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检索条件"任意字段=Proceedings of the 2019 International Communication Engineering and Cloud Computing Conference"

共 4939 条记录，以下是221-230 订阅

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A Systematic Analysis on Task Scheduling Algorithms for Resource Allocation of Virtual Machines on cloud computing Environments

A Systematic Analysis on Task Scheduling Algorithms for Reso...

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2023 IEEE international conference on Recent Trends in Electronics and communication, ICRTEC 2023

作者： Nayak, Ankitha A Shetty, Shashank Department of Computer Science and Engineering Udupi574110 India

ISBN: (纸本)9798350396195

In the emerging field of technology and science, cloud computing is an effective IT service model providing computing services like database storage, network, analytics and software to the consumer and user over the internet. The cloud computing archetype offers various benefits over the traditional self-hosting computing solution, though it has the critical challenge of assigning the task to the computing resource. Task scheduling is a prominent issue for all cloud providers and having an efficient task scheduling algorithm is a must for processing. Plentiful task scheduling algorithms are implemented with different techniques and defined by various performance factors like increasing throughput, minimizing time and makespan. This paper outlines a survey on existing task scheduling algorithms in the cloud platform. A set of contexts and scenarios are created to examine and contrast how different task scheduling algorithm work. Task scheduling algorithms, namely FCFS, Short Job First (SJF), Round Robin, Priority algorithm, Modified Short Job First, Enhanced Round Robin and Genetic algorithm, are investigated in this paper based on performance metrics like makespan, turnaround time, No. of Virtual Machines and cloudlets. © 2023 IEEE.

关键词： Virtual machine

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A STRUCTURED IMPLEMENBTATION OF INTELLEGENT DECISION MAKING FOR INDETIFYING TH ELEVEL OF LUNG CANCER USING AI

A STRUCTURED IMPLEMENBTATION OF INTELLEGENT DECISION MAKING ...

引用

2024 international conference on Sustainable computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Harikumar, Priya Batra, Raman Makhija, Priya Department of ISME ATLAS SkillTech University Maharashtra Mumbai India Department of Mechanical Engineering Noida Institute of Engineering & Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9798350355338

Lung cancer continues to be a major cause of cancer-related death, which emphasises how crucial early identification is to higher survival rates. Because it is difficult for radiologists to identify lung nodules by hand, a novel computer-aided decision-support equipment for lung a nodule detection was created. Our system, which is built on a 3D a Deep Convolutional Neural (3DDCNN), is a useful tool to help radiologists make lung cancer diagnostic decisions. In order to use the three-dimensional data from CT scans, we automatically selected possible areas of interest using a multi-Region Research Network (mRPN) and median intensity projection. Our Computer-Aided Detection (CAD) system performed better than expected thanks to training and validation using datasets in LUNA16, ANODE09, and LIDC-IDR. With a low false positive rate and a sensitivity rating of 98.4%, generality of 92%, AUROC = 96%, and accuracy of 98.51%, our method performs very well. With a low number of false positives and an amazing 98.7% sensitivity, our cloud-Based 3DDCNN outperformed previous systems once cloud computing was included. This highlights the potential elderly deep learning in conjunction with cloud computing to provide precise and effective lung nodule diagnosis using CT imaging, hence offering significant assistance to healthcare providers during the treatment of patients with lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Service Weaver: A Promising Direction for cloud-Native Systems? 14

Service Weaver: A Promising Direction for Cloud-Native Syste...

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14th international conference on cloud computing and Services Science, CLOSER 2024

作者： Johnson, Jacoby Kharel, Subash Mannamplackal, Alan Abdelfattah, Amr S. Cerny, Tomas Computer Science of Baylor University WacoTX United States Systems and Industrial Engineering University of Arizona TucsonAZ United States

ISBN: (纸本)9789897587016

cloud-Native and microservice architectures have taken over the development world by storm. While being incredibly scalable and resilient, microservice architectures also come at the cost of increased overhead to build and maintain. Google’s Service Weaver aims to simplify the complexities associated with implementing cloud-native systems by introducing the concept of a single modular binary composed of agent-like components, thereby abstracting away the microservice architecture notion of individual services. While Service Weaver presents a promising approach to streamline the development of cloud-native applications and addresses nearly all significant aspects of conventional cloud-native systems, there are existing tradeoffs affecting the overall functionality of the system. Notably, Service Weaver’s straightforward implementation and deployment of components alleviate the overhead of constructing a complex microservice architecture. However, it is important to acknowledge that certain features, including separate code bases, routing mechanisms, resiliency, and security, are presently lacking in the framework. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Architecture

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Secure cloud Data Storage with Double Encryption and Efficient Space Utilization 4

Secure Cloud Data Storage with Double Encryption and Efficie...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Kirubasankar, K. Mohandas, R. Hari, V. Gnanaguru, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331529635

In today's digital landscape, robust data protection measures are imperative to safeguard sensitive information. Encryption serves a cornerstone of data security, ensuring access. This project explores cloud data protection, focusing on a novel approach to enhance security. By integrating integer-based encryption with the SHA-256 algorithm followed by AES encryption, the system strengthens data resilience against sophisticated threats. The proposed system facilitates seamless file encryption and storage in the cloud, providing end to end security. Key features include secure file upload and recovery mechanisms, ensuring authorized access while preserving confidentiality, through a user-friendly interface and robust backend infrastructure, the system offers a comprehensive solution for modern data protection challenges. This research highlights the importance of proactive measures in mitigating risks and protecting sensitive data in the digital age. © 2024 IEEE.

