Department of Information Technology-Scopes
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2179
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Publication Embargo Symptomatic Analysis Prediction of Kidney Related Diseases using Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Lansakara, D.; Gunasekera, T.; Niroshana, C.; Weerasinghe, I.; Bandara, P.; Wijendra, D.Sri Lanka has been witnessing an increase in kidney disease issues for a while. Elderly kidney patients, kidney transplant patients who passed the risk level after the surgery are not treated in the emergency clinic. These patients are handed over to their families to take care of them. In any case, it is impossible to tackle a portion of the issues that emerge regarding the patient at home. It is hoped to enter patient’s data from home every day and to develop a system that can use that entered data to predict whether a patient is in an essential circumstance or not. Additionally, individuals in high-hazard regions cannot know whether they are in danger of creating kidney disappointments or not and individuals in danger of creating kidney sickness because of Diabetes Mellitus. Thus, we desire to emphasize the framework to improve answers for this issue. The research focuses on developing a system that includes early kidney disease prediction models involving machine learning classification algorithms by considering the relevant variables. In predictive analysis, six machine learning methods are used: Support Vector Machine (SVM with kernels), Random Forest (RF), Decision Tree, Logistic Regression, and Multilayer Perceptron. These classification algorithms' performance is evaluated using statistical measures such as sensitivity (recall), precision, accuracy, and F-score. In categorizing, accuracy determines which examples are accurate. The experimental results reveal that Support Vector Machine outperforms other classification algorithms in terms of accuracy.Publication Embargo Standalone Application and Chromium Browser Extension-based System for Online Examination Cheating Detection(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kariyawasam, S.; Lakshan, A.; Liyanage, A.; Gimhana, K.; Piyawardana, V.; Mallawarachchi, Y.Educational organizations and institutes that provide services to the public use e-learning frequently than before. The incapacity to evaluate the knowledge acquired is a flaw in education. Due to the current situation, traditional evaluation and examinations are not possible. In a developing country like Sri Lanka, the conduct of online examinations has not been efficient, resulting in cheating at examinations due to vulnerabilities resulting from organizational policies and the difficulty to track down candidates who are prone to cheating, therefore use of facial features for candidate verification and to monitor the background interactions the use of audio and video is taken into consideration with the aid of two cameras; the system mounted camera and a wearable camera containing a microphone allowing audio detection. In this research, we suggest using the training data set generated from individuals to undertake a training approach to improve the robustness for background interactions through audio and video to detect the level of cheating of candidates.Publication Embargo Smart Monitoring and Disease Detection for Robotic Harvesting of Tomatoes(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Pasindu, I.; Viraj, S.; Dilshan, R.; Kalhara, A.; Senaweera, O.; de Silva, R.; Jayawardena, C.Tomato is a one of the most popular produced and extensively consumed vegetables in the world. Typical agricultural systems make extensive use of human labor which is more costly and less effective. This research explores the minimization of human labor through automation. The diseases infected by tomato plants are hard to detect. Identifying these diseases in advance would save the cultivation of the disease from spreading, thereby saving the crop.It is also a difficult task to recognize the ripe harvest and experienced labor is required. The efficiency of the harvesting method will be increased by automating the identification process of ripened fruits. Manually picking tomatoes can cause some harm to the fruits during plucking due to inconsistencies in human labor. Such damage will be reduced through a better implemented robotic scheme. This paper presents the development of autonomous system for tomato harvesting and disease detection.Publication Embargo Smart Intelligent Floriculture Assistant Agent (SIFAA)(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Samaratunge, U.S.S.; Amarasinghe, D.H.L.; Kirindegamaarachchi, M.C.; Asanka, B.L.Technology has become a vital aspect for various functional purposes throughout the world and some industries like floriculture have not adapted technology to solve and facilitate currently facing problems and provide the supply to the demand. Consequently, we have identified and implemented a solution that will address major aspects of such industry barriers. To address these major aspects we proposed a system Smart Intelligent Floriculture Assistant Agent (SIFAA), which uses expert knowledge with solutions and guideline such as identify diseases based on deep learning techniques. It also suggests remedies for diseases based on the expert knowledge, recommend best products for customers by using Reinforcement Learning (RL) technique, motivate cultivators by using demand forecasting, and apply feature engineering by using Linear Regression (LR) and ensemble advance LightGBM Regressors techniques.Publication Embargo SentinelPlus: A Cost-Effective Cyber Security Solution for Healthcare Organizations(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Janith, K.; Iddagoda, R.; Gunawardena, C.; Sankalpa, K.; Abeywardena, K.Y.; Yapa, K.Electronic Protected Health Information (ePHI) has proven to be quite lucrative by cybercriminals due to their long shelf life and multiple possible avenues of monetization. These highly sensitive data has become an easy target for cyber attackers due to the poor cyber resiliency strategies exercised by Healthcare Organizations. The reasoning behind the poor cyber security management in the healthcare sector sums to the collective impact of budgetary restriction, lack of cyber security competency and talent in the domain, prioritizing convenience over security, and various work culture malpractices. Further-more, a substantial number of data breaches in the healthcare sector are known to be caused by human errors, security misconfigurations, and information mismanagement. Secondly, the increasing prevalence of ransomware and botnet attacks has hampered the efficiency and availability of healthcare services. As a result, in order to provide a holistic security mechanism, this paper presents "SentinelPlus," a machine learning-based security management suite.Publication Embargo Sensor-Based Emotion Tracking System for Computer Games(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Wickramasinghe, W.R.M.G.K.; Devduni, R.M.B.; Dasanayaka, D.T.C.B.; Mohomed, M.N.N.; Kumari, S.; Dassanayake, T.The game development industry is among the leading industries globally, and in 2020, gaming emerged as a popular entertainment