Browsing by Author "Jayalal, S."

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A Study on the factors that affect establishment of businesses by rural entrepreneurs
(2008) Siri, J.G.S.; Jayalal, S.
In Sri Lanka, as in other developing countries community development is considered as the development of the rural poor. It is estimated that 88% of the poor communities live in rural areas in Sri Lanka. Rural entrepreneur is a crucial entity of the rural community. As far as the economic development is concerned it is important to address their needs and problems when bridging the gap between poor and rich. A policy strategy recognizing the challenges facing rural entrepreneurs could do much to reduce the regional disparities and rural poverty in Sri Lanka. Rural entrepreneurship is one of the newest areas of research in the entrepreneurship field. It has become one of the significant supportive factors for rural economic development. This study which explored the factors that affect rural entrepreneurs was carried out in selected villages of seven districts namely Anuradhapura, Monaragala, Nuwara Eliya, Kegalle, Kaluthara, Puttalam and Ratnapura. The study covered the rural areas in each district. The Vidatha Resources Centers1, initiated by the government is facilitating technology transfer with a view to promote rural entrepreneurship. Even though many resources have been spent through the Vidatha Resources Centers, the majority of existing and potential entrepreneurs are yet to realize the way to run their business successfully. The main objective of this study was to identify the factors influencing rural entrepreneurs when establishing and expanding their business with a view to fill the knowledge gap in real problems of rural entrepreneurs to be addressed during the process of planning and thereby to maximize output from rural entrepreneurship development programmes. The research process was divided into two phases. In the first phase, secondary document analysis and informal interviews with key entities were carried out. The second phase was characterized by in-depth interviews with female and male entrepreneurs in rural villages, Science and Technology Officers and Field Officers at the Vidatha Resources Centers. The hypotheses tested were that, whether the level of education, age of the entrepreneur and a marketing plan at the beginning influence the success of the establishment and the level of income from the enterprise.Key findings of this study were that the level of education, age of the entrepreneur and a marketing plan at the beginning influence the success ofthe establishment and the level of income from the enterprise. Most of the entrepreneurs who had a marketing plan at the beginning (before establishing the enterprise) have been able to carry out their business successfully and withstand the market forces. It was found that previous experience in the relevant business field, gender of the entrepreneur, availability of new technology, limited access for micro-credits, availability of training programmes and lack of business development for service providers at rural level do not have a significant influence on the success or failure of the enterprise. Since majority of them have utilized their own savings to start up the enterprise, there is no significant influence of limited access to sources of micro credit at the point of establishing the business. However, it was observed that they face difficulties due to limited access to sources of micro-credits when they are going to expand their business further. The reasons for failure of enterprises were also surfaced in this study. Lack of training,difficulty to find a market, inability to find micro-credits and market competition are some of the reasons for failure as given by the entrepreneurs.
Analysis and detection of potentially harmful Android applications using machine learning
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Kavneth, G.A.S.; Jayalal, S.
With the rapid advancement of technology today, smartphones have become more and more powerful and attract a huge number of users with new features provided by mobile device operating systems such as Android and iOS. Android extended its lead by capturing 86% of the total market in 2017 (Gartner, 2017) and became the most popular mobile operating system. However, this huge demand and freedom has made the hackers and cybercriminals more curious to generate malicious apps towards the Android operating system. Thus, research on effective and efficient mobile threat analysis becomes an emerging and important topic in cybersecurity research area. This paper proposes a static-dynamic hybrid malware detecting scheme for Android applications. While the static analysis could be fast, and less resource consuming technique and dynamic analysis can be used for high complexity and deep analysis. The suggested methods can automatically deliver an unknown application for both static and dynamic analysis and determine whether Android application is a malware or not. The experimental results show that the suggested scheme is effective as its detection accuracy can achieve to 93% ∼ 100%. The findings have been more accurate in identifying Android malwares rather than separating those two static and dynamic behaviors. Furthermore, this research compares the machine learning algorithms for static and dynamic analysis of the Android malwares and compare the accuracy by the data used to train the machine learning models. It reveals Deep Neural Networks and SVM can be used for and higher accuracy. In addition, era of the training and testing dataset highly effect the accuracy of the results regarding Android applications.
Classification of vehicles by video analytics for unorganized traffic environments
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Arachchi, I.M.R.; Jayalal, S.; Rajapakse, C.
