Faculty of Computing

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4776

Browse

Filters

20253

Advanced Search

Filter by

Settings

Subject: search.filters.subject.Machine learning

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemEmbargo
    Predictive Policing with Neural Networks: A Big Data Approach to Crime Forecasting in Sri Lanka
    (Institute of Electrical and Electronics Engineers Inc., 2025) Nauzad, H; Dayawansa, D; Dias, N.Y; 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.
  • Thumbnail Image
    ItemEmbargo
    UrbanGreen - E-Waste Detection and Analysis using YOLOv5
    (Institute of Electrical and Electronics Engineers Inc., 2025) Madusanka A.R.M.S; Nawaratne D.M.R.S.; Gamage, N; Attanayaka, B
    E-waste has become a global concern that challenges environmental sustain ability. The disposal of electronic devices is often poorly managed, especially in urban areas. This research aims to develop an innovative e-waste management system suitable for urban areas, focusing on accurately identifying electronic devices and their harmful components through advanced image processing techniques. (Y olov5) The system identifies various electronic devices, harmful components and materials and assesses their recyclability, improper disposal's environmental and health impacts, empowering users to make informed decisions about disposal and recycling. The system will integrate tools to identify E-waste, promote the reuse of electronic devices, educate the public through interactive educational platforms, and locate nearby e-waste collection centers. By addressing these critical aspects of e-waste management, the project aims to provide a useful platform to manage e-waste effectively in urban areas. This paper was developed to discuss E-waste detection and analysis using YOLOv5 object detection model.
  • Thumbnail Image
    ItemOpen Access
    Intelligent Systems for Comprehensive Dog Management
    (Association for Computing Machinery, 2025-06-28) Katipearachchi, M.E; Sachethana, O; Gunawardena, G. N.A; Ruwanara, D.C; Krishara, J; Kasthurirathna, D
    In recent years, the integration of advanced technologies with canine welfare has gained significant attention, leading to the development of comprehensive platforms for dog management. The "Research Pooch-Paw"initiative addresses the multifaceted needs of dog owners and stray dog populations through an innovative platform that incorporates machine learning, wearable sensors, and real-time data processing. The platform facilitates early disease detection, behaviour analysis, and health monitoring using IoT-enabled devices, and provides personalized care guidance. Additionally, it includes features for stray dog identification and emergency response using deep learning algorithms and image processing techniques. The research underscores the potential of leveraging modern technology to enhance the quality of life for dogs and improve the effectiveness of canine welfare strategies.