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- Ali, Munwar, et al.
(författare)
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A Confidentiality-based data Classification-as-a-Service (C2aaS) for cloud security
- 2022
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Ingår i: Alexandria Engineering Journal. - : Alexandria University. - 1110-0168 .- 2090-2670. ; 64, s. 749-760
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Tidskriftsartikel (refereegranskat)abstract
- Rapid development and massive use of Information Technology (IT) have since produced a massive amount of electronic data. In tandem, the demand for data outsourcing and the associated data security is increasing exponentially. Small organizations are often finding it expensive to save and process their huge amount of data, and keep the data secure from unauthorized access. Cloud computing is a suitable and affordable platform to provide services on user demand. The cloud platform is preferable used by individuals, Small, and Medium Enterprises (SMEs) that cannot afford large-scale hardware, software, and security maintenance cost. Storage and processing of big data in the cloud are becoming the key appealing features to SMEs and individuals. However, the processing of big data in the cloud is facing two issues such as security of stored data and system overload due to the volume of the data. These storage methods are plain text storage and encrypted text storage. Both methods have their strengths and limitations. The fundamental issue in plain text storage is the high risk of data security breaches; whereas, in encrypted text storage, the encryption of complete file data may cause system overload. This paper propose a feasible solution to address these issues with a new service model called Confidentiality-based Classification-as-a-Service (C2aaS) that performs data processing by treating data dynamically according to the data security level in preparation for data storing in the cloud. In comparison to the conventional methods, our proposed service model is strongly showing good security for confidential data and is proficient in reducing cloud system overloading.
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| 2. |
- Saif-Ul-Allah, Muhammad Waqas, et al.
(författare)
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Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
- 2022
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Ingår i: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author's knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models' development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.
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