Traffic monitoring is essential for infrastructure planning and transportation. The objective of traffic monitoring is to have an effective traffic management system. Traffic management systems would be effective in well-organized traffic environments, where it has very disciplinary behaviors and less in inefficiencies. But in unorganized urban environments like Sri Lanka, road traffic behaviours are varying from standard structured ways which lead to discompose the traffic management. An effective monitoring system requires short processing time, low processing cost and high reliability. The paper proposes a novel vehicle detection and classification algorithm based on background filtering and re-engineered with suitable changes in order to be applicable to challenging unorganized traffic environments. The solution is successfully classifying vehicles individually and their trajectories in unorganized traffic environments in order to monitor the behaviors of the drivers. The system gives 74.4% average accuracy in vehicle detection and 55% accuracy in vehicle classification while counting each vehicle passed by. We used OpenCV functions for implementing and testing algorithms. Data was collected through pre-recorded video clips from footbridge crossing at Colombo Fort in western province Sri Lanka, for the testing. The ultimate objective of this research was to come up with a best-suited algorithm for vehicle detection and classification (hybrid solution) in unorganized traffic environments which would help to analyze the behaviors of road users. The solution will lead to help reduce unorganized traffic congestions by enhancing the efficiency and effectiveness of traffic monitoring and analyzing systems those are used for intelligent traffic management systems and traffic simulation models.
Dengue mosquito larvae identification using digital images
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) De Silva, W.D.M.; Jayalal, S.
Dengue is one of the highest spreading mosquitoborne diseases in tropical and subtropical regions all over the world. This disease is mainly spread by the mosquito vector called ‘Aedes’. In Sri Lanka, the number of infected patients reported is increasing, and it has become a public health problem. Health Inspectors are using different methods to reduce the spread of this viral disease and one of the main methods used is the fumigation by identifying the Aedes Larvae breeding locations. Currently, this identification is done manually by the specialized health inspectors and it is totally observer-biased and consumes a considerable amount of time, which could lead to false decision making and inefficient identification. The purpose of this research is to build an automated computational model to identify Aedes Larvae in real-time with more accuracy and convenience. Even though there are good results in previous researches done in Convolutional Neural Networks (CNN) on Aedes Larvae identification, the method of capturing Larvae Images is a bit complicated since they have used a Microscope lens of amplification capacity 60-100 times to get the magnified images. In this research, we propose the method of identifying Aedes mosquito larvae with a digital amplification of 8-12 times without using any microscope lenses attached, using ResNet50 CNN. The proposed model will identity the mosquito larvae by their genus ‘Aedes’ or ‘non- Aedes’ using a digital photo taken by a smartphone or camera in the upside of the larvae body. Hence it would help Health Inspectors, even the general public on identifying Aedes Larvae more efficiently, accurately and conveniently than the traditional method. This study shows that the trained model can identify images of Aedes and Non-Aedes Larvae separately with an accuracy of 86.65%. Furthermore, with using pre-processing techniques, the accuracy level can be enhanced to 98.76% for magnified images.
Detection of cyber bullying on social media networks
(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Priyangika. S.; Jayalal, S.
Social Media is becoming an integral part of people’s daily lives today. It is an effective way of sharing one’s life experiences, special occasions, achievements and other events with their friends and family. Although it is a fruitful way to communicate with groups, some people find themselves being insulted or offended by others who are involved in certain post or conversations. These insulations can be based on racism, using profanity or any other vulgar or lewd language. This cyber bullying needs to be monitored and controlled by the social media site owners since it will highly effect on the number and safety of the active site membership. Currently, there is no automated process of identifying offensive comments by the social network site itself. It can be only diagnosed by humans after reading the comments, flagging or reporting them to the owner of the site or blocking the offender. Considering the massive big data set generated in social media daily, automatically detection of offensive statements is required to reduce insulation effectively. For this purpose, text classification approach can be applied where a given text will be categorized as insulting or not, through learning from a pre-learned model. In order to develop the model, data was collected from the popular data repository site named www.kaggle.com. The dataset consists of comments posted on Facebook and Twitter. Firstly the dataset was divided into training data set and test data set. Then the collected data was preprocessed by removing the unwanted strings, correcting words and eliminating duplicate data fields. In the next step, features or keywords were extracted which are qualified to distinguish a statement as ‘insulting’ using N-grams model and counting methods. Feature selection is done using Chi- Squared test and finally apply classification algorithms for separating insulting comments and non-insulting comments from a dataset given. Machine learning algorithms such as Support Vector Machines (SVM), Naïve Bayes, Logistic Regression and Random Forest are used for this. Out of the classification algorithms, SVM is to be performed better than other algorithms since this is a two-class classification problem and a comment is to be classified only into two separate classes which are ‘insulting’ and ‘neutral’. With an exact separation of a given comment into ‘insulting’ and ‘neutral’ category, cyberbullying happening through offensive comments posted on social media sites can be detected.
