Research Papers - Dept of Information Technology
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/593
Browse
18 results
Filters
Advanced Search
Filter by
Settings
Search Results
Publication Embargo Surveillance based Child Kidnap Detection and Prevention Assistance(IEEE, 2022-07-18) Kodikara, K. A. O. V; Hettiarachchi, P; Prathapa, D. M. J; Jayakody, J. M. A. M. S; Haddela, P. SThis research paper is based on child kidnap detection and prevention to identify susceptible child kidnap by unauthorized persons. The intelligent surveillance system proposed for this is known as "AICare". The purpose behind developing a proper kidnap detection methodology is to enhance and strengthen the existing child security systems. The key is to identify the main characteristics of a kidnapper in real-time, which follows face recognition, speed detection and object detection theories. Face recognition is used to identify whether the outlined individual has covered his body, especially his face, to hide the true identity, or else the person's face is directly processed for authentication. Speed detection is helpful in calculating movement speed and capturing whether the targeted individual is moving in a hurry. Finally, the stranger is subjected to object detection in order to classify whether he/she is handling a sharp object or not. The captured outcomes are subjected to a decision tree to resolve the person as a kidnapper suspect. The system results in an overall accuracy that is above 90%. As this solution is child-sensitive and responsive, it provides a long term platform that can real-time monitor for potential kidnap based on the kidnapper characteristics to support the working from home parents to take care of their children during crucial times.Publication Embargo Student Teaching and Learning System for Academic Institutions(IEEE, 2022-07-18) Kumarasiri, A. D. S. S; Delwita, C. E. M. S. M; Haddela, P. S; Samarasinghe, R. P; Udishan, R. P. I; Wickramasinghe, LIn today's global online environment, automation is essential for establishing a competitive advantage. Conversational Artificial Intelligence Systems are an example of an automation technology that has been embraced by some of world's most famous companies. In the field, higher education, these handy gadgets come in handy for administrators.Students' responses and performances are highly important and desired areas to improve the teaching-learning environment in the education organism. Evaluate the feedback to aid in the identification of flaws and actions. Institutes and universities in the field of education collect both quantitative and qualitative responses in order to improve the teaching-learning environment. However, most educational institutions and universities lack a sufficient student feedback evaluation system for determining students' feelings. The grading technique is currently being utilized to collect input. However, the grading process does not reveal the students' genuine feelings about the educational system, whereas written feedback allows students to describe specific areas and situations. Sentiment analysis is a type of qualitative feedback analysis that is based on records. The Nave Bayes (NB) classifier, Support Vector Machine (SVM), and Random Forest were employed in the majority of recent machine learning-based Sentiment Analysis prototypes. When it comes to measuring performance, most people utilize the kids' average grades, however this is not an accurate way. Because students can benefit from the experience indefinitely.This proposal offered a method for analyzing the sentiment of students' responses by combining Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence algorithms (AI). The goal is to combine machine learning algorithms and natural language processing approaches to achieve high accuracy. In this proposal, I suggested a collective model that connects machine learning algorithms to improve accuracy and performance. At the end of the presentation, AI planned to collect student responses in real time. The proposed method analyzes student comments from course reviews to determine mood, emotions, and satisfaction vs. displeasure. The approach categorizes sentimentalities into two categories: positive and negative, and senders' feelings into eight categories (8 emotion groups). The Fuzzy logic technique will be used to assess student performance. We intend to make our program available for broad use in the education industry so that organizations can improve the teaching-learning environment's quality.Publication Embargo EEG Based Neuromarketing Recommender System for Video Commercials(IEEE, 2022-01-03) Bandara, S. K; Wijesinghe, U. C; Jayalath, B. P; Bandara, S. K; Haddela, P. S; Wickramasinghe, L. MVideo commercials are well known and extremely popular because of the innovative progression in technology and competition in the business fields. Therefore, breaking