Research Papers - Dept of Computer Systems Engineering
Permanent URI for this collection https://rda.sliit.lk/handle/123456789/1253
Browse
Publication Open Access 6-REXOS: Upper limb exoskeleton robot with improved pHRI(SAGE Publications, 2015-04-29) Gunasekara, M; Gopura, R; Jayawardena, T. S. SClose 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.Publication 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, DIn 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.Publication 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, KThe 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)Publication 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, UCabbage (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.Publication Embargo 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.Publication Embargo 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.Publication 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, SBelt 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.Publication Embargo 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. BAccording 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.Publication Embargo An adaptive based approach to improve the stability of two wheel mobile manipulator(IEEE, 2007-11-05) Abeygunawardhana, P. K. W; Toshiyuki, MMobile 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.Publication Embargo 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. WThis 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.Publication Embargo 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. WThis 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.Publication Embargo 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, CThis 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.Publication Embargo The advanced remote PC management suite(IEEE, 2011-08-16) Wijekoon, J; Wijesundara, M; Dassanayaka, T; Samarathunga, D; Dissanayaka, R; Perera, DDeveloping a system that helps system administrators to perform their administration task more effectively and efficiently is of great importance to reduce downtime, cost and man power requirement. The Advanced Remote PC Management Suite facilitates centralized management of PC infrastructure employing the Intel Active Management Technology (AMT). This technology enables the system administrators to monitor and manage computers via a dedicated channel regardless of whether the computer is powered on. This is known as Out-of-Band (OOB) management. Currently AMT is available in Desktops and Laptops with The 2nd generation Intel Core vPro processors. Using features of AMT, the The Advanced Remote PC Management Suite provides a real-time and intelligent asset management facility in addition to monitoring and administration capabilities. The system also features automated operating system deployment and centralized disk cloning mechanisms. It is also possible to isolate any computer in the network using the system, during incidents such as virus infections. Therefore, this system is able to drastically reduce the number of desk-side-visits by system administrators to setup and troubleshoot PCs in large enterprise networks.Publication Embargo Agro-Mate: A Virtual Assister to Maximize Crop Yield in Agriculture Sector(IEEE, 2021-12-09) Dayalini, S; Sathana, M; Navodya, P. R. N; Weerakkodi, R. W. A. I. M. N; Jayakody, A; Gamage, NInformation Technology plays a vital role in the agriculture industry. The main goal of the project is to develop a mobile application to support farmers to take accurate decisions and help them with activities such as soil quality determination, best crop selection, rice disease prediction, and disaster prediction for the wet zone of Sri Lanka. To achieve the main goal the project has incorporated advanced technologies such as Deep Learning, Image Processing (IP), Internet of Things (IoT), and Machine Learning that can support farmers or investors in a way to maximize yield. ‘Agro-Mate’ application is developed in a way to facilitate the agriculture industry. ‘Agro-Mate’ consists of four components such as soil quality determination and fertilizer recommendation, best crop selection, rice disease prediction and recommendation, and natural disaster prediction and providing the recommendation. Also, the application suggests fertilizer when soil is lacking quality and provides recommendations whenever rice diseases or natural disasters are identified. The usage of android mobile devices in agriculture is one of the key components of the sector's growth, which facilitates the farmer's inaccurate decision-making to gain more quality and quantity of crops. Agro-mate’ is more likely to increase the productivity of crops and indirectly increase the GDP of Sri Lanka.Publication Embargo Agro-Mate: A Virtual Assister to Maximize Crop Yield in Agriculture Sector(IEEE, 2021-12-09) Dayalini, S; Sathana, M; Navodya, P. R. N; Weerakkodi, R. W. A. I. M. N; Jayakody, A; Gamage, NInformation Technology plays a vital role in the agriculture industry. The main goal of the project is to develop a mobile application to support farmers to take accurate decisions and help them with activities such as soil quality determination, best crop selection, rice disease prediction, and disaster prediction for the wet zone of Sri Lanka. To achieve the main goal the project has incorporated advanced technologies such as Deep Learning, Image Processing (IP), Internet of Things (IoT), and Machine Learning that can support farmers or investors in a way to maximize yield. ‘Agro-Mate’ application is developed in a way to facilitate the agriculture industry. ‘Agro-Mate’ consists of four components such as soil quality determination and fertilizer recommendation, best crop selection, rice disease prediction and recommendation, and natural disaster prediction and providing the recommendation. Also, the application suggests fertilizer when soil is lacking quality and provides recommendations whenever rice diseases or natural disasters are identified. The usage of android mobile devices in agriculture is one of the key components of the