Undergraduate

Undergraduate Research Projects Supervised

RKMTN Ranaweera, Prediction of Potentially Hazardous Asteroids using Deep Learning
B.Sc. (Special) Degree in Computer Science (2020)
Supervisor(s): TGI Fernando

Abstract: An impact by an asteroid is one of the very few natural disasters that could be mitigated or even entirely prevented if accurate predictions are made early enough. Classifying the Near Earth Asteroid population in order to identify Potentially Hazardous Asteroids well before an impact is one of the most important problems that needs to be solved to avoid humanity from facing the same fate as that of the dinosaurs 65 million years ago. Therefore, we present an approach to classify the Near Earth Asteroid Population as potentially hazardous or non-hazardous by allowing deep neural networks to learn complex representations that exist in the distribution of available asteroid orbital data. We believe that the generation of an automatic potentially hazardous asteroid detector would contribute to speed up the characterization rate of the rapidly growing asteroid data that are made available through advances of science.
EMVN Amarajeewa, A Neural Network to Automate Planetary surface Dating By Crater Counting
B.Sc. (Special) Degree in Computer Science (2020)
Supervisor(s): TGI Fernando

Abstract: Planetary dating is an important aspect in astronomy to learn about the evolution of the universe. This is carried out by counting the craters that appear in an image of a certain geological area of a terrestrial body. But doing this process manually is highly time consuming and it is practically difficult to detect craters in an image. Therefore, the science community is paying attention to design models that can automate the manual counting of these impact craters. This research is also one such approach to automate the subjected process with a model implemented using YOLOv5 with PyTorch which is an effective object detection algorithm. For the study 250 lunar satellite images were obtained from Jmars, a Java based geospatial information system and were annotated with 850 crates using CVAT an online labelling tool. After image augmentation the image size was increased to 641 with 1200 annotated craters. The training followed by inference on test images gave out 88% and 86% coverages of the craters for two selected test images. These outputs were evaluated with the results obtained from the manual procedure that was carried out for the same two test images. However, some craters remained undetected due to their smaller size and rough edges. Considering these results the model was recognized as successful and future developments were suggested such as more training and application of different hyper-parameters together with different formats of images to increase its performance with a higher confidence level.
MD Welikala, Komposer V2 – A Hybrid Approach to Intelligent Musical Composition based on Generative Adversarial Networks with a Variational Autoencoder
B.Sc. (Special) Degree in Computer Science (2019)
Supervisor(s): TGI Fernando

Abstract: Music is an art form and cultural activity whose medium is sound organized in time. In many cultures, music is an important part of people’s way of life and hence the creation, performance, significance and the definition of music vary according to culture and social context. Music can be divided into genres and genres can be further divided into sub-genres. Although the dividing lines and relationships between music genres are often subtle, sometimes it is open to personal interpretation and occasionally controversial. Even though research on musical composition has been carried out using various technologies during the past few years, each research has had its positives and negatives. Hence, researchers have been experimenting new methods of musical composition. The purpose and the aim of this research is to identify aforementioned genre specific features and generates music notes which reflects a particular genre so that we can reproduce music that carry similarities of genre that are extinct as well as the genre that are currently being practiced, by using Generative Adversarial Networks with a Variational Autoencoder. In this study, we were able to successfully generate musical melody notes provided a genre, with a consistency of over 78%. Further a web-based inference tool that allows us to generate musical melody was also developed as a result of this study.
AC Gammanpila, Comparison Study of Quantum Inspired Genetic Algorithms and Classical Genetic Algorithms with Travelling Salesman Problem executed on real Quantum Computers
B.Sc. (Special) Degree in Computer Science (2019)
Supervisor(s): TGI Fernando

