Faculty of Computing
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Publication Embargo Pre-Interview Reviewer Using Natural Language Processing for Software Engineers in The IT Industry : Pre-interview reviewer(IEEE, 2023-03-28) Shaman, M.A.M.; Samarasinghe, S.M.; Tharinda, D; Gamage, D.C.; Gamage, AThe internet use to look for work has increased dramatically in recent years, and the number of job applicants has also increased. Numerous conventional websites promote online job postings using graphical and writeable ways. But the respective websites do not support HR (Human resources) in selecting the most suitable person from the candidates applying for the individual jobs. So, The HR department, faced with a large pool of applicants, faces the task of selecting the best one. To address this problem, we've developed a new web tool called "Pre-interview reviewer" that can be used by any company in the IT sector to quickly and easily narrow down the pool of applicants for software engineering roles. We use various methods to narrow down the pool of potential hires. We are training an algorithm to identify the best candidates by assessing the Curriculum Vitae (CV), providing selected questionaries relevant to the HR-provided job description requirement, and using leadership and teamwork skills to analyze questions. Using this system, the companies can utilize the pre-interview sections. Then time consummation will happen. Here, we take a natural language processing-based approach to creating a web app and web-dev languages, among others. In the end, this approach is meant to take advantage of the intricate nature of company recruitment.Publication Embargo Emission Activity Parts Extraction using custom Named Entity Recognition(IEEE, 2022-12-09) Mannavarasan, M; Gamage, A; Sivarajah, V; Chandrasiri, SDental health-related disorders have proliferated worldwide due to the excessive intake of fast food and sugary foods, which was followed by bad oral hygiene practices. The cost of dental examinations may change based on how critical the condition is, regardless of whether they are not regular. For a person, diagnosing an oral health problem, particularly locating the disease’s underlying cause, can be challenging. To properly diagnose and treat such conditions, advanced dental diagnostic techniques may be necessary. By offering convenience and enhancing their oral health knowledge, the system seeks to serve as a prediction tool that regular people can utilize to detect potential tooth illnesses at an early stage. It is encompassed as a mobile application where a Mask R-CNN model is used in the core that accepts a dental radiograph as the input. The trained model will be able to identify diseases related to the bone and teeth. Based on the performance evaluations, the accuracy of the results that are obtained in tooth type, restoration quality, dental caries, and periodontal disease identification falls in the range of 75%-80%.Publication Embargo Analyzing Fisheries Market, Shrimp Farming & Identifying Fish Species using Image Processing(IEEE, 2022-12-09) Sumeera, S; Pesala, N; Thilani, M; Gamage, A; Bandara, PThe fisheries industry is vital to the Sri Lankan economy because it provides a living for more than 2.5 million coastal communities and meets more than half of the country’s animal protein needs. Today, the fishery community in Sri Lanka is facing several grant problems. Among them, not getting a decent fish price for their harvesting, the inability to identify diseases in shrimp cages in the early stages, and the inability to identify fish species by observing their external appearance. This research developed a prototype mobile application “Malu Malu” to avoid the above-mentioned problems. It facilitates to the prediction of market fish prices, identifying shrimp diseases in their early stages, and identifying fish species by observing their external appearance. The proposed predictive models of the “Malu Malu” contains three main models developed using inseption V3 Convolutional Neural Network (CNN) model for image classification and Linear Regression is used for creating a model for predictions. The experimental results of these models showed above 85% of accuracy.Publication Embargo A Novel Ranked Emission-Factor Retrieval for Emission Calculation(IEEE, 2022-11-22) Paskaran, S; Gamage, A; Chandrasiri, SEmission Factors (EF) selection is a vital task during Carbon Management Systems (CMS) emission calculation. Due to Carbon footprint reduction regulations, there is a demand increase for CMS with better usability and scalability. However, most CMS assumes users know emission technologies well. To circumvent these problems, authors have proposed an approach to building an EF ranking system with a combined scoring approach. It has considered each EF as a document unit and emission activity information provided by the user as the search query. This system uses a linear combination of the Vector Space Model (VSM) and Natural Language Processing (NLP) Word Embedding techniques to rank EF documents for exact and non-exact search queries. This approach's user satisfaction measured with Mean Average Precision (MAP) for “glove-wiki-gigaword-300” at 0.41 linear combination parameter was nearly 30% better than the VSM model and 127% more than the word embedding. In addition, the paper discusses performance metrics such as speed, future EFs scalability, and system