Factors Influencing the Success of Software Startups in Sri Lanka: A Comparative Analysis using SmartPLS & SEMinR
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Attygalle, T.I.; Withanaarachchi, A.S.; Jayalal, S.
IT industry is one of the fast-growing industries in Sri Lanka. In that industry the software development sector plays a massive role. Out of these software development firms, a considerable number of companies are startups. But compared to other countries, the contribution from software startups to the country’s economy is very low in Sri Lanka. Further with the current economic crisis Sri Lanka faces it is even harder for startups to continue their businesses and also it is challenging for an entrepreneur-minded person who wants to establish a software startup in Sri Lanka. This study focuses on the factors influencing the success of software startups in Sri Lanka and how those factors will be affected by the current economic crisis in Sri Lanka. The study has been conducted using a systematic literature review to discover and validate influential factors from past studies. Then the conceptual framework was formed to assess the variables. To validate the model, data was collected through an online questionnaire survey. Testing and validation of collected data were done using a comparative analysis between Smart PLS and SEMinR. The results of both studies show that the availability of finance is the only factor that has a significant relationship with the success of software startups in Sri Lanka. With that the study also recommends taking necessary actions to improve the availability of funds for software startup companies.
Factors influencing the usability of web based learning material for higher education sector: A case study of Advanced Technological Institute Kegalle
(University of Kelaniya, 2011) Jayalal, S.; Jayathilake, M.V.M.
Web based learning systems and web based learning materials (WBLM) have been introduced with the expansion of the Internet. In Sri Lanka, many higher education institutions use web based learning systems such as MOODLE and WBLM for teaching purposes. The success of this depends on the usability of both web based learning systems and WBLM. Although much has been written on the usability of web based learning systems, literature on usability of WBLM is rare. This research, focused on the usability of WBLM, attempts to identify the relationship between the material type and usability of web based learning material identified in terms of nine factors. Four individual material types and six combined material types are selected for the experiment. The selected teaching module for the experiment is Web Design for the students of the Advanced Technological Institute Kegalle. The most usable WBLM type is identified as Presentation & Video combined material type. By using a questionnaire survey the factors were prioritized according to their importance as Applicability, Material type, Goal orientation, Feedback, Added value, Valuation of previous knowledge, Learner activity, Learner control and Cooperative learning. The findings of the study would be helpful to formulate a set of guidelines to develop a WBLM for Higher Education.
Grammatical error detection and correction model for Sinhala language sentences
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Pabasara, H.M.U.; Jayalal, S.
As the national language of Sri Lanka, the greater part of the exercises at most of all the services are completed in Sinhala whereas it is imperative to guarantee the spelling and syntactic accuracy to convey the ideal significance from the perspective of automated materials with the unavailability of resources even though there are enough amount of available materials as hard copy and books. With the high multifaceted nature of the language, it sets aside extensive effort to physically edit the substance of a composed setting. The necessity to overcome this problem has risen numerous years back. But with the complexity of grammar rules in morphologically lavish Sinhala language, the accuracy of the grammar checkers developed so far has been contrastingly lower and thus, to overcome the issue a novel hybrid approach has been introduced. Spell checked Sinhala active sentences being pre-processed, separated nouns and verbs were analyzed with the help of a resourceful part-of- speech-tagger and a morphological analyzer and alongside the sentences were sent through a pattern recognition mechanism to identify its sentence pattern. Then a decision tree-based algorithm has been used to evaluate the verb with the “subject” and output feedback about the correctness of the sentence. To train this decision tree, a dataset consisting of 800 records which included information about 25 predefined grammar rules in Sinhala was used. Finally, the error correction was provided using a machine learning algorithm-based sentence guessing model for the three possible tenses. Conducted research results paved the way to identify the sentence pattern, grammar rules and finally, suggest corrections for identified incorrect grammatical sentences with an acceptable accuracy rate of 88.6 percent which concluded that the proposed hybrid approach was an accurate approach for detecting and correcting grammatical mistakes in Sinhala text.
Interpretation of Sri Lankan Sign Language: A Wearable Sensor-based Approach
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Kusalanga, M.N.; Jayalal, S.; Wijayasiriwardhane, T. K.
