Faculty of Computing
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Publication Embargo GreenEye: Smart Consulting System for Domestic Farmers(IEEE, 2022-12-09) Mendis, O; Perera, A; Ranasinghe, S; Chandrasiri, SAlways it is challenging for typical domestic farmers to maintain a good homestead in today’s world and with the ever-growing economic concerns. To save time, money, and energy, they must keep up with the advancements of incorporating technology in their farming practices to ensure that their crops are up to standard and optimized for the maximum yield. Domestic farmers may grow crops for economic gain, pleasure, stress relief, decorative purposes, Etc. However, regardless of the purpose, everyone must be aware of good farming practices. No matter the intention, challenges, and outcomes, everyone engaged with plant growth is the same. In today’s highly advanced technological world, a lot of domestic farmers are using modern technology in their growing practices. Experimenting with intelligent growth mechanisms and intend to use modern technologies to provide advice that is useful for all gardeners who prefer home gardening. Additionally, the most crucial aspects of plant care are recognizing the ideal plants for each season, identifying stress factors, identifying diseases, identifying soil moisture levels, and predicting the harvest based on the current environmental conditions. Green Eye mobile application aims to provide a comprehensive solution to technologized domestic farmers using image processing technologies for their most common concerns.Publication Embargo Social media based personalized advertisement engine(IEEE, 2018-02-19) De Silva, H; Jayasinghe, P; Perera, A; Pramudith, S; Kasthurirathna, DOnline advertising has become a global phenomenon that affects the retail market substantially. Advertisements engines are an effective solution to the mobile application market to push advertisements. This paper reports evidence that AdSeeker, User Preference Based Advertisement Engine Based on Social Media is an effective solution to improve the business value of the marketing and advertising. Since the internet is used by vast number of people, it essentially needs a comprehensive method to push personalized advertisements to the right people. Adseeker is a system built using ontological mapping and social media content based semantic analysis to direct personalized. Identifying personal relationship hierarchy, and ontological approach for advertisement classification helps to identify the most appropriate advertisement for each user. AdSeeker uses the tweets posted by users to capture the preference of each and every user. Each user pushed advertisements based on their individual preferences. Based on the social experiments done using Adseeker, we could demonstrate that the social media profile based advertising is effective in providing highly relevant advertisements.Publication Embargo The Next Gen Security Operation Center(IEEE, 2021-04-02) Perera, A; Rathnayaka, S; Che, C; Madushanka, W. W; Senarathne, A. NDue to the evolving Cyber threat landscape, Cyber criminals have found new and ingenious ways of breaching defenses in networks. Due to the sheer destruction these threat actors can cause to an organization, most modern-day organizations have focused their attention towards protecting their critical infrastructure and sensitive information through multiple methods. The main defense against both internal and external threats to an organization has been the implementation of the Security Operations Center (SOC) which is responsible for monitoring, analyzing and mitigating incoming threats. At the heart of the Security Operations Center, lies the Security Information and Event Management system (SIEM) which is utilized by SOC analysts as the centralized point where all security notifications from various security technologies including firewalls, IPS/IDS and Anti-Virus logs are collected and visualized. The effective operation of SOC in an organization is dependent on how well the SIEM filters log events and generates actual alerts. Here lies the major problem faced by SOC analysts in detecting threats. If proper alert correlation is not accomplished, analysts would have to deal with too much alert noise due to a high false positive count. This would ultimately cause analysts to miss critical security incidents, thus causing severe implications to the organization's security. The performance of a SIEM can be enhanced through adding various functionalities such as Threat Hunting, Threat Intelligence and malware identification and prevention in order to reduce false positive alarms, threat framework and machine learning which would increase the accuracy and efficiency of the overall Security Operations process of an organization. Even though many products which provide these additional functionalities exist in the current market, they can be too expensive for smaller scale organizations to handle. Our aim is to make security operations deliverable to any organization regardless of the size and scale without any financial implications and enhance its functionalities with the aid of Advanced Machine Learning Techniques.Publication Embargo Ai based greenhouse farming support system with robotic monitoring(IEEE, 2020-11-16) Fernando, S; Nethmi, R; Silva, A; Perera, A; De Silva, R; Abeygunawardhana, P. K. WGreenhouses plays a major role in today's agriculture since farmers can grow plants under controlled climatic conditions and can optimize production. The greenhouses are usually built in areas where the climatic conditions for the growth of plants are not optimal so requires some artificial setups to bring about productivity. Automating process of a greenhouse requires monitoring and controlling of the climatic parameters. This paper is an attempt to minimize the cost of maintaining greenhouse environments using new technologies. The end goal of this research an automated system to optimally monitor and control the environmental factors inside greenhouse by monitoring temperature, soil moisture, humidity and pH through a cloud connected mobile robot which can detect unhealthy plants using image processing and machine learning. The mobile robot navigates through a predefined map of greenhouse. Database server has created to store gathered real-time data. And the necessary accurate data represent by using proper application for analyzing.Publication 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. WGreenhouse 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.