Research Publications

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Subject: search.filters.subject.Neural Networks

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Now showing 1 - 8 of 8
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    PublicationOpen Access
    A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis
    (PLOS ONE, 2025-12-29) Wijethilaka R.W.K.S; Yapa, K; Siriwardena, D
    In an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic to detect and classify various cyber attacks. The research begins with an extensive review of existing intrusion detection techniques, highlighting the limitations of traditional methods when addressing the unique security challenges posed by distributed networks. To overcome these limitations, advanced machine learning algorithms, including Random Forest, Long Short Term Memory (LSTM) networks, Artificial Neural Networks (ANN), XGBoost, and Naive Bayes, are employed to create a robust and adaptive intrusion detection system. The practical implementation utilizes a Raspberry Pi as the central processing unit for real time traffic analysis, supported by hardware components such as Ethernet cables, LEDs, and buzzers for continuous monitoring and immediate threat response. A comprehensive alert system is developed, sending email notifications to administrators and activating physical indicators to signify detected threats. Our proposed NIDS achieves 96.5 detection accuracy on the NF-UQ-NIDS dataset, with a significantly reduced false positive rate after applying SMOTE. The system processes real time network traffic with an average response time of 50 milliseconds, outperforming traditional IDS solutions in accuracy and efficiency. Evaluation using the NF-UQ-NIDS dataset demonstrates a significant improvement in detection accuracy and response time, establishing the system as an effective tool for safeguarding networks against emerging cyber threats.
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    PublicationOpen Access
    Bat.CG: Development of a Customizable Cricket Character Generator Web Application for Enhanced Broadcasting Experience
    (SLIIT City UNI, 2025-07-08) Kiriyalagammana, P. P; Niranga, G.D.H
    Cricket broadcasting has evolved significantly with technological advancements, yet traditional systems remain fragmented and technically complex for broadcasters. This research presents Bat.CG (Cricket Character Generator), an innovative web-based application that integrates customizable broadcasting graphics with realtime scoring, neural network-driven predictions, and enhanced audience interaction. The system addresses critical gaps in existing broadcasting infrastructure by providing a unified platform that eliminates the dependency on separate Character Generator (CG) and ball-by-ball scoring systems. Through comprehensive market research involving 81 industry professionals, this study identified key requirements including customizable graphics (99% demand), emergency score updating capabilities (87.7% essential), and integrated CG-scoring systems (49.4% extremely valuable). The proposed solution utilizes the MERN stack (MongoDB, Express.js, React, Node.js) architecture with hybrid neural network and regression models for match predictions. Key innovations include drag-and-drop graphic customization without programming knowledge, real-time data synchronization with sub-second latency, role-based access control, and interactive viewer engagement features. The system's modular design ensures scalability, security, and accessibility while maintaining professional broadcast quality. This research contributes to democratizing cricket broadcasting technology and establishing a foundation for future sports media innovations in developing regions.
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    PublicationEmbargo
    SMART DIARY: Autonomous System for Daily Diary Creation and Prioritization of Daily Activities for Improved Well-Being Using Neural Networks and Machine Learning
    (IEEE, 2022-12-09) Abraar, S.F.M.; Thuduhenage, D.T.; Balasubramaniyam, V.P.; Mohanraj, S.R.; Wimalaratne, G; Rajapaksha, S
    In the present world, the IT (Information Technology) industry is so advanced that it has opened many opportunities to communities with numerous roles. Even though the industry is growing day by day and providing more opportunities, it has had serious effects on human well-being. If a person fails to control the demands of work or study, such as tasks with higher complexity, an unmanageable workload, pressure, enduring conflicts within the team, and other physical and emotional demands, it could lead that person to exhaustion, anxiety, and stress. Such factors can affect the health of a person in an extremely negative way. The proposed topic “Smart Diary: Auto generation of diary and Prioritization of Daily Activities for Improved Well-Being” is a solution for people with uncontrolled job demands and busy work schedules. This helps to keep track of day-to-day life activities and review them to make better plans for the future. It also helps the user prioritize their daily tasks and provides suggestions for people who are stressed and showcasing negative emotions based on text analysis.
