Applying the Modular Encryption Standard to Mobile Cloud Computing to Improve the Safety of Health Data

Kumar, Mukesh; Siddardha, B.; Reddy, A. Hitesh; Reddy, Ch.V.Sainath; Shaik, Abdul Bari; Ganesh, D.

doi:10.47750/pnr.2022.13.s08.231

Cited by 7 publications

(1 citation statement)

References 11 publications

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“…Chen et al, [27] developed a two-stage approach that includes training separate convolutional neural networks (CNNs) using the CK+ and BU-4DFE datasets. On the other hand, Dosovitskiy et al, [33] utilized Flownet 2.0, a novel automated micro-expression analysis technique , to improve the performance of their dual-template CNN model [33][34][35][36][37][38][39]. Although the proposed model did not perform as well as traditional methods, it still achieved accuracy rates of 95.4% and 77.4% on the CK+ and BU-4DFE datasets [27], respectively.…”

Section: An Analysis Of Prior Research In the Relevant Fieldmentioning

confidence: 99%

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

Anbananthan Pillai Munanday,

Norazlianie Sazali,

Arjun Asogan

et al. 2023

ARASET

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The primary objective of this study is to develop a real-time system that can predict the emotional states of an individual who commonly undergoes various experiences. The primary methodology suggested in this research for detecting facial expressions involves the integration of transfer learning techniques that incorporate convolutional neural networks (CNNs), along with a parameterization approach that minimizes the number of parameters. The FER-2013, JAFFE, and CK+ datasets were jointly used to train the CNN architecture for real-time detection, which broadened the range of emotional expressions that may be recognized. The proposed model has the capability to identify various emotions, including but not limited to happiness, fear, surprise, anger, contempt, sadness, and neutrality. Several methods were employed to assess the efficacy of the model's performance in this study. The experimental results indicate that the proposed approach surpasses previous studies in terms of both speed and accuracy.

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Section: An Analysis Of Prior Research In the Relevant Fieldmentioning

confidence: 99%

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

Anbananthan Pillai Munanday,

Norazlianie Sazali,

Arjun Asogan

et al. 2023

ARASET

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Enhancing mobile data security using red panda optimized approach with chaotic fuzzy encryption in mobile cloud computing

Garg,

Kaur,

Jangra

2024

Concurrency and Computation

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SummarySmartphone devices have occupied an indispensable place in human life. These devices have some restrictions, like short lifetime of battery, imperfect computation power, less memory size and unpredictable network connectivity. Hence, a number of methods previously presented to decrease these restrictions as well as increase the battery lifespan with the help of offloading strategy. This manuscript proposes a new enhancing mobile data security using red panda optimized approach with chaotic fuzzy encryption in mobile cloud computing (RPO‐CFE‐SMC) to offload intensive computation tasks from mobile device to the cloud. The proposed model utilizes a red panda optimization algorithm (RPOA) to scale dynamically the offloading decision under energy consumption, CPU utilization, execution time, memory usage parameters. Before the work is transferred to the cloud, an innovative security layer is applied for encrypting the data using AES chaotic fuzzy encryption (CFE) technology. The proposed RPO‐CFE‐SMC method provides 20.63%, 25.25%, 25.28%, and 32.47% lower encryption time and 23.66%, 24.25%, and 26.47% lower energy consumption compared with existing EFFORT‐SMC, EESH‐SMC, and CP‐ABE‐SMC models respectively. In conclusion, the simulation results prove that the improved efficiency of proposed model in offloading computation to the cloud with enhanced data protection using chaotic fuzzy encryption.

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Wireless 6G Cloud Communication Based Security Analysis Using Machine Learning in Internet of Medical Things (IoMT)

Chen,

Xu,

Zhu

et al. 2024

Wireless Pers Commun

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Applying the Modular Encryption Standard to Mobile Cloud Computing to Improve the Safety of Health Data

Cited by 7 publications

References 11 publications

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

Enhancing mobile data security using red panda optimized approach with chaotic fuzzy encryption in mobile cloud computing

Wireless 6G Cloud Communication Based Security Analysis Using Machine Learning in Internet of Medical Things (IoMT)

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