Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification

Vinaykumar, V. N.; Babu, J. Ananda; Frnda, Jaroslav

doi:10.1186/s13634-023-00980-w

Cited by 25 publications

(7 citation statements)

References 38 publications

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“…In discrete scenarios, where WOA operates on continuous representations, discretization becomes a key preprocessing step. The integration of discretization with WOA proves influential in solving diverse optimization challenges in domains such as classification [20]. This approach harnesses the power of discretization techniques to convert continuous variables into discrete states, optimizing them efficiently with WOA in applications across various domains [21]− [23].…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

An improve unsupervised discretization using optimization algorithms for classification problems

Mohamed,

Samsudin

2024

IJEECS

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This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. Recognizing the critical role of discretization in enhancing classification performance, the study integrates equal width binning (EWB) with two optimization algorithms: the bat algorithm (BA), referred to as EB, and the whale optimization algorithm (WOA), denoted as EW. The primary objective is to determine the optimal technique for predicting relevant class labels. The paper emphasizes the significance of discretization in data preprocessing, offering a comprehensive approach that combines discretization techniques with optimization algorithms. An investigative study was undertaken to assess the efficiency of EB and EW by evaluating their classification performance using Naive Bayes and K-nearest neighbor algorithms on four continuous datasets sourced from the UCI datasets. According to the experimental findings, the suggested EB has a major effect on the accuracy, recall, and F-measure of data classification. The classification performance using EB outperforms other existing approaches for all datasets.

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Section: Whale Optimization Algorithmmentioning

confidence: 99%

An improve unsupervised discretization using optimization algorithms for classification problems

Mohamed,

Samsudin

2024

IJEECS

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“…The optimization problem is formulated in (18) in terms of the network-constrained parameters as follows:…”

Section: Fn17mentioning

confidence: 99%

“…Exploitation is the fast convergence ability of an algorithm towards a suboptimal or optimal solution. Exploration is the broad navigation of an algorithm toward an optimal solution in the search space [ 18 ]. Some metaheuristic algorithms have inherently higher exploitation, while others are identified with higher exploration in the global search space.…”

Section: Introductionmentioning

confidence: 99%

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations

Mohammed¹,

Sha’aban

Umoh

et al. 2023

PLoS ONE

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This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization.

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“…Modeling of Land-use and land-cover change (LULCC) through machine learning techniques was collected and discussed in Wang et al (Wang et al 2022); the review summarized that machine and deep learning may be limited in "(i) describing occurrence, transition, and spatial patterns of changes ; (ii) unavailability of training data for all the change drivers, particularly sequence data, and (iii) lack of inclusion of local ecological, hydrological, and social-economic drivers when addressing the spectral feature change". Bi-directional long short-term memory (Bi-LSTM) was integrated with the optimal guidance-whale optimization algorithm (OG-WOA) technique to classify and map the LULC (Vinaykumar et al 2023). The accuracy of Bi-LSTM was found to be better than that of CNN and RNN in LULC classification.…”

Section: Introductionmentioning

confidence: 99%

Novel Deep Learning Approaches for Mapping Variation of Ground Level from Spirit Level Measurements

Zarzoura

Kaloop

Samui

et al. 2023

Preprint

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Abstract. This study investigates the use of new machine learning techniques in mapping variation in ground levels based on ordinary spirit levelling (SL) measurements. Convolution Neural Network (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and bi-directional LSTM (BI-LSTM) were developed and compared in the current study to estimate the leveling through SL measurements. SL measurements of the Manzalla region, Egypt, were used in the current study. 3253 datasets of SL observation points, including 229 benchmarks of precise levelling (PL), were used to design and verify the proposed model’s results. The results show the developed LSTM model outperforms CNN, RNN, and BI-LSTM in modeling ground leveling in the training and testing stages. The root mean square error and correlation determination of the LSTM model are 7.4 cm and 0.99, respectively, in the testing stage. The accuracy of mapping ground levelling through the developed LSTM model is close to 99 % in terms of model error.

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Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification

Cited by 25 publications

References 38 publications

An improve unsupervised discretization using optimization algorithms for classification problems

An improve unsupervised discretization using optimization algorithms for classification problems

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations

Novel Deep Learning Approaches for Mapping Variation of Ground Level from Spirit Level Measurements

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