Efficient Nonlinear Regression-Based Compression of Big Sensing Data on Cloud

Yang, Chi; Chen, Jinjun

doi:10.1016/b978-0-12-809393-1.00004-0

“…Innovation and competition powered by advances in cloud computing have culminated in discovering hidden information from data in the big data age. The key big data processing problems are capturing, storage, recovery, sharing, search, interpretation, analysis and visualization (Yang and Chen, 2017).…”

Section: K 516mentioning

confidence: 99%

“…However, due to the internal restrictions of their forecasting models, the above compression methods may lose impact in terms of scalability and compression during the big data processing phases of data gathering and data preparing. Yang and Chen (2017) developed a novel nonlinear regression prediction model to increase the efficacy and reliability of processing real-world big sensing data. The relevant specifics are also addressed, including least squares, regression layout and triangular transform.…”

Section: Compressionmentioning

confidence: 99%

How does cloud computing help businesses to manage big data issues

Latifian

¹

2022

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PurposeBig data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.Design/methodology/approachThis paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.FindingsFrom the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.Research limitations/implicationsThere were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.Practical implicationsThe research presents new insight into the business value of cloud computing in interfirm collaborations.Originality/valuePrevious research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.

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New Artificial Neural Network Models for Bio Medical Image Compression

Kumari

¹

,

Rao

²

,

Rao

³

2022

Research Anthology on Artificial Neural Network Applications

0

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This article presents an image compression method using feed-forward back-propagation neural networks (NNs). Marked progress has been made in the area of image compression in the last decade. Image compression removing redundant information in image data is a solution for storage and data transmission problems for huge amounts of data. NNs offer the potential for providing a novel solution to the problem of image compression by its ability to generate an internal data representation. A comparison among various feed-forward back-propagation training algorithms was presented with different compression ratios and different block sizes. The learning methods, the Levenberg Marquardt (LM) algorithm and the Gradient Descent (GD) have been used to perform the training of the network architecture and finally, the performance is evaluated in terms of MSE and PSNR using medical images. The decompressed results obtained using these two algorithms are computed in terms of PSNR and MSE along with performance plots and regression plots from which it can be observed that the LM algorithm gives more accurate results than the GD algorithm.

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New Artificial Neural Network Models for Bio Medical Image Compression

Kumari

¹

,

Rao

²

,

Rao

³

2019

International Journal of Applied Metaheuristic Computing

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This article presents an image compression method using feed-forward back-propagation neural networks (NNs). Marked progress has been made in the area of image compression in the last decade. Image compression removing redundant information in image data is a solution for storage and data transmission problems for huge amounts of data. NNs offer the potential for providing a novel solution to the problem of image compression by its ability to generate an internal data representation. A comparison among various feed-forward back-propagation training algorithms was presented with different compression ratios and different block sizes. The learning methods, the Levenberg Marquardt (LM) algorithm and the Gradient Descent (GD) have been used to perform the training of the network architecture and finally, the performance is evaluated in terms of MSE and PSNR using medical images. The decompressed results obtained using these two algorithms are computed in terms of PSNR and MSE along with performance plots and regression plots from which it can be observed that the LM algorithm gives more accurate results than the GD algorithm.

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Efficient Nonlinear Regression-Based Compression of Big Sensing Data on Cloud

Cited by 3 publications

References 21 publications

How does cloud computing help businesses to manage big data issues

How does cloud computing help businesses to manage big data issues

New Artificial Neural Network Models for Bio Medical Image Compression

New Artificial Neural Network Models for Bio Medical Image Compression

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