Smart Computing and Systems Engineering - 2025 (SCSE 2025)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/30037

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Author: search.filters.author.Shaik, A.Has files: Yes

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    AI-Driven Approach for Measuring and Classifying Diabetic Retinopathy Severity
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Shaik, A.; Little Flower, K.; Veerabhadraiah, S.; Nandini, A.; Sai Kiran, C.; Yashwanth Goud, K.
    Diabetic Retinopathy is one of the most common complications affecting people with diabetes and is a leading cause of blindness worldwide. Advanced technological methods through image analysis and artificial neural networks have become major assets in addressing the escalating problem of DR. This paper discusses various approaches to implementing automation in DR detection, focusing on image acquisition and preprocessing, feature extraction, and classification using AI. We review the utility of these systems in terms of cost, benefits, and performance, as well as the challenges related to data quality, model interpretability, and regulatory requirements. The study demonstrates that automation holds the key to delivering higher patient-impact opportunities in clinical applications such as screening programs and telemedicine. Finally, we discuss directions for future research and community implementation, emphasizing the importance of highly controlled, naturalistic designs and the translation of research findings into clinical practice.
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    Hybrid CNN-LSTM Framework for Robust Speech Emotion Recognition
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Shaik, A.; Reddy, G. P.; Vidya, R.; Varsha, J.; Jayasree, G.; Sriveni, L.
    A key component of emotional computing is voice Emotion Recognition (SER), which is concerned with recognizing and categorizing human emotions from voice data. Human communication is heavily influenced by emotions, and giving machines the ability to recognize these emotional states improves their capacity for intelligent and sympathetic interaction. A reliable SER system that accurately classifies emotions using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is presented in this research. The suggested approach integrates sophisticated feature extraction methods that efficiently capture both temporal and spatial emotional patterns in speech, including Mel-Frequency Cepstral Coefficients (MFCC), pitch, and chroma data. Metrics including accuracy, precision, recall, and F1-score were used to assess the system on two common datasets, RAVDESS and EMO-DB. The trial findings show that the hybrid CNN-LSTM model outperformed traditional machine learning approaches, with an overall accuracy of 89.4%. The system also demonstrated resistance to external noise and emotional overlap, making it appropriate for real-world applications.