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Integration of IoT and cloud computing to manage the patient e- prescription 2

Integration of IoT and cloud computing to manage the patient...

引用

2nd IEEE international conference on Advances in computing, communication and Applied Informatics, ACCAI 2023

作者： Chandel, Garima Niwazi Qurashi, Shahazad Bindu Madhavi, N. Madhu, G.C. Singh, Charanjeet Chandigarh University Department of Electronics and Communication Engineering Mohali India Jazan University College of Public Health and Tropical Medicine Department of Health Informatics Jazan Saudi Arabia Andhra Pradesh Vaddeswaram India Mohan Babu University Department of ECE Tirupati India DeenbandhuChhotu Ram University of Science and Technology Electronics and Communication Department Murthal India

ISBN: (纸本)9798350315905

Healthcare data management affects software performance. Current healthcare systems do not use the newest technology to connect with stakeholders. Pharmacists' existing system flexibility could be more efficient. This research proposes an Android and web application system that allows doctors to prescribe to patients using a stylus pen. It will enable patients, receptionists, pharmacists, and admins to interact with the system via their web access. Arduino and E-Health sensors integrate the Internet of Things and cloud computing in the suggested solution. Registered IoT devices help healthcare stakeholders, making the system efficient, user-friendly, and error-free. We present a superior healthcare data management solution and an intuitive system interface. © 2023 IEEE.

关键词： cloud computing

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Electric Vehicle Range Prediction using Random Forest Regression 8

Electric Vehicle Range Prediction using Random Forest Regres...

引用

8th international conference on Electronics, communication and Aerospace Technology, ICECA 2024

作者： Sagar, B.S. Rajasekaran, Mothiram Sakthisaravanan, B. Jyothi, R. Leela Lalitha, K. Murugan, S. Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Pine Candle Way Saint AugustineFL United States Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

This research introduces a new method for predicting electric vehicles (EVs) range that combines cloud computing with random forest regression (RFR) approaches. Predicting the range properly is now critical for user comfort and efficient route planning due to the rising popularity of EVs. Factors like changing driving conditions and battery depletion might make traditional range calculation techniques inaccurate. It uses the processing power of the cloud to collect and analyze massive volumes of real-time data on variables like traffic, weather, and topography that have an impact on EVs range. Next, it uses a powerful machine learning (ML) algorithm for RFR to accurately forecast EVs range by modeling the intricate interactions between these factors. Results from experiments this technique works, with far better range estimate advanced approaches. cloud computing scalability and flexibility also make it easy to predict models into preexisting EVs navigation systems, so drivers may get accurate range estimations in real time based on their own circumstances. It will improve EVs' usability and dependability, which will lead to more people buying and using these eco-friendly vehicles. © 2024 IEEE.

关键词： Prediction models

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5G Application for Methodologies for Big Data Application 5

5G Application for Methodologies for Big Data Application

引用

5th IEEE international conference on computing, Power, and communication Technologies, IC2PCT 2024

作者： Singh, Amanveer Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350383522

The advantages and benefits of big data and cellular networks are discussed in this essay. First, a summary of their interactions, together with big data-driven and big data-assisted communication networks, is given. The erstwhile caters for big data characteristics like number, pace, and variation by utilizing diverse means such as transmission, storage, and processing in cellular networks in order to accommodate big data apps and services. In order to gather wireless big data and gain detailed information about the networks and users for better network management and scheduling, the latter makes use of big data methods. Two examples that focus on channel data collection and big data-aided peripheral information storage are offered that will further highlight the reciprocal advantages. The discussion concludes with a few intriguing open study questions. © 2024 IEEE.

关键词： 5G Convergence Big Data Cellular Networks cloud computing Data Analytics Data Collection Data Processing Data Storage and Data Security Data Transmission Edge computing Network Infrastructure Network Management Resource Allocation Wireless communications

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Machine Learning Techniques for Twitter Spam Detection: Comparative Insights and Real-Time Application 14

Machine Learning Techniques for Twitter Spam Detection: Comp...

