Kanwal Yousaf

Work place: Department of Software Engineering, University of Engineering and Technology Taxila, Pakistan

E-mail: kanwal_400@yahoo.com


Research Interests: Image Processing, Image and Sound Processing, Application Security, Image Manipulation, Software Organization and Properties


Engr Kanwal Yousaf is MSc Scholar in Department of Software Engineering at University of Engineering and Technology, Taxila. She completed her Bachelor’s degree in Software Engineering from UET, Taxila in 2010. She is currently working on Web 2.0 based Elearning by using social media. Her area of interest is Software Quality Assurance, Internet application development, Digital Image Processing and Wireless networks.

Author Articles
Improvement in Quality of Software Architecture via Enhanced-Pattern Driven Architecture (EPDA)

By Muhammad Fahad Khan Kanwal Yousaf Anam Mustaqeem Muazaam Maqsood

DOI: https://doi.org/10.5815/ijitcs.2012.12.03, Pub. Date: 8 Nov. 2012

No doubt software plays an important role in improvement of our lives. Great demand of software makes software architecture more complex. Flaws in any software have direct impact on diverse fields of life (such as business, science, engineering etc). The main reason of any software failure is due to poor software architecture or quality attributes. This paper focuses on factors that affect the quality of software architectures and highlighted the major reason of the defects through questionnaire and survey. In the light of this survey a technique is proposed to improve the quality of any software architecture. The proposed architecture is Enhanced-Pattern driven architecture (EPDA). This architecture focuses on the improvement of design phase in any architecture. This will also help in resolving lots of problems which arise due to usage of different traditional architectural styles.

[...] Read more.
Comparative Analysis of Automatic Vehicle Classification Techniques: A Survey

By Kanwal Yousaf Arta Iftikhar Engr Ali Javed

DOI: https://doi.org/10.5815/ijigsp.2012.09.08, Pub. Date: 8 Sep. 2012

Vehicle classification has emerged as a significant field of study because of its importance in variety of applications like surveillance, security system, traffic congestion avoidance and accidents prevention etc. So far numerous algorithms have been implemented for classifying vehicle. Each algorithm follows different procedures for detecting vehicles from videos. By evaluating some of the commonly used techniques we highlighted most beneficial methodology for classifying vehicles. In this paper we pointed out the working of several video based vehicle classification algorithms and compare these algorithms on the basis of different performance metrics such as classifiers, classification methodology or principles and vehicle detection ratio etc. After comparing these parameters we concluded that Hybrid Dynamic Bayesian Network (HDBN) Classification algorithm is far better than the other algorithms due to its nature of estimating the simplest features of vehicles from different videos. HDBN detects vehicles by following important stages of feature extraction, selection and classification. It extracts the rear view information of vehicles rather than other information such as distance between the wheels and height of wheel etc.

[...] Read more.
Other Articles