A coupled artificial neural network with artificial rabbits optimizer for predicting water productivity of different designs of solar stills

Alsaiari, Abdulmohsen; Moustafa, Essam B.; Alhumade, Hesham; Abulkhair, Hani; Elsheikh, Ammar H.

doi:10.1016/j.advengsoft.2022.103315

Cited by 81 publications

(15 citation statements)

References 66 publications

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“…The Artificial Rabbits Optimization (ARO) method, which is a novel bio-inspired meta-heuristic approach, is used to fine-tune the suggested PD-PIDA parameters. This algorithm has demonstrated better performance when it comes to the handling of complex real-world situations [37], [38], [39], [40]. The following are some of the most important contributions that this study presents:…”

Section: B Motivation and Contributionsmentioning

confidence: 89%

Improvement of Frequency Regulation of a Wind-Integrated Power System Based on a PD-PIDA Controlled STATCOM Tuned by the Artificial Rabbits Optimizer

Neamah,

AbuHussein,

Hossam-Eldin

et al. 2023

IEEE Access

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In this research, a novel optimized Proportional-Derivative with Proportional-Integral-Derivative-Acceleration (PD-PIDA) driven static synchronous compensator (STATCOM) is proposed with the goal of improving the frequency performance of power systems through the manipulation of reactive power. When there is a variation in load or when there is a failure of generation, the presented controller is able to maintain the frequency of the system within the allowable limits (±2% as per IEC 60034-1 standard). A recently developed metaheuristic optimization strategy known as the Artificial Rabbits Optimizer, or ARO, is used to do the fine-tuning the PD-PIDA controller's gain settings. In addition to this, the functionality of the ARO-based PD-PIDA-controlled STATCOM is evaluated in the context of two IEEE standard systems: the two-area four-machine system (Kundur system) and the New England IEEE 39-bus system. According to the outcomes, the ARO-based PD-PIDA controller can give a superior frequency response than the PIDA controller whose parameters are optimized by Marine Predator Algorithm (MPA) that was published in the prior literature. This was determined by comparing the two controllers. In addition, the proposed controller's resiliency is tested by integrating case studies with wind generation. This helps to ensure that the controller is fit for purpose. In every single one of the examples that were researched, the application of the ARO-based PD-PIDA-controlled STATCOM resulted in a remarkable boost in the frequency performance. Additionally, the outputs are introduced in the form of time domain simulations that were carried out with MATLAB/SIMULINK.

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Section: B Motivation and Contributionsmentioning

confidence: 89%

Improvement of Frequency Regulation of a Wind-Integrated Power System Based on a PD-PIDA Controlled STATCOM Tuned by the Artificial Rabbits Optimizer

Neamah,

AbuHussein,

Hossam-Eldin

et al. 2023

IEEE Access

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“…DL models have made significant advances in a variety of fields including, but not limited to, deep fakes [ 22 , 23 ], satellite image analysis [ 24 ], image classification [ 25 , 26 ], the optimization of artificial neural networks [ 27 , 28 ], the processing of natural language [ 29 , 30 ], fin-tech [ 31 ], intrusion detection [ 32 ], steganography [ 33 ], and biomedical image analysis [ 14 , 34 ]. CNNs have recently surfaced as one of the most commonly used techniques for plant disease identification [ 35 , 36 ].…”

Section: Related Workmentioning

confidence: 99%

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

et al. 2023

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Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.

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“…Conventional artificial tools suffer low accuracy due to the well-known drawbacks of the conventional optimizers embedded in the artificial intelligence models such as trapping at local minima, low convergence rates, and high computational costs [ 48 ]. Metaheuristic optimizers such as Harris Hawk’s Optimizer [ 49 ], mayfly optimizer [ 50 ], chimp optimizer [ 51 ], heap-based optimizer [ 52 ], transient search optimizer [ 53 ], pigeon optimizer [ 54 ], cat swarm optimizer [ 55 ], rabbit optimizer [ 56 ], and parasitism-predation algorithm [ 57 ] have been proposed as internal optimizers to optimize different artificial intelligence models. Datta et al [ 58 ] employed four different metaheuristics optimizers genetic algorithm, grey wolf optimizer, Jaya optimizer, bonobo optimizer, and sine-cosine optimizer to optimize two different ANN models, namely feed-forward neural network and recurrent neural network, used to predict the characteristics of laser-welded butt joints made of nickel–titanium alloy.…”

Section: Introductionmentioning

confidence: 99%

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer

Moustafa

Elsheikh

2023

Polymers

Self Cite

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This study investigates the application of a coupled multi-layer perceptrons (MLP) model with Archimedes optimizer (AO) to predict characteristics of dissimilar lap joints made of polymethyl methacrylate (PMMA) and polycarbonate (PC). The joints were welded using the laser transmission welding (LTW) technique equipped with a beam wobbling feature. The inputs of the models were laser power, welding speed, pulse frequency, wobble frequency, and wobble width; whereas, the outputs were seam width and shear strength of the joint. The Archimedes optimizer was employed to obtain the optimal internal parameters of the multi-layer perceptrons. In addition to the Archimedes optimizer, the conventional gradient descent technique, as well as the particle swarm optimizer (PSO), was employed as internal optimizers of the multi-layer perceptrons model. The prediction accuracy of the three models was compared using different error measures. The AO-MLP outperformed the other two models. The computed root mean square errors of the MLP, PSO-MLP, and AO-MLP models are (39.798, 19.909, and 2.283) and (0.153, 0.084, and 0.0321) for shear strength and seam width, respectively.

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A coupled artificial neural network with artificial rabbits optimizer for predicting water productivity of different designs of solar stills

Cited by 81 publications

References 66 publications

Improvement of Frequency Regulation of a Wind-Integrated Power System Based on a PD-PIDA Controlled STATCOM Tuned by the Artificial Rabbits Optimizer

Improvement of Frequency Regulation of a Wind-Integrated Power System Based on a PD-PIDA Controlled STATCOM Tuned by the Artificial Rabbits Optimizer

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer

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