Hearing-impaired and speech-impaired people communicate not only with themselves but also with ordinary people using visual languages. Sri Lankan Sign Language (SSL) is the standard visual language used in Sri Lanka. Like other sign languages, the SSL relies on a distinct combination of hand gestures, body movements, and facial expressions for communication. As a result, SSL is more challenging for individuals without knowledge of SSL to understand. On the other hand, the steep learning curve associated with SSL makes it even more difficult to acquire. Thus, the interpretation of SSL has become a need. However, Sri Lanka is suffering from a severe dearth in the availability of SSL interpreters. This justifies the need to use either vision- based or sensor-based technological approaches to help the interpretation of SSL. However, vision-based approaches are susceptible to conditions such as skin tone, background color, ambient light intensity, and real-time constraints, whilst the sensor-based solutions are generally better in gesture recognition. Further, there is no attempt has been made on developing a cost-effective, portable, and real-time solution to accurately interpret the hand gestures of SSL. In this paper, we, therefore, present a novel, wearable, sensor-based, real-time gesture recognition glove, and a machine-learning Long Short-Term Memory (LSTM) model to recognize the hand and finger positions in three-dimensional space for classification and interpretation of SSL. The proposed approach has achieved 320ms of lowest inference time while showing a promising result of 83% for categorical accuracy. Our aim is to help the interpretation of SSL with an affordable, portable as well as a real-time solution.
An investigation of WAP based applications in Sri Lanka
(University of Kelaniya, 2011) Wijesinghe, H.; Wickramarachchi, A.P.R.; Jayalal, S.
Mobile usage around the world, including Sri Lanka, has been growing rapidly. Statistics show that 428 million mobile devices were sold in the first quarter of 2011. In addition to voice based services, mobile service providers are offering services based on Wireless Access Protocol (WAP), mobile broadband, mobile television etc. WAP is one of the most commonly used data transfering technoloies. Email by mobile phones, e-commerce transactions, tracking stock markets, music downloads, and providing news headlines and sports scores are some of the examples of WAP based applications. A number of studies have shown that there is a high, positive influence of WAP based mobile commerce applications on business and society today. As Internet penetration rates are low is Sri Lanka, WAP could be a viable alternative to access e- commerce application. It should be noted that the majority of mobile phones support WAP. Previous studies have indicated that use of WAP based applications is very popular in many countries. A preliminary survey was conducted to determine usage of WAP based application by mobile phone users in selected areas of Sri Lanka. However, it was found that usage of such applications is very low. The initial findings of this research have shown that mobile users‟ poor knowledge of WAP technology, lack of awareness of the availabe WAP based applications, reluctance to use WAP because availability of Internet access through other ways, vary low bandwith on WAP when accessing the Internet, unavailability of high quality WAP based applications and small size of mobile phone screens as main reasons for poor WAP based application usage in Sri Lanka. Small customber base may discourage service providers in promoting and facilitiating use of such applications.Mobile usage around the world, including Sri Lanka, has been growing rapidly. Statistics show that 428 million mobile devices were sold in the first quarter of 2011. In addition to voice based services, mobile service providers are offering services based on Wireless Access Protocol (WAP), mobile broadband, mobile television etc. WAP is one of the most commonly used data transfering technoloies. Email by mobile phones, e-commerce transactions, tracking stock markets, music downloads, and providing news headlines and sports scores are some of the examples of WAP based applications. A number of studies have shown that there is a high, positive influence of WAP based mobile commerce applications on business and society today. As Internet penetration rates are low is Sri Lanka, WAP could be a viable alternative to access e- commerce application. It should be noted that the majority of mobile phones support WAP. Previous studies have indicated that use of WAP based applications is very popular in many countries. A preliminary survey was conducted to determine usage of WAP based application by mobile phone users in selected areas of Sri Lanka. However, it was found that usage of such applications is very low. The initial findings of this research have shown that mobile users‟ poor knowledge of WAP technology, lack of awareness of the availabe WAP based applications, reluctance to use WAP because availability of Internet access through other ways, vary low bandwith on WAP when accessing the Internet, unavailability of high quality WAP based applications and small size of mobile phone screens as main reasons for poor WAP based application usage in Sri Lanka. Small customber base may discourage service providers in promoting and facilitiating use of such applications.
Machine learning based model for Android malware analysis and detection.
(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Kavneth, G. A. S.; Jayalal, S.
