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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Kumari, K. Aruna Muvva, Vijaya Bhaskar Reddy Kumar, K. Sudheer Rai, B. Karunakara Shakir, Aboothar Mahmood Department of CSE SRKR Engineering College Bhimavaram Chinaamiram India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences -Shinas Shinas Oman School of Computer Science and Artificial Intelligence SR University Warangal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9789819778751

The resource allocation is focus on enhancing the service provider proceedings and obtaining customer satisfaction by Service Level Agreements (SLA). Cloud technology has significantly transformed the organizations to manage and allocate their resources. The cloud users are choosing sources of application distribution exclusive of cooperating Quality of Service (QoS) necessities. To overcome this issue, this paper proposed a Quasi-Opposition Learning based Aquila Optimizer (QOL-AO) for resource allocation in cloud. The virtualization supports to implement task using resource availability and reduces the response time. The QOL-AO employs the hunting approach for obtain the effective and efficient resource allocation in cloud. The Performance of QOL-AO is estimate through execution time, response time and energy consumption for various number of Virtual Machines (VMs). The QOL-AO attained less execution time of 18.568 ms and response time of 0.095 s than existing algorithms like Emperor Penguin Optimization (EPO) and Particle Swarm Optimization (PSO). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

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computer Systems science & Engineering 2024年第3期48卷 571-608页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Abdulatif Alabdulatif Ruba Abu Khurma Maribel Garcia-Arenas Pedro A.Castillo University Institute of Computing Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Uttaranchal UniversityUttarakhand248007India Department of Computer Science College of Computer Qassim UniversityBuraydah52571Saudi Arabia MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

As cloud computing usage grows,cloud data centers play an increasingly important *** maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance *** purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data *** aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management *** review revealed three task allocation research topics and seven performance management *** allocation research areas are resource allocation,load-Balancing,and *** management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network *** study proposes new techniques to enhance cloud computing work allocation and performance ***-comings in each approach can guide future *** research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and *** methodologies can steer future research to fill gaps in the literature.

关键词： Cloud computing data centre task allocation performance management resource utilization

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Studying Risks of Hybrid Reengineering on Air-Transportation Systems Management 'A Case Study on Jordanian Civil Aviation Regulatory Commission (CARC)' 24

Studying Risks of Hybrid Reengineering on Air-Transportation...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Al Refai Mohammed, N. Taani, M.K. Otoom, Ahmed Ali Samara, Ghassan Hanandeh, S.E. Khafajeh, Hayel Zarqa University Software Engineering Dep. Zarqa Jordan Applied Science Private University Cybersecurity and Cloud Computing Dep. Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Artificial Intelligence Dep. Zarqa Jordan

ISBN: (纸本)9798350384307

This study examines decision-making in software re-engineering within businesses, focusing on the challenges and considerations involved. Financial constraints, time, and staff efforts are critical factors in these decisions. The study identifies difficulties faced by top-level management with legacy systems, existing systems, underused systems, and packaged software due to user resistance and customer dissatisfaction. The primary goal of the study is to assess the importance of software quality factors, especially from the users' perspective, in making re-engineering decisions. It uses the ISO 9126 Model to analyze quality elements in the case study software, aiming to provide guidance for deciding whether to proceed with re-engineering. The study explores functionality, reliability, usability, efficiency, maintainability, and portability as factors influencing re-engineering decisions, based on the ISO 9126 model. To understand user interactions and program performance, the study used unstructured participant observation in the case study software, highlighting the challenges faced by top management due to user resistance. These findings were further confirmed through face-to-face interviews with software users and department employees, offering insights into their perspectives on software quality elements. The results reveal that, despite some flaws, the system has valuable features and capabilities. Most employees express a desire to undergo re-engineering to enhance service quality, align with ISO 9126 standards, and leverage the expertise of competent staff for re-engineering activities. © 2023 IEEE.

关键词： customer dissatisfaction ISO 9126 model decision-making software quality factors software re-engineering User acceptance

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule 4

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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Smart Traffic Control: Adaptive Signal Management Based on Real-time Lane Detection using YOLOv8

Smart Traffic Control: Adaptive Signal Management Based on R...

