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Machine Intelligence and Smart Innovation (ICMISI), International Conference on

作者： Sarah El-Dawy Abdallah Hussien Mostafa Ammar Ahmed El-Dawy Software Development Engineer S2S Vista Virtual Platforms Siemens EDA Cairo Egypt Electrical and Computer Engineering Department University of Waterloo Waterloo Canada Computer Network and Datacenter Department Arab Academy for Science Technology and Submarine Alexandria Egypt Electrical Power Department University of Alexandria Alexandria Egypt

ISBN: (数字)9798350365740

ISBN: (纸本)9798350365757

With the increasing integration of Controller Area network (CAN) in modern vehicles, securing the communication network from malicious attacks is imperative. A novel Graph Neural network (GNN)-based Intrusion Detection System (IDS) is proposed for real-time detection of several attacks, such as Denial of Service (DOS), fuzzy, and spoofing attacks. Our proposed model represents the CAN network as a graph, where nodes correspond to message arbitration ID and edges denote the communication sequence. Using Graph Attention networks (GAT), our model captures the intricate relationships and dependencies between Electronic Circuit Units (ECUs), in real-time, enhancing its ability to discern normal behavior from malicious activity. The key to our system's success is its emphasis on achieving real-time capabilities. The GAT architecture optimizes computational efficiency for swift analysis of CAN traffic without compromising detection accuracy. Our system minimizes model complexity for enhanced deployability in resource-constrained vehicular environments. Comprehensive experiments on a diverse dataset showcase its ability to swiftly and accurately detect a range of attacks in real-time, with an overall accuracy of 99.75% for DOS attacks, 99.50% for fuzzy attacks, and 100% for spoofing attacks. Moreover, attack detection time is calculated to be 6.76 ms for DOS, 6.79 ms for fuzzy, and 6.69 ms for spoofing. These results made the model meet real-time detection constraints.

关键词： Technological innovation Accuracy Computational modeling Image edge detection Intrusion detection Real-time systems Graph neural networks

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Defensive Auto-Updatable and Adaptable Bot Recommender System (DAABRS): A New Architecture Approach in Cloud Computing Systems

Defensive Auto-Updatable and Adaptable Bot Recommender Syste...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Sufian Khwaldeh Mohit Y Khushwant S Department of Network and Information Security College of Information Technology University of Fujairah UAE Department of Information Technology Faculty of Information Technology and Systems The University of Jordan Aqaba Jordan Department of Mathematics University Institute of Sciences Chandigarh University Mohali Punjab India Department of Computer Science & Engineering University Institute of Engineering and Technology M. D. University Rohtak India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Cloud server data security makes extensive use of data centers and instantly accessible edge systems for possible transactions involving sizable and confidential data. These Edge Systems are chosen for rental based on their usefulness in situations where new privacy and security difficulties must be overcome. The use of Edge Systems is subject to dynamic mobility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recommender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge Systems contain a variety of users and environments, making it simple for attackers to enter anonymously and compromise Edge System privacy. The most effective security measure against increasing risks and assaults for Edge Systems that rent more cloud storage is to use Stealth Mode Bots inside of data segments. In addition to anticipating and averting data breaches in server partitioning, tenant systems, edge systems, and containers by integrating stealth mode bots into each. Suggested use cases: Tenants, Edge Systems, or Containers for effective security-enhanced Stealth Mode Bots that are activated when a particular data segment is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth Mode Bots to stop heavily attacked Data Containers, and machine learning will be used to update specifications to identify and prevent further assaults.

关键词： Cloud computing Data centers Privacy Machine learning Containers Chatbots Servers