Abstract: In this study, a quantum inspired genetic algorithm is implemented on a real IBM quantum computer and is compared with a classical genetic algorithm based on the Travelling Salesman Problem. Since the late 90s numerous quantum inspired genetic algorithms have been proposed and out of which, the algorithms which use qubit rotation and measure the qubit state by duplicating its value to a separate variable are incompatible in implementing in real quantum devices. Also the majority of the previous studies have been simulated on classical computers which do not simulate a high performance quantum environment. Therefore in this study a selected quantum inspired genetic Algorithm is initially executed on a high performance IBM quantum simulator with the gr24.tsp and an 8 city simulated dataset. Due to the limitations of the algorithm, a modified version is designed and implemented both on a high performance IBM quantum simulator with and without quantum noise models using the above datasets and is also implemented in a real IBM quantum computer using a 5 city simulated dataset. However, the generated results did not converge to a satisfiable value when compared with the implementation of the classical genetic algorithm for the same dataset. As a result of the modified quantum inspired genetic algorithm proposed in this study and the generalized Python package implemented for quantum inspired evolutionary Algorithms open a new window of opportunity for the currently available quantum inspired algorithms which are constrained by the discussed limitations that are to be implemented on real quantum computers. To the best of our knowledge, it is the first time, this caliber of quantum inspired genetic algorithms have been implemented on a real IBM quantum computer.
Dulan S. Dias, Komposer – Automated Musical Note Generation Based on Lyrics with Recurrent Neural Networks
B.Sc. (Special) Degree in Computer Science (2018)
Supervisor(s): TGI Fernando

Abstract: Musical creativity being one of the stronghold characteristics that differentiates humans from computers in todays technologically advanced society, algorithmic composition and songwriting are the research areas that aim to bridge this gap. With the introduction and development of various neural network based methodologies that have shown quite a promise in applications to a wide range other fields, it is promising to see how these new technologies can cater to the domain of musical creativity. Even though there have been significant amount of research done focusing on musical composition, it is not the same for musical songwriting. The main objective of this research study is to apply Long Short-Term Memory Recurrent Neural Networks in constructing a machine learning model that can generate musical melody notes when it is provided with a lyrical input (musical songwriting). In this study, we were able to successfully generate musical melody notes for provided lyrical inputs with a consistencies of over 80%. In addition to that, a web-based inference tool was developed as a result of this study, that allows us to easily generate musical melody sheets when we provide with a lyrical input.

Web-based Interface Tool – Komposer
Thisuri C Lekamge, Gender Classification and Finding Effect of Gana Based on Personal Names with Recurrent Neural Networks
B.Sc. (Special) Degree in Computer Science (2018)
Supervisor(s): TGI Fernando

Abstract: Naming a baby is a very integral part of our life. As well as, naming a business with an attractive and suitable name takes first place in business world. In Asian countries, especially in Sri Lanka people tend to follow rituals and customs introduced by astrology when naming a baby or a business. Because they believe that good luck can be achieved by following astrological concepts like ‘Mathra’ and ‘Gana’ in naming. As a result of that, people tend to spend more money in finding a suitable name which follows astrological concepts. But most of the names given by many astrologers contain many malicious facts according to the astrology. A person without any knowledge about astrology has to blindly belief astrologers. There are many people who take advantage of this situation and earn money in tortuous ways. Also, when naming a new born, parents think many times whether that name is suitable for their boy/ girl or not according to gender. On the other hand, as web services grow, personal names have become one of the most abundant sources of data on the web. So, if one can predict or suggest proper gender for personal names; services such as web browsers or mobile applications can benefit from knowing each user’s gender. Actually, research studies on predicting gender and effect of gana based solely on personal names with deep learning algorithms have been poorly studied. Therefore, our main goal was to implement separate models for gender classification and finding effect of gana based on personal names (forenames/ most use name) which are in text format using Recurrent Neural Network. As a result of that, we could develop two models with higher accuracies and a web application using these models. Eventually, we can conclude that using Long Short Term Memory (a special unit of Recurrent Neural Network) is the best approach for gender classification and finding effect of gana based on personal names with higher accuracies. Also, this research study is relevant to an increasing amount of applications, particularly since the rise of social platforms and social media.