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    PublicationEmbargo
    Plant recognition system based on Neural Networks
    (IEEE, 2013-01-23) Rankothge, W. H; Dissanayake, D. M. S. B; Gunathilaka, U. V. K. T; Gunarathna, S. A. C. M; Mudalige, C. M; Thilakumara, R. P
    With the evolution of technologies, people have adopted their day today lives to utilize the benefits of highly advanced technologies. Artificial Intelligence and Neural Networks are playing major roles in this process and they have been involved in fields of medicine, automobiles, aeronautical science, military and many more. Unfortunately very little concern is devoted to the botanical science field, especially in taxonomic researches of plants. Even today, identification and classification of unknown plant species are performed manually by expert personnel who are very few in number. It takes a long time and the results are not very accurate. Advanced Plant Identification System (APIS) is an intelligent system which has the ability to identify tree species from photographs of their leaves and it provides more accurate results within less time.
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    PublicationEmbargo
    A Geophone Based Surveillance System Using Neural Networks and IoT
    (IEEE, 2020-12-10) Hettigoda, S; Jayaminda, C; Amarathunga, U; Thaha, S; Wijesundara, M; Wijekoon, J
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.
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    PublicationEmbargo
    Domain Specific Conversational Intelligence: Voice Based E-Channeling System
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Weerathunga, W.A.H.; Lokugamage, G.N.; Hariharan, V.; Yahampath, A.D.N.H.; Kasthurirathna, D.
    In this research the application of Automatic Speech Recognition, Natural Language Understanding, Neural Networks and Text To Speech Conversion is investigated to create a domain specific end to end voice based E-Channeling system. The novel idea in this research can be extended to any other domain(e.g.: Taxi Application) and build a conversational intelligence system. This system enables the user to avoid the shortcomings in the traditional doctor appointment channeling procedures. The system also have the ability to predict the doctor specialization according to the symptoms of the patient and can give emergency health tips by using the powerful Neural Network module. Domain-specific speech recognition model is created according to Sri Lankan accents and handles the context-specific to this domain(94% accuracy). Extracting the entities, handling e-channeling functions and selecting the most suitable API is done by the RASA backend. Neural Network will give the first aid and doctor specialization recommendations according to user input with a validation accuracy of 90%. Speech synthesis model will output the response in user preferred language(Sinhala, English or Tamil).
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    PublicationEmbargo
    A Geophone Based Surveillance System Using Neural Networks and IoT
    (IEEE, 2020-12-10) Hettigoda, S; Jayaminda, C; Amarathunga, U; Thaha, S; Wijesundara, M; Wijekoon, J
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.
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    PublicationEmbargo
    Stock Market Prediction Using Machine Learning Techniques
    (IEEE, 2019-12-05) Sirimevan, N; Mamalgaha, I. G. U. H; Jayasekara, C; Mayuran, Y. S; Jayawardena, C
    Predicting stock market prices is crucial subject at the present economy. Hence, the tendency of researchers towards new opportunities to predict the stock market has been increased. Researchers have found that, historical stock data and Search Engine Queries, social mood from user generated content in sources like Twitter, Web News has a predictive relationship to the future stock prices. Lack of information such as social mood was there in past studies and in this research, we discuss an effective method to analyze multiple information sources to fill the information gap and predict an accurate future value. For this, LSTM - RNN models were employed to analyze sperate sources and Ensembled method with Weighted Average and Differential Evolution technique were used for more accurate prediction of the stock prices. And highly accurate predictions were made to one-day, seven-days, 15-days and 30 days for the future. So that investors could gain an insight into what they are inventing for and the companies to track how well they will perform in the stock market.