2020
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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 Aerodynamic modeling of simplified wind turbine rotors targeting small-scale applications in Sri Lanka(Elsevier, 2020-09-11) Sugathapala, T. M; Boteju, S; Withanage, P. B; Wijewardane, SA design and optimization procedure of simplified wind turbine rotors for small-scale applications is presented. The need for this research has arisen from the recent national initiative of the government of Sri Lanka titled ‘Battle for Wind Energy’ in promoting small scale grid connected wind plants for electricity customers under Net Metering scheme. The main objective of this research is to assist local developers to design optimum rotors for given electrical generators (as determined by customer requirements), suitable for wind characteristics at specific locations. Another objective is to enhance local manufacturing capabilities by providing a design option of a simplified rotor blade geometry. A study on the correlation between population density of electricity customers and wind energy potentials was carried out to categorize the demand centres based on wind energy potentials in proposing series of small-scale wind turbine designs. A unique and improved rotor design procedure is presented which attempts to match the point of maximum performance of a rotor (design tip speed ratio) with the design wind speed of a given location by considering generator performance. The new design procedure showed successful convergence on a unique blade diameter for each rotor configuration that allowed the design tip speed ratio to match the design wind speed. The performance evaluation of rotor designs showed that high solidity rotors work better on the low wind potential region while low solidity rotors dominate medium and high wind potential regions. The performance reductions of simplified rotor designs are not significant and therefore would be an effective way to enhance value addition through local manufacture.Publication Open Access Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series(researchgate.net, 2020-10-21) Aryal, S; Nadarajah, D; Rupasinghe, P.L; Jayawardena, C; Kasthurirathna, DFinancial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has shown impressive accomplishments across various tasks including sequence learning and time series forecasting. Although most previous studies are focused on applications of deep learning models for single-step ahead prediction, multi-step financial time series forecasting has not been explored exhaustively. This paper aims at extensively evaluating the performance of various state-of-the-art deep learning models for multiple multi-steps ahead prediction horizons on real-world stock and forex markets dataset. Specifically, we focus on Long-Short Term Memory (LSTM) network and its variations, Encoder-Decoder based sequence to sequence models, Temporal Convolution Network (TCN), hybrid Exponential SmoothingRecurrent Neural Networks (ES-RNN) and Neural Basis Expansion Analysis for interpretable Time Series forecasting (N-BEATS). Experimental results show that the latest deep learning models such as NBEATS, ES-LSTM and TCN produced better results for all stock market related datasets by obtaining around 50% less Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) scores for each prediction horizon as compared to other models. However, the conventional LSTM-based models still prove to be dominant in the forex domain by comparatively achieving around 2% less error values.Publication Embargo Arogya -An Intelligent Ayurvedic Herb Management Platform(IEEE, 2020-11-04) Pathiranage, N; Nilfa, N; Nithmali, M; Kumari, N; Weerasinghe, L; Weerathunga, IAyurvedic means a science of life and well-being with its unique approaches to social and spiritual life. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs which have been utilized by generations as medicinal treatments for a variety of diseases. Absence of specialists in this area makes proper identification as well as classification of valuable herbal plants a tedious task, which is essential for better treatment. Hence, a fully automated system for herb detection and classification, information visualization regarding them is highly desirable. There are existing applications which can identify plants with low prediction accuracies, as well as to give information regarding them. However, these applications are based on foreign plant data sets that do not include valuable herbs and shrubs with medicinal qualities. Hence this research proposes an application unique to medicinal plants, which can perform all these functionalities in both online and offline approach. Here, a new Ayurvedic plant dataset prepared from scratch, and preliminary results for classification of 5 types of herbs, compared with several deep Convolutional Neural Network (CNN) models based on transfer learning are presented. Experimental results indicate Marker-based Watershed algorithm as the best object detection algorithm in a complex background, VGG-16 as the best deep CNN classification model which reached a promising testing accuracy of 99.53%, and Seq2Seq LSTM model as the best deep learning model with optimum accuracy in abstractive information summarization.Publication Embargo An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayaweera, K.N.; Kallora, K.M.C.; Subasinghe, N.A.C.K.; Rupasinghe, L.; Liyanapathirana, C.According to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.Publication Open Access 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.Publication Embargo Smart Intelligent Advisory Agent for Farming Community(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Illandara, T.S.; De Silva, H.L.H.; Madurawala, K.S.H.; Dayasena, B.R.D.; Srimath, U.; Samaratunge Arachchillage, S.; Buddhika, T.The currently available agricultural services have few limitations because of the traditional cultivation methods and the unavailability of experts. This research attempts to solve the major problems faced by farmers using an Intelligent Expert Advisory Agent (EAA) that would act as a human