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2,858 results on '"Azam, Muhammad"'

2. AI-Assisted Laryngeal Examination System

Author

Baldini, Chiara, Azam, Muhammad Adeel, Thorniley, Madelaine, Sampieri, Claudio, Ioppi, Alessandro, Peretti, Giorgio, Mattos, Leonardo S., Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ali, Sharib, editor, van der Sommen, Fons, editor, Papież, Bartłomiej Władysław, editor, Ghatwary, Noha, editor, Jin, Yueming, editor, and Kolenbrander, Iris, editor

Published
2025
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20. Improving Dynamic Security of the Least Significant Bit Using Block-Based Chaotic Multi-level LSB (BCM-LSB)

Author

Azam, Muhammad Harith Noor, Ridzuan, Farida, Norazizi Sham Mohd Sayuti, M., Azni, A. H., Zakaria, Nur Hafiza, Harjito, Bambang, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hewage, Chaminda, editor, Nawaf, Liqaa, editor, and Kesswani, Nishtha, editor

Published
2024
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23. Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet

Author

Shehzad, Muhammad Karam, Rose, Luca, Azam, Muhammad Furqan, and Assaad, Mohamad

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Channel state information (CSI) is of pivotal importance as it enables wireless systems to adapt transmission parameters more accurately, thus improving the system's overall performance. However, it becomes challenging to acquire accurate CSI in a highly dynamic environment, mainly due to multi-path fading. Inaccurate CSI can deteriorate the performance, particularly of a massive multiple-input multiple-output (mMIMO) system. This paper adapts machine learning (ML) for CSI prediction. Specifically, we exploit time-series models of deep learning (DL) such as recurrent neural network (RNN) and Bidirectional long-short term memory (BiLSTM). Further, we use NeuralProphet (NP), a recently introduced time-series model, composed of statistical components, e.g., auto-regression (AR) and Fourier terms, for CSI prediction. Inspired by statistical models, we also develop a novel hybrid framework comprising RNN and NP to achieve better prediction accuracy. The proposed channel predictors (CPs) performance is evaluated on a real-time dataset recorded at the Nokia Bell-Labs campus in Stuttgart, Germany. Numerical results show that DL brings performance gain when used with statistical models and showcases robustness., Comment: 6

Published
2022

34. An Efficient Framework for Short-Term Electricity Price Forecasting in Deregulated Power Market

Author

Alireza Pourdaryaei, Mohammad Mohammadi, Munir Azam Muhammad, Junaid Bin Fakhrul Islam, Mazaher Karimi, and Amidaddin Shahriari

Subjects
Backtracking search algorithm, electricity market, electricity price forecasting, feature selection, support vector machine, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant in the price-setting market, nowadays. Nevertheless, electricity price became a complex signal due to its non-stationary, non-linearity, and time-variant behavior. Consequently, a variety of artificial intelligence techniques are proposed to provide an efficient method for short-term electricity price forecasting. Backtracking search algorithm as the recent augmentation of optimization technique, yield the potential of searching a closed-form solution in mathematical modeling with a higher probability, obviating the necessity to comprehend the correlations between variables. Concurrently, this study also developed a feature selection technique, to select the input variables subsets that have a substantial implication on forecasting of electricity price, based on a combination of mutual information and support vector machine. For the verification of simulation results, actual data sets from the Ontario energy market in the year 2020 covering various weather seasons are acquired. Finally, the obtained results demonstrate the feasibility of the proposed strategy through improved preciseness in comparison with the distinctive methods.

Published
2024
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35. Modelling spatiotemporal impact of flash floods on power distribution system and dynamic service restoration with renewable generators considering interdependent critical loads

Author

Suhail Afzal, Hazlie Mokhlis, Hazlee Azil Illias, Abdullah Akram Bajwa, Saad Mekhilef, Marizan Mubin, Munir Azam Muhammad, and Hussain Shareef

Subjects
disasters, hazards, micro grids, power system resilience, power system restoration, power systems, Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841
Abstract

Abstract In recent decades, flash floods have become more common because of climate change and are considered a substantial risk for many cities worldwide. This catastrophic natural hazard presents a significant threat to critical infrastructure in urban areas, particularly the power distribution system. As modern societies are much more dependent on electrical energy these days, it is essential and imperative to make existing distribution systems resilient against flash flooding. Although researchers in this area have proposed various algorithms to impart resilience to a distribution system, however, the focus in these works is on wind‐related events such as hurricanes, cyclones, and windstorms. Therefore, here, the spatiotemporal effects of a flash flood on the distribution system are modelled using a grid‐based hydrodynamic model. The evolving line faults are then included in the proposed resilience‐oriented time horizon‐based service restoration model that also considers dynamic load demand, heavy uncertainties related to renewable generation, and interdependence among critical loads. Finally, the resilience of the distribution system's response is assessed using an operational resilience metric. The efficacy of the proposed framework is evaluated on IEEE 33‐bus and 69‐bus systems and the results show that the model provides an efficient restoration solution despite increased complexity caused by varying conditions.

Published
2024
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