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Showing papers by "Santanu Phadikar published in 2022"

Journal Article•DOI•
An efficient IDS in cloud environment using feature selection based on DM algorithm

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Partha Ghosh, Shashwat Sinha, Ritu Sharma, Santanu Phadikar
13 Jan 2022-Journal of computer virology and hacking techniques
TL;DR: The authors proposed a nature-inspired Dolphin Mating (DM) algorithm to determine pertinent features from the dataset and used the NSL-KDD dataset and Kyoto dataset and found that the proposed DM algorithm selects the most relevant feature subset which made the IDS efficient in the Cloud environment.

4 citations

Book Chapter•DOI•
ABID: Attention-Based Bengali Image Description

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Bidyut Das, Arif Ahmed Sekh, Mukta Majumder, Santanu Phadikar
01 Jan 2022-Lecture Notes in Electrical Engineering

3 citations

Journal Article•DOI•
An efficient IDS in cloud environment using feature selection based on DM algorithm

[...]

Partha Ghosh, Shashwat Sinha, Ritu Sharma, Santanu Phadikar
13 Jan 2022-Journal of computer virology and hacking techniques

3 citations

Journal Article•DOI•
A novel pre-processing technique of amplitude interpolation for enhancing the classification accuracy of Bengali phonemes

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Bachchu Paul, Santanu Phadikar
08 Aug 2022-Multimedia Tools and Applications

2 citations

Journal Article•DOI•
An efficient SGM based IDS in cloud environment

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Partha Ghosh, Zaid Alam, Ritu Sharma, Santanu Phadikar
16 Feb 2022-Computing

2 citations

Journal Article•DOI•
A novel plant disease prediction model based on thermal images using modified deep convolutional neural network

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Ishita Bhakta, Santanu Phadikar, Koushik Majumder, Himadri Mukherjee, Arkaprabha Sau 
15 Jun 2022-Precision Agriculture

2 citations

DOI•
Automatic Sign Language Identification Using Convolutional Neural Network

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Himadri Mukherjee1, Ankita Dhar1, S. K. Md. Obaidullah2, Santanu Phadikar3, Kaushik Roy1 •
West Bengal State University1, Aliah University2, Islamic Azad University3
01 Jan 2022
TL;DR: Here, a system is presented to automatically identify sign language and the highest accuracy of 99.98% was obtained in this study with a convolutional neural network-based approach.
Abstract: Sign language is the mode of communication for such people who are not blessed with the gift of hearing and speech. It involves the use of hands and facial expressions, Understanding sign language is difficult for people with no prior knowledge. This leads to a communication gap for such people. An automated sign language identification system can aid in reducing this gap. Here, a system is presented to automatically identify sign language. Experiments were performed on the publicly available sign language MNIST dataset. The highest accuracy of 99.98% was obtained in this study with a convolutional neural network-based approach.