Faculty of Computing

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Non-Communicable Diseases Detection System
(IEEE, 2021-12-09) Thudawehewa, H. R; Jayawardhana, W. A. P. T; Wellehewa, C. G; Silva, C; Rathnayake, P
This research paper presents a Non-communicable Diseases Detection System which is a centralized medical system designed for general public usage. The system aims to provide help for people with non-communicable diseases. In a pandemic situation like this where people find it difficult to reach medical facilities and staff, the system is more advantageous. The system covers areas related to the medical report analysis, BMI value prediction, and breast cancer analysis related to non-communicable diseases. Presently health reports are taken for every disease. BMI is a factor essential to everyone to lead a healthy life. The majority of women suffer from breast cancer. As per the findings of the report, the report analysis predicts possible diseases that can occur in the person concerned. In BMI prediction, particularly the possible BMI value and weight value for the next month is predicted. In Mammogram detection, it gives the current status of the breast. The report analysis model has 90.6% accuracy while the BMI prediction model has 99.7% accuracy. The mammogram detection model proved that it has 96.5% accuracy. All the aforesaid procedures were carried out by analyzing related data systematically. Machine learning, Deep learning, and Image processing techniques were used to develop this system. The main purpose of this system is to make the persons aware of their current health status and to prevent them from having non-communicable diseases.
Embargo
WANHEDA: A Machine Learning Based DDoS Detection System
(IEEE, 2020-12-10) Sudugala, A. U; Chanuka, W. H; Eshan, A. M. N; Bandara, U. C. S; Abeywardena, K. Y
In today's world computer communication is used almost everywhere and majority of them are connected to the world's largest network, the Internet. There is danger in using internet due to numerous cyber-attacks which are designed to attack Confidentiality, Integrity and Availability of systems connected to the internet. One of the most prominent threats to computer networking is Distributed Denial of Service (DDoS) Attack. They are designed to attack availability of the systems. Many users and ISPs are targeted and affected regularly by these attacks. Even though new protection technologies are continuously proposed, this immense threat continues to grow rapidly. Most of the DDoS attacks are undetectable because they act as legitimate traffic. This situation can be partially overcome by using Intrusion Detection Systems (IDSs). There are advanced attacks where there is no proper documented way to detect. In this paper authors present a Machine Learning (ML) based DDoS detection mechanism with improved accuracy and low false positive rates. The proposed approach gives inductions based on signatures previously extracted from samples of network traffic. Authors perform the experiments using four distinct benchmark datasets, four machine learning algorithms to address four of the most harmful DDoS attack vectors. Authors achieved maximum accuracy and compared the results with other applicable machine learning algorithms.
Embargo
Intelligent disease detection system for greenhouse with a robotic monitoring system
(IEEE, 2020-12-10) Fernando, S; Nethmi, R; Silva, A; Perera, A; De Silva, R; Abeygunawardhana, P. K. W
Greenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attention-grabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning.
Embargo
Computer Vision Enabled Drowning Detection System
(IEEE, 2021-12-09) Handalage, U; Nikapotha, N; Subasinghe, C; Prasanga, T; Thilakarthna, T; Kasthurirathna, D
Safety is paramount in all swimming pools. The current systems expected to address the problem of ensuring safety at swimming pools have significant problems due to their technical aspects, such as underwater cameras and methodological aspects such as the need for human intervention in the rescue mission. The use of an automated visual-based monitoring system can help to reduce drownings and assure pool safety effectively. This study introduces a revolutionary technology that identifies drowning victims in a minimum amount of time and dispatches an automated drone to save them. Using convolutional neural network (CNN) models, it can detect a drowning person in three stages. Whenever such a situation like this is detected, the inflatable tube-mounted self-driven drone will go on a rescue mission, sounding an alarm to inform the nearby lifeguards. The system also keeps an eye out for potentially dangerous actions that could result in drowning. This system’s ability to save a drowning victim in under a minute has been demonstrated in prototype experiments' performance evaluations.

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