International Conference on Advancements in Computing [ICAC]
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The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.
The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.
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Publication Embargo Banana Disease Identification Using Machine Learning Based Technologies and Weather-Based Dispersion Analysis(IEEE, 2022-12-09) Kothalawala, M.U.; Gaveshith, M.G. K; Tharaka, A.H.D.H.; Punchihewa, I.A; Sriyaratna, DBanana is the fourth most important food crop in the world as well as the most important and popular fruit crop in Sri Lanka. Banana leaf diseases are becoming one of the most important factors affecting agricultural products. As a result of these diseases, the quantity and quality of agricultural produce have drastically decreased. Hence, early detection and classification of banana leaf diseases are becoming more important than ever. But the ancient method of disease identification, visual observation is no longer helpful in this matter as it requires significant knowledge and experience related to banana diseases and symptoms which present farmers severely lacks. Therefore, using ICT-based approaches such as autoML, deep learning, natural language processing and APIs are very important towards the efficiency of the disease identification process and the accuracy of the diagnosis as well as keeping farmers synced with the information related to their plantation such as recent threats and nearby threats.Publication Embargo A Gamified Approach for Screening and Intervention of Dyslexia, Dysgraphia and Dyscalculia(2019 International Conference on Advancements in Computing (ICAC) -SLIIT, 2019-12-05) Kariyawasam, R.; Nadeeshani, M.; Hamid, T.; Subasinghe, I.; Ratnayake, P.This paper aims to diagnose children with specific learning disabilities and provide treatments via a mobile game. Learning disabilities are neurological disorders that affect the brain. Children with learning disabilities have trouble with learning compared to their fellow peers and quite often fall back academically since a majority of them go undiagnosed. The specific learning disabilities for which this paper provides screening are dyslexia a reading disability, dyscalculia a mathematical disability, letter dysgraphia and numeric dysgraphia are both writing disabilities. Deep learning and machine learning techniques are used in the screening process of these specific learning disabilities. Trained convolutional neural networks are used to detect the spoken letter/word, detect the written letter/word and detect the written number on the mobile application. Outputs from the convolutional neural network are fed into the models used for screening learning disabilities. The machine learning algorithms used in building the models include k-nearest neighbors, random forest and support vector machine. Screening results from the models built in this research provided an accuracy of 89%, 90%, 92%, 92% for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia respectively. This is the first game based screening and intervention tool for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia.Publication Open Access Utalk: Sri Lankan Sign Language Converter Mobile App using Image Processing and Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Dissanayake, I.S.M.; Wickramanayake, P.J.; Mudunkotuwa, M.A.S; Fernando, P.W.N.Deaf and mute people face various difficulties in daily activities due to the communication barrier caused by the lack of Sign Language knowledge in the society. Many researches have attempted to mitigate this barrier using Computer Vision based techniques to interpret signs and express them in natural language, empowering deaf and mute people to communicate with hearing people easily. However, most of such researches focus only on interpreting static signs and understanding dynamic signs is not well explored. Understanding dynamic visual content (videos) and translating them into natural language is a challenging problem. Further, because of the differences in sign languages, a system developed for one sign language cannot be directly used to understand another sign language, e.g., a system developed for American Sign Language cannot be used to interpret Sri Lankan Sign Language. In this study, we develop a system called Utalk to interpret static as well as dynamic signs expressed in Sri Lankan Sign Language. The proposed system utilizes Computer Vision and Machine Learning techniques to interpret sings performed by deaf and mute people. Utalk is a mobile application, hence it is non-intrusive and cost-effective. We demonstrate the effectiveness of the our system using a newly collected dataset.Publication Embargo NoFish; Total Anti-Phishing Protection System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Atimorathanna, D.N.; Ranaweera, T.S.; Pabasara, R.A.H.D.; Perera, J.R.; Abeywardena, K.Y.Phishing attacks have been identified by researchers as one of the major cyber-attack vectors which the general public has to face today. Although many vendors constantly launch new anti-phishing products, these products cannot prevent all the phishing attacks. The proposed solution, “NoFish” is a total anti-phishing protection system created especially for end-users as well as for organizations. This paper proposes a machine learning & computer vision-based approach for intelligent phishing detection. In this paper, a realtime anti-phishing system, which has been implemented using four main phishing detection mechanisms, is proposed. The system has the following distinguishing properties from related studies in the literature: language independence, use of a considerable amount of phishing and legitimate data, real-time execution, detection of new websites, detecting zero hour phishing attacks and use of feature-rich classifiers, visual image comparison, DNS phishing detection, email client plugin and especially the overall system is designed using a level-based security architecture to reduce the time-consumption. Users can simply download the NoFish browser extension and email plugin to protect themselves, establishing a relatively secure browsing environment. Users are more secure in cyberspace with NoFish which depicts a 97% accuracy level.Publication Embargo ChildPath: Diagnose depression in pre-schoolers based on daily activ(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kirthika, L.; Abeykoon, J.To determine depression in pre-schoolers and validation of identifying depression based on daily activities. A comprehensive literature search, interviews with accredited mental health practitioners and a survey was conducted to validate the background aspects and existing diagnosis theories to map out based on daily activities. The results of the evaluation suggest a gap around diagnosis of depression