Web-based Interface Tool – EXYONA
AS Nuwanthika, Automatic Hose/Pipe Routing Using Convolutional Neural Networks
B.Sc. (Special) Degree in Computer Science (2018)
Supervisor(s): TGI Fernando
Piriyankan Kirupaharan, A Mobile Application to Identify Fish Species Using Convolutional Neural Networks
B.Sc. (Special) Degree in Computer Science (2017)
Supervisor(s): TGI Fernando

Abstract: Object detection is one of the sub-component of computer vision. With recent development in the deep neural networks, many day-to-day problems can be solved. One of the practical problems faced by people is the difficulties in identifying the fish species correctly. Even though there are few studies to solve this problem, those implemented solutions are not easily accessible. The main objective of this study is to implement a mobile application based on deep learning that can detect the fish species and provide information on vitamins and recipes. For this study top selling 16 Sri Lankan fish species are used. In this study, we were able to obtain an accuracy of 77% using YOLO, a convolutional neural network approach. The mobile application takes 12-20 seconds to detect the fish species based on the internet speed.

YouTube Video
PM Jayanka, A Computer-based System to Identify the Ayurvedic Herbal Plant leaves in Sri Lanka
B.Sc. (Special) Degree in Computer Science (2016)
Supervisor(s): TGI Fernando

Abstract: Different parts of ayurvedic herbal plants are used to make ayurvedic medicines in Sri Lanka. Recognizing these endemic herbal plants is a challenging problem in the fields of Ayurvedic medicine, computer vision, and machine learning. In this research, a computer system has been developed to recognize ayurvedic plant leaves in Sri Lanka based on a recently developed machine learning algorithm: convolutional neural networks (CNNs). Convolutional neural networks with RGB and grayscaled images and multi-layer neural network with RGB images have been used to recognize the ayurvedic plant leaves. To train neural networks, images of 17 types of herbal plant leaves were captured from the plant nursery of Navinna Ayurveda Medical Hospital. As a CNN requires a large number of images to train it, various data augmenting methods have been applied to the collected dataset to increase the size of the dataset. Backgrounds of images were removed and all images were resized to 256 by 256 pixels before submitting them to a neural network. The results obtained were highly significant and CNN with RGB images was able to achieve an accuracy of 97.71% for recognizing ayurvedic herbal plant leaves in Sri Lanka. The study suggests that CNNs can be used notably to recognize ayurvedic herbal plants.
WDNS Wijesuriya, Predicting Successfulness of Radio Advertisements Using Recurrent neural Networks
B.Sc. (Special) Degree in Computer Science (2016)
Supervisor(s): TGI Fernando

Abstract: Advertisers spend a lot of money on creating advertisements which may be successful or may fail. Successful advertisements will increase the profit of the advertiser whereas failed advertisements will be a heavy loss. Therefore, it is required to find a method of classifying an advertisement before putting an advertisement on the air. This research proposes a solution to classify advertisements using Recurrent Neural Networks. In the research collection of 100 advertisements were used in order to train and validate the models. Altogether there were 4 different models implemented in the research. Each model was trained six times with a different number of epochs and batch sizes in order to optimize the final output. Deep Neural network with two LSTM layers was selected as the best network to be used for classification. Therefore, by this research, we have managed to prove that RNN can be used for classifying radio advertisements successfully.
TC Matharage, Training Convolutional Neural Networks Using the Firefly Algorithm
B.Sc. (Special) Degree in Computer Science (2016)
Supervisor(s): TGI Fernando

Abstract: Nowadays in all fields, Neural networks are very popular in solving practical problems. Convolutional Neural Network (CNN) is a kind of Deep Neural Network that has wide applications in fields such as image recognition, handwritten character recognition, speech recognition and natural language processing, etc. When considering solving these problems, the most important thing is that, training the neural network as quickly as possible with an optimal solution. Although backpropagation algorithm is the most common and traditional method of training the neural networks, there are some limitations with this method. Therefore, researchers tended to find new algorithms to train neural networks. As a result of that many research studies have been conducted to observe the usability of nature-inspired algorithms in neural network training. Nature-inspired algorithms are created by mimicking the patterns and processes in nature. In this research, a nature-inspired algorithm called Firefly algorithm is implemented to test as training algorithm for Convolutional Neural Networks. To train the implemented algorithm, a benchmark dataset MNIST is used and the results are compared to select the best training algorithm for Convolutional Neural Network training using negative log likelihood error and CPU time.
SL Heenatigala, Search for an Efficient Algorithm Without the Derivatives to Numerically Solve Nonlinear Equations
B.Sc. (Special) Degree in Mathematics (2016)
Supervisor(s): S Weerakoon and TGI Fernando

