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50 results on '"Akbar, Mariam"'

5. Hourly Electricity Load Forecasting in Smart Grid Using Deep Learning Techniques

Author

Khan, Abdul Basit Majeed, Javaid, Nadeem, Nazeer, Orooj, Zahid, Maheen, Akbar, Mariam, Hameed Khan, Majid, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Xhafa, Fatos, editor, and Hussain, Omar K., editor

Published
2020
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7. Weighted Cuckoo Search Based Load Balanced Cloud for Green Smart Grids

Author

Rahim, Muhammad Hassan, Javaid, Nadeem, Rahim, Sahar, Naz, Muqaddas, Akbar, Mariam, Javed, Farhana, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Xhafa, Fatos, editor, Javaid, Nadeem, editor, and Enokido, Tomoya, editor

Published
2019
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8. An Efficient Home Energy Management and Power Trading in Smart Grid

Author

Aslam, Sheraz, Javaid, Sakeena, Javaid, Nadeem, Mohsin, Syed Muhammad, Sulman Khan, Saad, Akbar, Mariam, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Xhafa, Fatos, editor, Javaid, Nadeem, editor, and Enokido, Tomoya, editor

Published
2019
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9. Hierarchical Based Coordination Strategy to Efficiently Exchange the Power Among Micro-grids

Author

Jamil, Aqib, Javaid, Nadeem, Iqbal, Zafar, Abdullah, Muhammad, Riaz, Muhammad Zaid, Akbar, Mariam, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Xhafa, Fatos, editor, Javaid, Nadeem, editor, and Enokido, Tomoya, editor

Published
2019
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13. Normative cerebral microvascular blood flow waveform morphology assessed with Diffuse Correlation Spectroscopy

Author

Urner, Tara, primary, Cowdrick, Kyle, additional, Brothers, Rowan, additional, Boodooram, Tisha, additional, Zhao, Hongting, additional, Goyal, Vidisha, additional, Sathialingam, Eashani, additional, Quadri, Ayesha, additional, Turrentine, Katherine, additional, Akbar, Mariam, additional, Triplett, Sydney, additional, Bai, Shasha, additional, and Buckley, Erin, additional

Published
2023
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26. Towards Void Hole Alleviation:Enhanced GEographic and Opportunistic Routing Protocols in Harsh Underwater WSNs

Author

Awais, Muhammad, Ali, Ishtiaq, Alghamdi, Turki Ali, Ramzan, Muhammad, Tahir, Muhammad, Akbar, Mariam, Javaid, Nadeem, Awais, Muhammad, Ali, Ishtiaq, Alghamdi, Turki Ali, Ramzan, Muhammad, Tahir, Muhammad, Akbar, Mariam, and Javaid, Nadeem

Abstract

Internet of Things enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are quite useful in monitoring different tasks including: from instrument monitoring to the climate recording and from pollution control to the prediction of natural disasters. However, there are some challenges, which affect the performance of a network, i.e., void hole occurrence, high Energy Consumption (EC) and low Packet Delivery Ratio (PDR). Therefore, in this work, two energy efficient routing protocols are proposed to maximize the PDR by minimizing the ratio of void hole occurrence. Scalability analysis of the proposed routing protocols is also performed. Additionally, feasible regions are computed to check the optimality of the proposed protocol in terms of EC. Furthermore, proposed protocols are compared with benchmark routing protocols in counterparts. Simulation results clearly show that proposed routing protocols achieved 80-81% higher PDR than GEographic and opportunistic routing with Depth Adjustment based topology control for communication Recovery (GEDAR) and Transmission Adjustment Neighbor-node Approaching Distinct Energy Efficient Mates (TA-NADEEM). Moreover, the ratio of void hole occurrence is minimized upto 30% approximately.

Published
2020

49. Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index.

Author

Khan, Mahnoor, Javaid, Nadeem, Sajjad, Abdullah, Naseem, Adnan, Ahmed, Salman, Riaz, Muhammad Sajid, Akbar, Mariam, and Ilahi, Manzoor

Subjects
KNOWLEDGE management, SMART power grids, ENERGY consumption, LOAD forecasting (Electric power systems), DEMAND forecasting
Abstract

Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer's behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting. [ABSTRACT FROM AUTHOR]

Published
2018
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50. Stand-alone segmentation of blood flow pulsatility measured with diffuse correlation spectroscopy.

Author

Bharadwaj S, Urner TM, Cowdrick KR, Brothers RO, Boodooram T, Zhao H, Goyal V, Sathialingam E, Wu YC, Quadri A, Turrentine K, Akbar MM, Triplett SE, Bai S, and Buckley EM

Abstract

We present a stand-alone blood flow index (BFI) pulse segmentation method for diffuse correlation spectroscopy that uses a wavelet-based representation of the BFI signal at the cardiac frequency in place of an exogenous physiological reference. We use this wavelet-based segmentation method to quantify BFI waveform morphology in a cohort of 30 healthy adults. We demonstrate that the waveform morphology features obtained with the wavelet approach strongly agree with those obtained using an exogenous blood pressure reference signal. These results suggest the promise of stand-alone wavelet-based BFI segmentation for quantifying BFI waveform morphological features., Competing Interests: The authors declare that there is no conflict of interests in this article., (© 2024 Optica Publishing Group.)

Published
2024
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