关键词： Ciphertext

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Advancing Healthcare Privacy with Homomorphic Encryption 1

Advancing Healthcare Privacy with Homomorphic Encryption

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1st international conference on Computational Intelligence for Security, communication and Sustainable Development, CISCSD 2024

作者： Rani, Shreenagamanjula Ramani, Roja Chinta, Sanjana Shet, Sathvik S Biradar, Shivani New Horizon College of Engineering Department of Cse Bengaluru India

ISBN: (纸本)9798350365405

This research introduces an innovative approach to bolster security in cloud computing by incorporating homomorphic encryption (HE) within a symmetric key cryptography framework. Our methodology facilitates Safe Multi-Party Computation (SMPC) in stable cloud environments, obviating the need for data decryption and ensuring the confidentiality of sensitive information. The study explores the practical applications of this approach in encrypted control systems and secure cloud-based data analytics processing, utilizing the Fast Additive Partially Homomorphic Algorithm within a keyed homomorphic public key encryption system. Through the implementation of homomorphic encryption, our solution enables data modification in an encrypted format without necessitating decryption, resulting in a swifter and more efficient execution of analytical operations on securely stored cloud data. This research addresses a critical gap in the medical industry, presenting a straightforward yet dependable solution to concerns regarding data privacy and authenticity. By advancing the adoption of homomorphic encryption, our study contributes to securing sensitive information within cloud environments, promoting trust and reliability in data processing systems. © 2024 IEEE.

关键词： Cryptographic Techniques Fast additive partially Algorithm Homomorphic Encryption

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Enhancing cloud Security: A Hybrid AI Approach for Intrusion Detection Using Convolutional Neural Networks and Stochastic Gradient Descent Algorithms 5

Enhancing Cloud Security: A Hybrid AI Approach for Intrusion...

引用

5th international conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Gayatri, Malyala Chintamaneni, Vijayalakshmi Swapna, Revuri Chanti, Malladi Devarasetty, Lavanya Athiraja, A. Telangana Hyderabad India Vignan Institute of Technology and Science Department of Ece Telangana Hyderabad India Malla Reddy Engineering College Department of Cse Telangana Hyderabad India Vignan Institute of Information and Technology Department of Cse Andhrapradesh Visakhaptnam India Malla Reddy College of Engg. Department of Cse Telangana Secunderabad India

ISBN: (纸本)9798350379945

With the rapid expansion of cloud computing, ensuring the security of data and infrastructure has become paramount. One of the most important functions of Intrusion Detection Systems (IDS) is to protect cloud environments against online attacks. This research study proposes a hybrid Artificial Intelligence (AI) strategy by combining the techniques of Stochastic Gradient Descent (SGD) and Convolutional Neural Networks (CNN) for intrusion detection in cloud computing environment. The proposed methodology integrates the robust feature extraction capabilities of CNN with the optimization power of SGD for efficient model training. By utilizing a comprehensive dataset comprising network traffic patterns and attack scenarios, the hybrid model is trained to accurately classify normal and malicious activities. Through extensive experimentation and evaluation, the proposed approach demonstrates superior performance in detecting intrusions with high accuracy and low false positive rates. Furthermore, the scalability and effectiveness of the proposed method is validated in real-world cloud environments. The research findings highlight the efficacy of hybrid AI techniques in enhancing intrusion detection capabilities, thereby enhancing cloud security and mitigating potential risks in terms of data integrity and confidentiality. © 2024 IEEE.

关键词： cloud security

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Optimisation of continuous overlapping point cloud data for laser cladding 7

Optimisation of continuous overlapping point cloud data for ...

引用

7th international conference on Data Science and Information Technology, DSIT 2024

作者： Zhong, Sheng-Jiang Zhu, Hao Zhu, Cong-Ying Nanjing Institute of Technology School of Communication and Artificial Intelligence Nanjing China

ISBN: (纸本)9798350384093

To address the issue where 3D scanning of complex structural metallic targets during the laser cladding process frequently generates overlapping noisy point cloud data that significantly deviates from the inherent characteristics of the object, this paper proposes a multi-layer point cloud extraction and data optimization method to eliminate overlapping noise and restore the original scanning data. The proposed method is based on point cloud surfaces extracted using the Random Sample Consensus (RANSAC) algorithm. By delineating object boundaries, the point cloud surface is segmented into distinct point cloud blocks. Subsequently, noisy blocks are identified based on their overlapping nature and limited size, and these blocks are systematically removed in a block-by-block manner. As the method exclusively processes noisy point cloud blocks, it preserves the integrity, precision, and original morphology of the non-noisy point cloud data. © 2024 IEEE.

