Research Papers - Dept of Information Technology
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Publication Embargo Betel Plant Tech: Betel Disease Forecasting System and Finding Marketplace(IEEE, 2022-12-26) Maitipe, C.N; Bandara, G.P.C.C; Anuradi, M.R.C; Marambage, M. H. B. Y; Weerasinghe, LBetel in Sri Lanka extends back to 340 B.C. and it has a significant cultural value in Sri Lanka. Betel is currently planted throughout the country, and it is the primary source of income for numerous farmers. Betel leaves are easily exposed to diseases. Taking that into consideration, it is clear that anticipating the spread of betel diseases, which have been identified as having a significant influence on the country’s economy, and creating a better marketplace is critical. The "Betel Plant Tech" smartphone application was created in this project with a focus on image processing, sequential models, regression and classification, in machine learning techniques. The findings indicate that predictions of betel yield identification produces high accuracy of 98% in random forest regression (RFR), identification of betel diseases with 92% accuracy with Restnet 34, and disease propagation level consists of a 92% accuracy level in the sequential model while predicting the spread of viral/fungal diseases has a 97% accuracy rate in Decision Tree Regression. The planned study would determine and predict the yield of betel cultivation, identify the diseases of betel leaf, predict the prevalence of diseases, and identify the level of disease prevalence. Besides, it will be easy to find a good marketplace for betel growers.Publication Embargo An Interactive E-Learning Tool(IEEE, 2022-07-18) Kodagoda, D. G; Ishara, K.G.R.U; Kumara, R. M. R. P; Dilshan, W. A. D.T; Weerasinghe, L; Premadasa, NUse of E-learning systems has surged immensely during the Covid-19 pandemic, which started around 2020. This research specifically conducted to introduce novel features with the purpose of enhancing traditional E Learning platforms. The suggested features are namely, avoid unauthorized users from accessing private video sessions using face recognition, manipulating 3D objects by hand gestures, analyzing student’s attention using face landmarks, smart QAs using voice recognitions. These features will provide not only an enhancement for e-Learning platforms but also it will improve user experience, efficiency, and effectiveness of current tools up to a certain distinguishable level.Publication Embargo Yuwathi: Early Detection of Breast Cancer and Classification of Mammography Images Using Machine Learning(IEEE, 2022-07-18) Diddugoda, D; Fernando, D. B; Munasinghe, S. M; Weerasinghe, L; Weerathunga, IAccording to the World Health Organization's (WHO) data and records, breast cancer is one of the most common diseases among women. As a result of the mutations of the genes within a cell, the cell starts growing uncontrollably and rapidly. Such a condition is known as cancer. Cancer tumors can be categorized into two major categories, benign and malignant. However, there is no existing solution in practice to automate early breast cancer identification and risk prediction using medical images (Mammograms). This paper discusses automating breast cancer detection, breast density identification, risk prediction, and solution suggestion using machine learning, image processing, and computer vision techniques. All the mentioned features can be accessed using the application "YUWATHI", and a user can take advantage of this application by using a smartphone also a web application. The objectives of the present study are mammographic mass detection without user intervention, identifying pectoral muscles and removing them, training a machine learning model to identify the future risk of breast cancers by obtaining clinical reports from the OCR application and suggesting solutions for the above problems using a computer-aided diagnosis (CADx) system that helps doctors to make decisions swiftly. The algorithms used for breast cancer detection, breast density classification, and future breast cancer risk prediction are Convolutional Neural Network (CNN), CNN and Logistic Regression with the accuracies 97.32%, 71.97% 74.76%, respectively.Publication Embargo ARChem: Augmented Reality Chemistry Lab(IEEE, 2021-12-06) Menikrama, M. R. L. Y; Liyanagunawardhana, C. S; Amarasekara, H. G. D. M. I; Ramasinghe, M. S; Weerasinghe, L; Weerasinghe, IOne of the technologies that has been gaining ground in recent years is Augmented Reality (AR), which allows to insert virtual objects into a real-world view using a device's camera and screen. This form of