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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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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing

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Procedia computer Science 2024年 238卷 385-395页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems (INDUCE) Lab 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

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

关键词： cloud computing edge computing dataset generation energy-efficiency green computing machine learning internet of things power consumption modeling resource management scheduling

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

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Procedia computer Science 2023年 220卷 470-477页

作者： Rima Kaafarani Leila Ismail Oussama Zahwe ICCS-Lab Computer Science Department American University of Culture and Education 1507 Beirut Lebanon Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.

关键词： Blockchain Decision-Map Health Insurance Frauds Detection Hyperledger Fabric Neo Smart Contract Smart Healthcare

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Simulating Cyber Attacks and Designing Malware using Python 10

Simulating Cyber Attacks and Designing Malware using Python

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Ankit, Akash Inder, Shubham Sharma, Anuj Johari, Rahul Vidyarthi, Deo Prakash University School of Information Communication and Technology Guru Gobind Singh Indraprastha University SWINGER: Security Wireless IoT Network Group of Engineering and Research Dwarka New Delhi110078 India MelbourneVIC3000 Australia Jawaharlal Nehru University Parallel and Distributed System Lab School of Computer and Systems Sciences New Delhi110067 India

ISBN: (纸本)9781665490993

In this Internet era, we have become heavily dependent on the World Wide Web and it has become a basic amenity for humans. A normal user can be trapped easily in this vicious web if not careful enough. It is a field that requires users to be on their toes every time they try to connect to this enormous interconnection of networks. Reports and Surveys have said that in the coming years, Cybercrime could become the biggest threat. In this proposed work, the authors have deployed applications and designed malware for the purpose of simulating various kinds of cyber-attacks so that they could study their effects & affects and ultimately devise solutions to resolve the vulnerabilities in a system. These applications included networking concepts & protocols and a software based keylogger which was developed with no malicious intent, but to analyse the architectural view or design of a malware and to study the stages of a malware attack like entry, distribution, exploitation, infection and execution. Efforts have been made to demonstrate spooting attacks with an idea of identifying vulnerabilities and security loopholes in a particular system that can be exploited by an attacker if not resolved within time. © 2023 IEEE.

关键词： Network security

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Using Deep Learning for Simulation of Real time Video Streaming Applications

Using Deep Learning for Simulation of Real time Video Stream...

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Simulation Winter Conference

作者： Abdolreza Abhari Dipak Pudasaini Department of Computer Science Distributed Systems & Multimedia Processing Laboratory (DSMP lab) Ryerson University Toronto Ontario CANADA

ISBN: (纸本)9781665476621

The traditional approaches for simulation of video analytics applications suffer from the lack of real-data generated by employed machine learning techniques. Machine learning methods need huge data that causes network congestion and high latency in cloud-based networks. This paper proposes a novel method for performance measurement and simulation of video analytics applications to evaluate the solutions addressing the cloud congestion problem. The proposed simulation is achieved by building a model prototype called Video Analytic Data Reduction Model (VADRM) that divides video analytic jobs into smaller tasks with fewer processing requirements to run on edge networking. Real data generated from VADRM prototype is characterized and tested by curve fitting to find the distribution models for generating the larger number of artificial data for resource management simulation. Distribution models based on real data of CNN-based VADRM prototype are used to build a queueing model and comprehensive simulation of real-time video analytics applications.

关键词： Analytical models Visual analytics Computational modeling Buildings Prototypes Data models Real-time systems

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Assessing the computational limits of GraphDBs' engines - A comparison study between Neo4j and Apache Spark 24

Assessing the computational limits of GraphDBs' engines - A ...

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24th Pan-Hellenic Conference on Informatics, PCI 2020

作者： Ballas, Ioannis Tsakanikas, Vassilios Pefanis, Evaggelos Tampakas, Vassilios Distributed Intelligent Systems and Data Lab Department of Electrical and Computer Engineering University of Peloponnese Greece

ISBN: (纸本)9781450388979

Big Data paradigm has placed pressure on well-established relational databases during the last decade. Both academia and industry have proposed several alternative database schemes in order to model the captured data more efficiently. Among these approaches, graph databases seem the most promising candidate to supplement relational schemes. Within this study, a comparison is performed among Neo4j, one of the leading graph databases, and Apache Spark, a unified engine for distributed large-scale data processing environment, in terms of processing limits. The results reveal that Neo4j is limited to the physical RAM memory of the processing environment. Yet, until this limit is reached, the processing engine of Neo4j outperforms the Apache Spark engine. © 2020 ACM.

