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.

https://icac.lk

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    Mobile-based Malware Detection and Classification using Ensemble Artificial Intelligence
    (IEEE, 2019-12-05) Somasundaram, S; Kasthurirathna, D; Rupasinghe, L
    The Android operating system is one of the most used operating systems in the world and has become a target to malware authors. Traditional malware detection methods such as signatures find it impossible to deal with detecting complex and intelligent malware which are capable of obfuscating and repackaging to avoid being detected. There is therefore an increase in the need to have more efficient and intelligent forms of malware detection. Artificial intelligence has now been brought to the field of malware detection and classification. Due to its accuracy and intelligence it has become an ideal solution to bridge the gap between traditional classifiers and the intelligent malware. Currently, research is mainly being conducted using either machine learning or deep learning techniques to target all or a given malware family. This paper proposes a methodology which brings an ensemble solution between the Support Vector Machine algorithm and the Convolutional Neural Network to create a solution that provides a higher accuracy than available techniques.
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    DenGue CarB: Mosquito Identification and Classification using Machine Learning
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Mohommed, M.; Rajakaruna, P.; Kehelpannala, N.; Perera, A.; Abeysiri, L.
    This research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.