counterpart to provide reliable solutions in real-time to the farmers using Machine Learning (ML), Image Processing (IP), and Internet of Things (IoT) technologies. A web application is developed to provide meaningful information to the user by representing agriculture instructors. Using the web application, the farmer can obtain information about predicted weather up to two months. Once the crop is selected, suitable organic fertilizers are suggested to maximize the productivity of the cultivation. After planting, the farmer can continuously monitor the condition of the plants in real-time using the IoT system. Based on this information, the farmer can check if the conditions are optimum for the growth of the plant by interacting with the knowledge base system. If the plants get infected with diseases, the user can capture an image of the diseased plant using the implemented mobile application and send to the IP system to identify the diseases and suggests remedies to overcome the situation.Publication Embargo 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.Publication Embargo Assist: Rendering, Pipeline Management, and Pipeline Tracking Software Publisher: IEEE Cite This PDF(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Salgado, M.V.I; Hettiarachchi, H.A.D.D.; Munasinghe, T.U.; Fernando, K.A.U.; Gamage, I.; Thilakarathna, T.; Cooray, N.C.Video production is one of the most dominant industries in the 21st century, and research into the automation of tasks associated with it has drastically increased. The production of videos take place in three stages: pre-production, production, and post-production. These three stages consist of script writing, scheduling, logistics, and other administration work. There are commercial products to automate these individual tasks. Incorporating all these software into video production can be expensive and difficult to manage. This study proposes the “Assist” software to handle all processes in video production. It has resulted in a product that covers the three main stages featuring scripts, storyboards, inventory management, production progress tracking and management, and rendering. The mentioned features were designed and developed using decision tree algorithm, PyQt5, general decimation algorithm, mesh simplification algorithm, and multi-variable regression.Publication Embargo A Story of Two Surveys: for the Advancement of Sinhalese Mobile Text Entry Research(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Reyal, S.; Piyawardana, V.; Kaveendri, D.This paper presents two surveys: a literature survey on the current progress on Sinhalese mobile text entry research and a user survey on how Sri Lankans experience Sinhalese mobile text entry. The first survey concludes that Sinhalese mobile text entry is limited in scope and size compared to western text entry research. The second survey attempts to bridge this gap by providing deep insight into aspects in Sinhalese mobile text entry such as language switching, using English within Sinhalese e.g. mixed-mode and Singlish, and the popularity of various input modalities, keyboard vendors, and keyboard layouts. This is also the first research publication that unveils the current state-of-the-art in Sinhalese mobile text entry, along with user-preferences such as using autocorrect, glide-typing, and speech. Results from this survey deepens our understanding of the Sinhalese mobile text entry domain resulting in a stronger empirical footing and more innovative Sinhalese mobile text entry solutions.Publication Embargo Smart Backpack for Travelers(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gunarathne, P.D.R.P.; Amarasuriya, R.M.C.I.; Wickramasinghe, W.A.D.D.; Witharana, A.H.T.N.; Abeygunawardhana, P.K.W.Smart backpack is an application-specific design which guarantees a safe journey for travelers. The smart backpack has a different combination of services connected to a single system. It has a unique design that helps to fulfill its services. The system provides the health status of travelers and environmental status by measuring the quality level of the nearby atmosphere. As a security feature, it contains a human detective sensor-based security system. As well as the research consists of an undying power resource which charges by solar cells, the power source can be used to power up the system and to recharge traveler's devices through a USB power outlet. The Backpack has a user-friendly mobile application. This system also provides a health monitoring feature, which monitors the heart rate and body temperature of the traveler. The traveler can share his/her health status with the system and compute the real-time health condition from the outputs of the health sensors integrated into the backpack. The bag model design and building play a major role and has removable unique mini compartments for all hardware components. It should carry maximum weight with minimum pressure for the back of the traveler with minimum cost.Publication Embargo Aspect Based Sentiment Analysis for Evaluating Movies and TV series Publisher: IEEE Cite This PDF(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Cooray, T.; Perera, G.; Chandrasena, D.; Alosius, J.; Kugathasan, A.Aspect-based sentiment analysis (ABSA) is used in different fields for analyzing customer reviews to project an overall customer opinion on certain products. With the expansion of the internet, people are provided with an inexpensive and time-saving method to express their opinion to a larger audience, while various industries are handed with the opportunity to gather free information from it to obtain market value. The implementation of machine learning methods for the evaluation of aspects related to movies and television series has not been commenced, and it could be a new development for the industry. This study focuses on conducting an ABSA on a movie or a television series based on genre, story as well as cast and crew aspects. The data collected from social media through web scraping is processed to produce adequate results to get a broad understanding on how the popularity of the movie or the television series