Hand Gesture Identification for Improving Accuracy Using Convolutional Neural Network(CNN)
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2022.13.247Keywords:
Hand Gesture, Machine Learning, ASL Data Set, Convolutional Neural Network;Dimensions Badge
Issue
Section
License
Copyright (c) 2022 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Hand gestures are a type of non-verbal communication that uses visible body movements to convey important messages. This paper presents a much better approach of hand gesture prediction. Image Identification is an important step in most of the modern hand gesture prediction system. A convolutional neural network are used for improving the accuracy of the system. Proposed system tested for large number of hand gesture images using Tensor flow tool . The convolutional neural network (ConvNet) is a deep learning algorithm for learning and classifying hand gestures and achieved accuracy 93.61%.Abstract
How to Cite
Downloads
Similar Articles
- Sachin V. Chaudhari, Jayamangala Sristi, R. Gopal, M. Amutha, V. Akshaya, Vijayalakshmi P, Optimizing biocompatible materials for personalized medical implants using reinforcement learning and Bayesian strategies , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Krishna P. Kalyanathaya, Krishna Prasad K, A framework for generating explanations of machine learning models in Fintech industry , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Ayesha Shakith, L. Arockiam, Enhancing classification accuracy on code-mixed and imbalanced data using an adaptive deep autoencoder and XGBoost , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Suprabha Amit Kshatriya, Jaymin K Bhalani, Fire and smoke detection with high accuracy using YOLOv5 , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Amanda Quist Okronipa, Isaac Asampana, Jones Yeboah Nyame, Exploring e-learning system loyalty: The role of system quality and satisfaction , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Jayaganesh Jagannathan, Dr. Agrawal Rajesh K, Dr. Neelam Labhade-Kumar, Ravi Rastogi, Manu Vasudevan Unni, K. K. Baseer, Developing interpretable models and techniques for explainable AI in decision-making , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Nitin J. Wange, Sachin V. Chaudhari, Koteswararao Seelam, S. Koteswari, T. Ravichandran, Balamurugan Manivannan, Algorithmic material selection for wearable medical devices a genetic algorithm-based framework with multiscale modeling , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Krishna P. Kalyanathaya, Krishna Prasad K, A novel method for developing explainable machine learning framework using feature neutralization technique , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Nithya R, Kokilavani T, Joseph Charles P, Multi-objective nature inspired hybrid optimization algorithm to improve prediction accuracy on imbalance medical datasets , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- S. Sindhu, L. Arockiam, A lightweight selective stacking framework for IoT crop recommendation , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
<< < 18 19 20 21 22 23 24 25 26 27 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Rajesh Kumar Singh, Genetic Variability in Aromatic Rice , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper

