Faculty of Computing

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Open Access
6-REXOS: Upper limb exoskeleton robot with improved pHRI
(SAGE Publications, 2015-04-29) Gunasekara, M; Gopura, R; Jayawardena, T. S. S
Close interaction can be observed between an exoskeleton robot and its wearer. Therefore, appropriate physical human-robot interaction (pHRI) should be considered when designing an exoskeleton robot to provide safe and comfortable motion assistance. Different features have been used in recent studies to enhance the pHRI in upperlimb exoskeleton robots. However, less attention has been given to integrating kinematic redundancy into upper-limb exoskeleton robots to improve the pHRI. In this context, this paper proposes a six-degrees-of-freedom (DoF) upperlimb exoskeleton robot (6-REXOS) for the motion assistance of physically weak individuals. The 6-REXOS uses a kinematically different structure to that of the human lower arm, where the exoskeleton robot is worn. The 6-REXOS has four active DoFs to generate the motion of the human lower arm. Furthermore, two flexible bellow couplings are attached to the wrist and elbow joints to generate two passive DoFs. These couplings not only allow translational motion in wrist and elbow joints but also a redundancy in the robot. Furthermore, the compliance of the flexible coupling contributes to avoiding misalignments between human and robot joint axes. The redundancy in the 6- REXOS is verified based on manipulability index, mini‐ mum singular value, condition number and manipulability ellipsoids. The 6-REXOS and a four-DoF exoskeleton robot are compared to verify the manipulation advantage due to the redundancy. The four-DoF exoskeleton robot is designed by excluding the two passive DoFs of the 6- REXOS. In addition, a kinematic model is proposed for the human lower arm to validate the performance of the 6- REXOS. Kinematic analysis and simulations are carried out to validate the 6-REXOS and human-lower-arm model.
Open 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.
Open Access
A Deep Learning-Based Dual-Model Framework for Real-Time Malware and Network Anomaly Detection with MITRE ATT&CK Integration
(Science and Information Organization, 2025) Migara H.M.S; Sandakelum M.D.B; Maduranga D.B.W.N; Kumara D.D.K.C; Fernando, H; Abeywardena, K
The contemporary world of high connectivity in the digital realm has presented cybersecurity with more advanced threats, such as advanced malware and network attacks, which in most cases will not be detected using traditional detection tools. Static cybersecurity tools, which are traditional, often fail to deal with dynamic and hitherto unseen attacks, including signature-based antivirus systems and rule-based intrusion detection. To ad-dress this issue, we would suggest a two-part, AI-powered solution to cybersecurity which would allow real-time threat detection on an endpoint and a network level. The first element uses a Feedfor-ward Neural Network (FNN) to categorize Windows Portable Ex-ecutable (PE) files, whether they are benign or malicious, by using structured static features. The second component improves net-work anomaly detection with a deep learning model that is aug-mented by Generative Adversarial Networks (GAN) and effec-tively addresses the data imbalance issue and sensitivity to rare cyber-attacks. To enhance its performance further, the system is integrated with the MITRE ATT&CK adversarial tactics and techniques, which correlate real-time detection results with adver-sarial tactics and techniques, thus offering actionable context to incident response teams. Tests based on open-source datasets pro-vided accuracies of 98.0 per cent of malware detection and 96.2 per cent of network anomaly detection. Data augmentation using GAN was very effective in improving the detection of less popular attacks, including SQL injections and internal reconnaissance. Moreover, the system is horizontally scalable and responsive in real-time due to Docker-based deployment. The suggested frame-work is an effective, explainable and scalable cybersecurity de-fense system, which is perfectly applicable to Managed Security Service Providers (MSSPs) and Security Operations Centers (SOCs), greatly increasing the precision rate and contextual in-sight of threat detection. © (2025), (Science and Information Organization)
Open Access
A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation
(Elsevier B.V., 2025-03-06) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I, U; Jayakody, A; Rathnayake, U
Cabbage (Brassica oleracea var. capitata) is commonly cultivated in high altitudes and features dense, tightly packed leaves. The Green Coronet variety is well-known for its robust growth and culinary versatility. Maximizing yield is crucial for food sustainability. It is essential to predict the soil’s major nutrients (nitrogen, phosphorus, and potassium) to maximize the yield. Artificial intelligence is widely used for non-linear predictions with explainability. This research assessed the predictive capabilities of soil nitrogen, phosphorus, and potassium levels with explainable machine learning methods over an 85-day cabbage growth period. Experiments were conducted on cabbage plants grown in central hills of Sri Lanka. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to clarify the model’s predictions. SHAP analysis showed that high feature values of the number of days and plant average leaf area negatively impacted for nutrient predictions, while high feature values of leaf count and plant height had a positive effect on the nutrient predictions. To validate the results, 15 greenhouse-grown cabbage plants at various growth stages were selected. The nitrogen, phosphorus, and potassium levels were measured and compared with the predicted values. These insights help refine predictive models and optimize agricultural practices. A user-friendly application was developed to improve the accessibility and interpretation of predictions. This tool is a user-friendly platform for end-users, enabling effective use of the model’s predictive capabilities.