interaction associated with education can improve teaching and experiencing practical knowledge in schools, especially in more difficult subjects such as Chemistry. This study focused on virtual education by providing a platform for students to follow practical oriented subjects like Chemistry. As a result, a mobile application is created with four main functions that assist students during their learning process of Chemistry using the AR technique. The main functions are, AR with Artificial Intelligence (AI), Chemical equation identification and correction with Image Processing, Chabot with sentiment analysis and text summarization. The application is developed by using Machine Learning, AI with Deep Learning and Mobile Application development technologies. ARChem shows 3D models of flasks with important descriptions with the use and also features a Chabot with text summarization for frequently asked questions.Publication Open Access An Efficient Automated Attendance Entering System by Eliminating Counterfeit Signatures using Kolmogorov Smirnov Test(Global Journal, 2019-05-27) Weerasinghe, L; Sudantha, B. HMaintaining the attendance database of thousands of students has become a tedious task in the universities in Sri Lanka. This paper comprises of 3 phases: signature extraction, signature recognition, and signature verification to automate the process. We applied necessary image processing techniques, and extracted useful features from each signature. Support Vector Machine (SVM), multiclass Support Vector Machine and Kolmogorov Smirnov test is used to signature classification, recognition, and verification respectively. The described method in this report represents an effective and accurate approach to automatic signature recognition and verification. It is capable of matching, classifying, and verifying the test signatures with the database of 83.33%, 100%, and 100% accuracy respectivelyPublication Embargo ARChem: Augmented Reality Chemistry Lab(IEEE, 2021-10-27) Menikrama, M. R. L. Y; Liyanagunawardhana, C. S; Amarasekara, H. G. D. M. I; Ramasinghe, M. S; Weerasinghe, L; Weerasinghe, IOne of the technologies that has been gaining ground in recent years is Augmented Reality (AR), which allows to insert virtual objects into a real-world view using a device's camera and screen. This form of interaction associated with education can improve teaching and experiencing practical knowledge in schools, especially in more difficult subjects such as Chemistry. This study focused on virtual education by providing a platform for students to follow practical oriented subjects like Chemistry. As a result, a mobile application is created with four main functions that assist students during their learning process of Chemistry using the AR technique. The main functions are, AR with Artificial Intelligence (AI), Chemical equation identification and correction with Image Processing, Chabot with sentiment analysis and text summarization. The application is developed by using Machine Learning, AI with Deep Learning and Mobile Application development technologies. ARChem shows 3D models of flasks with important descriptions with the use and also features a Chabot with text summarization for frequently asked questions.Publication Embargo SMARKET-Shopping in Supercenters (Hypermarkets) with Augmented Reality(IEEE, 2021-12-17) Jayagoda, N. M; Jayawardana, O. R; Welivita, W. W. T. P; Weerasinghe, L; Dassanayake, TNot so long ago, online shopping for groceries, electronics, and furniture items seemed futuristic. But today, it has become a norm to order requisites through online platforms using smart devices and deliver them to customers' doorstep. With the emerge of technologies such as artificial intelligence, machine learning, deep learning, augmented reality, retail becomes progressively effortless. One such emerging futuristic technology involved recently in online shopping is Augmented Reality (AR) which is rapidly adopted by many industries. In multi-story supercenters, also known as “Hypermarkets”, the customer often feels lost due to difficulty in finding exactly what they looking for, and also in conventional online shopping, often customers are in two minds whether to purchase an item or not since it lacks the proper visualization, touch, and feel of the product. In this research study, we propose a mobile-based solution with augmented reality, which assists the customer when shopping in-store as well as when shopping online to mitigate the difficulties and hesitancies faced while shopping. The results are commendable with 96.21 % accuracy in suggesting visually similar items and 89.59% accuracy in