Abstract: The research was mainly conducted to explore the possibility of formulating an efficient algorithm to find roots of nonlinear equations without using the derivative of the function. We know that Newton’s method also carries the derivative part in the formula. But the Secant method can be used to overcome this difficulty. In Secant method, it uses forward difference method to replace the derivative part. But the order of convergence of the Secant method is only 1.68. Which is lower than Newton’s method and the Improved Newton’s method. Comparing Newton’s method over Improved Newton’s method, Improved Newton’s method is better than Newton’s method because of its higher order of convergence. Therefore the Improved Newton’s method was used in this project to find a new method without the derivative. We call our method as Finite-Difference Improved Newtons Method (FDINM). The FDINM we have derived has given 2.4 or more as the computational order of convergence for all examples we have tested. This higher order of convergence was retained even when the FDINM was implemented on nonlinear equations with complex roots and also on systems of non-linear equations. So the computational order of convergence of the FDINM is not only higher than secant method but it exceeds the computational order of convergence of the most popular Newton’s method as well. We followed the procedure in Broyden’s method to solve systems of nonlinear equations using the FDINM due to the involvement of the Jacobian. For almost all of the test functions, the FDINM returned a computer order of convergence higher than that of Newton’s method, the secant method and also of the Broyden’s method. The computational order of convergence of the FNINM is in fact closed to 2.5
UR Weeratne, Recurrent Neural Network Based Approach for Sinhala Speech Recognition
B.Sc. (Special) Degree in Computer Science (2015)
Supervisor(s): TGI Fernando

Abstract: Speech is the most powerful mode of communication among human beings. Speech recognition involves the conversion of acoustic signals into text format which is readable. It has been an active research area over the past years, due to its applicability in different fields. Most of the research studies are based on few popular languages like English, Mandarin and French. The number of approaches tested on Sinhala speech is limited. Therefore, the research has been conducted to test the applicability of one of the latest research approaches on Sinhala speech recognition. Diﬀerent models of selected approach have been used to implement a speech recognizer with the highest rate of recognition accuracy. Recurrent Neural Networks, which is proven to be an effective neural network architecture when processing temporal data, has been selected as the core concept of building the recognizer. `Keras’ – a Theano based Deep Learning Library developed to facilitate neural network based experiments, is used as the main library in implementing the speech recognizer. Mel Frequency Cepstral Coefficients (MFCC) derived from Python speech features library are used as the feature vectors. Training data have been collected from both male and female speakers. Considerably large database with noiseless discrete speech utterances is created with the support of many speakers. The performance evaluation carried out on the accuracy of the recognizer shows around 98% accuracy over speaker independent scope. Since this is a primary approach to applying neural networks on speech recognition, it has been tested on noiseless speech samples. But, future work will be carried out related to continuous speech recognition in both noisy and noiseless environments.
ARAS Weerathunga, Convolutional Neural Network Based Approach for Sinhala Speech Recognition
B.Sc. (Special) Degree in Computer Science (2015)
Supervisor(s): TGI Fernando

Abstract: Speech is one of the most powerful ways of communicating between humans and they are finding ways to use the speech for communicating with machines as well. This has been a major research area for more than 5 decades and there is state of art speech recognition systems available today as a result of that. But the main limitation is that there are a lot of differences between languages of all around the world. Most of the state of art Automatic Speech Recognition (ASR) systems available today is based on the English language. The number of studies has been carried out based on the Sinhala language is very limited. Therefore this study was carried out based on Sinhala speech recognition. Although there are lots of traditional approaches available for speech recognition we have decided to carry out this study based on Convolutional Neural Network (CNN) approach which is a novel area of speech recognition. Two open source deep learning libraries which are Keras and NVIDIA Digits were used for the implementation of speech recognition classifier based on CNN. Since CNN have shown state of art result on image classifications, we used spectrograms of speech data as the inputs for the CNN architecture. This study was carried out using Sinhala digits dataset which is a discrete speech dataset. The dataset was created by getting speech samples from 90 different speakers of different ages. The performance was measured based on the accuracy rates of the CNN classifier for speech samples and the results were surprisingly well. We were able to achieve 96% accuracy rate for this implementation under noiseless environment.
HTM Perera, Deep Learning Approach for Sinhala Hand-written Character Recognition
B.Sc. (Special) Degree in Computer Science (2015)
Supervisor(s): TGI Fernando

