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Abstract Energy harvesters based on nanomaterials are getting more and more popular, but on their way to commercial availability, some crucial issues still need to be solved. The objective of the study is to select an appropriate nanomaterial. Using features of the Reinforcement Deep Q-Network (DQN) in conjunction with Fuzzy PROMETHEE, the proposed model, we present in this work a hybrid fuzzy approach to selecting appropriate materials for a vehicle-environmental-hazardous substance (EHS) combination that operates in roadways and under traffic conditions. The DQN is able to accumulate useful experience of operating in a dynamic traffic environment, accordingly selecting materials that deliver the highest energy output but at the same time bring consideration to factors such as durability, cost, and environmental impact. Fuzzy PROMETHEE allows the participation of human experts during the decision-making process, going beyond the quantitative data typically learned by DQN through the inclusion of qualitative preferences. Instead, this hybrid method unites the strength of individual approaches, as a result providing highly resistant and adjustable material selection to real EHS. The result of the study pointed out materials that can give high energy efficiency with reference to years of service, price, and environmental effects. The proposed model provides 95% accuracy with a computational efficiency of 300 s, and the application of hypothesis and practical testing on the chosen materials showed the high efficiency of the selected materials to harvest energy under fluctuating traffic conditions and proved the concept of a hybrid approach in True Vehicle Environmental High-risk Substance scenarios.

Abstract The research objective in the context of the study relates to the major concern of corrosion affecting the wind turbines in operation to find materials with high durability in relation to environmental conditions of operation, strength, and cost. A method is an integration of the Analytical Hierarchy Process (AHP) and VIKOR Multi-Criteria Decision Making (MCDM) techniques that will assess seven different material options on sixteen criteria that comprise corrosion resistance, mechanical properties, cost, and a negative environmental impact. From this result, the AHP method calculated the weights for the indicators and chose potential materials, and finally, the VIKOR method used these materials and compared and ranked them to obtain a compromise solution. The research novelty integrates the AHP and VIKOR MCDM methods to address corrosion in wind turbines. By evaluating seven materials against challenging sixteen criteria—including corrosion resistance, mechanical properties, cost, and toxicity, AHP ranks and weights the criteria, while VIKOR identifies the optimal material choice. This dual approach enhances the selection process, ensuring the chosen material improves turbine performance and durability, offering a significant advancement in the sustainable development of wind energy technology. In conclusion, by integrating AHP and VIKOR, it comprehensively evaluates multiple material options based on corrosion resistance, mechanical properties, cost, and environmental impact. This methodology effectively identifies materials that enhance wind turbine performance and extend their lifespan, addressing a critical industry challenge. The alternative exhibits a similarity to the positive ideal solution (Si) of 0.3704 and a relative closeness to the ideal solution (Ri) of 0.0750. Additionally, its priority ranking (Qi) is 0.001, placing it in the first rank for Carbon Fiber Reinforced Polymers (CFRP) within the selection methodology.

Abstract The classification of chronic diseases has been a prominent research focus in public health, extensively leveraging machine learning algorithms. One of these chronic diseases that has significant rates of occurrence all around the world is diabetes, which is a disease by itself. Many academics are working to construct robust machine-learning algorithms for accurate categorization, given the prevalence of this chronic disease. A revolutionary methodology that can accurately categorize diabetic disease is the focus of this study, which aims to provide new methods. The proposed technique in this work is based on developing a novel feature selection method, DWWPA, which stands for dynamic waterwheel plant algorithm. The DWWPA algorithm is utilized in the process of optimizing the K-nearest neighbors (KNN) model in order to improve the accuracy of its classification. In the feature selection process, a binary representation of this method is called binary DWWPA (bDWWPA). Several different machine learning models and optimization techniques are compared to the strategy that has been presented. When categorizing diabetes cases in the dataset, the findings demonstrate the superiority and success of the proposed method. Furthermore, several different statistical analysis techniques, such as Analyses of variance (ANOVA) and Wilcoxon signed-rank test, are carried out to investigate the statistical difference and importance of the suggested strategy in contrast to the other ways at the same level of competition. The conclusions of these tests were consistent with what was anticipated they would be. Based on the suggested feature selection and the optimization of the KNN model, the proposed method has an accuracy of 98.9% when taken as an entire. The suggested method was useful in accurately classifying diabetic disease, as evidenced by the fact that it achieved a higher level of accuracy than the contemporary approaches.

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences.

The stability of smart grids is crucial for ensuring reliable and efficient power distribution in modern energy systems. This paper presents an optimized Long Short-Term Memory model for predicting smart grid stability, leveraging the Novel Guide-Waterwheel Plant Algorithm (Guide-WWPA) for enhanced performance. Traditional methods often struggle with the complexity and dynamic nature of smart grids, necessitating advanced approaches for accurate predictions. The proposed LSTM model, optimized using Guide-WWPA, addresses these challenges by effectively capturing temporal dependencies and nonlinear relationships in the data. The proposed approach involves a comprehensive preprocessing pipeline to handle data heterogeneity and noise, followed by the implementation of the LSTM model optimized through Guide-WWPA. The Guide-WWPA combines the strength of the WWPA with a novel guidance mechanism, ensuring efficient exploration and exploitation of the search space. The optimized LSTM is evaluated on a real-world smart grid dataset, demonstrating superior performance compared to traditional optimization techniques. Experimental Results indicate significant improvements in prediction accuracy and computational efficiency, highlighting the potential of the Guide-WWPA optimized LSTM for real-time smart grid stability prediction. This work contributes to the development of intelligent energy management systems, offering a robust tool for maintaining grid stability and enhancing overall energy reliability. On the other hand, statistical evaluations were carried out to prove the stability and difference of the proposed methodology. The results of the experiments demonstrate that the Guide-WWPA + LSTM strategy is superior to the other machine learning approaches.