Embargo
Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks
(IEEE, 2019-08) Perera, S; Bell, M; Kurauchi, F; Kasthurirathna, D
Recent developments in the area of network science has encouraged researchers to adopt a topological perspective in modelling Supply Chain Networks (SCNs). While topological models can provide macro level insights into the properties of SCN systems, the lack of specificity due to high level of abstraction in these models limit their real-world applicability, especially in relation to assessing the impact on SCNs arising due to individual firm or supply channel level disruptions. In particular, beyond the topological structure, a more comprehensive method should also incorporate the heterogeneity of various components (i.e. firms and inter-firm links) which together form the SCN. To fill the above gap, this work proposes using the idea of absorbing Markov chains to model disruption impacts on SCNs. Since this method does not require path enumeration to identify the number of supply chains which form the SCN, it is deemed more efficient compared to the other traditional methods.
Open Access
Abstract concepts: A contemporary requirement for Rich Internet Applications engineering
(2016) Dissanayake, N. R; Dias, G. K. A
Rich Internet Applications are very advanced and complex systems, and for their development there are numerous tools, frameworks, libraries, techniques, and technologies are available. The underplaying concepts of the Rich Internet Applications are still have not been defined well, and the tools, frameworks, or libraries do not improve these underlying concepts; instead they might use their own forms of the concepts. If we can understand the abstract fundamental concepts of Rich Internet Applications, we can gain some advantages like: increased realization, knowledge sharing, and lower learning curves. These aspects have not being much discussed or researched within the domain; therefore, we attempt to pinpoint the importance of having abstract concepts for Rich Internet Applications engineering, as a contemporary requirement. This knowledge will help to look at the researching in Rich Internet Application engineering in a different perspective, and will lead to introduce abstract concepts, for Rich Internet Applications.
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Academic Depression Detection Using Behavioral Aspects for Sri Lankan University Students
(2021 3rd International Conference on Advancements in Computing (ICAC) -SLIIT, 2021-12-09) Gamage, M.A.; Matara Arachchi, R.; Naotunna, S.; Rubasinghe, T.; Silva, C.; Siriwardana, S.
Academic Depression is a widespread problem among undergraduate students in Sri Lanka. It is exhausting and has a detrimental impact on students' academic life. Therefore, the development of a technique to estimate the probability of depression among undergraduates is a blessed respite. Depression is mostly caused by a failure to check students' psychological well-being on a regular basis. Identifying depression at the college level, leading the students to get proper therapy treatments. If a counselor detects depression in a student early enough, he/she can successfully assist the student in overcoming depression. However, keeping track of the substantial changes that occur in students because of depression becomes challenging for the counselor with a considerable number of undergraduates. The advancement of image processing and machine learning fields has contributed to the creation of effective algorithms capable of identifying depression probability. Depression Possibility Detection Tool (DPDT) is considered an effective automated tool that brings the depression probability of a certain student. In DPDT, the result is generated by concerning four main strategies. They are facial expressions, eye movements, behavior changes (step count and phone usage), and physical conditions (heart rate and sleep rate). Convolutional Neural Network (CNN) with Visual Geometry Group 16 (VGG16) model, Residual Neural Network (ResNet-50), Random Forest (RF) classifier is the main models and techniques used in the system. More than 93% of accuracy was generated in every trained model. The paper concludes the system overview along with four strategies, literature review, methodologies, conclusion, and future works.