resource utilization concerning the solution's overall scalability. This approach can provide better usability and scalable for EF selection tasks compared to single-ranking approaches (VSM or Word Embedding).Publication Embargo Sinhala Part of Speech Tagger using Deep Learning Techniques(IEEE, 2022-12-21) Sathsarani, M.W.A.R.; Thalawaththa, T.P.A.B.; Galappaththi, N.K.; Danthanarayana, J.N.; Gamage, ANatural Language Processing (NLP) is a sub-field of Artificial Intelligence (AI) that consists of a collection of computational methods motivated by theory for the automated classification and reflection of human languages. The foundation for many sophisticated applications of NLP, including named entity recognition, sentiment analysis, machine translation, in-formation retrieval, and information processing, is laid by Part of Speech (POS) tagging, which is part of the lexical layer of NLP systems. In contrast to English, French, German, and other languages from the same geographical region, the development of high-accuracy, stable POS taggers for the Sinhala language is still in its early stages. Hence, Sinhala is identified as a low-resource language. The main objective of this research is to create a POS tagger for the Sinhala language to solve this issue. An innovative and novel strategy that has never been used with the Sinhala language has been designed. This approach has been suggested specifically to evaluate the possibility of enhancing the accuracy compared to other methodologies. So, deep learning algorithms have been applied in this study, which has a significant impact on improving tagger performance. First, highly accurate individual classifiers for primary POS tags were implemented, and then they were combined into one composite model. As expected, all individual classifiers and the final composite model have achieved a higher accuracy level. Thus, it demonstrates that the proposed solution using deep learning algorithms outperformed other methods, such as rule-based and stochastic, in terms of accuracy.Publication Embargo Mobile Base Solution for Individuals with Limited Knowledge About Cars(IEEE, 2022-12-09) Nammunige, H; Chamuditha, T; Udara, S; Athapaththu, D; Gamage, A; Gamage, NDifferent modes of transportation were discovered by our ancestors from ancient times. Currently, the majority of people choose to purchase a personal automobile for transport needs. However, the vast majority of people are not automobile industry experts. As a result, the majority of people have trouble when recognizing cars. Due to numerous variations of a single vehicle model, even an expert has trouble correctly identifying a certain car model. People must take into account a number of factors before purchasing a specific automobile. Some of crucial factors are service costs and future market prices. Ordinary people require the assistance of a professional when estimating the market price of a car and calculating the cost of servicing a car. Accidents can also occur at any time when driving a car often. In similar circumstances, consumers require the assistance of an insurance agent or a technician to estimate the cost of damage repair. In this study, we provide a way for non-automotive experts to use their smartphones to identify car models, forecast future market prices, determine and forecast servicing costs, and estimate minor damage repair costs. This paper demonstrates how we accomplished aforementioned tasks using YOLO V4, Multiple Linear Regression, Random Forest Classifier and Faster RCNN.Publication Embargo Carbon Emission Optimization Using Linear Programming(IEEE, 2022-12-09) Magenthirarajah, V; Gamage, A; Chandrasiri, SIn this fast-growing modernization, excess carbon emission plays a crucial role in climate change. Targeting and experimenting with sustainable ways of Carbon neutrality and management is the pathway toward a greener society. Data show that factories and industries take a high market stake in carbon emission and management. In actions, Governments defined a limit for carbon emissions to each organization which is called carbon credit. Every organization must focus on reducing carbon emissions. This is a critical task for each organization, In some cases, it is still not possible to explore other sustainable options. An innovative solution proposed for the above scenario is to implement a real-time platform that can provide insights into the most up-to-date emission statistics of the organization. This paper provides advanced analytics and precise proactive planning and actions in the simplest form and a discussion on future elaborations and insights about conclusions. By finding the minimum optimal emission values of each emission source, organizations can maintain carbon emissions without exceeding their carbon credit. Also, how industries and factories can create a smart carbon optimization system that can create an even greener society.Publication Embargo Planning Marketing Strategies in Small-Scale Business Using Data Analysis(IEEE, 2022-12-29) Fernando, A.M.P.; Adhikari, A.M.T.T.; Wijesekara, W.H.A.T.K.; Vithanage, T.V.T.I.; Gamage, A; Jayalath, TThe proposed research work develops a system focused on business opportunities to enhance market returns and improve marketing strategies and new strategies by identifying how customers interact with products and their behavior. Existing research efforts attempt to identify and market consumer