| 1. |
- 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. |
- Ashraf, Waqar Muhammad, et al.
(författare)
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Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
- 2022
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Ingår i: Alexandria Engineering Journal. - 1110-0168 .- 2090-2670. ; 61:3, s. 1864-1880
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Tidskriftsartikel (refereegranskat)abstract
- The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine's bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment. (c) 2021 THE AUTHORS. Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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| 3. |
- Chhabra, Amit, et al.
(författare)
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Improved bald eagle search algorithm for global optimization and feature selection
- 2023
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Ingår i: Alexandria Engineering Journal. - : ELSEVIER. - 1110-0168 .- 2090-2670. ; 68, s. 141-180
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Tidskriftsartikel (refereegranskat)abstract
- The use of metaheuristics is one of the most encouraging methodologies for taking care of real-life problems. Bald eagle search (BES) algorithm is the latest swarm-intelligence metaheuris-tic algorithm inspired by the intelligent hunting behavior of bald eagles. In recent research works, BES algorithm has performed reasonably well over a wide range of application areas such as chem-ical engineering, environmental science, physics and astronomy, structural modeling, global opti-mization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency and has a tendency to stuck in local optima which affects the final outcome. This paper introduces a modified BES (mBES) algorithm that removes the shortcomings of the original BES algorithm by incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), and Transition & Pharsor operators. OBL is embedded in different phases of the standard BES viz. initial population, selecting, searching in space, and swooping phases to update the positions of individual solutions to strengthen exploration, CLS is used to enhance the position of the best agent which will lead to enhancing the positions of all individuals, and Transition & Pharsor operators help to provide sufficient exploration-exploitation trade-off. The efficiency of the mBES algorithm is initially evaluated with 29 CEC2017 and 10 CEC2020 global optimization benchmark functions. In addition, the practicality of the mBES is tested with a real-world feature selection problem and five engineering design problems. Results of the mBES algorithm are com-pared against a number of classical metaheuristic algorithms using statistical metrics, convergence analysis, box plots, and the Wilcoxon rank sum test. In the case of composite CEC2017 test func-tions F21-F30, mBES wins against compared algorithms in 70% test cases, whereas for the rest of the test functions, it generates good results in 65% cases. The proposed mBES produces best per-formance in 55% of the CEC2020 test functions, whereas for the rest of the 45% test cases, it gen-erated competitive results. On the other hand, for five engineering design problems, the mBES is the best among all compared algorithms. In the case of the feature selection problem, the mBES also showed competitiveness with the compared algorithms. Results and observations for all tested opti-mization problems show the superiority and robustness of the proposed mBES over the baseline metaheuristics. It can be safely concluded that the improvements suggested in the mBES are proved to be effective making it competitive enough to solve a variety of optimization problems.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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| 4. |
- Ehteram, Mohammad, et al.
(författare)
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Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
- 2021
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Ingår i: Alexandria Engineering Journal. - Netherlands : Elsevier. - 1110-0168 .- 2090-2670. ; 60:2, s. 2193-2208
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Tidskriftsartikel (refereegranskat)abstract
- In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level.
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| 5. |
- Hashim, Fatma A., et al.
(författare)
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An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization
- 2023
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Ingår i: Alexandria Engineering Journal. - : ELSEVIER. - 1110-0168 .- 2090-2670. ; 85, s. 29-48
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Tidskriftsartikel (refereegranskat)abstract
- The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the Coati optimization algorithm (CoatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mCoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mCoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law Algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mCoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mCoatiOA are significantly different from those of the other rival algorithms. mCoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method. The proposed mCoatiOA achieved better results in comparison with other published methods. It achieved the mean best results on 75% of the datasets.
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| 6. |
- Hashim, Fatma A., et al.
(författare)
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An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems
- 2024
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Ingår i: Alexandria Engineering Journal. - : ELSEVIER. - 1110-0168 .- 2090-2670. ; 93, s. 142-188
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi -modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p -best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels.
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| 7. |
- Hashim, Fatma A., et al.
(författare)
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Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems
- 2023
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Ingår i: Alexandria Engineering Journal. - : ELSEVIER. - 1110-0168 .- 2090-2670. ; 73, s. 543-577
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Tidskriftsartikel (refereegranskat)abstract
- Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that sim-ulates Archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to prevent being trapped in local optima. Despite the wide acceptance of AOA, it has some drawbacks, such as the assumption that individuals modify their locations depending on altered densities, volumes, and accelerations. This causes various shortcomings such as stagnation into local optimal regions, low diversity of the population, weakness of exploitation phase, and slow convergence curve. Thus, the exploitation of a specific local region in the conventional AOA may be examined to achieve a balance between exploitation and exploration capabilities in the AOA. The bird Swarm Algorithm (BSA) has an efficient exploitation strategy and a strong ability of search process. In this study, a hybrid optimizer called AOA-BSA is proposed to overcome the limitations of AOA by replacing its exploitation phase with a BSA exploitation one. Moreover, a transition operator is used to have a high balance between exploration and exploitation. To test and examine the AOA-BSA performance, in the first experimental series, 29 unconstrained functions from CEC2017 have been used whereas the series of the second experiments use seven constrained engi-neering problems to test the AOA-BSAs ability in handling unconstrained issues. The performance of the suggested algorithm is compared with 10 optimizers. These are the original algorithms and 8 other algorithms. The first experiments results show the effectiveness of the AOA-BSA in optimiz-ing the functions of the CEC2017 test suite. AOABSA outperforms the other metaheuristic algo-rithms compared with it across 16 functions. The results of AOABSA are statically validated using Wilcoxon Rank sum. The AOABSA shows superior convergence capability. This is due to the added power to the AOA by the integration with BSA to balance exploration and exploitation. This is not only seen in the faster convergence achieved by the AOABSA, but also in the optimal solutions found by the search process. For further validation of the AOABSA, an extensive statis-tical analysis is performed during the search process by recording the ratios of the exploration and exploitation. For engineering problems, AOABSA achieves competitive results compared with other algorithms. the convergence curve of the AOABSA reaches the lowest values of the problem. It also has the minimum standard deviation which indicates the robustness of the algorithm in solv-ing these problems. Also, it obtained competitive results compared with other counterparts algo-rithms regarding the values of the problem variables and convergence behavior that reaches the best minimum values. (c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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| 8. |
- Isleem, Haytham F., et al.
