Mohammad Zubair Khan

Work place: Department of CS, College of Computer Science and Engg., Taibah University, Medina, KSA



Research Interests: Data Structures and Algorithms, Data Mining, Parallel Computing, Distributed Computing, Computer Architecture and Organization


Mohammad Zubair Khan got the Ph.D. degree in CS&IT from Faculty of Engineering, M.J.P. Rohilkhand University, Bareilly India, and the Master of Technology in Computer Science and Engineering in 2006 from U.P. Technical University, Lucknow, India. He is currently working as Associate Professor in the Department of Computer Science, College of computer science and engineering Taibah University. Past he has worked as head and Associate professor, in the Department of Computer Science and Engineering, Invertis University, Bareilly India. He has published more than 40 journals papers. Mohammad Zubair Khan is a member of Computer Society of India since 2004. His current research interests are data mining, big data, parallel and distributed computing, and computer networks. He has more than 15 years teaching and research experience

Author Articles
Green Computing: An Era of Energy Saving Computing of Cloud Resources

By Shailesh Saxena Mohammad Zubair Khan Ravendra Singh

DOI:, Pub. Date: 8 Jun. 2021

Cloud computing is a widely acceptable computing environment, and its services are also widely available. But the consumption of energy is one of the major issues of cloud computing as a green computing. Because many electronic resources like processing devices, storage devices in both client and server site and network computing devices like switches, routers are the main elements of energy consumption in cloud and during computation power are also required to cool the IT load in cloud computing. So due to the high consumption, cloud resources define the high energy cost during the service activities of cloud computing and contribute more carbon emissions to the atmosphere. These two issues inspired the cloud companies to develop such renewable cloud sustainability regulations to control the energy cost and the rate of CO2 emission. The main purpose of this paper is to develop a green computing environment through saving the energy of cloud resources using the specific approach of identifying the requirement of computing resources during the computation of cloud services. Only required computing resources remain ON (working state), and the rest become OFF (sleep/hibernate state) to reduce the energy uses in the cloud data centers. This approach will be more efficient than other available approaches based on cloud service scheduling or migration and virtualization of services in the cloud network. It reduces the cloud data center's energy usages by applying a power management scheme (ON/OFF) on computing resources. The proposed approach helps to convert the cloud computing in green computing through identifying an appropriate number of cloud computing resources like processing nodes, servers, disks and switches/routers during any service computation on cloud to handle the energy-saving or environmental impact. 

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Hybrid Ensemble Learning Technique for Software Defect Prediction

By Mohammad Zubair Khan

DOI:, Pub. Date: 8 Feb. 2020

The reliability of software depends on its ability to function without error. Unfortunately, errors can be generated during any phase of software development. In the field of software engineering, the prediction of software defects during the initial stages of development has therefore become a top priority. Scientific data are used to predict the software's future release. Study shows that machine learning and hybrid algorithms are change benchmarks in the prediction of defects. During the past two decades, various approaches to software defect prediction that rely on software metrics have been proposed. This paper explores and compares well-known supervised machine learning and hybrid ensemble classifiers in eight PROMISE datasets. The experimental results showed that AdaBoost support vector machines and bagging support vector machines were the best performing classifiers in Accuracy, AUC, recall and F-measure.

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