Abstract: Handwritten character recognition can be considered as one of the main subfields in computer vision and machine intelligence. With the recent development of deep learning based computer vision methods, most of the complex image recognition became much easier and accurate. Some deep learning methods have performed better in other classical approaches that have been used for recognition of handwritten characters. Sinhala handwritten characters have considerable variations due to the unique shapes involved with Sinhala characters. Up to now, some studies have been carried out for recognition of Sinhala handwritten character recognition using only the classical image processing methods and basic machine learning methods. The main objective of this study was to identify and implement an efficient Sinhala handwritten character recognition method based on deep learning. In this study, we were able to obtain top 1 and top 5 error rate of 2.74% and 0.07% respectively using convolutional neural networks for Sinhala handwritten character recognition.
K Nanayakkara, Independent Eeg Enabled Affective Human-computer Interface
B.Sc. (Special) Degree in Computer Science (2015)
Supervisor(s): TGI Fernando

Abstract: In recent years, human emotion detection using Electroencephalogram (EEG) started playing a crucial role in developing a smarter Brain-Computer Interface (BCI). In this research, we are using DEAP physiological emotional database to identify emotions using the arousal valance model. Using wavelet transformation the EEG signal was decomposed into four frequency bands (theta, alpha beta and gamma). The Daubechies order 4 wavelet function (db4) was utilized to do the processing. From these frequency bands, linear and nonlinear statistical features were extracted to be used in the classification.The classification is performed using an artificial neural network (ANN). Different network architectures and features were used in the experiment. The document finally presents the evaluations and results obtained in the classification stage.
HN Gunasinghe, System for Choosing the Right Eyeglasses Based on Face Shape Using Deep Learning Algorithms
B.Sc. (Special) Degree in Computer Science (2014)
Supervisor(s): TGI Fernando

Abstract: Identifying one’s face shape is important for various purposes including choosing the matching hairstyle, haircut and eyewear. Face shapes are distinguished by the formation and composition of lines, edges and curves at the outline of one’s face. There are seven basic shapes to be compared in order to detect the accurate shape. The identification process of face shapes is highly technical and time-consuming. The naked eye observation by experts is the main approach adopted for detection and identification of face shapes. A computer-aided system can provide clues and technological support to confirm the prediction of the expert. The work described in this thesis is based on an attempt to implement a system to identify face shapes by classifying the images of human frontal faces. The underlying concept of building the system was designed by taking the advantage of the Image Processing techniques and Machine learning. Image processing was used to obtain a new and accurate dataset. Four types of machine learning architectures were created including single-layer neural network, support vector machine, multilayer perceptron and convolutional neural network. Each network was trained and obtained the accuracy of classification for seven face shapes. The convolutional neural network with the best architecture could identify the face shape with an overall accuracy of 67%. The experimental results indicate that the proposed approach is a valuable approach, which can significantly support an accurate detection of face shapes with a little computational effort.
CD Athuraliya, Deep Learning Approach for Logo Recognition in Images of Complex Environments
B.Sc. (Special) Degree in Computer Science (2014)
Supervisor(s): TGI Fernando