related to above mentioned aspects. Then, each aspect is further analyzed to gather precise information belonging to each aspect. The accuracy of the results of the proposed system has been achieved over 79%. The results proved that the solution is highly successful than the former works with high business value.Publication Embargo Smart Personal Intelligent Assistant for Candidates of IELTS Exams(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Senevirathne, S.S.; Fernando, G.U.D.; White, J.B.; Divyanjala, S.T.H.; Samaratunge Arachchillage, U.S.S.; Dias, D.P.N.P.Many IELTS candidates encounter problems at the examinations and majority of them are unable to achieve their goals even though they strive hard to accomplish their targets. Candidates strive to achieve higher band score in exams, but fail to achieve them due to the ignorance of prevailing weaknesses which have to be identified if they were to succeed. At present, IELTS seems to be the most demanding exam among applicants who are planning to embark their higher studies or migration purposes. Currently, there is no proper mechanism to assist candidates and generate an improvement plan by identifying the weaknesses of them. As a solution, Smart Personal Intelligent Assistant for Candidates Exams (SPIACIE) has been proposed to detect IELTS candidates’ weaknesses through an analysis of their answers. The SPIACIE assesses four components (Reading, Writing, Listening, and Speaking) in IELTS exams. This paper is specifically based on the Long Short-Term Memory (LSTM) network model used to analyze the score of grammar and cohesion. To analyze the similarity of the sentences, the cosine proximity technique is proposed to evaluate the paraphrasing of the graph explanations. The final outcome of this application is to generate an improvement plan, developed using Machine Learning (ML) algorithms. The proposed algorithms are; Gaussian naïve base for reading exam, support vector machines for listening exam, decision tree classifier for speaking exam, and k-neighbors classifier for writing exam. An improvement plan on the prediction model is provided to increase the band score of the IELTS exams, based on applicants’ weakness.Publication Embargo EasyTalk: A Translator for Sri Lankan Sign Language using Machine Learning and Artificial Intelligence(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kumar, D.M.; Bavanraj, K.; Thavananthan, S.; Bastiansz, G.M.A.S.; Harshanath, S.M.B.; Alosious, J.Sign language is used by the hearing-impaired and inarticulate community to communicate with each other. But not all Sri Lankans are aware of the sign language or verbal languages and a translation is required. The Sri Lankan Sign Language is tightly bound to the hearing-impaired and inarticulate. The paper presents EasyTalk, a sign language translator which can translate Sri Lankan Sign Language into text and audio formats as well as translate verbal language into Sri Lankan Sign Language which would benefit them to express their ideas. This is handled in four separate components. The first component, Hand Gesture Detector captures hand signs using pre-trained models. Image Classifier component classifies and translates the detected hand signs. The Text and Voice Generator component produces a text or an audio formatted output for identified hand signs. Finally, Text to Sign Converter works on converting an entered English text back into the sign language based animated images. By using these techniques, EasyTalk can detect, translate and produce relevant outputs with superior accuracy. This can result in effective and efficient communication between the community with differently-abled people and the community with normal people.Publication Embargo 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.Publication Embargo Smart Intelligent Troubleshooter to Solve Windows Operating System Specific Issues(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Rajapakshe, D.I.K.; Shamil, M.P.P.; Paththinisekara, P.M.C.P.; Liyanage, S.K.; Samaratunge Arachchillage, U.S.S.; Kuruppu, A.While working on computers, people frequently confront with various kinds of problems, those beyond their extensive expertise. Microsoft Windows is the widely used Operating System running on numerous personal computers and the reason which gives more irritating problems that require to be addressed. Currently, troubleshooting is considered as a costly and time-consuming approach. The SAITA is an Artificial Intelligent Troubleshooting Agent that utilizes natural language generation, machine learning, and dependency resolving and ontology-based methodologies for solving most common Windows-specific issues within a short period of time than the traditional approach. The assistant learns from the accessible data and accomplishes the task for users as performed by human experts. The main objective of this exploration venture is to distinguish the constraints of existing troubleshooting software and create an AI troubleshooting assistant to provide solutions to fix the identified user issues. The use of this assistant would be economical as an IT help desk alternative in the industry. SAITA is developed to serve as a representative troubleshooter for fundamental user issues, service issues, application issues, and perform environment setup by analyzing software. This system will be able to solve the common Windows user’s issues as same as a human with less time.Publication Embargo VirtualPT: Virtual Reality based Home Care Physiotherapy Rehabilitation for Elderly(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Heiyanthuduwa, T.A.; Amarapala, K.W.N.U.; Gunathilaka, K.D.V.B.; Ravindu, K.S.; Wickramarathne, J.; Kasthurirathna, D.This paper describes the development of Personal computer based Virtual Reality home-care Physiotherapy system aimed for rehabilitating full body function in elders. VirtualPT is a true virtual reality platform where the environment is completely replaced by a virtual reality platform based on the mental condition of the person at the time. While doing the home-based prescribed physiotherapy exercises, the key health metrics