International Conference on Advancements in Computing [ICAC]

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/312

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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End date: 2025Subject: search.filters.subject.Machine Learning

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Now showing 1 - 10 of 23
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    An Approach to detect Advanced Persistent Threats using Machine Learning Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Bary A.A; Wijerupa W.D.O.D; Padukka P.V.G.G; Atapattu A.L.V.J.; Pandithage, D; Wijesooriya, A
    Advanced Persistent Threats (APTs) pose significant risks to organizations due to their stealthy, prolonged nature and ability to evade traditional detection mechanisms. Traditional solutions often analyze separate data elements, such as network traffic or endpoint activity, limiting their effectiveness against sophisticated APT campaigns. This research proposes a holistic machine learning (ML)-driven approach to detect APTs by integrating three critical data dimensions: user behavior anomalies, endpoint activity monitoring, and network traffic analysis. The system further incorporates Tactics, Techniques, and Procedures (TTP) analysis using the MITRE ATT&CK framework to provide actionable intelligence. A real-time dashboard visualizes the threat detection results, TTP mappings, and mitigation strategies, enabling cybersecurity teams to respond proactively. The integration of multiple ML models enhances detection accuracy while bridging the gap between threat identification and contextual understanding. Experimental validation demonstrates the system's capability to detect APT indicators across diverse attack vectors and prioritize high-risk TTPs. This work contributes to advancing APT detection methodologies by offering a scalable, multi-dimensional solution tailored for modern cybersecurity operations.
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    AI-Driven Behavioral Assessment and Intervention for ADHD
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dharmasena U.D.S.V; Manamperi R.S; Dilshani H.T.D.P; Halliyadda H.U.M.S.; Kasthuriarachchi, S; Samaraweera, M
    Attention Deficit Hyperactivity Disorder (ADHD) affects millions of children worldwide, causing cognitive, behavioral,and academic challenges. Traditional diagnostic methods rely on subjective assessments, leading to inconsistent evaluations and delayed interventions. This research addresses these limitations by developing an AI-driven gamified behavioral assessment and intervention platform that integrates machine learning, real-time emotion recognition, and adaptive learning techniques. The system features interactive games that dynamically adjust difficulty based on behavioral and emotional responses captured through facial expression analysis. Data collection includes gameplay metrics, parent-reported questionnaires, and expert evaluations to train AI models for ADHD classification and personalized intervention. The emotion recognition module achieved 89% accuracy using Convolutional Neural Networks, while reinforcement learning algorithms enabled real-time game adaptation. Classification models, including Random Forest (91.3% accuracy), demonstrated strong predictive capabilities. The platform provides continuous monitoring dashboards for caregivers and educators, enabling data-driven decision-making. Results indicate that AI-based behavioral assessments offer improved accuracy and flexibility compared to traditional methods, with emotion-adaptive gaming enhancing engagement. This research demonstrates the potential of AI-powered solutions to transform ADHD diagnosis and intervention through improved efficiency, personalization, and accessibility.
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    Agile User Story Builder: Transform Real-Time User Requirement Conversations into Well-Structured Agile User Stories
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Sathsarani, S; Sandun, Y; Rusith, R; Himaranga, S; Kumari, S; Supunya Swarnakantha, N.H.P. R
    Agile software development methodologies rely heavily on well-structured user stories to bridge technical and business requirements. However, the traditional manual process of converting stakeholder conversations into structured user stories presents significant challenges including transcription errors, communication ambiguity, and inefficient processing. This research presents an automated Agile User Story Builder system that transforms requirement conversations into well-structured user stories. The system integrates cloud speech recognition, Natural Language Processing (NLP), machine learning, and Retrieval-Augmented Generation (RAG) for automated requirement processing within a microservices architecture. The system was developed using synthesized audio from 1,247 SRS documents (achieving 88% transcription accuracy) and validated on 75 real stakeholder conversations. Real-world validation demonstrates 78% speech-to-text accuracy (95% CI: 74-82%), 82% task classification precision, 73% reduction in manual documentation effort, and 86% format compliance in generated stories. The system achieves 20% timeline and 62% budget prediction improvement while providing measurable benefits including enhanced collaboration and streamlined Agile workflow efficiency.
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    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, D
    Banana 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.
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    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.
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    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.
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    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.
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    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.
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    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—
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    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.