Abstract: Logo recognition can be considered as a sub-field of object recognition which is found useful in many day-to-day applications such as enterprise identification and product recognition. Despite the fact that there are many success stories in handling object recognition using tools which are solely based on computer vision, utilizing machine learning on these problems has resulted in better outcomes. Deep learning is a sub-field of machine learning which attempts to model high-level abstractions in data by using architectures, composed of multiple non-linear transformations. In this study, we are looking for a more efficient approach for logo recognition using deep architectures which will replace a considerable amount of image processing tasks from recognition phase with novel approaches. The varying conditions that logos can appear and the ways photographs of them can be taken render generic object detection methods, such as SIFT, useless. This problem was taken into consideration by comparing and trying to improve current methods or as a more direct application of machine learning, developing a working logo recognition system. This study aims to conduct a comprehensive analysis on research domain and to implement a usable logo recognition system utilizing deep learning approaches. Logo recognition mainly involves two phases; namely object localization and object classification in a given image. It has been identified that a special type of neural networks, convolutional neural networks are more efficient in computer vision problems. In this study we are trying to implement a network architecture to extract low-level features from input images and gradually recognize more abstract features for complete logo recognition. The solution will be developed and implemented by applying programming language tools along with deep learning libraries. Finally, the performance and efficiency of the solution will be evaluated with regard to the objectives of the problem.
RMEJ Rathnayaka, Development of a Tool to Find the Astrological Effects of a Name
B.Sc. (Special) Computer Science Special Degree (2014)
Supervisor(s): TGI Fernando

Abstract: Sri Lankans, especially Sinhala Buddhists and Hindus follow customs and rituals introduced by astrology. These rituals start with the birth of a newborn and end with the time of his death. First of these rituals is naming a baby. At present many parents want to name their children with astrologically beneficial names. Although they spend money on this matter and there is no reliable way to find out whether a given name by an astrologer is according to the rules of astrology. A common situation is that names given by them are not according to the rules of astrology. Naming a baby is not only connected with astrology but also with the language. In this research, Sinhala Language and its concepts are taken into consideration. Many concepts in astrology have been absorbed by the Sinhala language. Sinhalese have practised astrology for centuries and Buddhist priests were the pioneers of preserving both astrology and language in ancient Sri Lanka. This might be the reason for the close relationship between the Sinhala language and the astrology. Astrology has some concepts of categorizing words using the pronunciation pattern of them. Using these concepts of astrology, the effect can be given when a word is pronounced or placed as the first word of a poem. In this research, both astrological concepts and linguistic concepts have been studied to develop a web-based application to implement the concepts used in predicting the effect of a given name. The algorithms and concepts introduced and studied through the research may give new ways of thinking about the pronunciation of a word, especially the concept of වර්ණ (warna) in the Sinhala language may help to find more reliable grapheme to phoneme conversions.
KS Ilmini, Recognize Person’s Characteristics Using Artificial Neural Networks
B.Sc. (Special) Computer Science Special Degree (2013)
Supervisor(s): TGI Fernando

Abstract: The context of this work is the development of persons’ personality recognition system using machine learning techniques. Identifying personality traits from a face image includes three separate algorithms; they are Artificial Neural Networks (ANN) with backpropagation learning algorithm, Support Vector Machine (SVM) and Deep Learning. Face area in an image is identified by a colour segmentation algorithm. Then that extracted image is input to personality recognition process. Features of the face are identified manually in ANN and SVM. The main research area of this project is the development of a Multi-class recognition system using an artificial neural network which is used to recognize personality traits using extracted face image.
TYT Totagamuwa, A Comparative Study on Weight Optimization of Neural Networks Using Nature-inspired Algorithms with Cuckoo Search Algorithm
B.Sc. (Special) Degree in Computer Science (2013)
Supervisor(s): TGI Fernando

Abstract: Nowadays neural networks are very popular in solving practical problems, especially in solving optimization problems. When solving these problems, training the neural network quickly with an optimal solution is very important. Common and traditional methods to train the neural networks are using the backpropagation algorithm. But due to the limitations in the backpropagation people tend to find new algorithms for training neural networks. As a result of that many research studies have been conducted to observe the usability of nature-inspired algorithms in neural network training. Nature-inspired algorithms are created by mimicking the patterns and processes in nature. Nature has unlimited patterns and processes, therefore still new nature-inspired algorithms are being developed. To choose a better algorithm for neural network training, it has to check the performance of nature-inspired algorithms in neural network training context. In this project, two common nature-inspired algorithms; Genetic algorithm, Particle Swarm Optimization and two newly implemented nature-inspired algorithms; Cuckoo Search algorithm, Firefly algorithm are implemented to test as training algorithms for neural networks. To test the implemented algorithms two benchmark problems, Iris flower classification and Wisconsin breast cancer classification were used and results are compared to select the best training algorithm for neural network training using mean square error and CPU time. According to the results, it has been found that the Cuckoo search algorithm has better performances among those four algorithms.
TMTM Perera, Multi-agent system for Doctor, Patient, Hospital and Pharmacy
B.Sc. (Special) Degree in Computer Science (2013)
Supervisor(s): TGI Fernando and B Hettige

