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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Poonam Ghuli
R.V. College of Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
React: A detailed survey Varun Komperla; Deenadhayalan Pratiba; Poonam Ghuli; Ramakanthkumar Pattar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1710-1717

Abstract

developments in technology and the ever-growing number of web applications, it has become a necessity to create fast and scalable applications to cater to the current market. There are a plethora of frameworks available for web development, and a developer must choose the most appropriate framework for their use. In this paper, a detailed analysis of the history, prominent features and advantages of React, an open-source JavaScript library is presented. A discussion on React Native, a framework for building native applications is also given. This paper has provided an insight into the reason React is the leading web development framework in the world.
Diabetic retinopathy classification using deep convolutional neural network Akshita L.; Harshul Singhal; Ishita Dwivedi; Poonam Ghuli
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp208-216

Abstract

Diabetic retinopathy (DR) is a diabetic impairment that affects the eyes and if not treated could lead to permanent vision impairment. Traditionally, Ophthalmologists perform diagnosis of DR by checking for existence and any seriousness of some subtle features in the fundus images. This process is not very efficient as it takes a lot of time and resources. DR testing of all the patients, a lot of which are undiagnosed or untreated, is a big task due to the inefficiency of the traditional method. This paper was written with the aim to propose a classification system based on an efficient deep convolution neural network (DCNN) model which is computationally efficient. Amongst other supervised algorithms involved, proposed solution is to find a way to efficiently classify the fundus images into 5 different levels of severity. Application of segmentation after the pre-processing and then use of deep convolutional neural networks on the dataset results in a high accuracy of 91.52%. The result achieved is high given the limitations of the dataset and computational powers.
Co-Authors Akshita L. Deenadhayalan Pratiba Harshul Singhal Ishita Dwivedi Ramakanthkumar Pattar Varun Komperla