Browsing by Author "Jayathunga, D.P."

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    Artificial Intelligence Based Traffic Light Control System for Emergency Vehicles
    (Uva Wellassa University of Sri Lanka, 2020) Arunprashath, G.; Kethaatan, J.; Kandasamy, K.; Rathnayake, A.M.B.; Jayathunga, D.P.
    Numerous nations on the planet are confronting the issue at traffic light convergence that causes mishaps between emergency vehicles and the other open vehicle. The quick reaction of the crisis administrations, for example, ambulances or fire administration vehicles has gotten a difficult circumstance nowadays. Some of the time the rescue vehicle stalls out in rush hour gridlock and those minutes can cost people life. There are existing systems to manage traffic light controls for emergency vehicles based on image processing, Radio Frequency, and IR technology. But the current framework gave an extraordinary opportunity to the emergency vehicles to release even non-emergency circumstance time. So, in this project, we proposed an “AI-based traffic light control system for emergency vehicles” that has allowed emergency vehicles to leave only in actual emergencies. which can get the maximum benefits and save many lives. The main objective of our research is identifying the emergency vehicles at intersections and doing the more accurate AI-based traffic light control system to release them when stuck in traffic jams to identify vehicles, we developed and trained object recognition models by using image processing techniques especially for the ambulance, fire truck, and VIP vehicles. The system identifies every object from the video, emergency vehicles were considered as specifications to differentiate emergency vehicles from other vehicles. we have designed the sound identification model to identify the siren sounds, here we have trained varies siren sounds, our system which gain sound as an input from a microphone and our system trained to filter noises to identify the emergency vehicle’s sirens sound and combine both Sound & Image identification process when both conditions are true, the system changes the red signal to green or extend the green signal duration by detect the siren sound and emergency vehicles, and release the emergency vehicles path/way in an emergency. Keywords: Artificial intelligent, Emergency vehicle, Image processing, Machine learning, Neural networks, Siren sound, Sound analysis, Traffic lights
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    Designed Artefacts for Analyzing and Evaluating Autism Spectrum Disorder (ASD)
    (Uva Wellassa University of Sri Lanka, 2020) Yapa, Y.M.U.I.; Nuska, M.N.F.; Imthath, A.M.; Pathirana, K.P.P.S.; Jayathunga, D.P.
    According to the recent statistics, 1 in 63 children are affected with Autism. Autism is a neurodevelopment disorder of early childhood, it is a condition that occurs due to the abnormal growth of mind, where these children exhibit extra-ordinary behavioral patterns. There is no well-defined treatment for Autism Spectrum Disorder (ASD), and early diagnosis is essential to manage the condition. An ICT based artifact (more specifically, a set of software) can be introduced as a novel approach, which intends to expose the child behavior. Furthermore, the outcomes of such an artifact could be used by any psychiatrist for predictions of ASD. These artifacts are designed by considering three main impaired areas of ASD which are Eye Contact, Maturity level, and Intelligence level. Therefore, the developed system is comprised of an Eye Movement Tracking tool where a common sample video is shown to the participants and a record of their eye movement is taken and this recorded data is then processed and finally displayed graphically. A module capable of identifying the Maturity Level provides a drawing canvas where participants are allowed to draw shapes and the analysis is done by the way they draw correct shapes with time in graphs. Moreover, an Intelligence Level Measuring Tool compromised with color and number-based activities is used and their responses are taken for decision making. Besides, these artifacts are capable of giving an analysis by comparing both ASD patients and a Neurotypical person. Testing and evaluation of the system were done with three (3) ASD patients and ten (10) Neurotypical persons from the age groups of 3-5 years. This experiment showed that, computer-based software tools are effective for acting as a platform to provide data and for taking decisions in ASD predictions. Keywords: Autism Spectrum Disorder (ASD), Eye contact, Intelligence level, Maturity level, Neurotypical person
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    A Novel Method to Measure The Water Content of The Leaves Using Digital Image Processing
    (Uva Wellassa University of Sri Lanka, 2018) Wagachchi, I.A.; Rassagala, R.D.K.; Abeykoon, B.B.D.S.; Kartheeswaran, T.; Jayathunga, D.P.
    Leafy product industries like Tea, Tobacco, Palmyra, Leafy vegetables, and Ayurveda productions play a significant role to uplift the Sri Lankan economy. The water content in the leaves is an essential factor for leafy productions to maintain their quality. Naked eye observation of an expert is the general method to identify the water content. The objective of this study is to introduce a novel and easy method to measure the water content of the detached plant leaves using digital image processing. As a result, a simple computational water content prediction method has been built using image processing techniques to obtain a quality output at the end of production processes. The findings of this study help to identify the water content without an expert in an efficient manner. First, the colour images were captured in a control environment, while leaves were drying and simultaneously the weight was measured traditionally to find the water loss. Features were analysed from images to find the best features, which show a better correlation with the changes of the water content in the leaves. The basic features such as homogeneity, energy, contrast, variance, mean, median, min, max, range, kurtosis, skewness, standard deviation, entropy, correlation and IQR were extracted. The best features among the selected features have been chosen through correlation test. The classification was done with the K-Nearest neighbour algorithm by training with the selected best features of the training set of images. The green matrix of the RGB image is taken for the feature extraction to get better results. Finally, a simple model was built using the significant features which have a relationship with the water content measurement. 65.3% accuracy has been achieved, and this model can be used to predict the water content of a particular green leaf through images. This model will be a turning point for measuring the water content of the leaves in the industries in an automated manner.
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    Recommender System Based on Food and Exercise Ontologies to Find the Suitable Fitness Exercise Plan with the aid of Python
    (Uva Wellassa University of Sri Lanka, 2021) Basnayake, P.B.M.C.S.; Peiris, H.C.S.; Wickramarathne, S.D.H.S.; Jayathunga, D.P.
    In the modern world, professionals of diverse industrial sectors have severely become victims of overweight and obese conditions which can be minimized by having proper dietary plans, physical activities, and minimizing alcohol-based relaxation. However, most of the exercise plans provided by fitness applications currently available for usage are not personalized and general exercises are given for every individual. In this research context, individuals are guided by recommending suitable exercises with exercise frequency, exercise environment, and unique time period to perform according to body parameters. According to domain experts, fitness plans highly depend on individual characteristics. Therefore height, weight, age, sex, diet details, medical history and user preferences for exercises taken from the front end which is a Tkinter Graphical User Interface. In this system, food ontology uses these details to calculate the daily calorie intake and extra calorie intake of the particular individual. Disease extraction using natural language processing techniques, computed with Python and integrated with the output of Food Ontology which is to be mapped with the exercise ontological knowledge base along with the predefined rules to match respective exercises suitable for the particular individual that is compatible with his preferences. Two ontologies for foods and exercises developed using Protégé 4.3 and data retrieved by running Simple Protocol and Resource Description Framework Query Language (SPARQL) queries inside the Python code using the RDFLib module and output is taken and directed to the front end. The entire system developed with Python 3, where two ontological files of Foods and Exercises are loaded and tested for consistency using the HermiT reasoner with the aid of Owlready2. The task-based ontology evaluation approach is performed by addressing the competency questions through the execution of SPARQL queries. In conclusion, this study provides an approach to integrate two ontologies and a disease extraction model using Python programming language. Correctness and qualitative evaluations of the system are verified by the domain experts, and recommendations from the ontological system are beneficial for physical trainers to improve and validate their manual exercise recommendations. Keywords: Exercises; Ontology; Food; Tkinter; Python