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Automation and Computation (AUTOCOM), International Conference on

作者： M. Ravichandran Kumar Laxmikant Annamalai Muthu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electrical and Electronics Engineering University of Technology & Applied Science Nizwa Oman

As cities grow, handling traffic in big urban are-as becomes a huge proble-m. More cars on the road and not enough roads le-ad to heavy traffic jams. This increases trave-l time and harms our environment. Our study tackle-s these problems with a ne-w approach. We use real-time- lane detection with YOLOv8 and adaptable- traffic lights. Using sharp computer vision, our system pinpoints vehicle-s. It adjusts traffic light timings on-the-go to improve traffic flow. Our model works e-xtremely accurately. During the- learning phase, it achieve-d a mAP50 score of 99.3% and a mAP50-95 score of 87.4%. In the te-sting phase, it got a mAP score of 99.2% and a mAP50-95 score of 86.2%. The re-sults highlight how the system can improve city trave-l. It’s valuable for city planners and traffic officials. It helps the-m understand smart transportation systems bette-r.

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A Cost-Effective Approach for NDN-Based Internet of Medical Things Deployment

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computers, Materials & Continua 2022年第1期70卷 233-249页

作者： Syed Sajid Ullah Saddam Hussain Abdu Gumaei Mohsin SAlhilal Bader Fahad Alkhamees Mueen Uddin Mabrook Al-Rakhami Department of Information Technology Hazara University MansehraKPK21120Pakistan Research Chair of Pervasive and Mobile Computing Department of Information SystemsKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Faculty of Applied SciencesTaiz UniversityTaiz6803Yemen Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Digital Science Faculty of ScienceUniversiti Brunei DarussalamJln Tungku linkGadongBruneiBE1410Darussalam

Nowadays,healthcare has become an important area for the Internet of Things(IoT)to automate healthcare facilities to share and use patient data anytime and anywhere with Internet *** present,the host-based Internet paradigm is used for sharing and accessing healthcare-related ***,due to the location-dependent nature,it suffers from latency,mobility,and *** this purpose,Named Data Networking(NDN)has been recommended as the future Internet paradigm to cover the shortcomings of the traditional host-based Internet ***,the novel breed lacks a secure framework for *** article constructs an NDN-Based Internet of Medical Things(NDN-IoMT)framework using a lightweight certificateless(CLC)*** adopt the Hyperelliptic Curve Cryptosystem(HCC)to reduce cost,which provides strong security using a smaller key size compared to Elliptic Curve Cryptosystem(ECC).Furthermore,we validate the safety of the proposed scheme through *** cost-efficiency,we compare the designed scheme with relevant certificateless signature *** final result shows that our proposed scheme uses minimal network ***,we deploy the given framework on NDN-IoMT.

关键词： Internet of Medical Things healthcare Named Data Networking

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Genetic Optimization Techniques for Enhancing Web Attacks Classification in Machine Learning

Genetic Optimization Techniques for Enhancing Web Attacks Cl...

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2023 IEEE International Conference on Dependable, Autonomic and Secure computing, 2023 International Conference on Pervasive Intelligence and computing, 2023 International Conference on Cloud and Big Data computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Jaradat, Ameera S. Nasayreh, Ahmad Al-Na'amneh, Qais Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Yarmouk University Dep. of Computer Science Irbid Jordan Yarmouk University Dep. of Computer Science Irbid211633 Jordan Applied Science Private University Dep. of Cyber Security and Cloud Computing Amman Jordan Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya

ISBN: (纸本)9798350304602

Web-based applications are now the preferred approach for delivering a variety of services via the Internet. As a result of the globalization of commerce, web applications have been growing quickly and becoming increasingly complicated. Such applications have a significant security vulnerability in the online environment since they were developed with little experience and without testing or validation. Numerous attackers use this security vulnerability to take control of the program, modify the data, and steal the most crucial information. They may also access all internal, unauthorized items. This work presents a hybrid model that classifies website attacks as benign through the integration of four machine learning algorithms: gradient boost (GB), multilayer perceptron (MLP), logistic regression (LR) and K nearest neighbor (KNN). The work deployed Tree-based Pipeline Optimization Tool (TPOT) that utilizes the Genetic algorithm (GA) to extract the optimal parameter and consequently enhance the model performance. The model underwent evaluation utilizing a data set from the Canadian Institute 2023 that contains various types of attacks. Among these algorithms, GB achieved the best accuracy scores of 95%, 94% and 95% for accuracy, recall and F1-score, respectively. © 2023 IEEE.