关键词： Laser Cladding Reverse engineering Special Point cloud Noise

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proceedings - 2024 international conference on Electronic engineering and Information Systems, EEISS 2024

Proceedings - 2024 International Conference on Electronic En...

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2024 international conference on Electronic engineering and Information Systems, EEISS 2024

ISBN: (纸本)9798350351033

The proceedings contain 42 papers. The topics discussed include: microgrid energy management strategy based on hierarchical federated deep reinforcement learning;efficient dynamic symmetric searchable encryption with forward and backward security;design and implementation of system communication computing acceleration algorithm under cloud edge collaboration;surface tiny defect detection based on gaussian distribution modeling and adaptive feature fusion;statistical characteristics of radar degree of polarization in multiple-source interference environment;autonomous localization method for railway trains based on multi-source information fusion;evolutionary reinforcement learning-based optimization method for ship concept schemes;and self-supervised monocular depth estimation with uncertainty-aware feature enhancement and depth fusion.

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MCHeRA: An Efficient Multi cloud Heuristic Resource Allocation using Grey Wolf Optimization 5

MCHeRA: An Efficient Multi Cloud Heuristic Resource Allocati...

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5th international conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Gandhimathi, P. Kiruthiga, S. BIndu, K.V. Shanthi, H.J. Velmurugan, V. Sangeethaa, S.N. Srm Madurai College for Engineering and Technology Pottapalayam Sivagangai District630612 India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science &engineering Chennai India Puduvoyal India Department: Computer Applications Hindustan Institute of Technology and Science Deemed to be University Padur India Vel Tech Rangarajan Dr Sagunlatha R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Bannari Amman Institute of Technology India

ISBN: (纸本)9798350389609

cloud computing is a cutting-edge technology that has gained immense popularity in the present era. The burgeoning demand for services has necessitated the development of a framework for efficiently allocating resources to incoming requests. This approach not only enhances the network's efficiency but also reduces costs significantly. The convergence of cloud and edge computing has emerged as a burgeoning field of study in the computing industry over the past few years. The exponential rise in the number of customers and requests for cloud data centers (CDCs) has highlighted the critical need for robust servers and energy-efficient mechanisms. As CDCs expand to meet growing demands, they face significant challenges related to energy consumption, environmental impact, and operational costs. Employing suitable algorithms for resource allocation in CDCs is crucial in order to minimize energy consumption. The objective of this research endeavor was to devise an ingenious approach for dynamically allocating resources in cloud networks by leveraging the power of the Gray Wolf Optimization algorithm. The proposed work performs Multi-cloud Heuristic Resource Allocation (MCHeRA) using Grey Wolf Optimization (GWO) algorithm and produces 61% of computational resource utilization, 79% of CPU utilization, 64% memory utilization, 74% bandwidth utilization. The algorithm that has been put forth is meticulously assessed and contrasted with its counterparts. The outcome of the simulation manifests the remarkable efficacy of the novel MCHeRA in comparison to alternative algorithms. © 2024 IEEE.

关键词： cloud platforms

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cloud-Enabled Facial Emotion Recognition: A Comprehensive AWS Rekognition Analysis 5

Cloud-Enabled Facial Emotion Recognition: A Comprehensive AW...

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5th international conference on Intelligent communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Chowdary, Yogendra Kishore, Batta Chaitanya Pingali, Sai Harsha Aditya, Chalavadi Kvss Pavan Kiran, K.V.D. Koneru Laks Hmaiah Education Foundation Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350385649

This research introduces a serverless cloud-based a system for identifying facial emotions that is highly accurate and scalable, but not too complex. It uses A WS services like Lambda, S3 and Rekognition to simplify deployment and increase scalability - and make it available anywhere. Depending on a serverless architecture that is implemented by using cloud infrastructure, the system makes an API for real- time emotion analysis of facial images accessible. Bypassing intricate models, the system focuses on optimal image preprocessing techniques for efficient feature extraction. Performance evaluation rigorously assesses the system's accuracy, speed, and scalability across diverse metrics. The results affirm the system's efficiency and precision in a serverless cloud-based environment. With its innovative approach, this project demonstrates the power of serverless cloud computing for facial emotion recognition. Beyond its technical capabilities, the system has a wide range of possible uses that could affect sentiment analysis, personalized user experiences, and human -computer interaction. © 2024 IEEE.

关键词： Emotion Recognition

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