attraction to products and marketplace in the marketplace. Current research focuses on the challenges of identifying consumer buying patterns and how consumers interact with products, Existing research has not, however, integrated the essential elements into a single system. Consequently, the recommended study has been conducted on a number of significant issues, such as determining the high-value client base and the number of sectors, understanding the purchasing pattern of products that comprise the customers' basket, identifying customer lifetime value, and Customer Trajectory Determination for identifying customer attractive shelf. This system focuses on various machine learning algorithms. Customer segmentation and value analysis using K Mean, Agglomerative, Clustering algorithm, and Arima model. Association rules are generated using the Apriori algorithm for market basket analysis, which is built on the idea that a set of frequently purchased items is a subset of a set of frequently purchased items. Also using RFM analysis to create and prepare our data frame by using BG/NBD model and the Gamma-Gamma model to calculate the customer lifetime value standardization. Using image processing algorithms and retail video analysis algorithms, background reduction technology clearly identifies moving objects/ tracks customer routes using different colors. Based on results from implementation and testing, it was determined that the suggested technique outperformed the use of CCTV to identify consumer behavior and satisfaction with the product in recognizing customer purchasing patterns. The proposed system can identify customers' buying patterns, how customers interact with product...Publication Embargo UNWIND – A Mobile Application that Provides Emotional Support for Working Women(IEEE, 2022-10-11) Kugapriya, P; Manohara, M; Ranganathan, K; Kanapathy, D; Gamage, ADepression is a common phenomenon affecting more than 264 million people worldwide. It is one of the leading causes of disability and a major contributor to the overall global burden of disease. Around twice as many women are affected by mental illness compared to men. This situation has worsened during the pandemic. The need to balance both work life and personal life has put them under immense pressure. Even though the diagnosis of mental illness almost exclusively depends on doctor-patient communication, it has its own set of disadvantages such as patient denial, recall bias, subjective biases, time-consuming and inaccuracy and it is a long-term health problem that needs to be continuously monitored and managed. Considering this social problem, we have planned to develop an Emotional Support Mobile application UNWIND – using modern technological concepts of machine learning and artificial intelligence. Which focuses especially on working women and would include several functionalities: a Chabot to detect mental health status in real-time and to provide counseling, an internal activities tracker to find the correlation between changes in lifestyle and mental health, an improvement tracker of the user’s current mental state using facial recognition and also Recommendation system with the support group, which recommends the most suitable professional counselors to the user as per their preferences and enabling into the support group to provide with necessary treatments and consultation at greater accuracy.Publication Embargo AI and Machine Learning Based E - Learning System For Secondary Education(IEEE, 2022-07-18) Wijayawardena, G. C. S; Subasinghe, S. G. T. S; Bismi, K. H. P; Gamage, AOne of the key functions directly shifted to online platforms under COVID-19 is education. The paper is about an E-learning system for secondary education in Sri Lanka. Learners and teachers can access information, resources, and tools through an E-Learning system, which is a Learning Management System that integrates a number of online activities. The main functions provided through the proposed system are chatbot, final grade prediction and weak area prediction of the students. Chatbots are becoming increasingly popular in a wide range of applications, especially in those that provide intelligence support to the user, according to recent research. So, in order to speed up the aid process, these systems are often integrated with Chatbots, which can quickly and accurately read the user's questions. This paper describes the implementation of a Chatbot prototype in the educational domain: a system for providing support to students. In the beginning, the goal was to design a special architecture and communication model that would help students get the proper answers. The final grade prediction component plays major role in the system. Because when the students are graded by their marks, they can review which areas that they have to improve and work on them. This is helpful for students as well as teachers. Weak area prediction also plays a significant role, because it can help to find out the weak areas of each subject and generate Individual Student Progress Plans to predict the students’ weak subjects and the subject areas of the students. This motivates students to get higher marks easily because this part is mainly focused on weak areas of students and improve those weak areas by providing several learning activities. These are the major parts of this system to have a good E-learning system for both students and Teachers.