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Joint Precoding and Reflection Optimization for IRS-Assisted MU-MIMO-OTFS Downlink Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Zou, Tingting Wang, Fengyu Xu, Wenjun Zhang, Yimeng Pan, Miao Han, Zhu Beijing University of Posts and Telecommunications State Key Laboratory of Network and Switching Technology Beijing100876 China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Mobile Research Institute Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Orthogonal time frequency space (OTFS) modulation has been proposed as a promising technique to achieve high-reliability communications in high-mobility scenarios. In this paper, we consider a multi-user (MU) multiple-input multiple-output (MIMO)-OTFS downlink communication scenario, where an intelligent reflecting surface (IRS) is deployed to enhance the performance. Specifically, the delay- Doppler (DD) domain input-output relationship of such a system is first derived, based on which, the joint optimization of the precoding matrices and IRS reflection phase is studied to maximize the weighted sum rate (WSR) considering user fairness. Note that the corresponding optimization problem is non-convex. To tackle this challenge, we first reformulate the problem into a more traceable equivalent form by the weighted mean minimum square error (WMMSE) approach. Utilizing the characteristics of the DD domain signal relationship, we then decouple the reformulated problem and further transform it into two subproblems, which are solved by the Lagrangian function method and the project-descent-retracting (PDR) algorithm, respectively. The precoding matrices and reflection phase are jointly optimized by alternative optimization to maximize WSR. Simulation results demonstrate that, compared with benchmark schemes, the proposed WMMSE-ALT can improve the rate more than 85% in IRS-assisted MU-MIMO-OTFS systems. © 1967-2012 IEEE.

关键词： Mean square error

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A Comparative Study for Predicting Burned Areas of a Forest Fire Using Soft Computing Techniques 2nd

A Comparative Study for Predicting Burned Areas of a Forest ...

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2nd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2020

作者： Al-Shourbaji, Ibrahim Alhameed, Mohammed Katrawi, Anwer Jeribi, Fathe Alim, Sophia Department of Computer and Network Engineering Computer Science and Information Technology College Jazan University Jazan82822-6649 Saudi Arabia Department of Computer Science Computer Science and Information Technology College Jazan University Jazan82822-6649 Saudi Arabia Faculty Member Academic Development Deanship Jazan University Jazan82822-6649 Saudi Arabia Department of Information Technology and Security Computer Science and Information Technology College Jazan University Jazan82822-6649 Saudi Arabia Independent Researcher Bradford United Kingdom

ISBN: (纸本)9789811636899

Forest fires is an important environmental disasters that have many consequences in our life. As a result, early forecasting and rapid action of fires are needed to control such a phenomenon and saving lives. In this paper, Montesinho Natural Park (MNP) dataset is used to determine the burned areas of a forest fire, three prediction techniques were analyzed;namely Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS) and Artificial Neural network (ANN). These algorithms are implemented to specify the technique that would provide best prediction results. The performance of these algorithms was assessed based on three statistical measures, the Mean Absolute Error (MAE), the Mean Squared Error (MSE) and the Root Mean Squared Error (RMSE). From the Statistical measures, it is inferred that MARS algorithm provides the best results in term of performance compared with other methods. The obtained results confirm that MARS improves the forecasting accuracy and has the capability to forecast forest fires effectively with a comparable computational cost. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking

arXiv

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arXiv 2024年

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Hu, Ye Liu, Yuchen Yin, Changchuan The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The Department of Computer Science North Carolina State University RaleighNC27695 United States

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Enhancing Efficiency and Empowering Institutions: Leveraging Wireless Sensor Devices, IoT Edge Computing, and Fog Computing in Educational Systems

Enhancing Efficiency and Empowering Institutions: Leveraging...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Shams Tabrez Siddiqui Md Mohammed Ali Sohail Aasif Aftab Mohd Sarfaraz Mohammad Imran Alam Abu Salim Department of Computer Science College of CS & IT Jazan University Jazan Saudi Arabia Department of Computer & Network Engineering College of CS & IT Jazan University Jazan K.S.A Department of Information Technology and Security College of CS & IT Jazan University Jazan Saudi Arabia

Wireless sensor devices, edge computing, and fog computing can transform education by improving efficiency and empowering institutions. This research paper explores the application of these technologies in educational settings and investigates their benefits, challenges, and potential solutions. Educational institutions can track classroom occupancy, ambient conditions, and student participation using wireless sensor systems. loT edge computing allows data processing and analysis at the network edge for rapid insights and decision-making. Fog computing integrates cloud resources with edge computing to support complicated computations and data storage. Wireless sensor devices, loT edge computing, and fog computing are used to optimize educational processes like resource management, student tracking, personalized learning, and campus security. Data privacy, security, scalability, and interoperability issues are studied and solutions suggested. This research shows how wireless sensor devices, loT edge computing, and fog computing may alter educational systems, helping institutions improve efficiency and empower stakeholders.

