Sajid Anwar | National University of Computer and Emerging Sciences (original) (raw)

Papers by Sajid Anwar

Neural Computing and Applications

Instead of planting new electricity generation units, there is a need to design an efficient ener... more Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where Demand Response (DR) strategy is used to modify the nature of demand of consumer. In return, utility pay incentives to the consumer. The increasing load demand in residential area and irregular electricity load profile have encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. In order to meet the electricity demand of the consumers, the energy consumption pattern of a consumer is maintained through scheduling the appliances in day-ahead and real-time bases. In this paper we propose a hybrid algorithm Bacterial foraging Ant colony optimization is proposed (HB-ACO) which contain both BFA and ACO properties. Primary objectives of scheduling is to shift load from On-peak hour to Off-peak hours to reduce elec...

2018 IEEE Congress on Evolutionary Computation (CEC), 2018

Telecom companies are facing a serious problem of customer churn due to exponential growth in the... more Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.

Journal of Intelligent & Fuzzy Systems, 2015

2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC), 2009

... M. Ramzan, M. Arfan Jaffar M. Amjad Iqbal, Sajid Anwar, Arshad A. Shahid National University ... more ... M. Ramzan, M. Arfan Jaffar M. Amjad Iqbal, Sajid Anwar, Arshad A. Shahid National University of Computer and Emerging ... AK Brohi Road, H11/4 Islamabad, Pakistan, {muhammad.ramzan, arfan.jaffar}@nu.edu.pk, {amjad.iqbal, sajid.anwar, arshad.a.shahid}@nu.edu.pk ...

2010 International Conference on Information Science and Applications, 2010

Neural Computing and Applications, 2022

IEEE Transactions on Computational Social Systems

Education Sciences

Student engagement in the learning process is the key to successful delivery of teaching and lear... more Student engagement in the learning process is the key to successful delivery of teaching and learning. Teachers face several challenges to engage learners in different disciplines, including computer science. This research conducts a review of BSc (Computer Science) programmes and introduces interactive activities to enhance learner engagement. The study was conducted using a repeated measure design involving 24 participants. The findings revealed that the use of technology, and collaborative and interactive activities in groups may positively influence learner engagement. The participants’ feedback before and after introduction of group tasks and interactive activities showed a significant (p < 0.001) and increasing trend in response to questions-related learner engagement. The participants agreed that their learning experience and engagement enhanced with the use of technology and interactive and collaborative activities.

Journal of Grid Computing

Advances in Intelligent Systems and Computing, 2015

Proceedings of the 2010 Seventh International Conference on Information Technology New Generations, 2010

... Islamabad, Pakistan a.rauf@nu.edu.pk Sajid Anwar Department of Computer Science National univ... more ... Islamabad, Pakistan a.rauf@nu.edu.pk Sajid Anwar Department of Computer Science National university of Computer & Emerging Sciences Islamabad, Pakistansajid.anwar@nu.edu.pk M. Arfan Jaffer Department of Computer ...

Expert Systems with Applications, 2012

ABSTRACT This study contributes to formalize customer churn prediction where rough set theory is ... more ABSTRACT This study contributes to formalize customer churn prediction where rough set theory is used as one-class classifier and multi-class classifier to investigate the trade-off in the selection of an effective classification model for customer churn prediction. Experiments were performed to explore the performance of four different rule generation algorithms (i.e. exhaustive, genetic, covering and LEM2). It is observed that rough set as one-class classifier and multi-class classifier based on genetic algorithm yields more suitable performance as compared to the other three rule generation algorithms. Furthermore, by applying the proposed techniques (i.e. Rough sets as one-class and multi-class classifiers) on publicly available dataset, the results show that rough set as a multi - class classifier provides more accurate results for binary/multi-class classification problems.

The Scientific World Journal, 2015

Software Engineering and Data Engineering, 2010