Anil Kumar Kakelli

Work place: School of Computer Science and Engineering (SCOPE), VIT University, Vellore 632014, India



Research Interests: Computer Networks, Computer Architecture and Organization, Wireless Networks, Sensor, Computational Science and Engineering, Data Structures and Algorithms, Analysis of Algorithms


Kakelli Anil Kumar is an Associate Professor of the School of Computer Science and Engineering at the Vellore Institute of Technology (VIT), Vellore, TN, India. He earned his Ph.D. in Computer Science and Engineering from Jawaharlal Nehru Technological University (JNTUH) Hyderabad in 2017, and graduated in 2009 and under-graduated in 2003 from the same university. He started his teaching career in 2004 and worked as an Assistant Professor, and Associate Professor, and HOD in various reputed institutions of India. His current research includes wireless sensor networks, the internet of things (IoT), cyber security and digital forensics, Malware analysis, block-chain, and crypto-currency. He has published over 40 research articles in reputed peer-reviewed international journals and conferences.

Author Articles
Convolutional Neural Network (CNN-SA) based Selective Amplification Model to Enhance Image Quality for Efficient Fire Detection

By Sagnik Sarkar Aditya Sunil Menon Gopalakrishnan T Anil Kumar Kakelli

DOI:, Pub. Date: 8 Oct. 2021

Fires spread quickly and are extremely difficult to contain, and cause a great deal of damage to people and property. Current domestic systems for detecting outbreaks of fire, such as smoke detectors, are prone to reliability issues and will benefit greatly from having a secondary system in place to confirm the presence of a fire in the premises. In this paper, we have proposed a novel image pre-processing algorithm known as the Selective Amplification. This technique enhances images that are to be used in Convolutional Neural Networks, which are then trained on pre-processed images to detect fires with high accuracy. The efficacy of the proposed technique is verified by training two identical Convolutional Neural Network models on the same dataset of images. We train the proposed model on a version of the dataset that uses Selective Amplification for data pre-processing. The proposed model then demonstrates an improvement in the accuracy of the detection of fire in real-time over by 12.85%, compared to an identical model trained on the dataset without any pre-processing performed beforehand.

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