关键词： Graph Databases

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GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation

GLoT: A Novel Gated-Logarithmic Transformer for Efficient Si...

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IEEE Future Networks World Forum (FNWF)

作者： Nada Shahin Leila Ismail Department of Computer Science and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of Information Technology UAE University Al-Ain United Arab Emirates Emirates Center for Mobility Research UAE University Al-Ain United Arab Emirates

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community.

关键词： Sign language Adaptation models Translation Accuracy Terminology Neural networks Semantics Logic gates Transformers Testing

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From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation systems: Survey, Taxonomy and Performance Evaluation

arXiv

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arXiv 2024年

作者： Shahin, Nada Ismail, Leila Intelligent Distributed Computing and Systems Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi United Arab Emirates

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation *** Codes I.2, I.2.7, I.4, I.4.9 © 2024, CC BY.

关键词： Neural machine translation

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Optimal Resource Utilization in Hyperledger Fabric: A Comprehensive SPN-Based Performance Evaluation Paradigm

Optimal Resource Utilization in Hyperledger Fabric: A Compre...

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IEEE Symposium on Network Operations and Management

作者： Carlos Melo Glauber Gonçalves Francisco A. Silva Leonel Feitosa Iure Fé André Soares Eunmi Choi Tuan Anh Nguyen Dugki Min Applied Research on Distributed Systems Lab (PASID) Federal University of Piauí (UFPI) Brazil School of Software College of Computer Science Kookmin University Seoul South Korea Konkuk Aerospace Design-Airworlhiness Research Inslilule (KADA) Konkuk Universily Seoul Soulh Korea Department of Computer Science and Engineering Konkuk University Seoul South Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Hyperledger Fabric stands as a leading framework for permissioned block-chain systems, ensuring data security and audit-ability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various block-chain parameters becomes vital. These configurations significantly affect the system’s performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyper-ledger Fabric’s performance, considering variations in block-chain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding block-chain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size’s role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.

关键词： Performance evaluation distributed ledger Computational modeling Data security Petri nets Stochastic processes Fabrics

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A sustainable and secure load management model for green cloud data centres

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Scientific Reports 2023年第1期13卷 1-22页

作者： Saxena, Deepika Singh, Ashutosh Kumar Lee, Chung-Nan Buyya, Rajkumar Department of Computer Applications National Institute of Technology Haryana Kurukshetra 136119 India Department of Computer Science Goethe University Frankfurt Germany Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung 804201 Taiwan Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems University of Melbourne Melbourne 804201 Australia

The massive upsurge in cloud resource demand and inefficient load management stave off the sustainability of Cloud Data Centres (CDCs) resulting in high energy consumption, resource contention, excessive carbon emission, and security threats. In this context, a novel Sustainable and Secure Load Management (SaS-LM) Model is proposed to enhance the security for users with sustainability for CDCs. The model estimates and reserves the required resources viz., compute, network, and storage and dynamically adjust the load subject to maximum security and sustainability. An evolutionary optimization algorithm named Dual-Phase Black Hole Optimization (DPBHO) is proposed for optimizing a multi-layered feed-forward neural network and allowing the model to estimate resource usage and detect probable congestion. Further, DPBHO is extended to a Multi-objective DPBHO algorithm for a secure and sustainable VM allocation and management to minimize the number of active server machines, carbon emission, and resource wastage for greener CDCs. SaS-LM is implemented and evaluated using benchmark real-world Google Cluster VM traces. The proposed model is compared with state-of-the-arts which reveals its efficacy in terms of reduced carbon emission and energy consumption up to 46.9% and 43.9%, respectively with improved resource utilization up to 16.5%. © 2023, The Author(s).

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