Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics JURNAL MAHAJANA INFORMASI
Kaur, Parminder
Unknown Affiliation
Electrical & Electronics Engineering Library & Information Science

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Concurrent Access Algorithms for Different Data Structures: A Research Review Kaur, Parminder
JURNAL MAHAJANA INFORMASI Vol 2 No 1 (2017): Mahajana Informasi
Publisher : Universitas Sari Mutiara Indonesia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (162.782 KB)

Abstract

Algorithms refers to a finite set of steps, which when followed solves a number of problems and algorithams for concurrent data structure have gained attention in recent years as multi-core processors have become ubiquitous. Several features of shared-memory multiprocessors make concurrent data structures significantly more difficult to design and to verify as correct than their sequential counterparts. The primary source of this additional difficulty is concurrency. This paper provides an overview of the some concurrent access algorithms for different data structures. Keywords: concurrency, lock-free, non-blocking, mem-ory management, compares and swap, elimination
Performance analysis of convolutional neural network architectures over wireless capsule endoscopy dataset Kaur, Parminder; Kumar, Rakesh
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5858

Abstract

Wireless capsule endoscopy is one of the diagnostic methods used to record the video of the gastrointestinal tract. The endoscopy capsule stays in the digestive system for at least eight hours. It is difficult for gastroenterologists to examine such a lengthy video and identify the ailment. Convolutional neural networks (CNN) are a powerful solution to several computer vision problems. CNN can speed up the reviewing time of the recorded video by classifying video frames into various categories. The primary emphasis of this research paper is to examine and evaluate the performance of three different CNN architectures-VGG, inception, and MobileNet-in classifying the disease. Experimental results demonstrate that MobileNetV2’s accuracy is 91%, whereas InceptionV3 and VGG16 have an accuracy of 94% which is better than the accuracy of MobileNetV3. However, MobileNeV2 performed relatively better than the other CNN models in terms of computational time and cost. The model’s F-score, precision, and recall values are computed and compared also.
Co-Authors Kumar, Rakesh