activity upon the COVID-19 outbreak. Thus, competition among gaming companies is high. Hence, they try to adopt new technologies often. Gaming brings multiple feelings for the gamer. At times, the conditions may get even worse from the game’s end where the gamer may end up venting out his rage and annoyance. Hence, there is a massive possibility for the gamer to switch to another game which may result in the company to lose its customers. In that scenario, this system can monitor the emotional states of the gamer while playing and manipulate the gaming environment, sound environment, enemy behavior, and gamer mechanism according to the emotional state of the gamer. The sensor-based emotion tracking system identifies the gamer's emotional state using facial emotions, detected through a webcam and heart rate, detected through sensors. The development was carried out through the machine learning models, open cv, Arduino techniques, and reactive programming. The emotional state and facial emotions that will be tracked will count to an accuracy of above 95%. Through that, the target will be to make the gamer satisfied by building appreciation for the services given and by improving the gamer's gaming experience and retain the gamer with the game provider.Publication Embargo SalFix: Solutions for Small Businesses Using Artificial Intelligence and Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Perera, T.; Kuganandamurthy, L.; Ameen, T.; Dassanayake, T.; Ganegoda, D.Every large organization was a small business before. There are many businesses starting every day. Most of them are small businesses. Managing a business is always a challenge. The owners face lots of challenges when they engage with a business. Small business owners do not have enough knowledge about advertising or promoting a product. New owners do not know the trendiest product at present, and they need to know to sell which product to be profitable. L ack o f communication with the customers will impact the customer base. These are the main problems that owners face. By introducing SalFix, these challenges can be conquered. SalFix is a web application that is suitable for current owners and new owners. SalFix uses Artificial I ntelligence t o g enerate a utomated a d i mages, predict what will happen to the business next year, predict which product is the trendiest. To improve customer communication, SalFix is embedded with a chatbot plugin that can be integrated into the small business’s website. SalFix can perform a SWOT analysis as well. Owners can use SalFix to fix t heir s ales and boost their income. SalFix is a yearly subscription service and will provide more accurate results.Publication Embargo Personalized Assistive Learning System for Primary Education(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Yapa, Y.M.T.S.; Fernando, W.S.I.; Sampath, W.H.M.K.; Kodithuwakku, K.D.D.I.; Samaratunge, U.S.S.; Lunugalage, D.Due to the COVID 19 pandemic, almost all educational institutions, including schools, remain closed. This caused a dramatic change in the educational systems. The sudden shift away from the classroom made the profound transformation of the teacher-centered education system prevail so far; consequently, the education of primary level students has been collapsed. Therefore, the primary students from grades 1 to 3 cannot acquire the primary education given by the school. This research proposes a personalized assistive learning system for primary education from grade 1 to 3 students, aiming to improve their learning skills. The proposed system aims to increase the automation and self-learning of students. A novel method is proposed to acquire personalized course materials for students of grades 1 to 3 according to their knowledge level. The system is founded on a solid theoretical foundation and enables children to grow cognitive and psycho-therapeutic skills such as drawing, writing, recognizing numbers, enabling self-learning, and focusing on measuring the progress of the students and reporting it to parents. CNN is the primary classifier used in image recognition and classification tasks in computer vision. The components' median accuracy is 94.74%.Publication Embargo Mobile Medical Assistant and Analytical System for Dengue Patients(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Jayampathi, K.T.K.; Jananjaya, M.A.C.; Fernando, E.P.C.; Liyanage, Y.A.; Pemadasa, M.G.N.M.; Gunarathne, G.W.D.A.Dengue fever is a vector-borne viral disease spread by the mosquito Aedes Aegypti. It is a public health problem, with an estimated 50-500 million infections each year and no effective vaccination. People's hectic schedules may not have enough time to see a doctor every time they have a fever. They may overlook their disease, believing it to be a common ailment. Prior medical assistance for dengue patients with fever to check their conditions reliably is a major problem. There is no easily accessible proper system to identify dengue patients at an early stage. This paper presents a mobile medical assistant and analytical system for dengue patients. With a novel approach, using the most appropriate technologies, the mobile application supports identifying dengue patients using the chatbot, analyzing skin conditions, analyzing blood reports, and analyzing dengue-infected areas' functionalities. The registered users can log in to the system and check their dengue condition. The development is carried out with Natural Language Processing, Artificial Neural Network (ANN), Machine Learning, Image Processing, Convolutional Neural Network (CNN), and Android technologies. A mobile application prototype is created and tested, with the possibility of future testing and implementation. The results show effective performances in analyzing dengue conditions.Publication Embargo Intelligent Violence Video Detection System(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Jayasanka, H.K.B.; Jayasanka, B.M.R.D.; Diyunuge, K.D.C.D.R.; Jayasekara, T.H.D.Y.M.; Lunugalage, D.; Samaratunge, U.S.S.Due to the busy and stressful lifestyle, humans tend to feel frustrated frequently. This harmful emotional behavior results in violations of several rules, regulations and legislation. Violence is one of the serious issues which emerges due to this situation. It also results in uncontrollable human behavior. This behavior can either be verbal arguments or even physical conflicts. A trend of recording and publishing videos related to these kinds of violations in various platforms can be observed widely at present. Therefore, the terms and conditions of these platforms are subjected to frequent changes. Difficulty in identifying and controlling of violent events will result in an increase of such cases. Due to these reasons, the demand for violence detection systems will be significantly increased. Efficient violent detection systems are lacking currently. But, the usage of artificial intelligence in these systems are further limited. Four major components have been used to achieve this goal. They are video-based, embedded audio-based, abused textbased and thumbnail-based violence detection. The machine learning and image processing techniques are used along with these components to improve the clarity of violence detection.