down the effect of these video commercials are essential before the start of marketing and promotions. Mainly this study addresses a strategy that can assess the viability of movie commercials which are basically trailers, utilizing Electroencephalogram (EEG) signals caught from a Brain-Computer Interface (BCI). This helps the business to have a thought regarding the effect of the commercial and the value. The study consists of a recommend system which suggests movie advertisements based on the preferences of the users. The particular video will be evaluated from emotion, attention and enjoyment aspect of the user. Random Forest prediction algorithm with 91.97% accuracy was used for emotion analysis, c-Support Vector Classifier (c-svc) algorithm with 91.70% accuracy was used for attention analysis while using statistical approach Central Limit theorem and Empirical rule was used for enjoyment analysis to isolate particular emotion state. From the initial results received, confirms that the proposed framework is producing promising results. Although this research is currently focused on movie and entertainment industry, and also has the potential to be developed and applied in many other industries.Publication Embargo Effective Mechanism to Mitigate Injuries During NFL Plays(IEEE, 2019-12-18) Ahamed, A. A. A; Arulanantham, A; Rajalingam, G; Ingran, K. S; Haddela, P. SNFL (American football), which is regarded as the premier sports icon of America, has been severely accused in the recent years of being exposed to dangerous injuries that proves to be a bigger crisis as the players' lives have been increasingly at risk. Concussions, which refer to the serious brain traumas experienced during the passage of NFL play, have displayed a dramatic rise in the recent seasons concluding in an alarming rate in 2017/18. Acknowledging the potential risk, the NFL has been trying to fight via NeuroIntel AI mechanism [3] as well as modifying existing game rules and risky play practices to reduce the rate of concussions. As a remedy, we are suggesting an effective mechanism to extensively analyse the potential concussion risks by adopting predictive analysis to project injury risk percentage per each play and positional impact analysis to suggest safer team formation pairs to lessen injuries to offer a comprehensive study on NFL injury analysis. The proposed data analytical approach differentiates itself from the other similar approaches that were focused only on the descriptive analysis rather than going for a bigger context with predictive modelling and formation pairs mining that would assist in modifying existing rules to tackle injury concerns. The predictive model that works with Kafka-stream processor real-time inputs and risky formation pairs identification by designing FP-Matrix, makes this far-reaching solution to analyse injury data on various grounds wherever applicable.Publication Embargo Moderate Automobile Accident Claim Process Automation Using Machine Learning(IEEE, 2021-01-27) Imaam, F; Subasinghe, A; Kasthuriarachchi, H; Fernando, S; Haddela, P. S; Pemadasa, NIn modern-day, traditional automobile accident claim process struggles to keep up with the recurring automobile accidents and furthermore, the claim itself is a critical point in which the policyholder may decide to switch to a different automobile insurance provider. In this paper, the authors present a system which can be used to automate the processing of claims for automobiles which were involved in less severe accidents in a much quicker manner. The presented system comprises of four components, each with a model developed using computer vision or machine learning techniques to facilitate the automation process. The models are built and fine-tuned using transfer learning and ensemble learning techniques in order to determine the damaged component of the automobile, determine the make and model of the automobile, compute an accurate repair estimate and also compute the likeliness of the policyholder may churn, to ensure that the policyholder is satisfied with the appraised amount and will be retained by the insurance provider.Publication Embargo Document Clustering with Evolved Single Word Search Queries(IEEE, 2021-06-28) Hirsch, L; Haddela, P. S; Di Nuovo, AWe present a novel, hybrid approach for clustering text databases. We use a genetic algorithm to generate and evolve a set of single word search queries in Apache Lucene format. Clusters are formed as the set of documents matching a search query. The queries are optimized to maximize the number of documents returned and to minimize the overlap between clusters (documents returned by more than one query in a set). Optionally, the number of clusters can be specified in advance, which will normally result in an improvement in performance. Not all documents in a collection are returned by any of the search queries in a set, so once