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
- Amanda Q. Okronipa, Jones Y. Nyame, Adoption of health information systems in emerging economies: Evidence from Ghana , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- G. Chitra, Hari Ganesh S., Cultural algorithm based principal component analysis (CA-PCA) approach for handling high dimensional data , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Nilesh M. Patil, P M. Krishna, G. Deena, C Harini, R.K. Gnanamurthy, Romala V. Srinivas, Exploring real-time patient monitoring and data analytics with IoT-based smart healthcare monitoring , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Isaac Asampana, Henry M. Akwetey, Ben Ocra, Jones Y. Nyame, Albert A. Akanferi, Hannah A. Tanye, Factors motivating the adoption of virtual learning environments in higher education. Is gender relevant? , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Sowmiya M, Banu Rekha B, Malar E, Assessment of transfer learning models for grading of diabetic retinopathy , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- N. Ruba, A. S. A. Khadir, Session password Blum–Goldwasser cryptography based user three layer authentication for secured financial transaction , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- M. Rajalakshmi, V. Sulochana, Enhancing deep learning model performance in air quality classification through probabilistic hyperparameter tuning with tree-structured Parzen estimators , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- J. M. Aslam, K. M. Kumar, Enhancing security of cloud using static IP techniques , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- M. A. Shanti, Optimizing predictive accuracy: A comparative study of feature selection strategies in the healthcare domain , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Rashmika Vaghela, Dileep Labana, Kirit Modi, Efficient I3D-VGG19-based architecture for human activity recognition , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
<< < 10 11 12 13 14 15 16 17 18 19 > >>
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
