Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition

The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph’s topology and then fix it across all input layers and samples. This research shows how to use the Statistical Fractal Search (SFS)-Guided whale optimization algorithm (GWOA). To get the best solution for the GWOA, we adopt the SFS diffusion algorithm, which uses the random walk with a Gaussian distribution method common to growing systems. Continuous values are transformed into binary to apply to the feature-selection problem in conjunction with the BlazePose skeletal topology and stochastic fractal search to construct a novel implementation of the BlazePose topology for action recognition. In our experiments, we employed the Kinetics and the NTU-RGB+D datasets. The achieved actiona accuracy in the X-View is 93.14% and in the X-Sub is 96.74%. In addition, the proposed model performs better in numerous statistical tests such as the Analysis of Variance (ANOVA), Wilcoxon signed-rank test, histogram, and times analysis

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model’s feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm’s feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm’s regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon’s rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EV type for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics such as accuracy, sensitivity, specificity, and F-score. The improvements observed in these metrics are 91.22%, 94.34%, 89.5%, and 88.5%, respectively. These results highlight the overall effectiveness of the proposed method. Hence, the suggested approach is seen more favorable for implementing the voting classifier in the context of the EV population across different geographical areas

Electrical power output prediction of combined cycle power plants using a recurrent neural network optimized by waterwheel plant algorithm

It is difficult to analyze and anticipate the power output of Combined Cycle Power Plants (CCPPs) when considering operational thermal variables such as ambient pressure, vacuum, relative humidity, and temperature. Our data visualization study shows strong non-linearity in the experimental data. We observe that CCPP energy production increases linearly with temperature but not pressure. We offer the Waterwheel Plant Algorithm (WWPA), a unique metaheuristic optimization method, to fine-tune Recurrent Neural Network hyperparameters to improve prediction accuracy. A robust mathematical model for energy production prediction is built and validated using anticipated and experimental data residuals. The residuals’ uniformity above and below the regression line suggests acceptable prediction errors. Our mathematical model has an R-squared value of 0.935 and 0.999 during training and testing, demonstrating its outstanding predictive accuracy. This research provides an accurate way to forecast CCPP energy output, which could improve operational efficiency and resource utilization in these power plants

Towards a fossil free energy production using GIS multi-criteria decision-making support tool

Egypt’s Sustainable Development Strategy: Vision 2030 calls for renewable energy plans and the adoption of a sustainable development approach. Given the government’s gradual removal of energy subsidies for local citizens and the current energy crises, the study in hand aims to detect potential investment zones for free fossil fuel energy production. Site analysis for renewable energy allocation using GIS to identify potential capability to locate a renewable energy source was applied in the Sinai Peninsula in Egypt. The study used an accumulative co-relation matrix between different development sectors, Sinai’s geographical location and promising future investment scenarios. A set of data analysis process was developed to examine potentials and constraints. The analysis revealed that 36% of the area is suitable for the development of solar farms and a further 4% for wind farms. These findings could help decision makers to fill the gap between the country’s future energy needs and its available natural sources. Applying this methodology across the different areas offering similar potential in Egypt would help to identify more likely locations for renewable energy production. Wider replication of the method could also point to the significant contribution that different zones in Egypt, and even in other zones within the Middle East region, could make towards a more sustainable future

CNN-Hyperparameter Optimization for Diabetic Maculopathy Diagnosis in Optical Coherence Tomography and Fundus Retinography

Diabetic Maculopathy (DM) is considered the most common cause of permanent visual impairment in diabetic patients. The absence of clear pathological symptoms of DM hinders the timely diagnosis and treatment of such a critical condition. Early diagnosis of DM is feasible through eye screening technologies. However, manual inspection of retinography images by eye specialists is a time-consuming routine. Therefore, many deep learning-based computer-aided diagnosis systems have been recently developed for the automatic prognosis of DM in retinal images. Manual tuning of deep learning network’s hyperparameters is a common practice in the literature. However, hyperparameter optimization has shown to be promising in improving the performance of deep learning networks in classifying several diseases. This study investigates the impact of using the Bayesian optimization (BO) algorithm on the classification performance of deep learning networks in detecting DM in retinal images. In this research, we propose two new custom Convolutional Neural Network (CNN) models to detect DM in two distinct types of retinal photography; Optical Coherence Tomography (OCT) and fundus retinography datasets. The Bayesian optimization approach is utilized to determine the optimal architectures of the proposed CNNs and optimize their hyperparameters. The findings of this study reveal the effectiveness of using the Bayesian optimization for fine-tuning the model hyperparameters in improving the performance of the proposed CNNs for the classification of diabetic maculopathy in fundus and OCT images. The pre-trained CNN models of AlexNet, VGG16Net, VGG 19Net, GoogleNet, and ResNet-50 are employed to be compared with the proposed CNN-based models. Statistical analyses, based on a one-way analysis of variance (ANOVA) test, receiver operating characteristic (ROC) curve, and histogram, are performed to confirm the performance of the proposed models