Rapid advancement of technology has enabled smartphones to become extremely powerful. They are capable of attracting a considerable amount of users with new features provided by mobile device operating systems such as Android and iOS. Android extended its lead by capturing 86 percent of the total market in 2017, and became the most popular mobile operating system. The Android operating system, which is found on a wide range of devices is owned by Google and powered by the Linux kernel. It is an open source operating system that enables mobile application developers to access unlocked hardware and develop new apps as they wish. However, this huge demand and freedom has made the hackers and cybercriminals more curious to generate malicious apps towards the Android operating system. They constantly target the security vulnerabilities in the operating system to gain access within the system. Even though, Google provides a primary set of security services, there are possibilities for potentially harmful applications in the Google Play store and other third party application stores. Thus, research on effective and efficient mobile threat analysis becomes an emerging and important topic in cybersecurity research area. Many researchers proposed various security analysis and evaluation strategies such as static analysis and dynamic analysis. In this research, we propose a hybrid approach, which aggregates the static and dynamic analysis for detecting security threats and attacks by Android malware application. This approach has two phases. First phase is the static analysis for applications, which will analyze the mobile application without execution. This focuses on extracting app APK file and examining permission requests made by Android apps that have declared in AndroidManifest.xml, as a means for detecting malwares. Because, in most of cases extra permissions granted by applications will allow the attacker to exploit the device. As the next phase, we perform dynamic analysis for mobile application. This phase focuses on runtime data obtained from the applications such as CPU, scheduler information from every running application, network calls, sensor data and so forth. For both phases, we have used supervised, machine learning based algorithms to train models and recognize malwares. In the first phase, potentially harmful applications can be identified as well as in the proposed hybrid mechanism, which is a combination of both phases. Data that was collected by several cybersecurity research centers were used for the evaluation of the proposed hybrid approach and both real-life malware and benign app data demonstrated a good detection performance with high scalability. The initial findings have been more accurate in identifying Android malwares rather than separating those two static and dynamic behaviors.
Passion Fruit Disease Detection using Image Processing
(IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Dharmasiri, S.B.D.H.; Jayalal, S.
Fruit diseases are a major problem in economic and production losses in the agricultural industry worldwide. In this paper, an image processing approach is proposed for identifying passion fruit diseases. According to the Sri Lankan context, treatment details are taken by the farmers from the field officers. However, it can take a few days. So, this proposed system can be used to identify passion fruit diseases quickly and automatically. This proposed approach is composed of the following main steps; Image Acquisition, Image Preprocessing, Image Segmentation, Feature Extraction, Dataset Preparation, Training & Testing. Healthy and two types of passion fruit diseases, namely passion fruit scab and woodiness images were used for this approach. This approach was tested according to passion fruit disease type and its’ stages, such as mild, moderate and severe. K-Means clustering was used for segmentation. Images were clustered according to k values, such as 2, 4, 6 and 8. Before the segmentation, images were converted to RGB, L*a*b, HSV and Grey colour models, because of find out the most suitable colour model for this approach. Local Binary Pattern was used for feature extraction and Support Vector Machine was used for creating the model. Seventy percent (70%) of each dataset was used to train the SVM and the other thirty percent (30%) was used to test the model. According to this approach, passion fruit diseases can be identified in the average accuracy of 79% and its’ stage can be identified in average accuracy 66%.
Performance Comparison Analysis of Docker Container and Virtual Machine in the Cloud Computing Environment for Database Management Systems
(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Weerarathna, M.L.; Jayalal, S.
Recently, Virtual machine and Container technology have become widely used as two of the most important virtualization technologies in the industry. The virtual machine provides better mechanisms for solving many existing manageability issues in database systems. Therefore, database systems are increasingly running on virtual machines. But after introducing container technology, it has gained increasing attention in recent years and has become an alternative to traditional virtual machines. Some of the core motivations for the enterprise to adopt containerization technologies include application integration and deployment, lightweight operations, as well as resource sharing efficiency and flexibility. This provides many opportunities for researchers in the database systems to deploy the databases with server consolidation, but it is also important to understand the cost of virtualization. The additional abstraction layers provided by virtualization come from the interchange between performance and cost in a cloud computing environment where everything is on a pay-per-use basis. So, containers which are considered to be the future of virtualization are being developed to address mainly this issue. However, a systematic comparative study of the performance of the database servers in the container environment and in the virtual machine environment is still missing. Accordingly, the main objectives of the research study are to monitor, analyze and evaluate the performances of different database servers on the virtual machine and Docker containers and to study which is better for microservice-related database deployments. The proposed comparison environment was designed on the Microsoft Azure cloud computing environment with separated virtual machines and Docker containers on top of the Linux operating system as the host. An experimental research study of comparing virtual machines and containers for the overhead of running a database workload and a critical assessment of each database metric and its behavior basically when subjected to Query execution performance, Load performance and Resource utilization of the standard databases are going to be presented. The initial results have shown that the container gets the manageability benefits of virtualization over the virtual machine. Although query execution is fast, the high query latency is quite noticeable when receiving a large number of data records from container-based database servers. After reviewing the results and discussing the limitations, the conclusion of this research study will be useful for future research as well as database server deployments
Predicting box office success of movies using sentiment analysis and opinion mining
(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Basnayake, H.; Jayalal, S.
