Hamza Ramzan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Hamza Ramzan has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Awais Yasin
Department of Computer Engineering, National University of Technology, Islamabad 44000, Pakistan
1 shared publication - Azlan Mohd Zain
Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia
1 shared publication - Haitham Nobanee
Abu Dhabi University Department of Accounting and Finance Abu Dhabi and Faculty of Humanities and Social Sciences, University of Liverpool, Liverpool, UK
1 shared publication - Mazhar Javed Awan
Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan
1 shared publication - Rabia Javed
Department of Computer Science, Lahore College for Women University
1 shared publication - Syed Arbaz Haider Gilani
Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan
1 shared publication
Latest Publications
- Open Access
- 26 September 2021
- Published by MDPI AG in Electronics
- Vol. 10 (19) , 2350
Cricket Match Analytics Using the Big Data Approach
Cricket is one of the most liked, played, encouraged, and exciting sports in today’s time that requires a proper advancement with machine learning and artificial intelligence (AI) to attain more accuracy. With the increasing number of matches with time, the data related to cricket matches and the individual player are increasing rapidly. Moreover, the need of using big data analytics and the opportunities of utilizing this big data effectively in many beneficial ways are also increasing, such as the selection process of players in the team, predicting the winner of the match, and many more future predictions using some machine learning models or big data techniques. We applied the machine learning linear regression model to predict the team scores without big data and the big data framework Spark ML. The experimental results are measured through accuracy, the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), respectively 95%, 30.2, 1350.34, and 28.2 after applying linear regression in Spark ML. Furthermore, our approach can be applied to other sports.
- 26 September 2021
- Published by MDPI AG in Electronics
- Vol. 10 (19) , 2350
Open AccessCricket Match Analytics Using the Big Data Approach
Cricket is one of the most liked, played, encouraged, and exciting sports in today’s time that requires a proper advancement with machine learning and artificial intelligence (AI) to attain more accuracy. With the increasing number of matches with time, the data related to cricket matches and the individual player are increasing rapidly. Moreover, the need of using big data analytics and the opportunities of utilizing this big data effectively in many beneficial ways are also increasing, such as the selection process of players in the team, predicting the winner of the match, and many more future predictions using some machine learning models or big data techniques. We applied the machine learning linear regression model to predict the team scores without big data and the big data framework Spark ML. The experimental results are measured through accuracy, the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), respectively 95%, 30.2, 1350.34, and 28.2 after applying linear regression in Spark ML. Furthermore, our approach can be applied to other sports.