Reinforcement Learning–Based Method for Thre...

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作者： Saeed, Muhammad Yousaf He, Jingsha Zhu, Nafei Farhan, Muhammad Dev, Soumyabrata Gadekallu, Thippa Reddy Almadhor, Ahmad College of Computer Science Beijing University of Technology Beijing China Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal Pakistan School of Computer Science University College Dublin Dublin Ireland Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia

Traditional techniques for detecting threats in mobile edge networks are limited in their ability to adapt to evolving threats. We propose an intelligent reinforcement learning (RL)–based method for real-time threat detection in mobile edge networks. Our approach enables an agent to continuously learn and adapt its threat detection capabilities based on feedback from the environment. Through experiments, we demonstrate that our technique outperforms traditional methods in detecting threats in dynamic edge network environments. The intelligent and adaptive nature of our RL-based approach makes it well suited for securing mission-critical edge applications with stringent latency and reliability requirements. We provide an analysis of threat models in multiaccess edge computing and highlight the role of on-device learning in enabling distributed threat intelligence across heterogeneous edge nodes. Our technique has the potential, significantly enhancing threat visibility and resiliency in next-generation mobile edge networks. Future work includes optimizing sample efficiency of our approach and integrating explainable threat detection models for trustworthy human–AI collaboration. © 2024 John Wiley & Sons Ltd.

关键词： Reinforcement learning

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regularity for stationary mean-field games with logarithmic coupling

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Nonlinear Differential Equations and Applications NoDEA 2024年第5期31卷 1-34页

作者： Bakaryan, Tigran Di Fazio, Giuseppe Gomes, Diogo A. King Abdullah University of Science and Technology CEMSE Division Thuwal Saudi Arabia University of Catania Department of Mathematics and Computer Science Catania Italy

This paper investigates stationary mean-field games (MFGs) on the torus with Lipschitz non-homogeneous diffusion and logarithmic-like couplings. The primary objective is to understand the existence of $$C^{1,\alpha }$$ solutions to address the research gap between low-regularity results for bounded and measurable diffusions and the smooth results modeled by the Laplacian. We use the Hopf-Cole transformation to convert the MFG system into a scalar elliptic equation. Then, we apply Morrey space methods to establish the existence and regularity of solutions. The introduction of Morrey space methods offers a novel approach to address regularity issues in the context of MFGs.

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A Neutrosophic Logic Ruled Based Machine Learning Approaches for Chronic Kidney Disease Risk Prediction

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Neutrosophic Sets and Systems 2025年 79卷 76-95页

作者： Mohanty, Bhabani S. Krishna Priya, R. Alias, Liya Vijaya Kumar Reddy, R. Raut, Prasanta Kumar Isaac, Samson Manibharathi, D. Saravanakumar, C. Panda, Nihar Ranjan Broumi, Said Department of Statistics and Applied Mathematics Central University of Tamil Nadu India Research and Consultancy Department University of Technology and Applied Sciences P.O. Box: 12 Khasab Governorate of Musandam Musandam811 Oman College of Engineering and Technology University of Technology and Applied Science Shinas Oman Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur AP India Department of Mathematics Trident Academy of Technology Odisha Bhubaneswar India Division of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electronics and Communication Engineering Government College of Engineering Tamilnadu Salem India Department of Electronics and Communication Engineering SRM Valliammai Engineering College Kattankulathur Tamilnadu Chengalpattu India Department of Medical Research IMS & SUM Hospital SOA Deemed to be university India Laboratory of Information Processing Faculty of Science Ben M’Sik University Hassan II Casablanca Morocco

Chronic kidney disease (CKD) represents a significant global health challenge in society, and early detection of risk is essential for on-time treatment and intervention. This research suggests a novel machine-learning technique to create a reliable and accurate CKD risk prediction model by combining neutrosophic logic with various classification algorithms. We use neutrosophic logic to address the inherent imprecision and uncertainty in medical data, resulting in a more realistic portrayal of real-world scenarios. We measure the effectiveness of the proposed neutrosophic logic-based models using various metrics, including precision, specificity, and sensitivity. The results show that the neutrosophic logic method is better than traditional machine learning methods at finding people who are likely to develop CKD because it is more accurate and stable. This study illustrates the potential for incorporating neutrosophic logic into machine learning frameworks to improve risk prediction in medical fields. © 2025, University of New Mexico. All rights reserved.

关键词： Prediction models

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Comparative Analysis of Detector-Tracker Architecture for Object Tracking Based on SBC for UAV

Comparative Analysis of Detector-Tracker Architecture for Ob...

