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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

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Procedia computer science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud Computing Diagnosis Digital Health Edge Computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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Correction to: A comprehensive performance evaluation of container migration strategies

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Computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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An Efficient Multi-band Infrared Small objects Detection Approach for Low-Altitude Artificial Intelligence of Thing

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IEEE Internet of Things Journal 2025年

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Hangzhou310024 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Masdar United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria

As a cutting-edge technology of low-altitude Artificial Intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multi-band infrared small object detection approach for low-altitude intelligent surveillance services. Firstly, we propose the Multi-band infrared Image Fusion algorithm based on Cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based Multi-scale Dense Small object Detection (MsDSOD) algorithm is proposed. The algorithm consists of the Global-Local object Detection (G-LOD) network, the object Dense Area Extraction (O-DAE) module, and the Weighted Boxes Fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean Average Precision (mAP) of MsDSOD is improved by 0.80% and the Average Precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Comparative Analysis of Machine Learning Models for Students’ Performance Prediction

Comparative Analysis of Machine Learning Models for Students...

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International Conference on Advances in Digital science, ICADS 2021

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Al Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9783030717810

Machine learning for education is an emerging discipline where a model is developed based on training data to make predictions on students’ performance. The main aim is to identify students who would have difficulty in their learning and to take precautionary measures to help them. In this paper, we conduct a comparative analysis of the most used machine learning classification models in the literature. We evaluate the performance of the models in terms of accuracy, F-measure, and execution time using two real-life education datasets. The performance of the models is data-driven. We give insights into the models’ performance and advise on the best model to use accordingly. We believe the results of this paper will be widely used by education professionals for accurate predictions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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A Resilient Hierarchical Checkpointing Algorithm for distributed systems Running on Cluster Federation 1

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7th International Symposium on Security in Computing and Communications, SSCC 2019

作者： Mansouri, Houssem Pathan, Al-Sakib Khan Laboratory of Networks and Distributed Systems Computer Science Department Faculty of Sciences Ferhat Abbas Setif University 1 Sétif Algeria Department of Computer Science and Engineering Southeast University Dhaka Bangladesh

ISBN: (数字)9789811548253

ISBN: (纸本)9789811548246

In distributed systems, checkpointing method can be used to ensure fault-tolerance, which in turn could help the system’s security and stability. In simple terms, checkpointing means saving status information of a system. In this work, a novel hierarchical checkpointing algorithm for distributed systems is proposed, which runs on cluster federation. This algorithm is based on two well-known techniques in the literature and it ensures that a locally consistent state is always maintained in each cluster with a global optimistic logging technique between clusters. The proposed algorithm synchronizes the intra- and inter-cluster checkpointing process in such way that each checkpoint taken locally in any cluster is a segment of the consistent global state. Compared to other works, our scheme has low message cost as it makes sure that only few processes in each cluster record checkpoints for any execution operation. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Fault tolerance

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Modern Computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning

arXiv

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arXiv 2023年

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Independent Data Scientist and Engineer Al Ain Abu Dhabi United Arab Emirates

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high *** Codes 68-04, 68T05, 68T07, 68T20 © 2023, CC BY-NC-ND.

关键词： Coronavirus

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PMU-Spill: Performance Monitor Unit Counters Leak Secrets in Transient Executions

PMU-Spill: Performance Monitor Unit Counters Leak Secrets in...

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IEEE Asian Hardware-Oriented Security and Trust (AsianHOST)

作者： Pengfei Qiu Qiang Gao Dongsheng Wang Yongqiang Lyu Chang Liu Xiaoyong Li Chunlu Wang Gang Qu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Tsinghua University Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing China Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland Maryland USA

ISBN: (纸本)9781665461153

The processor's Performance Monitor Unit (PMU) allows the recording of architectural and microarchitectural events for profiling purposes. In this study, we reveal a security issue caused by the fact that current PMU implementations are capable of recording some events that happened during transient executions. We propose the PMU -Spill attack, a new kind of attack that enables attackers to maliciously leak the secret data in transient executions. We demonstrate on real hardware that PMU -Spill attack can leak the secret data stored in Intel Software Guard Extensions (SGX). In addition, we perform a thorough study to reveal all the vulnerable PMU counters and find that 20 of them can be used to achieve PMU -Spill attack. Our experiments suggest that the throughput of PMU -Spill attack is up to 575.3 bytes per second (Bps) with an average error rate of 1.89% when leaking the SGX-protected secret data.

关键词： Side-channel attacks Throughput Phasor measurement units Hardware Software Recording Security

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