Malik Hassan Abdul Rehman

Malik Hassan Abdul Rehman
COMSATS University Islamabad | CUI · Department of Computer Science

MS(COMPUTER SCIENCE)
Looking for PhD position in the field of big data and healthcare analytics

About

3
Publications
600
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13
Citations
Introduction
Hi, I am Malik Hassan, an MS graduate in the field of computer science. I completed my master's in 2021 with a thesis on Image Steganalysis by using convolutional neural networks (CNN). Currently, I am working on a review article in the field of big data and IoT. My aim is to find a PhD position in a relevant field.

Publications

Publications (3)
Conference Paper
Full-text available
In this paper, we proposed a home energy management (HEM) scheme for minimization in electricity bills and reduction in peak load. This can be achieved by scheduling the usage timings of appliances (APP) for shifting load from peak hours (PHs) to OFF-peak hours (OPHs). In this study we proposed a technique which is hybrid of two bio-inspired optimi...
Conference Paper
Full-text available
In this paper, our goal is to minimize the electricity cost, electricity consumption at minimum user discomfort while considering the peak electricity consumption. Electricity consumption may not be the same in residential, commercial and industrial areas. It may vary from each and every area. It is a challenging task to maintain the balance betwee...
Conference Paper
Full-text available
Nowadays, different schemes and ways are proposed to meet the user's load requirement of energy towards the Demand Side (DS) in order to encapsulate the energy resources. However, this Load Demand (LD) increases day by day. This increase in LD is causing serious energy crises to the utility and DS. As the usage of energy increases with the increase...

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Projects

Projects (2)
Project
In this paper, the primary focus is to minimize the electricity cost, electricity consumption at minimum user discomfort while considering the peak electricity consumption. Electricity consumption may not be the same in residential, commercial and industrial areas. It may vary from each and every area. It is a challenging task to maintain the balance between the conflicting objectives: electricity consumption and user comfort. To meet the rising electricity demand in residential area, schedule-able devices can be equally distributed to the available time slots on the basis of average power consumption. The main objective is to minimize the electricity usage during the electricity peak hours by distributing the electricity load during the off-peak hours. In this regard, Genetic Algorithm (GA) and Pigeon Inspired Optimization (PIO), hybridization of GA and PIO (HGP) in Demand Side Management are applied for residential load management to optimize the fitness function. GA,PIO and HGP are evaluated on the basis of real time pricing scheme (RTP) for single home and for three different operational time interval (OTI) : 20 minutes OTI, 30 minutes OTI, 60 minutes OTI. Simulations results shows that GA,PIO and HGP are able to minimize electricity bill and electricity consumption while minimizing the user discomfort. The feasible region between electricity cost and electricity consumption is also represented. Moreover, the desired trade-off between electricity cost and user comfort is also achieve in both techniques.