Feature Centric Data Augmentation Model-Based Mobile Commerce for Efficient Retail Growth using BlockChain

Feature Centric Data Augmentation Model-Based Mobile Commerce for Efficient Retail Growth using BlockChain

© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Rajesh Kumar .M, Venkatesh .J, Zubair Rahman .A. M. J. Md

How to Cite?

Rajesh Kumar .M, Venkatesh .J, Zubair Rahman .A. M. J. Md, "Feature Centric Data Augmentation Model-Based Mobile Commerce for Efficient Retail Growth using BlockChain," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 179-184, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P220

The recent trends in mobile commerce have been well studied and identified several approaches available towards the growth of the retail industry. However, the methods suffer to identify the efficient tool to hike the growth of the retail industry due to the poor management of data and security. Towards improving the performance of the retail industry, and efficient Feature Centric Data Augmentation Model (FCDAM) is presented in this article. The method has been designed for the development of mobile commerce performance and supports effective retail growth. The model adapts feature centric data augmentation techniques in producing efficient data for the user. The method maintains a number of traces of different user purchases and estimates feature centric popularity (FCP), feature centric retail support (FCRS) and feature centric augmentation support (FCAS) values. Using all these values, the model selects different products to the cart of the user at the mobile devices. Using the value of other measures, the value of product support (PS) has been measured to rank the products. Similarly, the augmented and purchase values are maintained using blockchain. Towards data security, the method uses feature centric data encryption and blockchain technique in improving the performance and growth of the retail industry. By incorporating the model, the performance of mobile commerce, as well as retail growth, is improved.

BlockChain, Data Augmentation, Mobile Commerce, Retail Growth, FCDAM.

[1] Boping Zhang, Augmented virtual reality glasses try-on technology based on iOS platform, EURASIP Journal on Image and Video Processing , ( 2018).
[2] Dongsik Jo & Gerard, IoT + AR: pervasive and augmented environments for Digi-log shopping experience, Springer, Human-centric Computing and Information Sciences, (2019).
[3] Bhavik Vachchani, Data Augmentation Using Healthy Speech for Dysarthric Speech Recognition, Conference on Interspeech, (2018).
[4] Georg Waitner, MANGO - Mobile Augmented Reality with Functional Eating Guidance and Food Awareness, An Interactive Tool for Speed up the Analysis of UV Images of Stradivari Violins ,425-432.
[5] Xiaodong Cui; Vaibhava Goel; Brian Kingsbury, Data Augmentation for Deep Neural Network Acoustic Modeling: IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(9) (2015).
[6] N. Surv, Framework for client side AES encryption technique in cloud computing, IEEE (IACC), (2015) 525-528.
[7] N. Mohammed and N. Ibrahim, Implementation of New Secure Encryption Technique for Cloud Computing, IEEE (ICCISTA), (2019) 1-5.
[8] Z. H. Mahmood and M. K. Ibrahim, New Fully Homomorphic Encryption Scheme Based on Multistage Partial Homomorphic Encryption Applied in Cloud Computing, IEEE (AiCIS), (2018) 182-186.
[9] G. Raj, R. C. Kesireddi and S. Gupta, Enhancement of security mechanism for confidential data using AES-128, 192 and 256bit encryption in cloud, IEEE (NGCT), (2015) 374-378.
[10] K. Rani and R. K. Sagar, Enhanced data storage security in cloud environment using encryption, compression and splitting technique, IEEE (TEL-NET), (2017) 1-5.
[11] Y. S. Gunjal, M. S. Gunjal and A. R. Tambe, Hybrid Attribute Based Encryption and Customizable Authorization in Cloud Computing, IEEE (ICACCT), (2018) 187-190.
[12] V. Sreenivas, Performance Evaluation of Encryption Techniques and Uploading of Encrypted Data in Cloud, IEEE (ICCCNT), (2013) 1-6.
[13] P. More, S. Hybrid Encryption Techniques for Secure Sharing of a Sensitive Data for Banking Systems Over Cloud, IEEE (ICACCT), (2018) 93-96.
[14] W. Chen, Efficient Key-Aggregate Proxy Re-Encryption for Secure Data Sharing in Clouds, IEEE (DSC), (2018) 1-4.
[15] S. Mudepalli, V. S. Rao and R. K. Kumar, An Efficient Data Retrieval Approach using Blowfish Encryption on Cloud Ciphertext Retrieval in Cloud Computing, IEEE (ICICCS), (2017) 267-271.
[16] P. L. R. Chze and K. S. Leong, A Secure Multi-Hop Routing for IoT Communication, IEEE (WF-IoT), (2014) 428-432.
[17] G. Hatzivasilis, I. Papaefstathiou and C. Manifavas, SCOTRES: Secure Routing for IoT and CPS, IEEE Internet of Things Journal, 4(6) (2017) 2129-2141.
[18] A. E. Hajjar, G. Roussos and M. Paterson, Secure routing in IoT networks with SISLOF, Global Internet of Things Summit (GIoTS), (2017) 1-6.
[19] Shengli Mao, Crowd Cloud Routing Protocol Based on Opportunistic Computing for Wireless Sensor Networks, EURASIP Journal on Embedded Systems, 2016(1) (2016) 1.
[20] Uma Khemchand Thakur, QoS Aware Cloud-Based Routing Protocol for Security Improvement of Hybrid Wireless Network, Machine Learning Research, 4(1) (2019) 21-26.