Research Papers - Dept of Computer Systems Engineering

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Subject: search.filters.subject.NLP

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    Recognition and translation of Ancient Brahmi Letters using deep learning and NLP
    (IEEE, 2019-12) Wijerathna, K. A. S. A. N; Sepalitha, R; Thuiyadura, I; Athauda, H; Suranjini, P. D; Silva, J. A. D. C; Jayakodi, A
    Inscriptions are major resources for studying the ancient history and culture of civilization in any country. Analyzing, recognizing and translating the ancient letters (Brahmi letters) from the inscription is a very difficult work for present generation. There is no any automatic system for translating Brahmi letters to Sinhala language. However, they are using manual method for translating inscriptions. The method that used in epigraphy is being taken a long period to decipher, analyze and translate the inscribed text in inscriptions. This research mainly focuses on recognition of ancient Brahmi characters written the time period between 3 rd B.C and 1 st A. D. First, we remove the noise, segment the letters from the inscription image and convert it into the binary image using image processing techniques. Secondly, we recognize the correct Brahmi letters, broken letters and then identify the time period of the inscriptions using Convolution Neural Networks in deep learning. Finally, the Brahmi letters are translated into modern Sinhala letters and provide the meaning of the inscription using Natural Language Processing. This proposed system builds up solution to overcome the existing problems in epigraphy.
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    Autonomous Cyber AI for Anomaly Detection
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Madhuvantha, K.A.N.; Hussain, M.H.; De Silva, H.W.D.T.; Liyanage, U.I.D.; Rupasinghe, L.; Liyanapathirana, C.
    Since available signature-based Intrusion Detection systems (IDS) are lacking in performance to identify such cyber threats and defend against novel attacks. It does not have the ability to detect zero-day or advanced malicious activities. To address the issue with signature-based IDS, a possible solution is to adopt anomaly-based detections to identify the latest cyber threats including zero days. We initially focused on network intrusions. This research paper discusses detecting network anomalies using AIbased technologies such as machine learning (ML) and natural language processing (NLP). In the proposed solution, network traffic logs and HTTP traffic data are taken as inputs using a mechanism called beats. Once relevant data has been extracted from the captured traffic, it will be passed to the AI engine to conduct further analysis. Algorithms such as Word2vec, Convolution Neural Network (CNN), Artificial Neural networks (ANN), and autoencoders are used in order to conduct the threat analysis. HTTP DATASET CSIC 2010, that NSL-KDD, CICIDS are the benchmarking datasets used in parallel with the above algorithms in order to receive high accuracy in detection. The outputted data is integrated and visualized using the Kibana dashboard and blockchain model is implemented to maintain and handle all the data.
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    Accommodation Finder: An Augmented Reality Based Mobile Application Integrated with Smart Contracts
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Parameswaran, G.; Perera, M.J.F.R.; Aluthgedara, C.R.B.; Amanda, E.D.N.; Ishara, W.G.A.; Ganegoda, D.
    Accommodation is one of the basic needs for travelers, tourists, students, and employees. Accommodations range from low-budget lodges to world-class luxury hotels, but finding the preferable accommodation is undoubtedly a tedious task. And due to the COVID-19 pandemic, it has become problematic state to visit each accommodation property to check whether it's suitable for the accommodation seeker, considering the location, environment, and to check if the property matches the user’s preferences. There have been incidents reported where thousands of people have been victimized because of contract breaches in the accommodation and real estate sectors, recurring from contract alterations. Considering these problems, we have proposed a system to provide solutions using Natural Language Processing (NLP), Automatic Speech Recognition (ASR), Augmented Reality (AR), Block-chain, and K-Nearest Neighbor (KNN). This system provides an efficient approach to viewing the exterior and interior of an accommodation using 360-degree views, providing recommendations to the user based on user preferences using KNN and cosine similarity, providing security in a digital agreement using blockchain technology, and a map navigation system using ASR. With the aid of the previously mentioned techniques, a mobile application prototype is created with the possibility of future testing and implementation.