Smart Computing and Systems Engineering - 2025 (SCSE 2025)
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/30037
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Item BookStore: An Innovative Book Recommendation System Driven by Facial Expressions(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Neelananda, M.; Rathnayake, R.In the age of digital content consumption, personalization plays a crucial role in enhancing the user experience. However, many existing recommendation systems focus primarily on past interactions or ratings, often overlooking the user's current emotions and cultural background. This is particularly relevant in literature, where readers' mood and background can significantly influence their reception of various genres or topics. The proposed study addresses this gap by proposing an emotion-based book recommendation system that utilizes Facial Emotion Recognition (FER) to suggest culturally appropriate books aligned with the user’s emotional state. A deep learning model based on Convolutional Neural Networks (CNN) was trained to classify facial expressions and identify a person’s real-time emotions. The system features a culturally diverse dataset of books, categorized by emotion-relevant themes using Natural Language Processing (NLP) techniques. The user’s emotional state is determined through the FER model, and recommendations are made based on this identified mood and the user's cultural background. The model is trained to recognize facial emotions using labeled facial emotion datasets and has been tested across multiple scenarios to ensure it provides relevant book suggestions. The prototype system accurately identified users’ emotions with an 84% success rate and successfully recommended books of interest in 90% of the attempts. Users confirmed that the system could enhance their interest in reading by aligning book suggestions with their emotional and cultural inclinations. While these preliminary results are promising, they highlight the model’s potential to create a friendly reading environment by suggesting books that resonate with users' moods and cultural contexts.Item Predictive Modeling of Rubber Plant Growth Using Environmental Data and Machine Learning Techniques(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Liyanage, Y. L. S. N.; Hettiarachchi, P. L.; Dilpashan, L. T. C.; Navodya, M. L. H.; Jayasekara, B.Rubber plant nurseries require effective management to maximize agricultural resources and ensure good plant health. This paper presents the Rubber Plant Growth Prediction System, which uses environmental data to predict the diameter of the stem, an important index used in plant growth. In this work, initial plant growth was predicted using a vision-based technique, but due to difficulties in data acquisition and the accuracy point of view, that approach may not be feasible. The method based on stem diameter was then adopted, incorporating environmental parameters such as soil moisture, soil temperature, ambient temperature, and humidity. With a limited dataset, it was quite challenging to be accurate in the predictions, but developing a comprehensive data collection system filled those gaps, making the measurements more reliable. This forms the basis for increasing predictive accuracy in improving resource management in the nursery and therefore contributing to sustainable agricultural practices.Item Detection of Cyberbullying to Reduce Mental Health Problems using Machine Learning Algorithms(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Perera, M. V. V.; Piyumal, K. M.Social networks and other online platform services are where people are more likely to experience issues with cyberbullying, including kids, young and older adults who are addicted to them. Cyberbullying is an activity that takes place on digital platforms where victims are threatened or bullied individually or in groups by messages or comments online. Various cyberbullying detection techniques are continuously used on social media platforms; however, not all online platform services follow those mechanisms, which may lead to psychological problems that can cause depression and even suicide because people are unaware of taking action to prevent it. Many past cyberbullying detection studies used small datasets and omitted disclosing the total number of features used to train the model. To fill this gap, this study explores how model performance changes with the feature count and what happens when the dataset size increases. Therefore, two cyberbullying datasets with a combined total of 47,183 and 120,556 records were used, containing suspicious activities on Twitter and Facebook that most commonly belong to the cyberbullying category. To compare performance metrics of each model, three methods for feature extraction and three classifiers were used, namely Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). The highest accuracy for the models created utilizing 47,183 data under the three feature extraction approaches was 94.43%, while the highest accuracy for the 120,556 data was 89.96%.Item Enhancing network intrusion detection with stacked deep and reinforcement learning models(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Kalpani, N.; Rodrigo, N.; Seneviratne, D.; Ariyadasa, S.; Senanayake, J.This study investigates the effectiveness of Ensemble Learning (EL) techniques by integrating reproducible Deep Learning (DL) and Reinforcement Learning (RL) models to enhance network intrusion detection. Through a systematic review of the literature, the most effective DL and RL models from 2020 to 2024 were identified based on their F1 scores and reproducibility, focusing on recent advancements in network intrusion detection. A structured normalisation and evaluation process allowed for an objective comparison of model performances. The best performing DL and RL models were subsequently integrated using a stacking ensemble technique, chosen for its ability to combine the complementary strengths of the DL and RL models. Experimental validation in a benchmark dataset confirmed the high accuracy and robust detection capabilities of the model, outperforming the individual DL and RL models to detect network intrusions in multiple classes. This research demonstrates the potential of ensemble methods for advancing Intrusion Detection Systems (IDSs), offering a scalable and effective solution for dynamic cybersecurity environments.Item Predictive policing with neural networks: A big data approach to crime forecasting in Sri Lanka(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Nauzad, H.; Dayawansa, D.; Dias, N.; Haddela, P. S.; Ratnayake, S.The surge in crime rates, particularly in urban regions, has underscored the importance of predictive policing within law enforcement strategies. This research introduces a neural network-based crime prediction model, specifically tailored to address the complexities of Sri Lanka’s crime landscape. By combining big data analytics with advanced machine learning methods—including ensemble models such as Random Forest and Gradient Boosting, alongside Artificial Neural Networks (ANNs)—our study presents a robust framework to forecast crime incidents, locations, and time spans. While neural networks excel in predictive accuracy, their “black-box” nature can hinder practical applications in critical fields like law enforcement. To address this, our model integrates Explainable AI (XAI), making the decision-making process of the system transparent and interpretable for end-users. XAI helps break down complex neural network predictions, ensuring trust and clarity in the model’s insights. With a prediction accuracy rate of 85%, this approach demonstrates substantial potential to improve crime prevention efforts and optimize resource allocation. Our research not only highlights the predictive strengths of neural networks but also showcases the essential role of interpretability for deploying these models effectively in real-world policing.