Muhammad Usman

Dr. Muhammad Usman has completed PhD in Computer & Information Sciences from Auckland University of Technology, New Zealand. He is currently an Associate Professor and Head of Computer Science Department at Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad, Pakistan. His research interests include Data Mining, Data Warehousing, Machine Learning, Business Intelligence and Knowledge discovery. He is currently researching in the novel methods and techniques for the seamless integration of Data Mining and Data Warehousing technologies. He has published in international journals and conference proceedings, and he has served as reviewer for a number of premier journals and conferences.

Publications

Predictive Analysis on Large Data for Actionable Knowledge: Emerging Research and Opportunities
Predictive Analysis on Large Data for Actionable Knowledge: Emerging Research and Opportunities
Muhammad Usman, M. Usman. © 2018. 177 pages.
Data analysis forms the basis of many modes of research ranging from scientific discoveries to governmental findings. With the advent of machine intelligence and neural networks...
Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes
Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes
Muhammad Usman. © 2018. 12 pages.
In high dimensional environments, the sheer size and volume of data poses a number of challenges in order to generate meaningful and informative data cubes. Data cube...
Improving Knowledge Discovery through the Integration of Data Mining Techniques
Improving Knowledge Discovery through the Integration of Data Mining Techniques
Muhammad Usman. © 2015. 391 pages.
Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing...
Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes
Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes
Muhammad Usman. © 2015. 12 pages.
In high dimensional environments, the sheer size and volume of data poses a number of challenges in order to generate meaningful and informative data cubes. Data cube...