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Conference on Information Communication Technology and Society (ICTAS)

作者： Nokuthula Khoza Pius Owolawi Vusumuzi Malele Department of Computer Systems Engineering Faculty of Information and Communication Technology Soshanguve Campus Tshwane University of Technology Pretoria South Africa Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus North-West University Vanderbijlpark South Africa

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography.

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Exploring Machine Learning in IoT Smart Home Automation

Exploring Machine Learning in IoT Smart Home Automation

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International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Quadri Waseem Wan Isni Sofiah Wan Din Azamuddin Bin Ab Rahman Kashif Nisar Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pekan Malaysia AnalytiCray Kuala Lumpur Malaysia Professional Computing & Data Science Swinburne University of Technology Sydney Parramatta New South Wales Australia

The Internet of Things (IoT) has evolved in these years. Various types of organizations, industries, research domains and almost all types of intelligent future applications are utilizing the advantages of IoT. These applications include smart homes, smart cities, smart infrastructure smart communities, smart healthcare, smart agriculture and many more. “Smart Homes” has emerged as one the latest Internet of Things (IoT) applications known to automate household equipment's using remote or automated functioning from remote locations to improve the quality of life for its inhabitants. For a smart home system to function effectively, the machine learning (ML) implementation must go beyond basic remote control and simple automation. To fully realize its potential and provide homeowners with tremendous and unexpected benefits, more research and development in the fields of machine intelligence and smart home automation are required. In this research work, we aim to traverse ML in IoT smart home automation by classifying the home automation applications. We propose a taxonomy of machine learning (ML) for smart homes based on its application. This research also includes related surveys and literature reviews along with open challenges and issues as well as future directions in detail.

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges

Unleashing the Potential of Decentralized Federated Learning...

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Haibo Cheng Wenjian Liu Youyang Qu Kexue Fu Qinyuan Wang Faculty of Data Science City University of Macau Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL’s current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling.

关键词： Surveys data privacy Systematics Federated learning Medical services data models Servers Security Reliability Systematic literature review

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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Quantitative Scalability of Nodes and Geographical Coverage in LEACH Protocol

Quantitative Scalability of Nodes and Geographical Coverage ...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Foudil Mir Farid Meziane Ahcene Bounceur Abdelkader Laouid Medical Computing Laboratory (LIMED) Faculty of Exact Sciences University of Bejaia Bejaia Algeria Data Science Research Centre University of Derby Derby UK Information and Computer Science Department KFUPM Dhahran Saudi Arabia LIAP Laboratory University of El Oued El Oued Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

Energy optimisation using clustering remains a constant challenge in wireless sensor networks (WSNs). The high volume of data generated within these networks leads to excessive energy consumption when transferring it to the processing center. Therefore, clustering protocols, in particularly hierarchical routing protocols dedicated to WSNs, must imperatively integrate energy efficiency mechanisms in order to extend the functional lifetime of the nodes and the network. In this paper, we focus on an in-depth analysis of scalability in the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. The LEACH protocol, renowned for its random and distributed approach to WSNs energy optimisation, forms the focus of our analysis and exploration. Our study focuses in particular on the quantitative scalability of nodes and the scalability of geographical coverage. The aim is to improve our understanding of the behaviour of the LEACH protocol in large-scale environments, particularly with regard to node and network lifetime and energy management parameters.

关键词： Wireless sensor networks Protocols Scalability Voting Probabilistic logic Routing Routing protocols

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Design and Promotion of Cost-Effective IOT-Based Heart Rate Monitoring

Design and Promotion of Cost-Effective IOT-Based Heart Rate ...

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2022 International Conference on Cloud computing, Internet of Things, and Computer Applications, CICA 2022

作者： Moshayedi, Ata Jahangir Kolahdooz, Amin Roy, Atanu Shuvam Rostami, Seyyed Ali Latifi Xie, Xiaoyun School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China Faculty of Technology School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou China

ISBN: (纸本)9781510656673

The term telemedicine was first used in the 1920s, although used many years ago and has continued to evolve today. Medical diagnoses usually require visual information, but remote display systems have recently become a special place due to the constant unavailability of the treating physician or the remoteness of medical centers and the constant need of some patients for round-the-clock care. In this article, an Arduino-based heart rate information system is designed and implemented. Due to the reasonable price and easy accessibility of the created system in the fraction, it has many applications. The results of the designed system showed the system's capabilities to track and know the person's heart rate. © 2022 SPIE.

