DeepRing: Convolution Neural Network based on Blockchain Technology

Authors

  • Sura Hamed Mousa Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq. https://orcid.org/0009-0005-7556-3283
  • Narjis Mezaal Shati Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq. https://orcid.org/0000-0001-6850-3518
  • Nageswari Sakthivadivel Department of Computer Science and Engineering, CARE College of Engineering, Trichy, Tamilnadu, India.

DOI:

https://doi.org/10.23851/mjs.v35i2.1476

Keywords:

DeepRing, CNN, mnist, Fashion-MNIST, Blockchain technology

Abstract

Background: This paper addresses specific challenges in predictive modeling, namely transparency issues, susceptibility to data manipulation, and fairness concerns. To overcome these obstacles, the study introduces DeepRing, approach that combines Convolutional Neural Networks (CNNs) and blockchain technology. Objective: DeepRing aims to enhance prediction integrity, data security, and fairness, thereby improving the ethical considerations, reliability, and accountability of predictive models. Methods: involves iterative training of a CNN model on five diverse datasets, including CIFAR-10, Fashion-MNIST, MNIST, CIFAR-100, and a Hands dataset. The CNN architecture incorporates Conv2D layers, MaxPooling2D layers, and Dense layers. Training metrics such as accuracy and sparse categorical cross-entropy loss are monitored, with the Adam optimizer employed. While achieving high accuracy on Plam (0.5300), MNIST (0.9978) and Fashion MNIST (0.9673), DeepRing exhibits moderate performance on CIFAR-10 (0.9296) and lower accuracy on CIFAR-100 (0.5973). Results: demonstrate the effectiveness of DeepRing in improving accuracy and enhancing model performance across various datasets. However, further development and validation are essential for successful model implementation, further development and validation are essential for successful model implementation. Conclusions: Introduces DeepRing as an innovative solution to address key challenges in predictive modeling, specifically focusing on transparency issues, susceptibility to data manipulation, and fairness concerns. By combining Convolutional Neural Networks (CNNs) with blockchain technology, DeepRing aims to elevate prediction integrity, enhance data security, and promote fairness, thereby contributing to the improvement of ethical considerations, reliability, and accountability in predictive modelling.

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Key Dates

Received

04-09-2023

Revised

12-12-2023

Accepted

29-12-2023

Published

30-06-2024

Data Availability Statement

The data that support the findings of this study are openly available at:
https://www.cs.toronto.edu/ kriz/cifar.html
https://www.tensorflow.org/datasets/catalog/fashion_mnist
https://www.kaggle.com/datasets/haohaoxuexiba/hands-plam

Issue

Section

Original Article

How to Cite

[1]
S. H. Mousa, N. M. Shati, and N. . Sakthivadivel, “DeepRing: Convolution Neural Network based on Blockchain Technology”, Al-Mustansiriyah J. Sci., vol. 35, no. 2, pp. 61–69, Jun. 2024, doi: 10.23851/mjs.v35i2.1476.

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