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Detecting Activities of Daily Living in Egocentric Video to Contextualize Hand Use at Home in Outpatient Neurorehabilitation Settings

arXiv

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arXiv 2024年

作者： Kadambi, Adesh Zariffa, José Institute of Biomedical Engineering University of Toronto TorontoON Canada KITE - Toronto Rehabilitation Institute University Health Network TorontoON Canada Rehabilitation Sciences Institute University of Toronto TorontoON Canada Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoON Canada

Wearable egocentric cameras and machine learning have the potential to provide clinicians with a more nuanced understanding of patient hand use at home after stroke and spinal cord injury (SCI). However, they require detailed contextual information (i.e., activities and object interactions) to effectively interpret metrics and meaningfully guide therapy planning. We demonstrate that an object-centric approach, focusing on what objects patients interact with rather than how they move, can effectively recognize Activities of Daily Living (ADL) in real-world rehabilitation settings. We evaluated our models on a complex dataset collected in the wild comprising 2261 minutes of egocentric video from 16 participants with impaired hand function. By leveraging pre-trained object detection and hand-object interaction models, our system achieves robust performance across different impairment levels and environments, with our best model achieving a mean weighted F1-score of 0.78 ± 0.12 and maintaining an F1-score > 0.5 for all participants using leave-one-subject-out cross validation. Through qualitative analysis, we observe that this approach generates clinically interpretable information about functional object use while being robust to patient-specific movement variations, making it particularly suitable for rehabilitation contexts with prevalent upper limb impairment. Copyright © 2024, The Authors. All rights reserved.

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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

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computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

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Sum Computation Rate Maximization in Self-Sustainable RIS-Assisted MEC

Sum Computation Rate Maximization in Self-Sustainable RIS-As...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Han Li Ming Liu Bo Gao Ke Xiong Pingyi Fan Khaled B. Letaief Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

This paper studies a self-sustainable reconfigurable intelligent surface (SRIS)-assisted mobile edge computing (MEC) network, where a SRIS first harvests energy from a hybrid access point (HAP), and then enhances the users' offloading performance with the harvested energy. To improve computing efficiency, a sum computation rate maximization problem is formulated. Based on the alternating optimization (AO) method, an efficient algorithm is proposed to solve the formulated non-convex problem. Simulations show that when the SRIS is deployed closer to the HAP, a higher performance gain can be achieved.

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SSI−FL: Self-sovereign identity based privacy-preserving federated learning

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Journal of Parallel and Distributed Computing 2024年 191卷

作者： Haque, Rakib Ul Touhidul Hasan, A.S.M. Al-Hababi, Mohammed Ali Mohammed Zhang, Yuqing Xu, Dianxiang Department of Electrical & Computer Engineering University of Texas at San Antonio San Antonio United States School of Computer Science & Technology University of Chinese Academy of Sciences Beijing100049 China Division of Computing Analytics and Mathematics School of Science and Engineering University of Missouri-Kansas City Kansas CityMO64110 United States School of Computer Science & Technology University of Chinese Academy of Sciences Beijing101408 China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing100049 China

Traditional federated learning (FL) raises security and privacy concerns such as identity fraud, data poisoning attacks, membership inference attacks, and model inversion attacks. In the conventional FL, any entity can falsify its identity and initiate data poisoning attacks. Besides, adversaries (AD) holding the updated global model parameters can retrieve the plain text of the dataset by initiating membership inference attacks and model inversion attacks. To the best of our knowledge, this is the first work to propose a self-sovereign identity (SSI) and differential privacy (DP) based FL namely SSI−FL for addressing all the above issues. The first step in the SSI−FL framework involves establishing a secure connection based on blockchain-based SSI. This secure connection protects against unauthorized access attacks of any AD and ensures the transmitted data's authenticity, integrity, and availability. The second step applies DP to protect against model inversion attacks and membership inference attacks. The third step focuses on establishing FL with a novel hybrid deep learning to achieve better scores than conventional methods. The SSI−FL performance analysis is done based on security, formal, scalability, and score analysis. Moreover, the proposed method outperforms all the state-of-art techniques. © 2024

关键词： Blockchain

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