引用

14th international conference on cloud computing, Data Science and engineering, Confluence 2024

作者： Asthana, Yashvardhan Chhabra, Rahul Srivastava, Sweta Amity University Computer Science and Engineering Noida India

ISBN: (纸本)9798350344837

In today's world of digital communication, platforms like Twitter are essential for connecting people globally. But there's a problem - spam on Twitter can be a big issue for users. Our main goal is to make a system that can spot and stop spam using fancy computer techniques. We looked at many tweets on Twitter, trying to understand how spam and regular tweets are spread out. We found a problem with our data - there were way more regular tweets than spam ones. To fix it, we used something called Synthetic Minority Over-sampling Technique (SMOTE) to make the number of tweets more equal. After that, we made and tested 13 computer models, looking at how good they were using important measures like accuracy and recall. The result is a strong system that can tell the difference between spam and real tweets on Twitter. This helps make online talks better, keeps users safe, and makes sure Twitter stays a good place to be. Since spam tactics keep changing, our work is an important step in making social media safer. This means everyone can enjoy a safer and better time online. © 2024 IEEE.

关键词： Digital communication systems

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Adaptive cloud Load Balancing with Reinforcement Learning: Leveraging Google Cluster Data 5

Adaptive Cloud Load Balancing with Reinforcement Learning: L...

引用

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

作者： Jyothi, V Esther Alugoju, Sahithi Indraja, K. Chowdary, N. Sampreeth Madhuri, A. Sindhura, S. Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P. V.P. Siddhartha Institute of Technology Department of Cse Vijayawada India Nri Institute of Technology Department of Cse Agiripalli India

ISBN: (纸本)9798350379945

This research study aims to develop an RL-based cloud load balancer leveraging Google cluster data. The primary objective is to optimize cloud resource allocation and load distribution using RL techniques. The dataset includes various metrics that are useful for decision-making in load balancing. Some metrics include CPU utilization, memory usage, resource requests, and system configurations. The proposed methodology includes systematic data collection, exploratory data analysis (EDA), and preprocessing, including handling missing values and feature engineering. This study focuses on designing and training the RL agent using state-of-the-art algorithms like Q-learning and Deep Q Networks, calibrated to the intricacies of cloud load balancing. To replicate real-world dynamics, a simulated cloud environment having servers with different configurations is created and several tasks are assigned. Whenever different tasks are encountered, all the values of metrics are calculated and cumulative reward is considered for decision making by using the RL agent. The research outcomes include enhanced efficiency in load balancing, improved resource utilization, and adaptability to changing demands. This research study aims to significantly contribute to cloud computing, setting new standards of resource management and operational efficiency. © 2024 IEEE.

关键词： cloud platforms

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Identity-based Security Enhancement in cloud computing: Deep Learning-Powered Threat Detection Framework 8

Identity-based Security Enhancement in Cloud Computing: Deep...

引用

8th international conference on Electronics, communication and Aerospace Technology, ICECA 2024

作者： Pradeep, M. Anitha, G. Smile, N.Spandana Sathappan, S. Rajeswaran, N. Kanakaprabha, S. Shri Vishnu Engg. College for Women Department of Ece Andhrapradesh Bhimavaram India Marri Laxman Reddy Institute of Technology and Management Department of Cse Telangana Hyderabad India Malla Reddy Engineering College Department of Cse Telangana Secunderabad India Cmr Institute of Technology Medchal Department of Cse Telangana Hyderabad India Ims Unison University Department of Iqac Uttarakhand Dehardun India Telangana Secunderabad India

ISBN: (纸本)9798350367904

cloud computing has improved data management but also increased security vulnerabilities, notably identity-based threats including identity spoofing, data breaches, and illegal access. In dynamic cloud systems, typical security methods cannot handle these evolving concerns, highlighting need for complex security measures. Deep learning-powered threat detection was established to improve cloud computing identity-based security. It uses LSTM and CNNs to analyze massive volumes of user behavior data and identify dangers. After training on dataset of many cloud simulation log entries, the framework combats many attack approaches. With false positive rate of less than 1% and 99% accuracy in recognizing identity-based threats, it beat standard machine learning models by 6% and dramatically reduced false warnings. This reduction is necessary to reduce security monitoring teams operating load. The framework processed data in 0.02 seconds per log entry to detect risks in real time. Overall, this deep learning-driven system improves identity-based threat detection in cloud security. Businesses looking to enhance the cloud security can benefit from its false positive rate and accuracy. The framework outperformed older techniques in accuracy by about 6%, proving its superiority in threat identification-based management. Future research could focus on refining model's efficiency and expanding its relevance to more cloud-based environments. © 2024 IEEE.

关键词： cloud security

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cloud Security and Privacy Preservation - A Comprehensive Analysis 7

Cloud Security and Privacy Preservation - A Comprehensive An...