Abstract: Computer science caters solutions for different types of problems relevant to the medical industry. For example, online channelling applications are a result of an attempt at solving real life, day to day problems. These channelling applications are available worldwide. They have great value and high popularity since they save time and money – two very important facts in our busy lives. Buying medicine is a basic necessity. Every person has gone through this experience at least once in their life. People prefer to buy medicine at a pharmacy if it has the cheapest medicine but finding a pharmacy with the lowest priced medicine cannot be done by guessing. The simple fact is that the buyer may have to visit several pharmacies to find the cheapest medicine and this simply is not practical for busy lives. Making appointments and channelling doctors online by using basic knowledge like symptoms, is convenient for people. And searching online for best pharmacies with low priced medicines can be a safeguard against inadvertently wasting money on high priced pharmaceuticals. So an application which contains these two features is another valuable product of computer science. This application uses knowledge of expert systems and multi-agent systems to achieve the goal of producing a system that is capable of covering the above two features within one application.
MKA Ariyaratne, A Comparative Study of Nature-inspired Algorithms with Firefly Algorithm
B.Sc. (Special) Degree in Computer Science (2012)
Supervisor(s): TGI Fernando

Abstract: The processes of optimization can be defined simply as an attempt at making something better or finding the best solution for a maximization or minimization problem. The basic two approaches to optimization are classical and natural wherein some problems classical approach works better and for some other problems, natural methods are good. Natural optimizing techniques, which are extracted from the behaviour of the natural world, are known as Nature Inspired optimization techniques. Ant colonies which mimic the natural food finding behaviour of ants, particle swarm optimizations algorithms which take the advantage of schooling behaviour of fish or flocking behaviour of birds are some examples of them. In this research, the main purpose is to measure the optimizing performance of such nature-inspired algorithms with one new nature-inspired algorithm known as Firefly inspired algorithm, which came to the stage, extracting the flashing behaviour of fireflies. Two major areas of nature-inspired algorithms are evolutionary strategies and swarm intelligence. An evolutionary algorithm (EA) is a generic population-based metaheuristic optimization algorithm. An EA uses tools motivated by biological evolution: reproduction, mutation, recombination, and selection. Swarm intelligence is another problem-solving behaviour, inspired by the nature that emerges from the interaction of individual agents (e.g., bacteria, ants, termites, bees, spiders, fish, and birds) which communicate with other agents by acting on their local environments. For this research, genetic algorithms are taken as an algorithm from evolutionary strategies and Ant colonies, particle swarm optimization from swarm intelligence to make the comparison with the firefly-inspired algorithm, which is also an algorithm that belongs to swarm intelligence. Traveling salesman problem, which is a representative of NP-hard problems, was taken as the benchmark problem to employ all these algorithms. Each algorithm was used to solve four TSP instances with 16, 29, 51 and 100 cities that were taken from the TSPLIB library and statistics were taken appropriately. Another 5 instances of 29 cities were generated randomly and results were calculated for all four algorithms. The results of the study were manipulated using MATLAB 2008. For all 9 TSP instances, firefly algorithm gave the best results and sometimes ant colony systems too. Particle swarm optimization algorithm always scores the third place and Genetic algorithm performs last. With the results obtained, it can be clearly said that the firefly algorithm is remarkably successful and better than other three algorithms in its discrete version solving TSP.
Janaki Wanigasooriya, Multi-vehicle Passenger Allocation and Route Optimization for Employee Transportation Using Genetic Algorithms
B.Sc. (Special) Degree in Computer Science (2011)
Supervisor(s): TGI Fernando

