Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

Shaban, Hassan; Houssein, Essam H.; Pérez‐Cisneros, Marco; Oliva, Diego; Hassan, Amir Y.; Ismaeel, Alaa A. K.; AbdElminaam, Diaa Salama; Deb, Sanchari; Said, Mokhtar

doi:10.3390/math9182313

Cited by 67 publications

(26 citation statements)

References 69 publications

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“…Their recovery method is built upon the CBIR framework, which also does not require a formal confirmation but provides users with a range of matches. Abeysinghe et al [46] used multilayer Siamese networks to build a completely computerized system for identifying snake species. Even though the original snake dataset is limited, they could attain consistent results.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

Orban

Vadivel³

et al. 2022

Electronics

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In comparison to the competitors, engineers must provide quick, low-cost, and dependable solutions. The advancement of intelligence generated by machines and its application in almost every field has created a need to reduce the human role in image processing while also making time and labor profit. Lepidopterology is the discipline of entomology dedicated to the scientific analysis of caterpillars and the three butterfly superfamilies. Students studying lepidopterology must generally capture butterflies with nets and dissect them to discover the insect’s family types and shape. This research work aims to assist science students in correctly recognizing butterflies without harming the insects during their analysis. This paper discusses transfer-learning-based neural network models to identify butterfly species. The datasets are collected from the Kaggle website, which contains 10,035 images of 75 different species of butterflies. From the available dataset, 15 unusual species were selected, including various butterfly orientations, photography angles, butterfly lengths, occlusion, and backdrop complexity. When we analyzed the dataset, we found an imbalanced class distribution among the 15 identified classes, leading to overfitting. The proposed system performs data augmentation to prevent data scarcity and reduce overfitting. The augmented dataset is also used to improve the accuracy of the data models. This research work utilizes transfer learning based on various convolutional neural network architectures such as VGG16, VGG19, MobileNet, Xception, ResNet50, and InceptionV3 to classify the butterfly species into various categories. All the proposed models are evaluated using precision, recall, F-Measure, and accuracy. The investigation findings reveal that the InceptionV3 architecture provides an accuracy of 94.66%, superior to all other architectures.

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Section: Related Workmentioning

confidence: 99%

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

Orban

Vadivel³

et al. 2022

Electronics

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“…In addition, different methods were employed to develop the discrete version of a continuous algorithm [35]. The metaheuristic algorithms are applied for solving complex problems in different applications such as parameter identification of solar cells [36], feature selection [37][38][39][40][41], scheduling and planning [42][43][44], disease diagnosis [45,46], clustering [47], medical applications [48][49][50], industrial applications [51][52][53][54][55], and engineering optimization [56,57].…”

Section: Introductionmentioning

confidence: 99%

DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection

et al. 2021

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In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.

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“…Most researchers are interested in optimizing the layout of EV CSs. This problem can be solved with several optimization algorithms [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

et al. 2021

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The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer.

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Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

Cited by 67 publications

References 69 publications

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

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