A Review on Cloud, Fog, Roof, and Dew Computing

Ahammad, Ishtiaq; Khan, Ashikur Rahman; Salehin, Zayed Us

doi:10.4018/ijcac.2021100102

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…Another research work implemented a hybrid security model for securing medical patient's data stored in the cloud [27]. The authors have modified the AES algorithm and named it P-AES, which is used with RSA to provide privacy and security to medical data stored in the cloud [28].…”

Section: Related Work Of Cloud Securitymentioning

confidence: 99%

RSA and AES Based Hybrid Encryption Technique for Enhancing Data Security in Cloud Computing

Akter,

Khan,

Rahman

et al. 2023

International Journal of Computational and Applied Mathematics &Amp; Computer Science

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Cloud computing security has become the most crucial problem in its growth. Encryption has been developed as a solution and plays a vital role in the security of information systems in cloud computing. Many methods are required to secure the shared data. The advanced internet, networking firms, health information, and cloud applications have significantly increased our data every minute. The current work focuses on cryptography to protect sensitive data exchanged between personal users, companies, organizations, or in the cloud applications and others during data transfer across the network. Firstly, data sent from the sender to the network receiver must be encrypted using the cryptographic algorithm. Secondly, the recipient shows the original data using the decryption technique. In this paper, we have proposed a model to use a hybrid encryption and decryption process based on AES-128 and the RSA algorithm. Furthermore, we used the HMAC algorithm to ensure the integrity and authenticity of data. Our experiment work has been done to explain the time required and throughput for encryption and decryption of three encrypting algorithms, AES, RSA, and hybrid algorithms, based on different data sizes, and their efficiency is compared. The results of the experiments show that the hybrid algorithm is better in terms of security.

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Section: Related Work Of Cloud Securitymentioning

confidence: 99%

RSA and AES Based Hybrid Encryption Technique for Enhancing Data Security in Cloud Computing

Akter,

Khan,

Rahman

et al. 2023

International Journal of Computational and Applied Mathematics &Amp; Computer Science

View full text Add to dashboard Cite

show abstract

“…The literature review is performed on the studies which relates on the sectors of our work. When we want to study in depth on a research topic, then we first have to focus on the recent review papers on that topic [14]. That's why in this literature work, initially we performed the study on review works which are related to stock market prediction using AI and its communities.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock market prediction in Bangladesh perspective using artificial neural network

2022

IJATEE

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Stock market price prediction is now a prominent and significant issue in financial and academic studies as the stock market plays a vital role in the economy. The process of attempting to anticipate the future valuation of a company's share is known as stock market price prediction. Share prices are time-series information, and artificial neural networks (ANNs) can uncover non-linear associations among time-series information. This makes ANN the best method for predicting stock market values. Many researchers are working on this topic and trying to find the best algorithm which is suitable for predicting the stock price. But significant improvement in prediction is still not achieved. Therefore, in this work, an ANN model is proposed and implemented by using a multilayer feedforward backpropagation method. In this work, data of fifteen companies over a six years span have been analyzed. To predict the specific result, the proposed model has been trained with four different algorithms: Levenberg Marquardt (LM), Bayesian regularization (BR), scaled conjugate gradient (SCG) and Quasi Newton) by changing their parameters. Number of hidden layers, hidden neurons and percentage of training data have been changed to get better output. The split ratio of training, testing and validation data sets is70:15:15. The projected results are then compared to the actual data after the training and testing procedure to determine the accuracy. The accuracy of LM is 95.64%, BR is 91.26%, SCG accuracy is 88.91% and Quasi Newton is 84.20%. The result showed that, LM algorithm provides better accuracy than other models. In addition, less error has been found from the LM algorithm, making it the best algorithm for prediction in our proposed model.

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Fog Computing Complete Review: Concepts, Trends, Architectures, Technologies, Simulators, Security Issues, Applications, and Open Research Fields

Ahammad

2023

SN COMPUT. SCI.

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A Review on Cloud, Fog, Roof, and Dew Computing

Cited by 8 publications

References 19 publications

RSA and AES Based Hybrid Encryption Technique for Enhancing Data Security in Cloud Computing

RSA and AES Based Hybrid Encryption Technique for Enhancing Data Security in Cloud Computing

Stock market prediction in Bangladesh perspective using artificial neural network

Fog Computing Complete Review: Concepts, Trends, Architectures, Technologies, Simulators, Security Issues, Applications, and Open Research Fields

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