Abstract: Design of optimization of real-world problems are quite complicated and optimizing vehicle routing is most important to the today’s world. Vehicle routing problems are combinatorial and NP-hard. The research discusses the employee transportation optimization which uses split deliveries when the employee demand of a city greater than the vehicle capacity where vehicle capacities may be homogeneous or heterogeneous. The problem is purely multi-objective and the objectives conceded in the problem are minimizing travel time, minimizing total distance, and minimizing no of vehicles which are most concerned by companies and the employees. We successfully engaged with the popular meta-heuristic method, Genetic algorithms in the research work and we addressed the geography of the problem by designing and developing two new initialization methods and by proposing two algorithms for the employee transportation problem. The first algorithm uses the dominance relation between individual routing solutions and the second approach uses scalar weight mechanism. The algorithms implemented with C#.Net and the developed graphical user interface allows to tune the genetic parameters and also to take the routing decisions to the decision maker. The proposed algorithms for the employee transport optimization run efficiently and give invaluable support to the decision maker for taking right routing decisions.
KAG Udeshani, Lung Cancer Detection System Using Neural Networks and Image Processing Techniques
B.Sc. (Special) Degree in Computer Science (2010)
Supervisor(s): TGI Fernando

Abstract: Lung cancer is tumours arising from cells lining the airways of the respiratory system. Chest X-rays are used for lung cancer detection in early stages. This system uses digital images of chest X-rays as the inputs to the system. It categorizes a given suspicious area into two categories: nodule or non-nodule. Two approaches have been used as the Methodology of this system. Those are the Neural Networks and the Image Processing Techniques. Two methods have been used to train the Neural Network: the first one is a feature-based method and the other one is a pixel-based method. The concept of Connected Component Analysis has been introduced and using that technique the roundness of lung nodules of a chest X-ray are identified. According to the roundness of those nodules, we can classify suspicious areas into those two categories. As the database, images of 154 lung nodules (100 malignant cases, 54 benign cases), and 93 non-nodules were collected from the Digital Image Database developed by the Japanese Society of Radiological Technology (JSRT). To consider about Sri Lankan patients, digital images of chest X-rays were collected from the National Cancer Institute, Maharagama. This dissertation discusses this research with the background study. And the system has been implemented using MATLAB R2008a. This system is a flexible and practical system that can be used to develop and add more features later on.
PCP Peiris, Review of the Citations of the Research Paper: A Variant of Newton’s Method with Accelerated Third-order Convergence
B.Sc. (Special) Degree in Mathematics (2010)
Supervisor(s): Sunethra Weerakoon and TGI Fernando

Abstract: This report is a review of the citations of the research papers relevant to “A variant of Newton’s method with accelerated third-order convergence” published by S. Weerakoon and T.G. I. Fernando in the Elsevier journal of “Applied Mathematics Letters” in 2000. Within less than a decade it records over 200 citations. The majority of researchers refer this article to gain information regarding newly developed 3rd order iterative method for solving nonlinear equations while few researchers introduced modifications to the original method. Most subsequent researchers have made use of the technique in proving the third order convergence to prove their order of convergence. Almost all who came up with a new method of same or higher order used the set of sample functions used to test the Improved Newton’s Method. Some need INM as an efficient method for solving nonlinear equations arising in their particular research. Due to some modifications, sometimes the original method transforms to a higher order method. Thus the original method shows a way to develop other third order methods or higher order methods for solving nonlinear equations. Therefore the original method plays an important role in the branch of numerical methods. The paper entitled “New Variants of Newton’s Method for Nonlinear Unconstrained Optimization Problems” comments this and this paper saying that it indicates a way of solving nonlinear unconstrained optimization problems. In this report, we have highlighted some improvements done to the original method (i.e. INM). It may be useful for consequent researchers who interest in the development of new methods for solving nonlinear equations. In addition to the above objectives of this research, this is a collection of recently used iterative methods for solving nonlinear equations. It will helpful for the future researchers, who interest in this field.
DVS Hettiarachchi, Mobile Navigation System
B.Sc. (Special) Degree in Computer Science (2009)
Supervisor(s): TGI Fernando
MDL Salgado, Degree Reduction of Bezier Curves
B.Sc. (Special) Degree in Mathematics (2009)
Supervisor(s): TGI Fernando and Sunethra Weerakoon