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Accommodation Finder: An Augmented Reality Based Mobile Application Integrated with Smart Contracts
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Parameswaran, G.; Perera, M.J.F.R.; Aluthgedara, C.R.B.; Amanda, E.D.N.; Ishara, W.G.A.; Ganegoda, D.
Accommodation is one of the basic needs for travelers, tourists, students, and employees. Accommodations range from low-budget lodges to world-class luxury hotels, but finding the preferable accommodation is undoubtedly a tedious task. And due to the COVID-19 pandemic, it has become problematic state to visit each accommodation property to check whether it's suitable for the accommodation seeker, considering the location, environment, and to check if the property matches the user’s preferences. There have been incidents reported where thousands of people have been victimized because of contract breaches in the accommodation and real estate sectors, recurring from contract alterations. Considering these problems, we have proposed a system to provide solutions using Natural Language Processing (NLP), Automatic Speech Recognition (ASR), Augmented Reality (AR), Block-chain, and K-Nearest Neighbor (KNN). This system provides an efficient approach to viewing the exterior and interior of an accommodation using 360-degree views, providing recommendations to the user based on user preferences using KNN and cosine similarity, providing security in a digital agreement using blockchain technology, and a map navigation system using ASR. With the aid of the previously mentioned techniques, a mobile application prototype is created with the possibility of future testing and implementation.
Open Access
Accurate control position of belt drives under acceleration and velocity constraints
(Institute of Control, Robotics and Systems, 2003) Jayawardena, T. S. S; Nakamura, M; Goto, S
Belt drives provide freedom to position the motor relative to the load and this phenomenon enables reduction of the robot arm inertia. It also facilitates quick response when employed in robotics. Unfortunately, the flexible dynamics deteriorates the positioning accuracy. Therefore, there exists a trade-off between the simplicity of the control strategy to reject time varying disturbance caused by flexibility of the belt and precision in performance. Resonance of the system further leads to vibrations and poor accuracy in positioning. In this paper, accurate positioning of a belt driven mechanism using a feed-forward compensator under maximum acceleration and velocity constraints is proposed. The proposed method plans the desired trajectory and modifies it to compensate delay dynamics and vibration. Being an offline method, the proposed method could be easily and effectively adopted to the existing systems without any modification of the hardware setup. The effectiveness of the proposed method was proven by experiments carried out with an actual belt driven system. The accuracy of the simulation study based on numerical methods was also verified with the analytical solutions derived.
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AD Mini: Memory Improvement Tool for Alzheimer's Patients
(IEEE, 2018-08-08) Weerakoon, D. S. D; Kahandawaarachchi, K. A. D. C. P; Thilakasiri, W. P. M; Dissanayake, J. D. S. Y; Shanthakumara, W. D. M. B
According to National Center for Health Statistics, Alzheimer's disease is one of the major causes of deaths among elderly people. The current medicine does not provide any cure to this disease. Hence, managing the progression of disease is more important for the wellbeing of the patient. Approximately there are 44 million people are suffering with Alzheimer's or a relevant Dementia in the world. Which has triggered many institutions and associations forming to provide treatments. However, in Sri Lanka there is less attention to the disease because of the cultural reasons and the cost associated with the disease management process. With the limited amount of resources available most of the activities are paper base activities and care giver always should give his or her attention to the patient. All Sri Lankan Alzheimer's patients will not be able to get opportunity to go for that Alzheimer's foundation or else all patients might not attend to the clinics on date. At clinics in Sri Lanka, doctors are providing paper base activities for patient to answer. Then doctor gives marks for each activity and compare with the patient's earlier results. Furthermore, in Sri Lanka there is no computer based application tool for patients to use by themselves to keep them in touch with relevant information and develop their memory/Skills. To solve this problematic situation the research group has decided to develop an online memory improvement tool especially for Alzheimer's patients in Sri Lanka. For this researchers are observing Lanka Alzheimer's Foundation Secretariat and Service Center to gather issues related to there current paper base activity process issues and we are getting advices from Consultant Psychiatrists in Sri Lanka. Under supervision of the Psychiatrists the research team is developing an online application with recommended activities for specific areas in Alzheimer's. At the end of each game, the score save to the database and the system will generate the report.
