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Deniable Authentication Encryption for Privacy Protection using Blockchain
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Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
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QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
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Volume-3 | Issue-1
Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish Image
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
Volume-1 | Issue-2
Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2
Volume - 4 | Issue - 1 | march 2022
Published
16 April, 2022
A sign language recognition system for low-resource Sinhala Sign Language using Leap Motion (LM) and Deep Neural Networks (DNN) has been presented in this paper. The study extracts static and dynamic features of hand movements of Sinhala Sign Language (SSL) using a LM controller which acquires the position of the palm, radius of hand sphere and positions of five fingers, and the proposed system is tested with the selected 24 letters and 6 words. The experimental results prove that the proposed DNN model with an average testing accuracy of 89.2% outperforms a Naïve Bayes model with 73.3% testing accuracy and a Support Vector Machine (SVM) based model with 81.2% testing accuracy. Therefore, the proposed system which uses 3D non-contact LM Controller and machine learning model has a great potential to be an affordable solution for people with hearing impairment when they communicate with normal people in their day-to-day life in all service sectors.
KeywordsSinhala Sign Language Deep Neural Networks Naïve Bayes Support Vector Machine Hand Gesture Recognition Sign Language Classification Leap Motion Controller
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