Faculty of Computing
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Publication Embargo CertiMart: Use Computer Vision to Digitize and Automate Supermarket with Fruit Quality Measuring and Maintaining(IEEE, 2022-12-09) Rathnayake, W.P.D.N. P; Geeth Dulanjana, D; Punchihewa, A.V.B.W. G; Anjana, N.W. G; Suriya Kumari, P. K.; Samarakoon, USri Lanka has a tropical environment, which makes it easy for fruit and vegetable plants to thrive. Vitamins, proteins, and other nutrients are abundant in fruits. However, there is a time when the fruit is considered to be fresh. During this time, many fruit supplier firms continue to supply fruit that is unsafe for ingestion due to inaccuracy in the sorting process when the fruit is taken from the plantation and the introduction of other fruit into an incorrect packing. As a result, detecting food rotting from the point of production to the point of consumption is critical. Inside the market we realize that there is unavailability of sorting of fruits. Just after receiving the fruit into the supermarket, we should have a way to measure freshness of fruit and maintain it. In addition to this ripened method identification and disease identification will be great help to this help.Publication Embargo Face Skin Disease Detection and Community based Doctor Recommendation System(IEEE, 2022-12-09) Udara, M.A.A.; Wimalki Dilshani, D.G.; Mahalekam, M.S.W.; Wickramaarachchi, V.Y.; Krishara, J; Wijendra, DIn our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.Publication Embargo Success Factors of Requirement Elicitation in the Field of Software Engineering(IEEE, 2022-12-09) Attanayaka, B; Nawinna, D; Manathunga, K; Abeygunawardhana, P. K. WRequirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors.Publication Embargo AI-Based Child Care Parental Control System(IEEE, 2022-12-09) Jayasekara, U; Maniyangama, H; Vithana, K; Weerasinghe, T; Wijekoon, J; Panchendrarajan, RDue to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child’s activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application’s development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments.Publication Embargo TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates(IEEE, 2022-12-09) Legrand, T.R; Bandara, K.M.R.A.I; Stefania Crishani, J.A.D; Uvindu, L.W.P; Amarasena, N; Kasthurirathna, DSri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study.Publication Embargo Smart Advertising Based on Customer Preferences and Manage the Supermarket(IEEE, 2022-12-09) Wickramasinghe, A.Y.S. W; Eishan Dinuka, W.H.A.; Weerasinghe, W.S. H; Karunaratne, K.P. G; Liyanapathirana, C; Rupasinghe, LAs a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users’ feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users’ preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly.Publication Embargo Algorithmically Navigating Complex Tabular Structures in Images for Information Extraction(IEEE, 2022-12-26) Nugawela, M; Abeywardena, K. Y; Mahaadikara, HComputer vision has been in the forefront of automating workflows to replace manual repetitive tasks with convenience and accuracy. Recognizing text from images of commercial documents through optical character recognition (OCR) form the initial step of most such workflows where majority of their information are in the form of complex data structures such as tables and nested tables. Although OCR technology has evolved to effectively capture text from images, there is still room for improvement in recognizing complex data structures and extracting tabular data from images. This paper proposes an algorithmic approach based on keyword detection and the position of words relative to each other in order to recognize nested structures and successfully extract tabular data into a program and human readable format, which aims to take a different approach as opposed to using machine learning models or pre-defined templates for layout recognition. Furthermore, this approach is shown to yield successful results in correctly comprehending the layout and data of nested table structures in multiple rows in a table.Publication Embargo Intelligent Wheelchair with Emotion Analysis and Voice Recognition(IEEE, 2022-12-26) Perera, S; Gamage, S; Weerasinghe, C; Jayawardena, C; Pathinayake, K; Rajapaksha, SIntelligent wheelchairs are becoming more and more prevalent in contemporary life, and the peaceful interaction of humans with wheelchairs is one of the most popular research topics. The development of a voice recognition and emotion recognition based intelligent wheelchair framework is being addressed here for truly impaired/disabled people who are unable to operate the wheelchair by hand. The patient can operate the wheelchair using voice commands, and the wheelchair’s Emotion Analysis module recognizes the patient’s face and records the patient’s emotions before sending the information to a cell phone application. A portion of the intelligent wheelchair is made to gather crucial information given by other units and send out emergency calls or notifications to the caregivers. Face recognition technology uses image processing to identify facial expressions by detecting the patient’s face and facial expressions. This helps the other components collect and send data via Internet of Things technologies. Speech – to –Text and Text – to-Speech Methodology is used in the voice recognition module and it captures the voice command data set and extracts the features of the commands.The model is already built and trained to recognize the commands and to send action request to the relevant unit.The Responsive AI auto starts the timer when the patient moves away from the wheelchair, recognizes time and responses back. This unit auto also sends the alert and calls to the guardian when the user has no response.Publication Embargo Blockchain-based Secure Environment for Electronic Health Records(IEEE, 2022-11-26) Jayasinghe, J. G. L. A; Shiranthaka, K. G. S.; Kavith, T; Jayasinghe, M. H. D. V.; Yapa Abeywardena, K; Yapa, KElectronic health records (EHRs) have become the de facto standard for storing patient data in hospitals because of the data technology revolution. Many hospitals use server-based systems to keep track of patient medical records, however, this limits the scalability of those systems because they require a lot of storage space. Interoperability and security and privacy concerns, as well as cyber-attacks on the centralized storage, are among the issues they are dealing with. Lab report downloads can be compromised by a poor authentication mechanism that can be easily shared with a third party. Highlighted issues will be addressed by the proposed system, a Blockchain-based private patient information management system. Using a distributed, immutable, and secure ledger, the solution promises efficient system access and retrieval. Consensus can be achieved without consuming a big amount of energy or causing network congestion thanks to an enhanced consensus technique. Because of their tight zero-knowledge requirement, near-perfect data interchange across many platforms is possible thanks to Non-Fungible Tokens, which encourage openness and immutability in the data flow. In addition, the proposed system uses a mix of a hybrid access control system and public key cryptography to ensure high levels of data protection. Additionally, it is a fantastic accomplishment when Lab Report Download Portal and the report generator for medical lab reports can be connected to the main system, which can dynamically modify the report template format with multi-factor authentication enabled. Know your customer verification is also used to authenticate the user to the system. Decentralizing the medical industry’s data storage, sharing, and record-keeping is the general goal of this solution; this method eliminates the need for paper records.Publication Embargo Accuracy of Diabetes Patient Determination: Prediction Made from Sugar Levels Using Machine Learning(Springer, Cham, 2022) Krishnananthan, S; Puvanendran, S; Puvanendran, RThis study focuses on the prediction of the Diabetic Patients through the sugar levels. The Dataset is analyzed using the data mining techniques such as feature extraction, associate rule mining and classification. The Fast Blood Sugar (FBS) and Post-Prandial Blood Sugar (PPBS) sugar levels are selected as the important features, identification of a rule depending on the selected feature is identified and the performance metric for three classifiers is analyzed based on the selected attributes and choose the classifier with high accuracy. Classification algorithms like random forest, decision tree (J48), and Naïve Bayes were utilized to identify the patients with diabetes disease. The performance of these techniques is considered using the factors relating to the accuracy from the applied techniques. The accuracy is seeming to be higher for Naïve Bayes. The outcomes acquired demonstrated that Naïve Bayes outflanks from different strategies with most noteworthy precision of 74.8%.