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Adapting MaryTTS for Synthesizing Sinhalese Speech to Communicate with Children
(IEEE, 2021-12-01) Lakmal, M. A. J. A; Methmini, K. A. D. G; Rupasinghe, D. M. H. M; Hettiarachchi, D. I; Piyawardana, V; Senarathna, M; Reyal, S; Pulasinghe, K
The majority of the Sri Lankan population speak Sinhala, which is also the country's mother tongue. Sinhala is a difficult language to learn by children aged between 1–6 years when compared to other languages. Text to speech system is popular among children who have difficulties with reading, especially those who struggle with decoding. By presenting the words auditorily, the child can focus on the meaning of words instead of spending all their brainpower trying to sound out the words. In Sri Lanka, however, computer systems based on the Sinhala language especially for children are extremely rare. In this situation having a Sinhala text-to-speech technology for communicating with children is a helpful option. Intelligibility should be considered deeply in this system because this is specific for children. Recordings of a native Sinhalese speaker were used to synthesize a natural-sounding voice, rather than a robotic voice. This paper proposes an approach of implementing a Sinhalese text-to-speech system for communicating with children using unit selection and HMM -based mechanisms in the MaryTTS framework. Although a work in progress, the intermediate findings have been presented.
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An adaptive based approach to improve the stability of two wheel mobile manipulator
(IEEE, 2007-11-05) Abeygunawardhana, P. K. W; Toshiyuki, M
Mobile manipulator with two wheel will play vital role when robot working with limited space. On the other hand, improvement of two wheel vehicle will explore the technology to improve welfare and industrial robots like wheelchair robot. Two wheeled mobile manipulator has been already implemented using inverted pendulum control. But system error is relatively large. Although the stability improvement using passivity theory was reported, it was not succeed with trajectory motion. Therefore, performance improvement which will be achieved through changing the PD controller gains is proposed in this paper. Disturbance observer has been employed to cancel the disturbances.
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An adaptive routing algorithm for Cognitive Packet Network infrastructure based on neural networks
(IEEE, 2011-08-16) Madubashitha, D. K. D; Wijesinghe, W. M. S. S; Kamaladiwela, K. A. S. R; Ranaweera, M. G. P; Wijekoon, J; Abeygunawardhana, P. K. W
This paper examines the possibility of introducing an intelligent routing protocol to the Internet, based on the Cognitive Packet Network (CPN) architecture with respect to the Quality of Service (QoS) delivered to the end users. In the present with increasing populations of countries it is clear that present infrastructure does not hold the sufficient capacity to deliver the expected level of service to the end users. Since there is an eminent need for a solution for improving the QoS in the Internet, this research focuses to provide a new network architecture which would improve the QoS, provide reliable and efficient service which can fulfill the ever growing Internet usage demand. This is achieved through a new network architecture known as CPN which is based on the basis of providing the best and user desired QoS. The main underlying technology behind the CPN will be a neural network. The neural network will be learning the changes in the network and adapt to the situation through the knowledge gathered. The packets will collectively learn about the network thus the load on the routers will be minimized. This mechanism completely replaces the need of a routing table thus making routing far more efficient when comparing to current routing protocols like Open Shortest Path First (OSPF). Final outcome of the research is coming to the conclusion that the future of the Internet is with the neural network based intelligent, dynamically adapting and learning CPN infrastructure instead of current packet switched network.
