Browsing by Author "Tissera, M."

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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.
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PatientCare: Patient Assistive Tool with Automatic Hand-written Prescription Reader
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kulathunga, D.; Muthukumarana, C.; Pasan, U.; Hemachandra, C.; Tissera, M.; De Silva, H.
Most people in the world prefer to be conscious of the medications prescribed by physicians. Especially, the importance of handwritten prescriptions is prodigious in Sri Lanka because they are widely used in the healthcare sector. However, due to the illegible handwriting and the medical abbreviations of the physicians, patients are unable to find the prescribed medication information. This research is an attempt to assist the patients in identifying the prescribed medicine information and minimizes misreading errors of medical prescriptions. When a patient uploads the image of a prescription, the system converts it into unstructured text data by using OCR and segmentation, then NER is used to categorize medical information from given text. According to the other research, some solutions exist in other domains for the above mechanisms. But they gave less accuracy when tried to apply for this research due to the domain specialty. Therefore, as a solution to overcome the above discrepancy this approach allows users to scan handwritten medical prescriptions and blood reports and obtain analyzed reports in medical history. Results have shown that this approach will give 64%-70% accuracy level in doctor's handwriting recognition and 95%- 98% accuracy in medical information categorization of the prescription format.
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A Sensitive Data Leakage Detection and Privacy Policy Analyzing Application for Android Systems (PriVot)
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Atapattu, H.N.; Fernando, W.S.N.; Somasiri, J.P.A.K.; Lokuge, P.M.K.; Senarathne, A. N.; Tissera, M.
Mobile applications can have access to various sensitive information to accomplish the business requirements as well as user requirements. Due to the sensitivity of this information, app developers are bound by the regulations to provide a privacy policy that describes their data collection practices. However, there were many incidents where the privacy policies were inconsistent with the actual data practices. Additionally, the privacy policies are often too long and difficult to grasp just by reading them due to their complex language. To address this hurdle, we propose a mobile application “PriVot”. PriVot has a privacy policy analyzer built with a hierarchical classifier using convolutional neural networks to provide a detailed and unambiguous summary indicating the data that is being collected by each app and their purpose for being collected Furthermore, it monitors the network traffic of the device with the aid of a Transport Layer Security(TLS) proxy, a Forwarder, and a Traffic Analyzer that operates on-device without requiring root privileges to identify potential data leakages and privacy policy violations. We present "PriVot" which achieved a 67.4% accuracy on privacy policy analysis and a 72.5% throughput at a low latency overhead with the network traffic monitoring.
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Use of Natural Language Processing and Deep Learning towards Guiding Healthy Cholesterol Free Life
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Sasanka, D.; Malshani, H. K. N.; Wickramaratne, U.I.; Kavindi, Y.; Tissera, M.; Attanayaka, B.
High blood cholesterol is a key risk factor for cardiovascular diseases such as coronary heart disease and stroke. This has become a severe health problem, because it causes a considerable amount of deaths annually. The major risk factors that affect a person’s cholesterol level include unawareness of cholesterol risk, unhealthy dietary habits, lack of proper exercises, and high stress conditions. In this research, novel approaches are introduced to provide an automated and personalized guidance to maintain healthy cholesterol level and raise the awareness of each risk factors mentioned above. This research associates with four novel approaches. Natural Language Processing (NLP) based Cholesterol risk analyzer, Fuzzy based Food management with Meal predictor, Machine Learning based Physical exercise planner and Stress controller. Altogether with results, this research will provide a complete and facts-proven solution to reduce and guide people towards a cholesterol-free healthy lifestyle.