in pre-schoolers due to lack of awareness and its distinctive nature to adult depression. This establishes a need for depression status calculation mechanism based on analysis of daily activities using machine learning to examine behaviour and speech patterns. Further, rule-based machine learning, will be implemented to offer personalized treatment plans if diagnosed with a status of depression.Publication Embargo SURAKSHA e-Caretaker: Elders Falling Detection and Alerting System using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Mendis, L.; Hathurusinghe, S.; Epa, H.; Edirisinghe, T.; Wickramarathne, J.; Rupasinghe, S.People become unable to perform tasks that were done at the younger ages as they were when the ages pass with time. Falls play a major issue in the lives of elderly people as the physical and mental quality of life is dependable on the effects of falls. This research presents an e-Caretaker SURAKSHA which is an elder falling detection and alerting system based on Machine Learning concepts. Researchers that have been done in this area have produced different solutions to detect only the falls but not to automatically detect and notify them to the caretakers. This solution serves as a smart wearable device that is capable of automatically monitoring real-time postures, detecting sudden falls, possible arrhythmia conditions of the heart of the fallen person, and daily route deviations along with the fallen location which is finally notified to the caretakers through a mobile application. According to the performed studies, python model development was used to implement the system through Machine Learning concepts by referring to the Markov model, Prophet model, and Naïve Bayes algorithms. This solution provides the results of this research with an accuracy of around 89.9% leading to a successful product in the domain. Keywords—Publication Embargo WANHEDA: A Machine Learning Based DDoS Detection System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Sudugala, A.U.; Chanuka, W.H.; Eshan, A.M.N.; Bandara, U.C.S.; Abeywardena, K.Y.- In today’s world computer communication is used almost everywhere and majority of them are connected to the world’s largest network, the Internet. There is danger in using internet due to numerous cyber-attacks which are designed to attack Confidentiality, Integrity and Availability of systems connected to the internet. One of the most prominent threats to computer networking is Distributed Denial of Service (DDoS) Attack. They are designed to attack availability of the systems. Many users and ISPs are targeted and affected regularly by these attacks. Even though new protection technologies are continuously proposed, this immense threat continues to grow rapidly. Most of the DDoS attacks are undetectable because they act as legitimate traffic. This situation can be partially overcome by using Intrusion Detection Systems (IDSs). There are advanced attacks where there is no proper documented way to detect. In this paper authors present a Machine Learning (ML) based DDoS detection mechanism with improved accuracy and low false positive rates. The proposed approach gives inductions based on signatures previously extracted from samples of network traffic. Authors perform the experiments using four distinct benchmark datasets, four machine learning algorithms to address four of the most harmful DDoS attack vectors. Authors achieved maximum accuracy and compared the results with other applicable machine learning algorithms.Publication Embargo Smart Exam Evaluator for Object-Oriented Programming Modules(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wickramasinghe, M.L.; Wijethunga, H.P.; Yapa, S.R.; Vishwajith, D.M.D.; Samaratunge Arachchillage, U.S.S.; Amarasena, N.Worldwide educators considered that, automate the evaluation of programming language-based exams is a more challenging task due to its complexity and the diversity of solutions implemented by students. This research investigates and provides insight into the applicability and development of a java based online exam evaluator as a solution to traditional onerous manual exam assessment methodology. The proposed system allows students to take online exams in Java for an implemented source code in a practical exam, automatically reporting the results to the administrator simultaneously. Accordingly, this research examines existing methods, identifies their limitations, and explores the significance of introducing a smart object-oriented program-based exam evaluator as a solution. This method minimizes all human errors and makes the system more efficient. An automated answer checker checks and marks are given as human-counterpart and generate a report with possible suggestions for improvement of the answer scripts and generate a classification report to predict the student’s final exam marks. This software application uses a Knowledge base, Abstract Syntax tree (AST), ANTLR, Image processing, and Machine Learning (ML) as key technologies. The proposed system gains a higher accuracy of 95% as performed by a separate human-counterpart. These results show a high level of accuracy and automate marking is the major emphasis to save human evaluation effort and maximize productivity.Publication Embargo MOOCs Recommender Based on User Preference and Video Quality(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Sankalpa, R.; Sankalpani, T.; Sandeepani, T.; Ransika, N.; Kodagoda, N.; Suriyawansa, K.MOOCs (Massive Open Online Courses) are a new revolution in the field of e-learning. MOOCs are capable of providing several thousands of learners with access to courses over the internet. MOOCs are produced in many different video production styles and these styles play an important role in helping the consumer stay engaged and interested in the courses. MOOCs provide a large number of courses in different domains to a wide range of learners. It has become difficult and a timeconsuming task for a user to find the most suitable courses that suit a learner’s personal preferences. This paper describes how to recommend a course based on the preferred video style of the learner and the basic learning style of the learner which determines the learner’s preferences on other materials in a course. In the course recommendation process, this paper also describes how to classify the course in order to recommend the most appropriate massive open online courses for users according to their most preferred video production style.Publication Embargo Intelligent Disease Detection System for Greenhouse with a Robotic Monitoring System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Fernando, S.; Nethmi, R.; Silva, A.; Perera, A.; De Silva, R.; Abeygunawardhana, P.K.W.Greenhouse 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 attentiongrabbing 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.