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An adaptive routing algorithm for Cognitive Packet Network infrastructure based on neural networks
(IEEE, 2011-08-16) Madubashitha, D. K. D; Wijesinghe, W. M. S. S; Kamaladiwela, K. A. S. R; Ranaweera, M. G. P; Wijekoon, J; Abeygunawardhana, P. K. W
This paper examines the possibility of introducing an intelligent routing protocol to the Internet, based on the Cognitive Packet Network (CPN) architecture with respect to the Quality of Service (QoS) delivered to the end users. In the present with increasing populations of countries it is clear that present infrastructure does not hold the sufficient capacity to deliver the expected level of service to the end users. Since there is an eminent need for a solution for improving the QoS in the Internet, this research focuses to provide a new network architecture which would improve the QoS, provide reliable and efficient service which can fulfill the ever growing Internet usage demand. This is achieved through a new network architecture known as CPN which is based on the basis of providing the best and user desired QoS. The main underlying technology behind the CPN will be a neural network. The neural network will be learning the changes in the network and adapt to the situation through the knowledge gathered. The packets will collectively learn about the network thus the load on the routers will be minimized. This mechanism completely replaces the need of a routing table thus making routing far more efficient when comparing to current routing protocols like Open Shortest Path First (OSPF). Final outcome of the research is coming to the conclusion that the future of the Internet is with the neural network based intelligent, dynamically adapting and learning CPN infrastructure instead of current packet switched network.
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Adaptivo: A Personalized Adaptive E-Learning System based on Learning Styles and Prior Knowledge
(IEEE, 2022-12-09) Rishard, M.A.M; Jayasekara, S.L; Ekanayake, E.M.P.U; Wickramathilake, K.M.J.S; Reyal, S; Manathunga, K; Wickramarathne, J
The rapid advancement of technology and the internet has resulted in an increase in the number of learners seeking e-learning. Though E-Learning is widely used most e-learning systems provide the same set of learning resources and learning paths to each student, regardless of their personal preferences. In recent years there has been increasing attention towards the characteristics of learners such as the learning styles and the knowledge level of the learner. This research paper proposes a personalized adaptive E-learning system called “Adaptivo” that provides a personalized learning experience to the learners based on their learning style and knowledge level. To make the learning process more efficient and engaging, Adaptivo takes into account the specific differences between learners in terms of time, online interactions and learning duration. It then builds a personalized learning path depending on each learner's learning style and knowledge level. The main aim of this study is to investigate the impact of the proposed adaptive learning approach on learners. The results show that the students appreciate the approach, are highly satisfied, and performed better when content is personalized according to their learning style and prior knowledge.
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An add-on module to ECU for extending the functionalities of EFI tune-up process: An electronic device which extends the lifetime of fuel injectors
(Faculty of Graduate Studies and Research, 2017-01-26) Pathirana, S.; Gajanayake, C.; Vithanage, C.W.
The paper presents the ideology, procedures followed in order to implement, and the experimental results of an electronic device developed to extend the functionalities of an Electronic Control Unit (ECU) employed in the automobiles equipped with Electronic Fuel Injection (EFI) technology. The EFI system is empowered with a computing unit, ECU. A key responsibility of a typical ECU is to manipulate the fuel delivery for efficient combustion to optimize the engine's performance while minimizing the fuel wastage and emissions. Further, it is expected to self-adjust to fulfill the above requirements relying on several input sensors including a feed-back sensor. But, due to various reasons the objective is not fully achieved, therefore need to be rectified time to time. The process is called EFI Tune-up. Sensor malfunction and decayed fuel injectors are the major reasons for the incapability of ECU to control the situation. The especially developed add-on module was designed to assist the ECU when it loses its tolerance because of decayed fuel injectors and once the feedback sensor is beyond its range of operation. The strategy followed was to modify the Injector Pulse Width, a Pulse Width Modulated (PWM) control signal generated by the ECU to regulate the fuel release of fuel injectors, based on an analysis done regarding the emissions containing in exhaust gas. The experimentally obtained evaluation records conclude that the involvement of the introduced add-on module could reduce the fuel wastage and release of toxic emissions up to 70%, depending upon the condition of the fuel injector.
