Article Per Year (5 Year)
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES)
Kayalvizhi Jayavel
SRM Institute of Science and Technology
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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A fully integrated violence detection system using CNN and LSTM Sarthak Sharma; B. Sudharsan; Saamaja Naraharisetti; Vimarsh Trehan; Kayalvizhi Jayavel
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3374-3380

Abstract

Recently, the number of violence-related cases in places such as remote roads, pathways, shopping malls, elevators, sports stadiums, and liquor shops, has increased drastically which are unfortunately discovered only after it’s too late. The aim is to create a complete system that can perform real-time video analysis which will help recognize the presence of any violent activities and notify the same to the concerned authority, such as the police department of the corresponding area. Using the deep learning networks CNN and LSTM along with a well-defined system architecture, we have achieved an efficient solution that can be used for real-time analysis of video footage so that the concerned authority can monitor the situation through a mobile application that can notify about an occurrence of a violent event immediately.
Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches Akella S. Narasimha Raju; Kayalvizhi Jayavel; Tulasi Rajalakshmi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp738-747

Abstract

The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme.
ACCESS - IoT enabled smart lock Harshith Gadupu; Osa Mokharji; Raunak Kankaria; Shrey Kumar; Kayalvizhi Jayavel
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 10, No 3: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v10.i3.pp176-185

Abstract

ACCESS is a centrally controlled extensible security system - a system for enhancing accessibility and security methods. Security is an important matter of concern and everyone wants things easy and fast with the advancement of technology. Many IoT engineers are inclined towards home automation today. An area of recent interest is the automation of lock and key systems of homes and workplaces. This paper comprises mechanisms to view visitors of households, machinery, or any appliance that may be remotely controlled through a mobile application. Owners or supervisors can keep a watch on the guests and choose whom they want to grant entry to. This is conducted by providing the guests with temporary access permission for a validity period of the owner’s choice. They can also simultaneously monitor the activities of the guests.
Regional feature learning using attribute structural analysis in bipartite attention framework for vehicle re-identification Cynthia Sherin; Kayalvizhi Jayavel
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5824-5832

Abstract

Vehicle re-identification identifies target vehicles using images obtained by numerous non-overlapping real-time surveillance cameras. The effectiveness of re-identification is further challenging because of illumination changes, pose differences of captured images, and resolution. Fine-grained appearance changes in vehicles are recognized in addition to the coarse-grained characteristics like color of the vehicle along with model, and other custom features like logo stickers, annual service signs, and hangings to overcome these challenges. To prove the efficiency of our proposed bipartite attention framework, a novel dataset called Attributes27 which has 27 labelled attributes for each class are created. Our framework contains three major sections: The first section where the overall and semantic characteristics of every individual vehicle image are extracted by a double branch convolutional neural network (CNN) layer. Secondly, to identify the region of interests (ROIs) each branch has a self-attention block linked to it. Lastly to extract the regional features from the obtained ROIs, a partition-alignment block is deployed. The results of our proposed system’s evaluation on the Attributes27 and VeRi-776 datasets has highlighted significant regional attributes of each vehicle and improved the accuracy. Attributes27 and VeRi-776 datasets exhibits 98.5% and 84.3% accuracy respectively which are comparatively higher than the existing methods with 78.6% accuracy.
Co-Authors Akella S. Narasimha Raju B. Sudharsan Cynthia Sherin Harshith Gadupu Osa Mokharji Raunak Kankaria Saamaja Naraharisetti Sarthak Sharma Shrey Kumar Tulasi Rajalakshmi Vimarsh Trehan