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Muhammad Shiraz
Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan

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Journal article
Published: 28 July 2021 in Electronics
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Cloud Computing (CC) is a promising technology due to its pervasive features, such as online storage, high scalability, and seamless accessibility, in that it plays an important role in reduction of the capital cost and workforce, which attracts organizations to conduct their businesses and financial activities over the cloud. Even though CC is a great innovation in the aspect of computing with ease of access, it also has some drawbacks. With the increase of cloud usage, security issues are proportional to the increase. To address these, there has been much work done in this domain, whereas research work considering the growing constrained applications provided by the Internet of Things (IoT) and smart city networks are still lacking. In this survey, we provide a comprehensive security analysis of CC-enabled IoT and present state-of-the-art in the research area. Finally, future research work and possible areas of implementation and consideration are given to discuss open issues.

ACS Style

Abeer Tahirkheli; Muhammad Shiraz; Bashir Hayat; Muhammad Idrees; Ahthasham Sajid; Rahat Ullah; Nasir Ayub; Ki-Il Kim. A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges. Electronics 2021, 10, 1811 .

AMA Style

Abeer Tahirkheli, Muhammad Shiraz, Bashir Hayat, Muhammad Idrees, Ahthasham Sajid, Rahat Ullah, Nasir Ayub, Ki-Il Kim. A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges. Electronics. 2021; 10 (15):1811.

Chicago/Turabian Style

Abeer Tahirkheli; Muhammad Shiraz; Bashir Hayat; Muhammad Idrees; Ahthasham Sajid; Rahat Ullah; Nasir Ayub; Ki-Il Kim. 2021. "A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges." Electronics 10, no. 15: 1811.

Journal article
Published: 05 June 2020 in Energies
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Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques.

ACS Style

Waqas Ahmad; Nasir Ayub; Tariq Ali; Muhammad Irfan; Muhammad Awais; Muhammad Shiraz; Adam Glowacz. Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine. Energies 2020, 13, 2907 .

AMA Style

Waqas Ahmad, Nasir Ayub, Tariq Ali, Muhammad Irfan, Muhammad Awais, Muhammad Shiraz, Adam Glowacz. Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine. Energies. 2020; 13 (11):2907.

Chicago/Turabian Style

Waqas Ahmad; Nasir Ayub; Tariq Ali; Muhammad Irfan; Muhammad Awais; Muhammad Shiraz; Adam Glowacz. 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine." Energies 13, no. 11: 2907.

Journal article
Published: 29 October 2019 in Malaysian Journal of Computer Science
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ACS Style

Muhammad Shiraz; Laleh Boroumand; Abdullah Gani; Suleman Khan. AN IMPROVED PORT KNOCKING AUTHENTICATION FRAMEWORK FOR MOBILE CLOUD COMPUTING. Malaysian Journal of Computer Science 2019, 32, 269 -283.

AMA Style

Muhammad Shiraz, Laleh Boroumand, Abdullah Gani, Suleman Khan. AN IMPROVED PORT KNOCKING AUTHENTICATION FRAMEWORK FOR MOBILE CLOUD COMPUTING. Malaysian Journal of Computer Science. 2019; 32 (4):269-283.

Chicago/Turabian Style

Muhammad Shiraz; Laleh Boroumand; Abdullah Gani; Suleman Khan. 2019. "AN IMPROVED PORT KNOCKING AUTHENTICATION FRAMEWORK FOR MOBILE CLOUD COMPUTING." Malaysian Journal of Computer Science 32, no. 4: 269-283.