the search query evolution is completed a second stage is performed whereby a KNN algorithm is applied to assign all unassigned documents to their nearest cluster. We describe the method and compare effectiveness with other well-known existing systems on 8 different text datasets. We note that search query format has the qualitative benefits of being interpretable and providing an explanation of cluster construction.Publication Embargo Ads-In Site: Location based advertising framework with social network analyzer(IEEE, 2014-12-10) Perera, A. A. G. A. K; Jayarathne, R. P. E. T; Thilantha, B. Y; Kalupahana, S. I. G; Haddela, P. S; Kirupananda, A; Edirisinghe, E. A. T. DSocial networks contain a vast amount of data that holds very valuable information and is nowadays identified as a very effective source in marketing. The content of social networks can be used to identify the business needs and preferences of people. This research is carried out with the aim of providing suitable advertisements for people based on their preferences by analysing their social network content. Users' demographic details are also considered in providing suitable advertisements of the shops that are most conveniently located for a particular user. The primary goal of this research is to build an advertisement framework that supports targeted advertising by analysing social network content. The information extracted by analysing the content of social networks is used to predict the advertisement categories that interest a particular user. The framework applies location based services to filter advertisements based on the location of the shop.Publication Embargo Google map and camera based fuzzified adaptive networked traffic light handling model(IEEE, 2018-12-05) Nirmani, A; Thilakarathne, L; Wickramasinghe, A; Senanayake, S; Haddela, P. SRising traffic congestion has turned into a certain issue as the number of vehicles on roads are increasing. This research study was conducted to develop `Google Map and Camera Based Fuzzified Adaptive Networked Traffic Light Handling Model'. The main road with six major junctions was selected as the target route for the project. During this study, we were able to plan a limit and control traffic congestion utilizing two neural networks which process together to provide an efficient, productive and optimized solution based on real-time situations. Real-time video streams and Google Map traffic layer were used as primary input sources to the system. The Main algorithm was used to reduce traffic at a specific point whereas secondary algorithm was used to produce optimum decisions for the overall network. As a further advancement, REST endpoint was implemented to get the best route considering all the accessible data. With the aid of the previously mentioned techniques, an optimal traffic management model was developed.Publication Embargo Dynamic stopword removal for Sinhala Language(IEEE, 2019-10-08) Jayaweera, A. A. V. A; Senanayake, Y. N; Haddela, P. SIn the modern era of information retrieval, text summarization, text analytics, extraction of redundant (noise) words that contain a little information with low or no semantic meaning must be filtered out. Such words are known as stopwords. There are more than 40 languages which have identified their language specific stopwords. Most researchers use various techniques to identify their language specific stopword lists. But most of them try to define a magical cut-off point to the list, which they identify without any proof. In this research, the focus is to prove that the cut-off point depends on the source data and the machine learning algorithm, which will be proved by using Newton's iteration method of root finding algorithm. To achieve this, the research focuses on creating a stopword list for Sinhala language using the term frequency-based method by processing more than 90000 Sinhala documents. This paper presents the results received and new datasets prepared for text preprocessing.Publication Embargo Enhanced Tokenizer for Sinhala Language(IEEE, 2019-10-08) Senanayake, S. Y; Kariyawasam, K. T. P. M; Haddela, P. STokenization process plays a prominent role in natural language processing (NLP) applications. It chops the content into the smallest meaningful units. However, there is a limited number of tokenization approaches for Sinhala language. Standard analyzer in apache software library and natural language toolkit (NLTK) are the main existing approaches to tokenize Sinhala language content. Since these are language independent, there are some limitations when it applies to Sinhala. Our proposed Sinhala tokenizer is mainly focusing on punctuation-based tokenization. It precisely tokenizes the content by identifying the use case of punctuation mark. In our research, we have proved that our punctuation-based tokenization approach outperforms the word tokenization in existing approaches.