Abdul Basit Majeed Khan

Abdul Basit Majeed Khan
National University of Modern Languages | NUML · Computer Science

Master of Science
IT Risk Officer

About

15
Publications
4,334
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33
Citations
Introduction

Publications

Publications (15)
Chapter
Full-text available
In this paper, a Deep Learning (DL) technique is introduced to forecast the electricity load accurately. We are facing energy shortage in today’s world. So, it is the need of the hour that proper scenario should be introduced to overcome this issue. For this purpose, moving towards Smart Grids (SG) from Traditional Grids (TG) is required. Electrici...
Chapter
Full-text available
In this paper, enhanced Deep Learning (DL) method is implemented to resolve the accurate electricity load forecasting problem. Electricity load is a factor which plays major role in operations of Smart Grid (SM). For solving this problem, we propose a model which is based on preprocessing, selection and classification of historical data. Features a...
Chapter
With rapid increase in the use of technology, the world is now moving towards smart cities which require the communication and collaboration of Internet-of-Things (IoT) devices. The smart city enhances the use of technology to share information and data among devices. These devices are producing a huge volume of data that needs to be tackled carefu...
Chapter
Counterfeit medicines are increasing day by day and these medicines are damaging the health of people. Drug Regulatory Authorities (DRAs) are trying to overcome this issue. Synchronized electronic medicine record can mitigate this risk. We proposed a decentralized Blockchain (BC) based medicine licensing and authentication system to stop production...
Conference Paper
Full-text available
Counterfeit medicines are increasing day by day and these medicines are damaging the health of people. Drug Regulatory Authorities (DRAs) are trying to overcome this issue. Synchronized electronic medicine record can mitigate this risk. We proposed a decentralized Blockchain (BC) based medicine licensing and au-thentication system to stop productio...
Conference Paper
Full-text available
With rapid increase in the use of technology, the world is now moving towards smart cities which requires the communication and collaboration of Internet-of-Things (IoT) devices. The smart city enhances the use of technology to share information and data among devices. These devices are producing a huge volume of data that needs to be tackled very...
Thesis
In this thesis, an improved Deep Learning (DL) based technique is introduced to forecast the electricity load accurately. Energy shortage is one of the main issue in today’s world. So, an efficient mechanism is required to solve aforementioned issue. For this purpose, moving towards Smart Grids (SG) from Traditional Grids (TG) is required. Electric...
Chapter
Full-text available
In smart grid, precise and accurate electricity load forecasting is one of the most challenging tasks. It is due to the high volatile, non-stationary and non-linear behavior of electricity load data. In this paper, a Deep Convolution Neural Network (DCNN) model is proposed to forecast the electricity load for each day of the week of Victoria (Austr...
Chapter
Full-text available
There is emerging trend in power system, i.e., energy internet that provides energy production, transmission, storage and utilization. Which is used to manage and control energy centrally by using information and communication technologies. In this paper, coordinated management of renewable and traditional energy is focused. In proposed work, stora...
Chapter
In this paper, we attempt to predict short term price forecasting in Smart Grid (SG) deep learning and data mining techniques. We proposed a model for price forecasting, which consists of three steps: feature engineering, tuning classifier and classification. A hybrid feature selector is propose by fusing XG-Boost (XGB) and Decision Tree (DT). To p...
Conference Paper
Full-text available
In this paper, we attempt to predict short term price forecasting in Smart Grid (SG) deep learning and data mining techniques. We proposed a model for price forecasting, which consists of three steps: feature engineering, tuning classifier and classification. A hybrid feature selector is propose by fusing XG-Boost (XGB) and Decision Tree (DT). To p...
Chapter
Analysis of data is very important for accurate prediction. Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is used for load forcasting. Features are selected using PSO and redundant features are removed. Data is divided into training and testing data. Load forecasting is done by using SVM classifier. However, SVM classifier pred...
Conference Paper
Full-text available
Demand side management plays a vital role in load shifting to off peak hours from on peak hours in response to dynamic pricing. In this paper, we propose an optimal stopping rule (OSR) and firefly algorithm (FA) for the demand response based on cost minimization. Each appliance gets the best opportunistic time to start its operation in response to...
Conference Paper
Analysis of data is very important for accurate prediction. Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is used for load forcasting. Features are selected using PSO and redundant features are removed. Data is divided into training and testing data. Load forecasting is done by using SVM classifier. However, SVM clas-sifier pre...

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