MOBILE APP FOR HIDDEN DATA ANALYTICS OF ONLINE MARKETPLACE SYSTEMS

In this project, an extensive analysis and evaluation of the existing e-marketplaces is performed. The aim of this analysis is to improve the experience of end-users through an Android application that is capable of summarizing multiple heterogeneous hidden data sources and unify received responses to one single, structured and homogenous source. The proposed Android application is based on the multi-level conceptual analysis and modeling strategy. In which, the data is analyzed in a way that helps discovering the main entities of any unknown dataset captured from hidden web sources. Several experiments have been conducted that depend on static data analytics for discovering entities. The results showed that query results analysis and re-structuring the output before displaying to the end-user in conceptual multilevel mechanism are reasonably effective in response time to the user interaction with minimal number of screens and clicks. The proposed application can also predict user requirements from the initial query that built on the results obtained from different e-commerce marketplaces. Based on the proposed intelligent application that predict user required products, the interface is minimized to only two navigation screens, and the approximated time needed is 8 seconds to reach the targeted product. This solution is faster and easier to use than the current available application solutions by comparing the response time and the user interaction for the obtained results that met user requirements.This contribution was made possible by NPRP-07-794-1-145 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

Edge-based Compression and Classification for Smart Healthcare Systems: Concept, Implementation and Evaluation

Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the net- work edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart health- care systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make au- tonomous decisions for smart health applications. In particular, we present and im- plement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low com- putational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%

Novel Processing and Transmission Techniques Leveraging Edge Computing for Smart Health Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen