An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm

Tao, Hai; Zhou, Jincheng; Lu, Ye; Jawawi, Dayang Norhayati Abang; Wang, Dan; Shitharth, S.; Manoharan, Hariprasath; Ibeke, Ebuka

doi:10.1186/s13677-023-00449-z

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Each business listed on the platform is carefully vetted to meet specific social and environmental criteria. This includes factors such as fairtrade, sustainable production methods, ethical labor practices, and the other seven principles Mohammed Yunus mentioned about social business [18,19].…”

Section: Platform Introductionmentioning

confidence: 99%

Blockchain-based cyber-security proposal in commerce mobile platforms for social and sustainability businesses

Riskhan,

Almassri,

Hussain

et al. 2024

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<p>This study aims to propose the integration of cyber security measures with sustainable mobile commerce platforms and evaluate their efficacy in promoting social entrepreneurship and sustainability in the field of mobile commerce. A platform was developed for Android and iOS platforms using the Flutter Framework and Dart Programming Language, and it included features designed to help social entrepreneurs and non-profit organizations connect with potential donors, volunteers, and partners. The study includes participants from social entrepreneurs, non-profit organizations, and potential donors who are recruited to use the platform to create and promote their social business projects and to provide feedback on their experience using the platform. The results of the integration are expected to show that the mobile platform for social business is effective in promoting social entrepreneurship and sustainable development and in connecting social entrepreneurs and non-profit organizations with potential donors and partners due to the security measures that have been taken in the application and data protection, ensuring privacy with a comparatively lower risk than other platforms. In conclusion, this paper presents a blockchain-based secure mechanism for mobile platforms for m-commerce and social business, which is a valuable tool for promoting social entrepreneurship and sustainable development. It can be used to create a positive social impact in local and, conceivably, international communities. The proposal explicitly explains the methodology and implementation of the platform and highlights the significance and impact of this research in advancing the fields of mobile commerce, social entrepreneurship, and sustainability.</p>

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Section: Platform Introductionmentioning

confidence: 99%

Blockchain-based cyber-security proposal in commerce mobile platforms for social and sustainability businesses

Riskhan,

Almassri,

Hussain

et al. 2024

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“…Cloud computing provides the necessary infrastructure and services to implement, train, and deploy NN models [ 7 , 8 ]. However, using NNs on third-party infrastructures reveals several data privacy issues during the pre-processing, processing, and post-processing [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Self-learning activation functions to increase accuracy of privacy-preserving Convolutional Neural Networks with homomorphic encryption

Pulido-Gaytan,

Tchernykh

2024

PLoS ONE

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The widespread adoption of cloud computing necessitates privacy-preserving techniques that allow information to be processed without disclosure. This paper proposes a method to increase the accuracy and performance of privacy-preserving Convolutional Neural Networks with Homomorphic Encryption (CNN-HE) by Self-Learning Activation Functions (SLAF). SLAFs are polynomials with trainable coefficients updated during training, together with synaptic weights, for each polynomial independently to learn task-specific and CNN-specific features. We theoretically prove its feasibility to approximate any continuous activation function to the desired error as a function of the SLAF degree. Two CNN-HE models are proposed: CNN-HE-SLAF and CNN-HE-SLAF-R. In the first model, all activation functions are replaced by SLAFs, and CNN is trained to find weights and coefficients. In the second one, CNN is trained with the original activation, then weights are fixed, activation is substituted by SLAF, and CNN is shortly re-trained to adapt SLAF coefficients. We show that such self-learning can achieve the same accuracy 99.38% as a non-polynomial ReLU over non-homomorphic CNNs and lead to an increase in accuracy (99.21%) and higher performance (6.26 times faster) than the state-of-the-art CNN-HE CryptoNets on the MNIST optical character recognition benchmark dataset.

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Development of Secure Framework in Mobile Cloud Computing Using AES-HMAC Encryption Approach

Naveen,

Poongodi

2024

Communications in Computer and Information Science

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An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm

Cited by 10 publications

References 26 publications

Blockchain-based cyber-security proposal in commerce mobile platforms for social and sustainability businesses

Blockchain-based cyber-security proposal in commerce mobile platforms for social and sustainability businesses

Self-learning activation functions to increase accuracy of privacy-preserving Convolutional Neural Networks with homomorphic encryption

Development of Secure Framework in Mobile Cloud Computing Using AES-HMAC Encryption Approach

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