引用

7th international conference on computing Methodologies and communication, ICCMC 2023

作者： Mukthasree, P. Pavani, L. Nikhila, G. Adarsh, K. Sai Srinivasu, N. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781665464086

The on-demand availability of computer system resources is known as cloud computing. The term "the cloud" describes the software and databases that run on servers that may be accessed via the Internet. Data centers all throughout the world house cloud servers. Users and businesses can avoid managing physical servers or running software on their own computers by utilizing cloud computing. Now a days cloud is used in everywhere many it companies are moved into cloud because less infrastructure is required to maintain the cloud, you need fewer staff and fewer resources to maintain it. Moving to the cloud will help you reduce your operating costs over the long term and the cloud has more storage to store the any type of data but there are also some issues like data security and privacy. While the threats and data leaks were a top issue for Sony in the days and weeks that followed the attack, this was not the only problem. A lawsuit was brought by Sony employees who claimed that the theft of their personal data caused them to incur severe economic hardship. Hence, there is a dire need to protect the data from the hackers. © 2023 IEEE.

关键词： Operating costs

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A Shared Network Security System for cloud computing

A Shared Network Security System for Cloud Computing

引用

2023 IEEE international conference on Innovative Data communication Technologies and Application, ICIDCA 2023

作者： Arumugam, Thirumoorthy Senthilraja, P. Gnanabaskaran, A. Ks Rangasamy College of Technology Computer Science and Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9798350397208

cloud computing has hastened the creation of web and mobile apps that may reach tens of millions of users by enabling small firms to access virtual infrastructures. This preserves data and programs from several shared data center residents, blurring system barriers for each cloud user. Challenges of different inhabitants have varied safety requirements, while some demand other security procedures. Network visualization matches the underlying network with tenant-specific criteria, enabling tenant datacenters to immediately resolve a variety of tenant needs. This system proposes the framework implementation of shared network security, a partnership in multi-tenant data centers. A multitenant data center for cloud computing is a facility that provides computing infrastructure and services to multiple customers, or tenants, in a shared environment. This shared network security system is based on unified threat management system. Shared network security uses intelligent package verdict for intelligence flow analysis to avoid data center network attacks. © 2023 IEEE.

关键词： Network security

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Enhancing Secure Video Transmission and cloud-Based Storage through Advanced Encryption, Hashing, and Distributed Processing 4

Enhancing Secure Video Transmission and Cloud-Based Storage ...

引用

4th IEEE international conference on computing, communication, and Intelligent Systems, ICCCIS 2023

作者： Suryawanshi, Sanmitra Mhaskar, Yash Meghana, K.L.P. Disa, Rohan Gutte, Vitthal S. School of Computer Engineering and Technology MIT-World Peace University Pune India

ISBN: (纸本)9798350306118

It is critical in today's data-centric world to ensure the privacy and security of video data preserved in the cloud. This investigation looks at the issues of securing video data in cloud environments, where privacy and security are top priorities. The use of a hybrid cryptosystem that incorporates symmetric and asymmetric encryption algorithms is proposed as an innovative approach. This cutting-edge technology enhances the security, integrity, and availability of video data while decreasing the processing resources required for encryption and decryption operations. The study offers actual evidence demonstrating the feasibility and efficacy of the suggested technique by demonstrating improvements in data security, performance, and scalability. This analysis not only enhances data security but also the efficacy of cloud-based video management. This technique's ramifications for the future of video encryption and cloud computing research are also highlighted. © 2023 IEEE.

关键词： Cryptography

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An Advanced Keyword Searching Model with Data Security in cloud computing 4

An Advanced Keyword Searching Model with Data Security in Cl...

引用

4th international conference on Electronics and Sustainable communication Systems, ICESC 2023

作者： Jabeen, Afsha Prasad, C.V.P.R. Mallareddy Engineering College for Women's Dept of Cse India

ISBN: (纸本)9798350300093

The growing popularity of cloud computing has necessitated the development of more efficient and secure methods of searching and retrieving data from cloud storage. In this paper, we propose an advanced keyword-searching model that ensures data security in cloud computing environments. To ensure data confidentiality and safety, our model employs advanced encryption techniques such as symmetric-key, homomorphic, and attribute-based encryption. Also, this study introduces an optimized indexing technique using a binary search algorithm to reduce search time and improve search efficiency. Furthermore, our model employs a secure multi-party computation approach to enable fast computation between multiple parties while keeping private information private. Using a benchmark dataset, this study demonstrates that the proposed model achieves high accuracy and efficiency while maintaining data security. The proposed model can be used in various applications, such as healthcare, finance, and ecommerce, where sensitive data must be securely stored and retrieved. The proposed model provides an efficient and secure solution for keyword searching in cloud computing environments. © 2023 IEEE.

关键词： cloud storage

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