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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Pawar, Vijaya R.
Unknown Affiliation
Computer Science & IT Engineering

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Sign language emotion and alphabet recognition with hand gestures using convolution neural network Patil, Varsha K.; Pawar, Vijaya R.; Patil, Aditya; Bairagi, Vinayak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp954-962

Abstract

American sign language (ASL) is a special means of interaction for hard-of-hearing individuals and has precise conventional rules. Since the general public does not know these sign language protocols, there is a need to have an efficient automatic sign-emotion recognition system. The objective of this paper is, to develop a framework that recognizes standard hand gestures. The gesture represents emotions and alphabet. This paper covers the methodology, results and performance factors, for experimentations. This experimentation of ASL-based alphabet and emotion recognition is novel as till now many efforts of alphabets categorization are done but this is the new direction of research where emotions, such as together’, ‘happy’, ‘peace, ’sad’, ‘confused’, and ‘love’ are captured and automatically classified with hand signs. We mention our approach to increase ‘accuracy’, wherein we capture images and regions of interest (ROI). In this article, a specifically designed convolution neural network (CNN), is used to identify emotions from hand gestures and the addition of ROI enhances accuracy. The captured hand gesture dataset of the size of 94,000 images. “peace” sign emotion has the highest recognition rate (‘98.95%’). Alphabet’s “P” and “Q” sign ASL alphabets have the maximum recognition rate of signs. In all, very impressive accuracy of “92%” and above is detected. The limits of the experimentation are as mentioned i) there is no repeatability of accuracy for the same hand gesture; ii) The distance and angle of hand gestures with camera are crucial factors for an experiment; and iii) the alphabet recognition system is not working for the alphabets “J” and “Z”.
An innovative deep learning based approach for anomaly detection in intelligent video surveillance Pallewar, Megha G.; Pawar, Vijaya R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1105-1116

Abstract

Nowadays, anomaly detection has gained vital importance as security is a major concern everywhere. This work focuses on developing an intelligent video surveillance system capable of detecting anomalous activities in videos, utilizing the UCF Crime dataset as the primary source. The proposed model employed a multistage method uniting the convolutional neural networks (CNN) and long short-term memory (LSTM) networks. In the proposed approach, video frames serve as input to the CNN, which processes them to extract key features. These features are then passed to an LSTM network to capture temporal dependencies and identify anomalous events over time. This CNN-LSTM architecture successfully detects twelve distinct types of anomalous activities: abuse, arrest, arson, assault, burglary, explosion, fight, road accident, robbery, stealing, shoplifting, and vandalism. The dataset is divided into portions for training, testing, and validation, along with cross-validation to ensure model generalization. The system achieves an accuracy of 98.6%, reflecting a significant improvement of 4-5% over existing systems. This demonstrates the robustness of the proposed method in detecting anomalous behavior in video data.
Co-Authors Bairagi, Vinayak Pallewar, Megha G. Patil, Aditya Patil, Varsha K.