An Assessment of the Accuracy of MODIS Land Surface Temperature over Egypt Using Ground-Based Measurements

Space-based data have provided important advances in understanding climate systems and processes in arid and semi-arid regions, which are hot-spot regions in terms of climate change and variability. This study assessed the performance of land surface temperatures (LSTs), retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua platform, over Egypt. Eight-day composites of daytime and nighttime LST data were aggregated and validated against near-surface seasonal and annual observational maximum and minimum air temperatures using data from 34 meteorological stations spanning the period from July 2002 to June 2015. A variety of accuracy metrics were employed to evaluate the performance of LST, including the bias, normalized root-mean-square error (nRMSE), Yule–Kendall (YK) skewness measure, and Spearman’s rho coefficient. The ability of LST to reproduce the seasonal cycle, anomalies, temporal variability, and the distribution of warm and cold tails of observational temperatures was also evaluated. Overall, the results indicate better performance of the nighttime LSTs compared to the daytime LSTs. Specifically, while nighttime LST tended to underestimate the minimum air temperature during winter, spring, and autumn on the order of −1.3, −1.2, and −1.4 °C, respectively, daytime LST markedly overestimated the maximum air temperature in all seasons, with values mostly above 5 °C. Importantly, the results indicate that the performance of LST over Egypt varies considerably as a function of season, lithology, and land use. LST performs better during transitional seasons (i.e., spring and autumn) compared to solstices (i.e., winter and summer). The varying interactions and feedbacks between the land surface and the atmosphere, especially the differences between sensible and latent heat fluxes, contribute largely to these seasonal variations. Spatially, LST performs better in areas with sandstone formations and quaternary sediments and, conversely, shows lower accuracy in regions with limestone, igneous, and metamorphic rocks. This behavior can be expected in hybrid arid and semi-arid regions like Egypt, where bare rocks contribute to the majority of the Egyptian territory, with a lack of vegetation cover. The low surface albedo of igneous and limestone rocks may explain the remarkable overestimation of daytime temperature in these regions, compared to the bright formations of higher surface albedo (i.e., sandy deserts and quaternary rocks). Overall, recalling the limited coverage of meteorological stations in Egypt, this study demonstrates that LST obtained from the MODIS product can be trustworthily employed as a surrogate for or a supplementary source to near-surface measurements, particularly for minimum air temperature. On the other hand, some bias correction techniques should be applied to daytime LSTs. In general, the fine space-based climatic information provided by MODIS LST can be used for a detailed spatial assessment of climate variability in Egypt, with important applications in several disciplines such as water resource management, hydrological modeling, agricultural management and planning, urban climate, biodiversity, and energy consumption, amongst others. Also, this study can contribute to a better understanding of the applications of remote sensing technology in assessing climatic feedbacks and interactions in arid and semi-arid regions, opening new avenues for developing innovative algorithms and applications specifically addressing issues related to these regions

An archive-based multi-objective arithmetic optimization algorithm for solving industrial engineering problems

This research proposes an Archive-based Multi-Objective Arithmetic Optimization Algorithm (MAOA) as an alternative to the recently established Arithmetic Optimization Algorithm (AOA) for multi-objective problems (MAOA). The original AOA approach was based on the distribution behavior of vital mathematical arithmetic operators, such as multiplication, division, subtraction, and addition. The idea of the archive is introduced in MAOA, and it may be used to find non-dominated Pareto optimum solutions. The proposed method is tested on seven benchmark functions, ten CEC-2020 mathematic functions, and eight restricted engineering design challenges to determine its suitability for solving real-world engineering difficulties. The experimental findings are compared to five multi-objective optimization methods (Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Slap Swarm Algorithm (MSSA), Multi-Objective Ant Lion Optimizer (MOALO), Multi-Objective Genetic Algorithm (NSGA2) and Multi-Objective Grey Wolf Optimizer (MOGWO) reported in the literature using multiple performance measures. The empirical results show that the proposed MAOA outperforms existing state-of-the-art multi-objective approaches and has a high convergence rate.Web of Science1010669810667