(författare)
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Nonlinear finite element and machine learning modeling of tubed reinforced concrete columns under eccentric axial compression loading
- 2024
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Ingår i: Alexandria Engineering Journal. - : Elsevier. - 1110-0168 .- 2090-2670. ; 92, s. 380-416
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Tidskriftsartikel (refereegranskat)abstract
- There is still insufficient data on the behavior of tubed-reinforced concrete columns (TRCCs) under the eccentric compression. Thus, this research work comprehensively examines the eccentric compression behavior of TRCCs using nonlinear finite element modeling and machine learning (ML). To do this, numerical simulation and parametric analysis based on existing investigations were conducted. In addition to the existing 22 specimens with limited test variables, additional 188 specimens were developed to cover a wide range of parameters, including the load eccentricity, transverse reinforcement spacing, columns’ slenderness ratio, yield strength of steel, and outer steel tube diameter. Additionally, six ML models were created to estimate the ultimate load results. The results indicated that increasing the outer steel tube yield strength and diameter, and reducing the load eccentricity, slenderness ratio, and spacing of the transverse reinforcement enhanced the load-carrying capacity of the columns. The Gaussian process regression model demonstrated superior performance metrics in comparison to other ML models, with the highest R2 values (0.998613 in training and 0.99823 in testing stages) and lowest root mean square error values (0.007213 in training and 0.008471 in testing stages). To save money, time, and resources compared to laboratory testing, an online-based prediction program is finally presented to predict the columns’ ultimate load.
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| 9. |
- Izci, Davut, et al.
(författare)
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Enhancing time-domain performance of vehicle cruise control system by using a multi-strategy improved RUN optimizer
- 2023
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Ingår i: Alexandria Engineering Journal. - : ELSEVIER. - 1110-0168 .- 2090-2670. ; 80, s. 609-622
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the pressing concern of traffic safety by focusing on the optimization of vehicle cruise control systems. While traditional control techniques have been widely employed, their design procedures can be time-consuming and suboptimal. To overcome these limitations, metaheuristic algorithms have been introduced as promising solutions for complex optimization problems. In this study, an improved Runge Kutta optimizer (IRUN) is developed and applied to enhance the control performance of a real PID plus second-order derivative (RPIDD2) controller for vehicle cruise control systems. The IRUN optimizer incorporates advanced strategies such as quadratic interpolation, Laplacian segment mutation, Levy flight, and information-sharing-based local search mechanisms. By integrating these strategies, the IRUN algorithm demonstrates enhanced optimization capabil-ities, making it well-suited for tuning the controller. The proposed approach utilizes a master-slave system, where the ideal reference model sets the desired response and the RPIDD2 controller adjusts its parameters accordingly. The integral of the square error is employed as the objective function to evaluate the control sys-tems performance. Statistical analyses, convergence analyses, and stability evaluations and robustness analysis are performed to demonstrate the effectiveness of the IRUN-based RPIDD2 controller. Comparative studies are conducted against established approaches using PID, fractional-order PID (FOPID), and RPIDD2 controllers, showcasing the superiority and effectiveness of the proposed approach. Overall, this paper presents a compre-hensive study on enhancing the time-domain performance and stability of vehicle cruise control systems, providing significant improvements in control accuracy and efficiency. The subsequent sections delve into the proposed approach, experimental setup, and obtained results, further emphasizing the significance and potential impact of this research.
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| 10. |
- Liu, G., et al.
(författare)
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Numerical analysis of inner heating tube position for improving solid-phase transition in a shell-and-tube heat accumulator
- 2023
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Ingår i: Alexandria Engineering Journal. - : Elsevier B.V.. - 1110-0168 .- 2090-2670. ; 65, s. 771-784
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Tidskriftsartikel (refereegranskat)abstract
- Latent heat thermal storage (LHTS) system is vital to reduce environment pollution. In the shell-and-tube heat accumulator, the position of the inner heating tube plays a vital role in the thermal storage. To analyze the effect of the inner tube position on the phase transition, a two-dimensional numerical model is developed. The structure has the minimum full melting time of 3480 s when the inner tube is 12 mm (L = 12 mm) from the center. Compared with L = 0 mm, the full melting time at L = 12 mm can be reduced by 13.4%.
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