are continuously monitored and tracked by combining the immersive Virtual Reality with the wearable VirtualPT Sensor kit. Virtual Reality combined with 3D motion capture lets real time movements to be accurately translated onto the virtual reality avatar that can be viewed in a virtual environment to assist physiotherapist to add exercises to the system easily. This ultimate virtual reality Physiotherapy assistant avatar is used to provide guidance to elders at home, to demonstrate and assist elders in adhering to the prescribed exercises. As a significant aspect of social interactions, mirroring of movements has been added to focus on whether the elder is able to accurately follow the movements of avatar. Furthermore, the insightful dashboard offers the elders and physiotherapists an interactive platform through virtual reality capabilities. VirtualPT physiotherapy system is cost effective and makes recovery and more convenient to elders at home while the participatory and immersive nature of Virtual Reality offers a unique realistic quality that is not generally existing in clinical-based physiotherapy. When looking at the broader concept of VirtualPT; continuity of care, integration of services, quality of life and access are equally important criteria which add more value.Publication Embargo AI - Driven Smart Bin for Waste Management(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Abeygunawardhana, A. G. D. T.; Shalinda, R. M. M. M.; Bandara, W. H. M. D.; W. D. S. Anesta, D.; Kasthurirathna; Abeysiri, L.With increasing urbanization, waste has become a major problem in the present world. Therefore, proper waste management is a must for a healthy and clean environment. Though government authorities in most countries provide various solutions for waste management, solid waste tends to make a significant impact on the environment as they do not decompose easily. This research focuses on AI (Artificial Intelligence)-driven smart waste bin that can classify the most widely available solid waste materials namely Metal, Glass, and Plastic. The smart waste bin performs the separation of waste using image processing and machine learning algorithms. The system also performs the continuous monitoring of the collected waste level by using ultrasonic sensors. A dedicated mobile application will generate the optimal routes for the available waste collectors to collect the filled bins. Moreover, with this smart bin, the challenge of recognizing each waste item is overcome by using visual data as the source. Therefore, the usage of expensive sensor devices and filtration techniques to determine the category is disregarded. The smart bin can recognize the category of solid waste, collect it to the specified container, and notify the garbage level in each container. So, it is a portable waste management system.Publication Embargo Generating 2.5D Motion Graphics from 2D Designs(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ranawake, I.; Guruge, S.; Ahamed, A.; Kasthurirathne, D.As of the year 2020 , the video production industry is worth 31 billion dollars in the United States alone, with more than 6000 businesses and 57000 employees, and keeps growing. The global computer animation market size is anticipated to reach USD 28.30 billion by 2025, according to Grand View Research, Inc. Such significant growth demands the tech industry to introduce better tools for making animations. In this paper, we propose our contribution, specifically for User Interface designers in the field of motion graphics. UI/UX design is found to be one of the top 5 most trending opportunities for motion designers, and our proposed system allows them to generate a 2.5D animation based on a 2D Futuristic User Interface (FUI) design. The ultimate goal is to reduce the production time of FUI animations and minimize the cost of responding to the client’s changes. Obtaining clients’ feedback directly on animations rather than still images would improve the client’s involvement in production, resulting in greater confidence and loyalty. We implement several image processing techniques such as thinning and pixel clustering for pre-processing the 2D designs to segment the given design into an array of atomic shapes. Since the thinned shapes ensure that any pixel in the design does not have adjacent pixels which are also adjacent to each other, it is possible to utilize mathematical means to approximate the shapes. Our system converts a given 2D design to a collection of animated lines and arcs distributed in 3D space that eventually can be exported to the industry-standard tool, Adobe After Effects.Publication Embargo AI Approach In Monitoring The Physical And Psychological State Of Car Drivers And Remedial Action For Safe Driving(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Shanmugarajah, S.; Tharmaseelan, J.; Sivagnanam, L.Road Accidents and casualties incited by drowsiness are an overall important social and monetary issue. The connection between drowsiness and accidents is bolstered by logical confirmations that relate to small-scale sleep. This project has focused on Driver drowsiness detection by using ECG signal extraction. This work expects to extract and arrange the basic four types of sleep through Wavelet Transform and machine learning calculations. The report covers a short theoretical introduction about the medicinal topic, features the extraction, filtering techniques, and afterward trains the extracted information through machine learning software. After that is covered, it demonstrates the results with two types of machine learning algorithms (active or drowsiness status) with WEKA software. The main benefit of this system is it will send a notification to the driver's mobile every second when he goes to sleeping status. Nowadays artificial intelligence cars are available with sleep assistance, however, the devices used on these cars are very expensive. So, our approach is to develop a system to predict the driver's drowsiness to reduce accidents caused by sleepiness at a low cost. The sleep / awake status is determined by both the factors RR peak's distance and R's amplitude.