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Your search keyword '"Liaqat, Rehan"' showing total 32 results
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32 results on '"Liaqat, Rehan"'

4. Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data

Author

Nawaz, Ali, Anwar, Syed Muhammad, Liaqat, Rehan, Iqbal, Javid, Bagci, Ulas, and Majid, Muhammad

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. An early detection can prevent the patient from further damage of the brain cells and hence avoid permanent memory loss. In past few years, various automatic tools and techniques have been proposed for diagnosis of AD. Several methods focus on fast, accurate and early detection of the disease to minimize the loss to patients mental health. Although machine learning and deep learning techniques have significantly improved medical imaging systems for AD by providing diagnostic performance close to human level. But the main problem faced during multi-class classification is the presence of highly correlated features in the brain structure. In this paper, we have proposed a smart and accurate way of diagnosing AD based on a two-dimensional deep convolutional neural network (2D-DCNN) using imbalanced three-dimensional MRI dataset. Experimental results on Alzheimer Disease Neuroimaging Initiative magnetic resonance imaging (MRI) dataset confirms that the proposed 2D-DCNN model is superior in terms of accuracy, efficiency, and robustness. The model classifies MRI into three categories: AD, mild cognitive impairment, and normal control: and has achieved 99.89% classification accuracy with imbalanced classes. The proposed model exhibits noticeable improvement in accuracy as compared to the state-fo-the-art methods.

Published
2021

28. Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution

Author

Afzaal, Muhammad Umar, primary, Sajjad, Intisar Ali, additional, Awan, Ahmed Bilal, additional, Paracha, Kashif Nisar, additional, Khan, Muhammad Faisal Nadeem, additional, Bhatti, Abdul Rauf, additional, Zubair, Muhammad, additional, Rehman, Waqas ur, additional, Amin, Salman, additional, Haroon, Shaikh Saaqib, additional, Liaqat, Rehan, additional, Hdidi, Walid, additional, and Tlili, Iskander, additional

Published
2020
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32. Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer.

Author

Iqbal, Muhammad Muzaffar, Sajjad, Malik Intisar Ali, Amin, Salman, Haroon, Shaikh Saaqib, Liaqat, Rehan, Khan, Muhammad Faisal Nadeem, Waseem, Muhammad, and Shah, Muhammad Athar

Subjects
GENETIC algorithms, HOUSEHOLD appliances, ENERGY storage, RENEWABLE energy sources, SMART power grids, PHOTOVOLTAIC power systems
Abstract

The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system. [ABSTRACT FROM AUTHOR]

Published
2019
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