Faculty of Computing
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Publication Embargo Cocopal - A Deep Learning Based Intelligent System to Certify and Standardize the Quality of Coconut Based Products(IEEE, 2022-12-09) Gunawardana, K.H.R.; Deshan, M.P.N.; Hemachandra, M.G.S.P.; Ganegoda, D; Hettiarachchi, N. M; Weerasinghe, LThe procedure of certifying and standardizing the quality of the coconut-based products is done manually in Sri Lanka at precent. It is a time consuming and labor-intensive task and is conducted by experts. In most cases, the quality is decided solely by visual inspections by buyers and suppliers, with no scientific basis. The paper reports the capacity of bringing modern technology solutions such as Artificial Intelligence (AI), Machine Learning (ML), Image Processing (IP), and decentralized storage to aid in the certification and standardization of the quality of raw materials.Results showed that the accuracy of the proposed system is in the 86% to 90% range and showed that this technique could beimproved and used as an alternative to manual techniques.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 CXR Scan:X-Ray Image Scanning Application for Lung Cancer and Tuberculosis(Institute of Electrical and Electronics Engineers, 2022-10-15) Jayasooriya, A.M.U.J.; Wickramasekara, T.M.A.M; Jayasinghe, I.C.; Gunaratne, U.A.; Weerasinghe, L; Dassanayake, G. TThe initial criterion for identifying lung disorders is chest radiographs. The three major lung illnesses that pose the greatest threat to public health are tuberculosis, pneumonia, and lung cancer. Chest X-ray diagnosis of pulmonary illnesses is a challenging undertaking that requires high experience. In rural places, it can be difficult to locate skilled radiologists. Due to the high frequency of TB and lung cancer radiological similarities, many individuals with lung cancer are initially misdiagnosed as having TB and treated incorrectly. According to a recent WHO survey, millions of people die each year as a result of delayed or incorrect diagnoses of lung diseases. This death rate can be reduced, by early detection of certain disorders. This paper proposes a system with 4 main components; Image processing of chest X-rays to identify the disease using Convolutional Neural networks; Predicting the probability of having LC or TB using multivariate data classification techniques; Recommending medicine and related information to support the decision-making process using gaussian naïve bayes, logistic regression model and decision tree classification methods; Visualizing the X-ray image using Augmented Reality.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 Arogya-An Intelligent Ayurvedic Herb Management Platform(IEEE, 2020-10-15) Pathiranage, N; Nilfa, N; Nithmali, M; Kumari, N; Weerasinghe, L; Weerathunga, IAyurvedic means a science of life and well-being with its unique approaches to social and spiritual life. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs which have been utilized by generations as medicinal treatments for a variety of diseases. Absence of specialists in this area makes proper identification as well as classification of valuable herbal plants a tedious task, which is essential for better treatment. Hence, a fully automated system for herb detection and classification, information visualization regarding them is highly desirable. There are existing applications which can identify plants with low prediction accuracies, as well as to give information regarding them. However, these applications are based on foreign plant data sets that do not include valuable herbs and shrubs with medicinal qualities. Hence this research proposes an application unique to medicinal plants, which can perform all these functionalities in both online and offline approach. Here, a new Ayurvedic plant dataset prepared from scratch, and preliminary results for classification of 5 types of herbs, compared with several deep Convolutional Neural Network (CNN) models based on transfer learning are presented. Experimental results indicate Marker-based Watershed algorithm as the best object detection algorithm in a complex background, VGG-16 as the best deep CNN classification model which reached a promising testing accuracy of 99.53%, and Seq2Seq LSTM model as the best deep learning model with optimum accuracy in abstractive information summarization.Publication 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.