Improving Privacy and Security in Multicloud Architectures

Shukla, Piyush Kumar; Ahirwar, Mahendra Kumar

doi:10.4018/978-1-4666-9466-8.ch025

Cited by 2 publications

(4 citation statements)

References 24 publications

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“…Essentially, a wavelet caught energy at a certain frequency and orientation, and it was found to be extremely efficient. The colour-based CBIR technology incorporates two extra features that are computed using the Gabor wavelet to apply the texture feature, despite the fact that retrieval time is becoming more and more inefficient with each passing generation [ 22 , 35 ], it is used to extract texture from objects so that it may be retrieved later.…”

Section: Results and Analysismentioning

confidence: 99%

“…Each time a word appears, we look to see whether any further items have the same phrase as the one we are looking for. For each of the items on the ballot, the number of votes in the voting array [ 22 , 33 ] is raised by one. As seen in the example below, each item in the voting array is a list with one entry for each object that has a counter variable.…”

Section: Existing Methodsmentioning

confidence: 99%

“…The phrase “stuff” in this context refers to the colours, forms, surfaces, or other materials that might arise from the real thing. CBIR is beneficial since most picture search engines on the Internet concentrate primarily on metadata, resulting in a large amount of trash [ 22 ].…”

Section: Existing Studymentioning

confidence: 99%

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Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning

Malmurugan

Nelson

Altuwairiqi

et al. 2022

Computational Intelligence and Neuroscience

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Numerous forms of disasters and vandalism can occur in transmission lines, which makes them vulnerable. As a result, the transmission pipes must be protected by a reliable monitoring system. When a wireless sensor network is built from disparate devices that are positioned at varying distances from one another, it can be used to monitor physical and environmental conditions in the surrounding environment. In addition to the built-in sensor on the exterior of a pipeline and sensors positioned to support bridge structures, wireless sensor networks have a range of other applications. Other uses include robotics, healthcare, environmental monitoring, and a variety of other areas of technology. It is feasible to use wireless sensor networks to monitor temperature and pressure, as well as leak detection and transmission line sabotage, among other applications. There are several different sorts of attacks that can be launched against wireless sensor networks. When it comes to information security in wireless sensor networks, cryptographic approaches play a critical role in ensuring the integrity of the data. Different types of cryptographic algorithms are now available for use in order to maintain network security. Specific difficulties must be addressed, though, and these are as follows: To strengthen the power of these algorithms, a unique hybrid encryption approach for monitoring energy transmission lines and increasing the security of wireless sensor networks is created in this study. While wireless sensor networks are being used to monitor transmission pipelines, the proposed hybrid encryption method ensures that data is transferred securely and promptly. The proposed method must follow three cryptographic principles: integrity, secrecy, and authenticity. All of the subtleties and underlying principles of the algorithm are explained in detail so that the algorithm can be put into action immediately after it is introduced.

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Section: Results and Analysismentioning

confidence: 99%

Section: Existing Methodsmentioning

confidence: 99%

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Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning

Malmurugan

Nelson

Altuwairiqi

et al. 2022

Computational Intelligence and Neuroscience

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“…Using optimization approaches, this work offers a multi-iterative function for monitoring filter performances in real time and striving to enhance them as much as possible. The usage of cost functions might help you keep track of state corrections and save money [ 14 ]. The optimization of a new parameter is carried out using MCEHOs, which approximate the nonlinearity of the system, and multi-iterative function, which estimates the unknown parameters of a target [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

Jain

Alsanie

Gago

et al. 2022

Computational Intelligence and Neuroscience

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ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman filters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online filter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multi-iterative function (Optimized Iterative UKFs) predicts a target’s unknown parameters. To obtain optimal solutions theoretically, multi-iterative function takes less iteration, resulting in shorter execution times. The proposed multi-iterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.

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Improving Privacy and Security in Multicloud Architectures

Cited by 2 publications

References 24 publications

Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning

Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

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