Syed M. Shehram Shah ¹, John Wilson ², Vijdan Khalique ¹, Hira Noman ¹

Affiliations
1 Department of Software Engineering, Mehran University of Engineering and Technology, PK
2 Department of Computer and Information Sciences, University of Strathclyde, GB

Abstract

Objectives: This work provides an analysis of View maintenance of RDF structures for various data sizes using Jena. Methods/Statistical Analysis: We perform insert and deletion operations in files of three sizes and investigate how these operations are effected by the size of the file which is being worked upon. Five different experiments were conducted on the data files to provide a thorough analysis. Findings: The results from the experiments indicate that time taken for Insert operation increased proportionally however time remained the same for delete operations performed on the RDF data. It was also determined that first runs of any code in Jena took the longest time while time for operations reduced as more runs of the operations were made. Application/Improvements: The work provides a qualitative analysis of the updating of Semantic Web Content on the web.

Keywords

JENA, Resource Distribution Framework, Semantic Web, View Maintenance.

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Maria Panhwar ¹, Syed M. Shehram Shah ¹, Syed M. Zafi S. Shah ¹, Syed M. Z. Abbas Shah ¹, Bhawani Shankar Chowdhry ¹

Affiliations
1 Department of Software Engineering, Telecommunication Engineering, Electronic Engineering, Institute of Information and Communication Technologies (IICT), Mehran University of Engineering and Technology, PK

Abstract

Fall detection is an important aspect of the field of accident prevention, ambient assisted living as well as care of the elderly. To address this issue, researchers have employed several approaches including vision based systems, setups that require deployment in a special environment and inertial sensors. Inertial sensors have the advantage of being deployable in mobile systems such as wearable devices and smart phones. An important consideration in using inertial sensors for fall detection is the need to develop techniques that would work without enforcing positional requirements of the sensor device. This paper presents a method for the detection of falls using inertial sensors readings of the smart phone, a tri-axial accelerometer, tri-axial gyroscope and orientation data. We consider inertial sensor data for two falls and three activities of daily living. Using Auto-Regressive (AR) modeling to characterize the measurements from the sensors, we compare Support Vector Machines (SVM) and Neural Networks for use in classifying between these five events. Results indicate that the Neural Network provides better classification accuracy compared to SVM for the purpose of differentiating between falls and the activities of daily living.

Keywords

Fall Detection, Inertial Sensor, Machine Learning, Mobifall.

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