关键词： Genetic algorithms

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On the Scalability of computing Genomic Diversity Using SparkLeBLAST: A Feasibility Study

On the Scalability of Computing Genomic Diversity Using Spar...

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IEEE Conference on High Performance Extreme computing (HPEC)

作者： Ritvik Prabhu Bernard Moussad Karim Youssef Emil Vatai Wu-Chun Feng Department of Computer Science Virginia Tech USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA RIKEN Center for Computational Science Japan

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

Studying the genomic diversity of viruses can help us understand how viruses evolve and how that evolution can impact human health. Rather than use a laborious and tedious wet-lab approach to conduct a genomic diversity study, we take a computational approach, using the ubiquitous NCBI BLAST and our parallel and distributed SparkLeBLAST, across 53 patients (~40,000,000 query sequences) on Fugaku, the world's fastest homogeneous supercomputer with 158,976 nodes, where each code contains a 48-core A64FX processor and 32 GB RAM. To project how long BLAST and SparkLeBLAST would take to complete a genomic diversity study of COVID-19, we first perform a feasibility study on a subset of 50 query sequences from a single COVID-19 patient to identify bottlenecks in sequence alignment processing. We then create a model using Amdahl's law to project the run times of NCBI BLAST and SparkLeBLAST on supercomputing systems like Fugaku. Based on the data from this 50-sequence feasibility study, our model predicts that NCBI BLAST, when running on all the cores of the Fugaku supercomputer, would take approximately 26.7 years to complete the full-scale study. In contrast, SparkLeBLAST, using both our query and database segmentation, would reduce the execution time to 0.026 years (i.e., 22.9 hours) – resulting in more than a 10,000× speedup over using the ubiquitous NCBI BLAST.

关键词： COVID-19 Databases computer viruses Scientific computing Scalability Genomics Predictive models Supercomputers Bioinformatics Optimization

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Enhanced Diabetic Retinopathy Detection: An Explainable Semi-Supervised Approach Using Contrastive Learning

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Ali, Rashid Khan, Fiaz Gul Rehman, Zia Ur Kwak, Daehan Ali, Farman Comsats University Islamabad Department of Computer Science Abbottabad Campus KPK Abbottabad22060 Pakistan Kean University Department of Computer Science and Technology NJ07083 United States Sungkyunkwan University Department of Applied Ai School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Diabetic retinopathy (DR) is a leading cause of blindness and represents a critical challenge to global vision health. Early detection is essential to preventing irreversible eye damage. Automated medical image analysis plays a pivotal role in enabling timely diagnosis. However, the development of robust diagnostic models is challenged by the scarcity of labeled data and the prevalence of imbalanced and unlabeled datasets. Semi-supervised learning offers a potential solution by leveraging unlabeled data to enhance model performance. However, it is often limited by challenges such as unreliable pseudo-labeling, the exclusion of low-confidence data, and biases introduced by imbalanced datasets. To address these limitations, we propose a novel semi-supervised learning framework for DR detection that combines similarity and contrastive learning. Our approach utilizes class prototypes and an ensemble of classifiers to generate reliable pseudo-labels for unlabeled data. Unlike traditional methods that discard unreliable samples, our framework integrates them into the training process using contrastive learning. This allows us to extract valuable features and improve overall performance. Furthermore, we enhance the model's transparency and interpretability by incorporating the explainable AI technique GradCAM, which provides insights into the model's predictions for specific images. We evaluated the proposed method on the publicly available Kaggle DR dataset for diabetic retinopathy classification. Experimental results demonstrate that our approach achieves improved performance compared to existing semi-supervised learning methods. It also effectively leverages unreliable samples, highlighting its potential to advance DR diagnosis. © 2013 IEEE.

关键词： Contrastive Learning

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Harnessing the Power of Vocal Signals in COVID-19 Detection Utilizing Machine Learning

Harnessing the Power of Vocal Signals in COVID-19 Detection ...

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IEEE International Conference on Big Data

作者： Aleesa Mann Ajinkya P. Jadhav Richard Matovu Vibhuti Gupta Department of Computer Science and Data Science School of Applied Computational Sciences Meharry Medical College Nashville TN USA Department of Computing and Information Science Gannon University Erie PA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The global COVID-19 pandemic has strained health-care systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1-score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible.

关键词： COVID-19 Machine learning algorithms Limiting Costs Pandemics Scalability Biological system modeling Nose Machine learning Biosensors

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