Movies and social media come together as a result of people sharing their opinions on social media and movie makers using the same platforms for movie promotions. From movie makers to movie goers, many parties are interested in the success or failure of a movie. Forecasting the success of a movie before its release has been a difficult task for many industry analysts. Since film industry’s unpredictable nature, many analysts have come up with different algorithms and mechanisms to predict the success of a movie. One of the mechanisms to predict the box office success is hype analysis. Hype is one of the factors that drive people to the theatres to watch a new movie. Box office opening of a new movie depends on this hype and it will boost up the total box office collection. Hype can be estimated through social media platforms like Twitter. Twitter can be used as a corpus for sentiment analysis and opinion mining. A movie’s success cannot be predicted in a high accurate level solely based on social factors. Classical factors like movie’s brand name, cast, director, etc. are also important aspects in movie’s performance at box office and should be considered as well. However, a highly accurate method for movie box office prediction integrating both social and classical factors is yet to be introduced for this research area. In this study, tweets related to the particular movie before releasing are collected using an archiver tool and are used as input data. Then the collected data is preprocessed in order to get a clean dataset. As a part of sentiment analysis and opinion mining, feature selection is performed using N-gram method in order to filter out irrelevant data records and unlike Bag of words method, this does not require an extensive dictionary of words since it uses combinations of words and letters. Afterwards the data related to classical factors are integrated with the proposed formula in order to predict the opening box office collection of the movie. The proposed formula is an extension of a formula used in a previous research and the new extension represent the inclusion of classical factors. Finally, the results are compared with actual box office data and the previous formula results in order to compare and determine the level of accuracy. Based on initial results, the proposed formula showed of an accuracy level more than 85 percent when the results were compared with actual box office data. Even though it produced a higher accuracy level, the results produced were less than the actual box office values. Thus further testing is needed to determine the actual accuracy level.
Prediction of User Intentions Using Web History
(IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Samarasinghe, K.T.C.S.; Jayalal, S.
In the present internet has become much more necessary thing to humans and we use it as a way of sharing information and way of communication. If the networks can identify the user’s intentions, it will be affecting to increase productivity and personalization. Predicting user intention(s) is interesting and useful for many applications such as threat identification, imposing restrictions and cashing web details. The aim of this research is to develop a method to predict user intention using supervised machine learning methods with user’s past historical behaviours. Experiments in this study used access log on a local server and focused on creating single user prediction and multiuser generalize prediction models. Experimental models were created based on several multi-classifier algorithms, such as Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbor (KNN). KNN based models outperform other used algorithms. Also results in this study show that there is some sort of behavioural patterns for peoples to use the internet according to the time and the groups they interact
Real-time big data video analytics for unorganized traffic environments
(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Ranaweera Arachchi, I.; Jayalal, S.; Rajapakse, C.