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Adding Common Sense to Robots by Completing the Incomplete Natural Language Instructions
(IEEE, 2022-07-18) De Silva, G. W. M. H. P.; Rajapaksha, S; Jayawardena, C
This system is developed to identify and complete the human’s instructions or incomplete sentences given by a user as a command. It would facilitate the interaction between the human and mobile service robots. However, when humans give the instruction, there can be incompleteness or else missing the information related to the environment. That is because humans, generally based on common sense, depending on the environment. Then the human brain can complete all those incomplete sentences by using common sense knowledge. This paper itself introduced a model of a service robot who can compete with the given incomplete instructions, display the related sentences or words, and finally move to the related objects in the environment. First, it will consider and identify the objects in the environment and then consider the given natural language instruction by humans. As a first step of the approach, complete the incomplete sentences. Those sentences are coming as natural language instructions. By parsing it into as the frame can identify the related words by using the created model or can call as language model and here used some identify words from the human common sense also, then the service robot will learn about the commonsense knowledge automatically from the parsing sentences as a speaker. Considering all the parsing sentences, it calculates and measures the accuracy of this service robot model. Simply this is a commonsense reasoning model. The result of the provided solution can enable the robot model that works in a ROS environment to identify and automatically perform the tasks.
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Adding Commonsense to Robotic Application Using Ontology-Based Model Retraining
(IEEE, 2022-10-04) Pradeepani, M. K. T.; Jayawardena, C.; Rajapaksha, U. U. S.
In terms of the level of technological capability in the world today, the use of automated robotics is common in various fields. There are large projects going on in many industries that collaborate between robots and other robots, as well as humans and robots. In hospital environments, care for people with medical needs and their needs and used to make appropriate suggestions to their problems. Robots can also be found in certain areas that can respond quickly as an emergency rescue agent. Furthermore, robots, which can be seen in the hotel industry as waiters and as farm assistants in agriculture, have a great tendency to be used as multi-tasking agents in many fields. In each of these areas, robots must co-operate with humans. In that situation, the importance of the exchange of mutual knowledge between robots-robots and between humans-robots comes into the picture. What matters here is not only the quantitative vastness of knowledge but also the ability to understand each other in the same medium. Although the common sense that people need in their day-to-day work is completely obvious to humans, the commonsense knowledge domain needs to be implanted in robots. Whatever concept is defined for adding commonsense to robotics, it should be a consistent concept that can be logically constructed so that it can be understood by a machine. As will be discussed later in the paper, different methods have been used in various related works to add a different kind of domain knowledge to robotics. The objective of this paper is to provide an improved retrained model for robotics in order to give them the ability to act more human-like when performing tasks. By using the proposed model robots are able to answer the incomplete command or inquiries related to a given context. One of the objectives of this work is to use the ontology-based, commonsense-support existing knowledge base as a mechanism to retrain and build a new model.
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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. D
Social 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.
Open Access
Advance Technology for Kids to Improve Knowledge and Skills using Motion Gesture Recognition – Leap Mania
(SLIIT, 2014-12-16) Nandasiri, K. G. M. P; Nawarathna, N. H. C. E. M; Mohamad, M. M. R; Herath, H. M. C. K; Kasthuriarachchi, K. T. S; Wijendra, D
Leap mania is a gesture controlled e-leaning system which targets the nursery level kids to improve their knowledge and skills in a pleasurable learning environment. Game-based learning is becoming popular in the academic discussion of Learning Technologies. However, even though the educational potential of games has been thoroughly discussed in modern days, teaching to small kids became difficult due to the short attention spans of them. In addition to traditional methods of learning and teaching, such as reading books and newspapers, a huge variety of online educational resources are available to provide an atmosphere of fun and interactive designs to keep children engaged. However, there is no proper e-learning game tools with gesture control mechanism found among the tools and computer based applications for kids. This research focuses on building an enthusiastic and pleasurable learning environment to enhance the knowledge and skills of kids by implementing a game-based learning application using leap motion controller.