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IEEE International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

作者： Pylyp Prystavka Andrii Shevchenko Irina Rokitianska Department of Applied Mathematics Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

This paper investigates the tracking technology of moving objects from a UAV camera (or streaming video) for systems with limited computational resources, such as modern SBCs. A detector-tracker architecture is proposed, combining the advantages of object detection methods and tracking algorithms. A comparative analysis of the performance and accuracy of various detector and tracker combinations on modern SBCs is conducted. The results demonstrate the high efficiency of the proposed approach for object-tracking tasks under limited computational resources, which has practical significance for UAV-based systems.

关键词： Accuracy computer architecture Object detection Detectors Streaming media Autonomous aerial vehicles Cameras Computational efficiency Object tracking

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Trusting Computing as a Service for Blockchain Applications

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IEEE Internet of Things Journal 2023年第13期10卷 11326-11342页

作者： Li, Wen-Wei Meng, Weizhi Yeh, Kuo-Hui Cha, Shi-Cho National Taiwan University of Science and Technology Department of Information Management Taipei10607 Taiwan Technical University of Denmark Department of Applied Mathematics and Computer Science Copenhagen2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan

Blockchain and smart contracts have become one of the most popular technologies to establish decentralized applications. Due to public visibility of data stored in blockchain, the information processed by a smart contract is visible to every party in a blockchain. To improve data confidentiality, this study proposes a trusted computing as a service (TCaaS) framework based on the blockchain. The framework includes a trustworthy node to perform trusted computing. Using the proposed framework, users of blockchain systems can integrate trustworthy node into blockchain systems. Users can also upload general-purposed programs and associated parameters and discover trustworthy node for execution via related smart contracts. The programs and parameters are encrypted so that only selected trustworthy node can decrypt the data. The execution environment returns computed results to the corresponding blockchain system. We evaluate our concept with the ESP32 microcontroller with the ATECC508A security chip and the Quorum blockchain system. Therefore, the study contributes to construct a trusted computing environment for blockchain system and preserve confidentiality at the same time. © 2014 IEEE.

关键词： Smart contract

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Spline Approaches for Anomaly Detection in UAV-Based Aerial Surveillance

Spline Approaches for Anomaly Detection in UAV-Based Aerial ...

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IEEE International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

作者： Anhelina Zhultynska Pylyp Prystavka Department of Applied Mathematics Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

This article presents a new approach to detecting anomalies in data obtained from unmanned aerial vehicles using spline models. The relevance of the study is driven by the need for fast and accurate identification of anomalies in real time, which is critical for military intelligence and civilian monitoring. The proposed model is based on the use of momentary characteristics, such as average illumination intensity and standard deviation, which allows for effective data parameterization and detection of anomalous observations. The advantage of the spline model is its ability to adapt to complex and heterogeneous image textures, which significantly reduces the risk of missing critical anomalies. The model also demonstrates high robustness to variations in the data, making it a reliable tool for analyzing images in different lighting and landscape conditions. Experiments using forest, road, and car textures have shown that the spline model is able to accurately identify anomalies, which increases the efficiency of analysis and allows for a quick response to detected changes.

关键词： Adaptation models Analytical models Accuracy Surveillance Lighting Autonomous aerial vehicles Data models Real-time systems Splines (mathematics) Standards

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Development of an Immersive Learning Environment for Fundamentals of Nursing Labs 10

Development of an Immersive Learning Environment for Fundame...

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10th International Conference on Virtual Reality, ICVR 2024

作者： Obeid, Mohammad F. Ewais, Ahmed Asia, Mohammad Shenandoah University Division of Applied Technology Winchester United States Arab American University Palestine Computer Science Department Jenin Palestine Arab American University Palestine Health Sciences Department Jenin Palestine

ISBN: (纸本)9798350364231

The field of nursing is no stranger to the use of simulation and training systems for education and skill acquisition. Virtual reality (VR) has been utilized as an immersive learning environment (ILV) for many years as it facilitates repetitive training, scenario introduction, and information retention with no disturbance of the real system/clinic. In this work, the development of an ILV for fundamentals of nursing laboratory courses is presented. The project is carried out cross-institutionally to create a modular platform that allows the continuous addition of VR modules for nursing and medical procedures. An initial user study is conducted with faculty and senior nursing students hypothesizing the utility of this platform for efficient, scalable, and effective training;as well as investigating development decisions related to user interface design and user experience. © 2024 IEEE.

关键词： Contrastive Learning

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