Traffic on local roads has reached such a level that it is necessary to address the issue of traffic congestion and seek complex transport solutions for the city. Increase of the number of vehicles on the road becomes one of the key reasons for increasing traffic congestion. Traffic congestion is associated with massive financial and manhour loss and therefore attempts to alleviate this has been of keen interest. The basis of almost all those approaches is traffic monitoring and analysis, leading to having an effective traffic management system. Most traffic management systems are applied in well-organized traffic environments such as highways, where driver discipline is high. But in unorganized urban environments as seen in Sri Lanka, road traffic behavior vary from the accepted standards. Driver and pedestrian indiscipline cause huge traffic congestions in urban areas. Hence in such a scenario, a system that monitors road traffic on different traffic environments is very useful. There are several existing techniques such as Magnetic Loops, Microwave RADAR, Infrared Detectors, Ultrasonic Detectors and Camera Based Systems. Traffic monitoring systems require short processing time, low processing cost and high reliability. Therefore, according to the literature, camera-based monitoring is the best-suited technique for traffic monitoring. Real-time video analytics are part of a centralized approach to modern traffic management which is defined as computer vision-based surveillance that provides algorithms for object detection, tracking, classification and trajectory analysis using real-time traffic surveillance video. It usually uses roadside cameras (CCTV) to obtain traffic information and transmit it to central servers, exhibiting real-time operability of big data. In this study, several approaches and algorithms for moving object detection, based on temporal differencing method, optical flow method, background filtering are compared and a novel real-time vehicle detection and classification algorithm based on background filtering will be proposed and re-engineered in order to be applicable to challenging unorganized traffic environments. The solution will classify vehicles individually and their trajectories in real time in unorganized traffic environments in order to analyze the behaviors of the drivers as well as pedestrians on the road. We use OpenCV which is a library of programming functions mainly aimed at real-time computer vision, for implementing and testing algorithms. Data will be collected via pre-recorded video clips from Kiribathgoda junction in the western province, for the testing purpose and real- time CCTV surveillance video is going to be used as the input for implementation. A comprehensive data analysis is required to be conducted to address the higher processing requirement of such videos. The solution will be validated for performance subsequently. The final objective of this research is to come up with an optimum algorithm for vehicle detection and classification in unorganized traffic environments which would help to analyze the behavior of road users. The solution will lead to reduced traffic congestion in the country by enhancing the efficiency and effectiveness of traffic monitoring and analyzing systems.
Sinhala Handwritten Postal Address Recognition for Postal Sorting
(IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Ifhaam, M.F.A.; Jayalal, S.
Sri Lankan post office mail sorting process is done manually, even today. Though employees are well experienced, it takes considerable time and employees need to work overtime in places like Central Mail Exchange (CME). With major issues like unclear handwriting, having trouble to recognize some uncommon or ambiguous names, and carrying these duties twice a day create a negative impact on the efficiency of the postal delivery system. In the prevailing system, forward mails and delivery mails are the two categories of separating mails at the sorting centers. Delivery mails are the posts which can be delivered to its destination directly. Forward mails are the ones which need to be sent to an appropriate post office that can deliver the particular post to its destination. Majority of Sri Lankans use Sinhala language for their day to day activities. The primary objective of the research is to identify the automatic way of forwarding the letter to the next post office from the current post office. Proposed system is focused on the recognition of Sinhala handwriting using Optical Character Recognition (OCR) and image processing technologies. Data collected under different criteria were used for training and testing the solution. Genetic Algorithm (GA) was used to generate more optimized results faster with higher accuracy. Given addresses are written in the default format. This format can be extended to more formats as improvements in future. The algorithm shows accuracy over 92% for addresses which are recognized with 3 misrecognized characters. This algorithm can be used on practice scenario as the AI Recognition has more than 79 % of accuracy.
Sinhala language-based social media analysis to detect fake news
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Wijayarathna, W.M.S.N.P.; Jayalal, S.
In a rapidly evolving digital age, societies rely heavily upon social media to express opinions and to share the news, publicly. With billions of users, this fast mode of information exchange takes only a few minutes to take polarized opinions, oftentimes malicious or misleading, to go viral. The objective of this research is to propose a detection technique that can be used to identify fake news published in the Sinhala language to evade public unrest. Approaches to detect fake news generally rely upon features intrinsic to either the user/source or features based on the content in the text or any hybrid set of above features. The hybrid methodology which was applied in this study, mainly focused on the verifiability of the news text content against credible sources and the credibility of the source was used to obtain the news content. Ordinary user tweets and credible sources’ tweets (from 08 sources) were extracted from Twitter. The selected data set consisted of about 6000 credible sources’ tweets. Then, ordinary user tweets were labelled as fake (120) and non-fake (250) using the domain knowledge about the news published in the particular month. Both types of tweets were converted into a numerical format. The text encoding was done using FastText, which derives a word as the vector summation of character n-grams and converts words into a 300-dimensional vector. The average of word vectors in a sentence was taken as the overall sentence numeric representation. Then, the vector representation of each user tweet was compared against credible news tweet vectors to check whether semantically similar contents appeared on credible sources within a given period. Out of the list of similarity scores obtained by each ordinary user-tweet, the maximum similarity score was used for further analysis. Moreover, a point scheme was introduced for features of a user-account by considering their contribution to the overall credibility of the user- account (e.g.: for each of the 10 followers → 1 point). The summation of the points was taken as the user-account credibility score. Then, the formula T𝑣𝑎𝑙 (UC) + (1− T𝑣𝑎𝑙) TS [i.e. 𝑇𝑣𝑎𝑙 ∊ (0.5,1]], where UC is the account credibility score, and TS is the text verification score was generated. Here, 𝑇𝑣𝑎𝑙 > 0 decides the relative contributions of content verification and user-account credibility to the overall tweet’s credibility assessment. In the initial implementation, for T𝑣𝑎𝑙 = 0.7, results indicated a maximum accuracy of 70% with credibility detection of tweets, after comparison with human-annotated tags. While source credibility plays an important role in overall content’s credibility, the study demonstrates that the use of the verification-based method is more effective in Sinhala fake news detection.