detecting emotional implications of product reviews.Publication Embargo CEYLAGRO: Information Technological Approach for an Optimized and Centralized Agriculiture Platform(IEEE, 2020-12-10) Kaushalya, T. V. H; Wijewardana, B. Y. S; Karunasena, A; Kavishika, M. G. G; Gamage, S. T. A; Weerasinghe, LSri Lankan Agriculture sector can be considered as a crucial component as it contributes 18% of country GDP. As native farmers still cling to inapplicable traditional theorems and practices to track customer's vegetable consumption trends, they failed to assure a “good price” for their harvest. Also, the plants are prone to many diseases and pests' attacks which causes loss of the harvest. Unreliable problem identification, poor knowledge on application of fertilizers and pesticides have caused the farmers to lose their profits. As a solution to mitigate these problems, this study has built a computerized system with a vegetable price prediction system and a plant disease, pest identification system. Taking Potato as an example, the parameters of the time series model were analyzed through experiment and has built the price predictor using ARIMA model. Also, with advanced Image processing and CNN techniques Plant disease, pest identifier has built. Desirable results of the entire system have been achieved with more than 94%-97% rate of accuracy. The ultimate goal of this study is to achieve the optimal growth of the sector by navigating the users for a quality and effective decision making by reliable market trends and problem identification.Publication Embargo Plagiarism Detection Tool for Enhanced Entity-Relationship Diagrams(IEEE, 2021-12-01) Dahanayake, H; Samarajeewa, D; Jayathilake, A; Bandara, D; Karunasena, A; Weerasinghe, LPlagiarism is presenting someone else’s work as one’s own work without giving credit to the original owner. Recently, plagiarism has become a serious issue in the fields of Education and Technology. To address this issue, many systems have been implemented to detect plagiarism. However, most of them are designed to deal with plagiarism of text content. Detecting plagiarism in figures and diagrams is equally important. Although there is research done on detecting plagiarism in images and flow charts, there is no research done on detecting plagiarism in more complex diagrams such as Enhanced Entity-Relationship (EER) diagrams. This paper presents a methodology to detect plagiarism in EER diagrams using Deep Neural Networks (DNN), image processing techniques, Optical Character Recognition (OCR) techniques, and text similarity detection algorithms. Since the students are aware of the existence of a plagiarism detecting tool, it will encourage the students to do work on their own and it will reduce exam offenses. The similarity report can be presented as proof to the offenders who are not accepting that they have plagiarized others' work. Using the proposed system, the EER diagram plagiarism can be detected much faster and accurately. Therefore, the efficiency of marking examinations will be increased. The final outcome of the system will be a similarity report including the plagiarized content in the compared EER diagrams.Publication Embargo OMNISCIENT: A Branch Monitoring System for Large-scale Organizations(IEEE, 2020-12-10) Jayasekara, T; Omalka, K; Hewawelengoda, P; Kanishka, C; Samarasinghe, P; Weerasinghe, LOmniscient is a system that enables higher-level management of massive organizations to remotely monitor and scrutinize the activities that take place in the branches from the head office itself by providing exclusive insight in the form of detailed reports on the employees' behaviour and performance daily, weekly and monthly. The system further monitors the branch and provides reports on any suspicious behaviour and also on the customers' activity within the branch premises. Omniscient rates the customer's level of satisfaction by capturing the customer's facial expressions and analyzing their emotions while they are being served. The employee face and dress recognition models have accuracies of 90.90% and 87.00% respectively while, employee activity detection has an accuracy of 89.00%. Customer emotion and miscellaneous activities detection models have the accuracies of 91.50% and 83.00% respectively. All of the aforementioned procedures were made possible by systematically analyzing the IP camera video footage obtained throughout the day to analyze the work productivity and performance of the branch as accurately as possible using deep learning and modern visual computing techniques like CNN, OpenCV, Haar Cascade classifier, face recognition, Dlib and Darknet.