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 Deep learning based flood prediction and relief optimization(IEEE, 2019-12-05) Pathirana, D; Chandrasiri, L; Jayasekara, D; Dilmi, V; Samarasinghe, P; Pemadasa, NFlood is a major natural disaster that occurs recurrently in Sri Lanka. It is important to stay on alert and get early preparations to avoid unnecessary risks that cause damage to both life and property. This project developed a flood assistance application “DHARA” to support early flood preparation and flood recovery process. DHARA mobile application facilitates river water level prediction, safest evacuation route suggestion and provides relevant warnings and alert notifications and the web application provides affected area detection, victim and relief estimation to assist flood recovery management. This system is developed as a mobile application and a web application. A recurrent neural network architecture named Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), a path finding algorithm namely A star (A*) algorithm and a clustering technique named Fuzzy Clustering are used for the development of the system. The system is verified with sample data related to “Wellampitiya” and “Kaduwela” area based on river “Kelanl”. The river water level prediction model has successfully predicted the water level 4 hours in advance. The verification results of the river water level prediction showed a satisfactory agreement between the predicted and real records with 85.4% accuracy.Publication Embargo Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Aryal, S.; Nadarajah, D.; Kasthurirathna, D.; Rupasinghe, L.; Jayawardena, C.Forecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction.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.Publication Embargo Deepfake Audio Detection: A Deep Learning Based Solution for Group Conversations(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wijethunga, R.L.M.A.P.C.; Matheesha, D.M.K.; Al Noman, A.; De Silva, K.H.V.T.A.; Tissera, M.; Rupasinghe, L.The recent advancements in deep learning and other related technologies have led to improvements in various areas such as computer vision, bio-informatics, and speech recognition etc. This research mainly focuses on a problem with synthetic speech and speaker diarization. The developments in audio have resulted in deep learning models capable of replicating naturalsounding voice also known as text-to-speech (TTS) systems. This technology could be manipulated for malicious purposes such as deepfakes, impersonation, or spoofing attacks. We propose a system that has the capability of distinguishing between real and synthetic speech in group conversations.We built Deep Neural Network models and integrated them into a single solution using different datasets, including but not limited to Urban- Sound8K (5.6GB), Conversational (12.2GB), AMI-Corpus (5GB), and FakeOrReal (4GB). Our proposed approach consists of four main components. The speech-denoising component cleans and preprocesses the audio using Multilayer-Perceptron and Convolutional Neural Network architectures, with 93% and 94% accuracies accordingly. The speaker diarization was implemented using two different approaches, Natural Language Processing for text conversion with 93% accuracy and Recurrent Neural Network model for speaker labeling with 80% accuracy and 0.52 Diarization-Error-Rate. The final component distinguishes between real and fake audio using a CNN architecture with 94% accuracy. With these findings, this research will contribute immensely to the domain of speech analysis.Publication Embargo A Geophone Based Surveillance System Using Neural Networks and IoT(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Supun Hettigoda, Chamath Jayaminda; Amarathunga, U.; Wijesundara, M.; Wijekoon, J.; Thaha, S.Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.Publication Embargo A Deep Learning Approach to Outbreak related Tweet Detection(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayawardhana, B. A. S. S. B.; Rajapakse, R. A. C. P.Due to the popularity of social media around the world, people use to report and discuss real-world events, personal health complications, and disaster situations through these platforms. These social media data streams can be used to track and detect different types of outbreaks. A mechanism is needed to identify outbreak-related tweets to predict the outbreak in advance. In this paper, we propose a deep learning model that can detect tweets related to different outbreaks Epidemics, Public Disorders, and Disasters. GloVe (Global Vectors for Word Representation) embeddings are used as the feature extraction technique as it can capture the semantic meanings of the tweets. Long Short-term Memory (LSTM) which is a specialized Recurrent Neural Network architecture is used as the classification algorithm. In the process, first, outbreak-related tweets were manually collected and curated. Pretrained GloVe word embeddings of 100 dimensions were then used to represent the words of the tweets. As the next step, a Deep Learning Model was trained by using LSTM technique on the curated dataset. Finally, the performance of the model was evaluated using a different dataset. With the results, it can be concluded that the proposed deep learning model is an accurate approach for outbreak-related tweet 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 Machine Learning-based Prediction Model for Academic Performance(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Tharsha, S.; Dilogera, J.; Mohanashiyaam, B.; Kirushan, S.; Chathurika, K.B.A.B.; Swarnakantha, N.H.P.R.S.This paper represents the work of a new integrated and collaborative Smart application for managing students online through data mining techniques. Nowadays especially in this pandemic situation, there is a necessity for academic management to incorporate and change all study methods online. By considering all these conditions this research is focused to discuss the solution to manage and engage students smartly and easily. Thou technology advancements have a serious impact on the day-to-day life people face troubles when using complex applications, this implemented Smart application is simple to use and a great tool for Student Management systems. The survey feedback from students, academic staff, and the public illustrate that this project helps to improve the effectiveness and efficiency of learning capability among the targeted group. The main objective of this project is to build up a smart model using Machine Learning, Deep Learning, and Artificial Intelligence to overcome generic learning problems. Therefore, this paper aims to present the concept behind the development and implementation of the Smart Study Application for Student Management System.Publication Embargo Guided Vision: A High Efficient And Low Latent Mobile App For Visually Impaired(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Rizan, T.; Siriwardena, V.; Raleen, M.; Perera, L.; Kasthurirathna, D.This paper presents a novel solution for visually impaired individuals. A mobile app is connected to an ESP32CAM and a remote server to help visually impaired individuals to navigate around their environment safely. A deep learning model is deployed in the mobile app to detect obstacles in real-time without connecting to the internet. Other tasks such as reading texts, recognizing people, and describing objects are done in the remote server. We managed to connect the mobile app to the ESP32CAM and the remote server simultaneously. This was possible because the ESP32CAM is connected to the mobile app through Bluetooth. This gave the mobile the ability to connect to the remote server via the internet. To the best of our knowledge, no research has been done using Bluetooth to stream images to do object detection in a mobile app locally. Hence, our solution can detect obstacles locally and do other tasks mentioned previously in the remote server. This paper discusses how the ESP32CAM, obstacle detection module, face recognition module, text reading module, and object description module was implemented such that a low latent and highly efficient mobile app is created using minimal resources.Publication Embargo Crime Analysis, Prediction and Simulation Platform Based on Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12) Herath, I.S.; Dinalankara, R.; Wijenayake, U.As a global social-economical problem, crime has shown complex correlations with spatial-temporal, socio-economical, and environmental factors. Understanding patterns and interactions in the crimes is essential to prepare better to respond to those criminal activities. This study is focused on research and development of crime analysis, prediction and simulation platform that provides descriptive analysis, predictive crime analysis, Reinforcement learning based crime entity simulations and safest route navigation services based on crime data from the city of San Francisco. Ultimately, the proposed crime analysis, prediction and simulation platform provides critical information on root causes and statistical patterns of crime and future crime predictions for the policymakers and security officials to create strategies to minimise the crimes.