关键词： Cost effectiveness

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Privacy-Aware Edge Computation Offloading With Federated Learning in Healthcare Consumer Electronics System

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Yiming Fang, Hao Yan, Zheng Qi, Lianyong Bilal, Muhammad Li, Yang Xu, Xiaolong Zhou, Xiaokang Nanjing University of Information Science and Technology School of Software Nanjing210044 China NanJing University of Information Science and Technology Reading Academy Nanjing210044 China Xidian University School of Cyber Engineering Shaanxi Xi'an710026 China College of computer science and technology Qingdao China Lancaster University School of Computing and Communications United Kingdom Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China Kansai University Faculty of Business and Data Science Japan

The continuous iteration of consumer electronics has significantly promoted the development of medical devices, which has enabled the collection of large amounts of heterogeneous medical data. These data are offloaded from local devices to cloud servers for processing using traditional methods, which results in high transmission latency and the risk of privacy leakage. Additionally, researchers have employed federated learning to protect data privacy, but this approach can lead to a straggler effect due to the limited computational and communication resources of terminal devices. To address these issues, a computation offloading framework is designed to optimize task and resource allocation, addressing multi-optimization problems and mitigating the straggler effect in federated learning. Moreover, a novel computation offloading method within a federated learning framework assisted by edge computing, named DRWB, is proposed. Specifically, we develop a deep reinforcement learning-based approach to transfer lagging training tasks to idle edge servers, enhancing data processing speed, minimizing transmission delays, and protecting user privacy. Extensive experimental results demonstrate that the DRWB method outperforms baseline methods, showcasing superior performance in handling heterogeneous medical data tasks. © 1975-2011 IEEE.

关键词： Electronic health record

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SWAP Attack: Stealthy Side-Channel Attack on Multi-Tenant Quantum Cloud System

arXiv

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arXiv 2025年

作者： Lee, Wei Jie Bryan Wang, Siyi Dutta, Suman Maouaki, Walid El Chattopadhyay, Anupam College of Computing and Data Science Nanyang Technological University Singapore Applied Statistics Unit Indian Statistical Institute Kolkata India Quantum Physics and Spintronics Team LPMC Faculty of Sciences Ben M’Sik Hassan II University of Casablanca Morocco

The rapid advancement of quantum computing has spurred widespread adoption, with cloud-based quantum devices gaining traction in academia and industry. This shift raises critical concerns about the privacy and security of computations on shared, multi-tenant quantum platforms accessed remotely. In several recent works, crosstalk has been exploited on shared quantum devices, enabling adversaries to interfere with the victim circuits within a neighborhood. Though illuminating, these studies left several unresolved questions regarding the fundamental cause of crosstalk, effective countermeasures, and replicability across different circuits. In this paper, we revisit the crosstalk effect, tracing its origins to the SWAP path between various qubits in a circuit and demonstrating its effectiveness even over long distances. The results significantly improve our understanding of the phenomenon beyond prior works. Our proposed SWAP-based side-channel attack can be executed in both active and passive modes, as verified on real shared IBM quantum devices. In the active attack, an adversary executing a single CNOT gate can perturb victim circuits running Grover’s Algorithm from afar, reducing the expected output accuracy through 81.62% by strategically positioning qubits. Moreover, this phenomenon can be clearly explained through modeling, where specific qubits in a platform can be identified as more susceptible to an attacker. The passive attack, leveraging a stealthy circuit as small as 6.25% of the victim’s, achieves 100% accuracy in predicting the circuit size of the victim, running Simon’s Algorithm. These findings challenge the existing defense strategy of maximizing topological distance between circuits, demonstrating that attackers can still extract sensitive information or manipulate results remotely. Our work highlights the need for more robust security measures to safeguard quantum computations against such emerging threats. © 2025, CC BY.

关键词： Qubits

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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System

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Procedia Computer science 2023年 217卷 1266-1279页

作者： M.O. Okwu L.K. Tartibu E.O. Ojo S. Adume J.O. Gidiagba J. Fadeyi Department of Mechanical and Industrial Engineering Tech. University of Johannesburg South Africa Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Mathematics and Data Science Dept. Computing Science and Mathematics Division Faculty of Natural Science University of Stirling UK Faculty of Engineering and Applied Sc. Memorial University Newfoundland Canada Department of Industrial and Systems Engineering University of Buffalo USA

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach.

关键词： Adaptive Neuro-Fuzzy Inference System Dual Source Multi-Echelon Multi-Destinations

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Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

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Computers, Materials & Continua 2022年第1期70卷 715-738页

作者： Shidrokh Goudarzi Seyed Ahmad Soleymani Mohammad Hossein Anisi Domenico Ciuonzo Nazri Kama Salwani Abdullah Mohammad Abdollahi Azgomi Zenon Chaczko Azri Azmi Centre for Artificial Intelligent(CAIT) Universiti Kebangsaan MalaysiaBangi43600Malaysia School of Computer Engineering Iran University of Science and TechnologyResalat Sq.16846-13114TehranIran School of Computing Faculty of Engineering Universiti Teknologi MalaysiaJohorMalaysia School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Department of Electrical Engineering and Information Technologies University of Naples“Federico II”Naples80125Italy Razak Faculty of Technology and Informatics Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network *** address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood *** light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river *** framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a ***,an algorithm hybridized with Group Method data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative *** proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River *** performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

关键词： Unmanned aerial vehicles wireless sensor networks group method data handling particle swarm optimization river flow prediction

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