Study of machine learning algorithms for Sinhala speech recognition
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Shaminda, S.; Jayalal, S.
Speech is the primary mode of communication among humans and the most natural and efficient form of exchanging information. Therefore, it is logical that the next technological development in natural language speech recognition for Human Computer Interaction is, Artificial Intelligence. Speech recognition can be defined as the process of converting speech signal to a sequence of words by an algorithm implemented using a computer program. Speech processing is one of the challenging areas of signal processing. The main objective of the study was to conduct a study on speech recognition approaches to improve the accuracy level of Sinhala speech recognition. This study was conducted in order to find the optimal algorithm for accurate Sinhala speech recognition. According to the implementation architecture of speech recognition, feature extraction and the pattern recognition phases can be varied with different algorithms. The study identified that Linear Predictive Coding (LPC) and Hidden Markov Model (HMM) gives most accurate results than other combine algorithms.
A Study on the factors that affect establishment of businesses by rural entrepreneurs
(University of Kelaniya, 2008) Siri, J.G.S.; Jayalal, S.
In Sri Lanka, as in other developing countries community development is considered as the development of the rural poor. It is estimated that 88% of the poor communities live in rural areas in Sri Lanka. Rural entrepreneur is a crucial entity of the rural community. As far as the economic development is concerned it is important to address their needs and problems when bridging the gap between poor and rich. A policy strategy recognizing the challenges facing rural entrepreneurs could do much to reduce the regional disparities and rural poverty in Sri Lanka. Rural entrepreneurship is one of the newest areas of research in the entrepreneurship field. It has become one of the significant supportive factors for rural economic development. This study which explored the factors that affect rural entrepreneurs was carried out in selected villages of seven districts namely Anuradhapura, Monaragala, Nuwara Eliya, Kegalle, Kaluthara, Puttalam and Ratnapura. The study covered the rural areas in each district. The Vidatha Resources Centers1, initiated by the government is facilitating technology transfer with a view to promote rural entrepreneurship. Even though many resources have been spent through the Vidatha Resources Centers, the majority of existing and potential entrepreneurs are yet to realize the way to run their business successfully. The main objective of this study was to identify the factors influencing rural entrepreneurs when establishing and expanding their business with a view to fill the knowledge gap in real problems of rural entrepreneurs to be addressed during the process of planning and thereby to maximize output from rural entrepreneurship development programmes. The research process was divided into two phases. In the first phase, secondary document analysis and informal interviews with key entities were carried out. The second phase was characterized by in-depth interviews with female and male entrepreneurs in rural villages, Science and Technology Officers and Field Officers at the Vidatha Resources Centers. The hypotheses tested were that, whether the level of education, age of the entrepreneur and a marketing plan at the beginning influence the success of the establishment and the level of income from the enterprise. 1 Vidatha Resource Centers have been established in rural areas under the purview of Ministry of Science and Technology with a view to facilitate technology transfer from local R&D Institutions/Universities to the rural community Key findings of this study were that the level of education, age of the entrepreneur and a marketing plan at the beginning influence the success ofthe establishment and the level of income from the enterprise. Most of the entrepreneurs who had a marketing plan at the beginning (before establishing the enterprise) have been able to carry out their business successfully and withstand the market forces. It was found that previous experience in the relevant business field, gender of the entrepreneur, availability of new technology, limited access for micro-credits, availability of training programmes and lack of business development for service providers at rural level do not have a significant influence on the success or failure of the enterprise. Since majority of them have utilized their own savings to start up the enterprise, there is no significant influence of limited access to sources of micro credit at the point of establishing the business. However, it was observed that they face difficulties due to limited access to sources of micro-credits when they are going to expand their business further. The reasons for failure of enterprises were also surfaced in this study. Lack of training, difficulty to find a market, inability to find micro-credits and market competition are some of the reasons for failure as given by the entrepreneurs.