Smart Computing and Systems Engineering - 2025 (SCSE 2025)

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

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Start date: 2025Subject: search.filters.subject.classification

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    AI-Driven Solutions for Automated Fish Freshness Classification Using CNN Architectures
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Peries, R. F. S.; Adeeba, S.; Ahamed, M. F. S.; Kumara, B. T. G. S.
    Ensuring fish freshness is essential for market value, consumer health, and seafood quality. In Sri Lanka, traditional sensory-based methods for assessing freshness are subjective and often inaccessible to small-scale fishermen due to high costs and limited resources. This study addresses these challenges by employing Convolutional Neural Networks (CNNs) to automate fish freshness classification using image data from the Mannar coastal region. The approach involved capturing images of whole fish, fish eyes, and fish gills, followed by preprocessing steps such as labeling, resizing, and augmentation. Separate custom CNN models were developed for each dataset, with the gill dataset achieving the highest performance at 98.26% accuracy, along with excellent precision, recall, and F1-scores. Furthermore, advanced pre-trained models—including VGG16, ResNet50, MobileNetV2, InceptionV3, Xception, and DenseNet121—were evaluated on the gill dataset. Among these, DenseNet121 emerged as the best-performing model due to its high accuracy, precision, recall, F1-score, and stable learning curve. These findings highlight the potential of CNN-based and pre-trained models to provide scalable, cost-effective solutions for fish freshness assessment, promoting sustainable seafood practices, empowering small-scale fishers, and enhancing food safety standards.
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    Optimizing Predictive Maintenance in Industrial Machinery with Data Smoothing and Machine Learning
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Nirmal, W. C.; Lakmak, H. K. I. S.; Fernando, K. J. P.
    Unplanned machinery breakdowns result in significant financial losses, making predictive maintenance essential in industrial operations. This study focuses on fault detection in a single-phase induction motor used in jewelry manufacturing by analyzing vibration data under normal and abnormal conditions. Data was collected using an accelerometer, and three preprocessing techniques—Kalman Filter, Moving Average Filter, and Fast Fourier Transform (FFT)—were applied to reduce noise and improve data quality. Six supervised classification algorithms were evaluated on both raw and preprocessed data. Results demonstrate that preprocessing significantly enhances model accuracy, with the Moving Average Filter enabling Random Forest to achieve the highest accuracy of 99.77%. Kalman Filter also improved model performance, while FFT was particularly beneficial for Logistic Regression. This research highlights the importance of combining machine learning with effective preprocessing to optimize predictive maintenance strategies, reduce downtime, and minimize maintenance costs in industrial environments. By demonstrating the practical implications of these methods, this study contributes to the advancement of reliable fault detection systems for critical machinery components.
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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.