Browsing by Author "Jayasena, KPN"

Now showing 1 - 3 of 3

item: Conference-Full-text
Multimedia big data platform with a deep learning approach for flood emergency management
(Faculty of Information Technology, University of Moratuwa., 2020-12) Weerasinghe, IDTT; Jayasena, KPN; Karunananda, AS; Talagala, PD
Flood emergency management has been a major issue in the last few decades as it can disrupt human lives as well as the economy and property damage. A flood occurs when the overflow of water that melts relatively dry land. In the hydrology discipline, floods are a field of study and they are the most common and widespread unpredictable weather occurrence of natural sources. Floods can look quite different because anywhere from a few inches of water to several feet is affected by flooding. They can also come on suddenly, or slowly increase. Therefore frequent identification of flood impact levels is very important. This study aims to create a multimedia big data platform with a deep learning approach for flood emergency management. It uses multimedia data as they are freely available as social media data (Twitter and Facebook), satellite image data, crowdsourcing, and sensor network data for mining purposes. As this research based on deep learning and image processing cutting edge technologies, authorities can identify impact level using satellite images and provide a real-time warning for the people or people can use this for self-estimation of flood risk level when they want in their day to day life like detecting passable or low-risk roads in flooding time. The deep neural network plays a major role in feature extraction and data augmentation helps to increase the number of images in the dataset. This study provides a comparative study between VGG16, VGG19, Densetnet169, and MobileNet deep learning models and evaluates the performance by using training and testing data. Dataset compromises of over 1500 data and the conclusions drawn from work prove that the MobileNet model worked with 86% accuracy with high performance. In the latter part of the paper, it will describe future recommendations.
item: Conference-Full-text
Novel approach for load balancing in mobile cloud computing
(Faculty of Information Technology, University of Moratuwa., 2021-12) Ranapana, RAAIB; Jayasena, KPN; Ganegoda, GU; Mahadewa, KT
Mobile cloud computing (MCC) was used in many sectors in the current day world, and it combined mobile computing and cloud computing technology to provide mobile services. Mobile devices have short battery life and storage; MCC plays a significant work in compromising that issue. When users are in a hotspot, they face more difficulties and a bad user experience. Load balancing gives a better user experience in the MCC domain. Edge computing and edge clouds are beneficial for load balancing in MCC. There are various algorithms for load balancing in MCC. This research in a simulation environment rather than a natural environment. This research, is focusing on developing a hotspot migration mechanism, reducing mobile devices' battery usage, and developing a novel load balancing algorithm. This research focuses on providing solutions for the limited battery life of mobile devices and the gap in load balancing in mobile cloud computing and provides suggestions to future researchers.
item: Conference-Full-text
Real-time uber data analysis of popular uber locations in kubernetes environment
(Faculty of Information Technology, University of Moratuwa., 2020-12) Gunawardena, TM; Jayasena, KPN; Karunananda, AS; Talagala, PD
Data is crucial in today's business and technology environment. There is a growing demand for Big Data applications to extract and evaluate information, which will provide the necessary knowledge that will help us make important rational decisions. These ideas emerged at the beginning of the 21st century, and every technological giant is now exploiting Big Data technologies. Big Data refers to huge and broad data collections that can be organized or unstructured. Big Data analytics is the method of analyzing massive data sets to highlight trends and patterns. Uber is using real-time Big Data to perfect its processes, from calculating Uber's pricing to finding the optimal positioning of taxis to maximize profits. Real-time data analysis is very challenging for the implementation because we need to process data in real-time, if we use Big Data, it is more complex than before. Implementation of real-time data analysis by Uber to identify their popular pickups would be advantageous in various ways. It will require high-performance platform to run their application. So far no research has been done on real-time analysis for identifying popular Uber locations within Big Data in a distributed environment, particularly on the Kubernetes environment. To address these issues, we have created a machine learning model with a Spark framework to identify the popular Uber locations and use this model to analyze real-time streaming Uber data and deploy this system on Google Dataproc with the different number of worker nodes with enabling Kubernetes and without Kubernetes environment. With the proposed Kubernetes environment and by increasing the worker nodes of Dataproc clusters, the performance can be significantly improved. The future development will consist of visualizing the real-time popular Uber locations on Google map.