sector's growth, which facilitates the farmer's inaccurate decision-making to gain more quality and quantity of crops. Agro-mate’ is more likely to increase the productivity of crops and indirectly increase the GDP of Sri Lanka.Publication Embargo AI Based Cyber Threats and Vulnerability Detection, Prevention and Prediction System(IEEE, 2019-12-05) Amarasinghe, A. M. S. N; Wijesinghe, W. A. C. H; Nirmana, D. L. A; Jayakody, A; Priyankara, A. M. SSecurity of the computer systems is the most important factor for single users and businesses, because an attack on a system can cause data loss and considerable harm to the businesses. Due to the increment of the range of the cyber-attacks, anti-virus scanners cannot fulfil the need for protection. Hence, the increment of the skill level that required for the development of cyber threats and the availability of the attacking tools on the internet, the need for Artificial Intelligence-based systems, is a must to the users. The proposed approach is an automated system that consists of a mechanism to deploy vulnerabilities and a rich database with known vulnerabilities. The Convolutional Neural Networks detects the vulnerabilities and the artificial intelligence-based generative models do the prevention process and improves reliability. The prediction procedure implemented using the algorithm called “Time Series” and the model called “SARIMA”. These implementations give an output with considerable accuracy.Publication Embargo AI Based Cyber Threats and Vulnerability Detection,Prevention and Prediction System(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Amarasinghe, A.M.S.N.; Wijesinghe, W.A.C.H.; Nirmana, D.L.A.; Jayakody, A.; Priyankara, A.M.S.Security of the computer systems is the most important factor for single users and businesses, because an attack on a system can cause data loss and considerable harm to the businesses. Due to the increment of the range of the cyber-attacks, anti-virus scanners cannot fulfil the need for protection. Hence, the increment of the skill level that required for the development of cyber threats and the availability of the attacking tools on the internet, the need for Artificial Intelligence-based systems, is a must to the users. The proposed approach is an automated system that consists of a mechanism to deploy vulnerabilities and a rich database with known vulnerabilities. The Convolutional Neural Networks detects the vulnerabilities and the artificial intelligence-based generative models do the prevention process and improves reliability. The prediction procedure implemented using the algorithm called “Time Series” and the model called “SARIMA”. These implementations give an output with considerable accuracy.Publication Embargo Ai based greenhouse farming support system with robotic monitoring(IEEE, 2020-11-16) Fernando, S; Nethmi, R; Silva, A; Perera, A; De Silva, R; Abeygunawardhana, P. K. WGreenhouses plays a major role in today's agriculture since farmers can grow plants under controlled climatic conditions and can optimize production. The greenhouses are usually built in areas where the climatic conditions for the growth of plants are not optimal so requires some artificial setups to bring about productivity. Automating process of a greenhouse requires monitoring and controlling of the climatic parameters. This paper is an attempt to minimize the cost of maintaining greenhouse environments using new technologies. The end goal of this research an automated system to optimally monitor and control the environmental factors inside greenhouse by monitoring temperature, soil moisture, humidity and pH through a cloud connected mobile robot which can detect unhealthy plants using image processing and machine learning. The mobile robot navigates through a predefined map of greenhouse. Database server has created to store gathered real-time data. And the necessary accurate data represent by using proper application for analyzing.Publication Embargo AI Based Monitoring System for Social Engineering(IEEE, 2021-12-09) Abeywardana, K. Y; Udara, S. W. I; Wijayawardane, U. P. B; Kularatne, K. N. P; Navaratne, N. M. P. P; Dharmaphriya, W. G. V. USocial media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets; and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits.Publication Embargo Air Visio: Air Quality Monitoring and Analysis Based Predictive System(IEEE, 2019-12-05) Dissanayaka, A. D; Taniya, W. A. D; De Silva, B. P. A. N; Senarathne, A. N; Wijesiri, M. P. M; Kahandawaarachchi, K. A. D. C. PSri Lanka is facing a serious air pollution problem that severely impacts the daily life of every Sri Lankan. The main source of ambient air pollution in Sri Lanka is vehicular emissions. A methodology to monitor the air quality in real-time with an overall coverage of Sri Lanka, and automatically process these huge data to identify air quality levels in a specific area is now becoming a timely research topic. An air quality monitoring and analysis based predictive system is proposed to monitor the ambient air quality, provides the best route with minimum polluted air, maps the heatmaps to identify the current air quality of an area easily and predict the future air quality of each area. The prototype was implemented by hierarchically deploying two different gas sensors, an Arduino Uno board and a wifi module, to implement in open spaces between smart buildings, and transfers the sensor data back to the information processing center by using IoT technology for real-time display. The information processing center stores real-time information which is collected from the sensors to the database. By reading sensor data stored in the database, the front-end system draws real-time, accurate air quality levels included maps and predicts the less polluted routes and the air quality level over an area. Further, an energy harvesting system is also presented for the power consumption of the device. A route is suggested in an accuracy of 70% from this system. The final product provides a low cost, highly portable and easily maintainable system for the users.