Browsing by Author "Arachchi, S. P. Kasthuri"

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Enhanced Violence Detection Using Deep Learning
(Faculty of Computing and Technology, University of Kelaniya Sri Lanka, 2023) Bhawantha, Praveen; Arachchi, S. P. Kasthuri
Global violence needs to be stopped to increase public safety. With the increasing number of surveillance cameras, manual monitoring of all surveillance feeds is less practical. Because of that, the development of technology-driven solutions to detect real-time violence and inform authorities to prevent it has become necessary. This study focuses on finding a novel deep learning approach to enhance violence detection, specifically addressing the limitations and complexities of previous studies. Notably, the research utilizes proposed models and techniques to evaluate real-life violence scenarios captured in Closed-Circuit Television (CCTV) footage, overcoming the challenges identified and improving the accuracy of violence detection. Two models were proposed in this research paper. The model architecture consists of a multimodal approach, integrating two deep learning techniques, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The proposed model utilizing VGG-16 with CNN layers and LSTM, achieved 89% accuracy on the real life violence situations dataset. This emphasizes the effectiveness of applying multimodal deep learning technique in detecting violence, outperforming similar research in accuracy.
LSTM Based Emotion Analysis of Text in Tamil Language
(Faculty of Computing and Technology, University of Kelaniya Sri Lanka, 2022) Ahamed, M.R. Faiyaz; Arachchi, S. P. Kasthuri
The sentiments and emotions expressed by users on the internet greatly influence the decision-making process of business firms. Recent studies show that emotion analysis yields more precise information than sentiment analysis. Text emotion analysis has become popular for higher-demand languages like English, Chinese, French, and Arabic. However, no prior studies have been conducted on locally speaking languages, including Tamil, Malayalam, and Sinhala. Therefore, this paper presents a deep learning based novel model to identify the emotions expressed in Tamil texts using a Long Short-Term Memory (LSTM) network. Besides, to enhance the robustness of our proposed model, we conducted experiments with machine learning classifiers, including Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest Classifier (RFC). The experimental results prove that our Tamil text emotion analysis model significantly outperforms other machine learning models, achieving an accuracy of 80%.