Journal of Advances in Engineering and Technology [JAET]

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/3041

The Journal of Advances in Engineering and Technology (JAET) is an international, open access, double blind peer-reviewed journal. It is published by the Faculty of Engineering of Sri Lanka Institute of Information Technology (SLIIT). The JAET aims at fostering research and development work in Engineering and Technology and bringing researchers on to a common platform. Furthermore, JAET will also accept review articles on appropriate subject areas including concept papers of academic opinions, book reviews, etc. for publication therein.

Browse

Filters

2023 - 20252

Advanced Search

Filter by

Settings

Has files: YesSubject: search.filters.subject.Machine Learning

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen Access
    Pneumonia Detection and Lung Disease Assessment from Chest X-rays: Developing A Diagnostic Support System
    (SLIIT, Faculty of Engineering, 2025-01) Jayawardena, C.A; Wedasingha, N; Kolambage, N; Perera, S
    This research, dedicated to developing an accurate and efficient pneumonia detection system from Chest X-Ray images, highlights the significance of automated tools in enhancing healthcare diagnostics. Its significance lies in the fact that pneumonia is a prevalent respiratory condition that requires timely and accurate diagnosis for effective medical intervention. The project's objective was to make use of convolutional neural networks and image analyses to create an automated diagnostic tool that could assist healthcare professionals in identifying pneumonia with precision and efficiency. To achieve this, the system initially made use of two custom deep learning architectures but ultimately used a pretrained CheXNet-based model, developed by using transfer learning. This choice was made by considering CheXNet’s proven performance in identifying pneumonia and other pulmonary conditions. The project's results proved promising, with the CheXNet-based model achieving high diagnostic accuracy and providing valuable insights into the presence of pneumonia. The system's architecture, using deep learning and the use of DICOM images, demonstrated its effectiveness in improving the accuracy and efficiency of pneumonia diagnosis. Based on the results, this paper further demonstrates a web-based application for interaction with the system. Additionally, it provides information on the work that could be done in the future. Thus, this research contributes to the growing field of medical image analysis and highlights the significance of automated tools in enhancing healthcare diagnostics. The project's outcomes are meant to pave the way for more efficient and accessible methods for pneumonia detection, ultimately benefiting both healthcare providers and patients.
  • Thumbnail Image
    PublicationOpen Access
    Machine Learning-Based Indoor Localization System with Human- Computer Interaction System
    (SLIIT, Faculty of Engineering, 2023-10) Jayasundara, A; Malasinghe, L
    Understanding the indoor whereabouts of individuals and objects is important, especially for those who fall within the 71% of visually impaired individuals with a school education, students in 450 special education units and many other areas and aspects in Sri Lanka. Researchers have declared that, there isn’t any particularly good localization system, and the performance should be evaluated considering the approach and application. The most well-known indoor positioning (IP) technologies that have been historically deployed are Bluetooth, Wi-Fi, RFID (radio frequency identification), IR (Infrared), and UV (ultraviolet) whereas received signal strength (RSSI), fingerprinting, and triangulation methods have been used as common IP techniques. The combination of both IP technologies and techniques creates an IP system, and the integration of machine learning and IoT with the structured system essentially delivers an accurate and more advanced system. This paper contains a detailed, analytical review of a developed indoor positioning system derived from the existing indoor localization techniques, localization technologies, localization systems, algorithms, and performance matrixes. This also provides a comprehensive comparison between numerous existing systems to justify the proposed solution. This project has been developed to achieve better accuracy through low-cost deployment as an effective system to fill the gap in the scarcity of positioning systems in the world. This paper presents a descriptive introduction and problem definition, a critical discussion of results, machine learning models, benefits of the project, and future works. As later justified, ESP32 microcontroller and BLE beacons are utilized with RSSI fingerprinting method to develop this IP system and, as a part of the project, two data visualization methods have been introduced here using NodeRED dashboard and LC display. Overall, this project was developed with an effective combination of RSSI fingerprinting, IoT protocols, machine learning, and data interpretation methods.