Particularly, environmental pollution, such as air pollution, is still a significant issue of concern all over the world and thus requires the identification of good models for prediction to enable management. Blind Source Separation (BSS), Copula functions, and Long Short-Term Memory (LSTM) network integrated with the Greylag Goose Optimization (GGO) algorithm have been adopted in this research work to improve air pollution forecasting. The proposed model involves preprocessed data from the urban air quality monitoring dataset containing complete environmental and pollutant data. The application of Noise Reduction and Isolation techniques involves the use of methods such as Blind Source Separation (BSS). Using copula functions affords an even better estimate of the dependence structure between the variables. Both the BSS and Copula parameters are then estimated using GGO, which notably enhances the performance of these parameters. Finally, the air pollution levels are forecasted using a time series employing LSTM networks optimized by GGO. The results reveal that GGO-LSTM optimization exhibits the lowest mean squared error (MSE) compared to other optimization methods of the proposed model. The results underscore that certain aspects, such as noise reduction, dependence modeling and optimization of parameters, provide much insight into air quality. Hence, this integrated framework enables a proper approach to monitoring the environment by offering planners and policymakers information to help in articulating efficient environment air quality management strategies.

As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user’s present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles.

Diabetes is a chronic disease that is usually caused when the pancreas fails to produce sufficient insulin or when the body is unable to effectively utilize the insulin produced by the pancreas. Early detection of diabetes enables the implementation of a suitable treatment method, which can lead to a healthy lifestyle. A necessity arises for an automated system capable of diagnosing diabetes using clinical and physical data in cases when the conventional approach to detecting diabetes proves to be arduous. In this paper, a new diabetes classification model based on optimized long short-term memory (LSTM) is presented and evaluated on the Pima Indians Diabetes Database (PIDD). To improve the LSTM model, a novel hybrid waterwheel plant and stochastic fractal search (WWPASFS) is proposed for optimizing its parameters. To confirm the performance superiority of the proposed WWPASFS + LSTM model, it is compared to various machine learning models and metaheuristic optimization methods. In addition, the binary WWPASFS is proposed to extract the relevant features in the PIDD dataset, with the aim of improving the accurate classification of diabetes patients. The WWPASFS + LSTM model attained the highest accuracy of 98.2% in classifying diabetes patients on the dataset in hand. The WWPASFS + LSTM model exhibited superior performance compared to the other five models, namely decision tree, K-nearest neighbors, neural networks, random forest, and support vector machines. On the other hand, the statistical analysis of the proposed approach is studied and the results prove its difference and significance.

Environmental issues of rainfall are basic in terms of understanding and management of ecosystems and natural resources. The rainfall patterns significantly affect soil moisture, vegetation growth and biodiversity in the ecosystems. In addition, proper classification of rainfall types helps in the evaluation of the risk of flood, drought, and other extreme weather events’ risk, which immensely affect the ecosystems and human societies. Rainfall classification can be improved by using machine learning and metaheuristic algorithms. In this work, an Adaptive Dynamic Puma Optimizer (AD-PO) algorithm combined with Guided Whale Optimization Algorithm (Guided WOA) introduces a potentially important improvement in rainfall classification approaches. These algorithms are to be combined to enable researchers to comprehend and classify rain events by their specific features, such as intensity, duration, and spatial distribution. A voting ensemble approach within the proposed (AD-PO-Guided WOA) algorithm increases its predictive performance because of the combination of predictions from several classifiers to localize the dominant rainfall class. The presented approach not only makes the classifying of rain faster and more accurate but also strengthens the robustness and trustworthiness of the classification in this regard. Comparison to other optimization algorithms validates the effectiveness of the AD-PO-Guided WOA algorithm in terms of performance metrics with an outstanding 95.99% accuracy. Furthermore, the second scenario is applied for forecasting based on the long short-term memory networks (LSTM) model optimized by the AD-PO-Guided WOA algorithm. The AD-PO-Guided WOA- LSTM algorithm produces rainfall prediction with an MSE of 0.005078. Wilcoxon rank test, descriptive statistics, and sensitivity analysis are applied to help evaluating and improving the quality and validity of the proposed algorithm. This intensive method facilitates rainfall classification and is a base for suggested measures that cut the hazards of extreme weather events on societies.

Abstract Gross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy’s health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To enhance GDP prediction performance, we implement a parameter transfer approach, fine-tuning the parameters learned from Dataset A on Dataset B. Moreover, in this study, a preprocessing stage that includes median imputation and data normalization is performed. Mean Square Error, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Median Absolute Error, and determination coefficient (R2) evaluation metrics are utilized in this study to demonstrate the performance of the proposed model. The experimental results demonstrated that the proposed model gave better results than other regression models used in this study. Also, the results show that the proposed model achieved the highest results for R2, with 99.99%. This paper addresses a critical research gap in the domain of GDP prediction through artificial intelligence (AI) algorithms. While acknowledging the widespread application of such algorithms in forecasting GDP, the proposed model introduces distinctive advantages over existing approaches. Using PC-LSTM-RNN which achieves high R2 with minimum error rates.

Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.

Lead-based deep brain stimulation (DBS) electrodes have been employed to treat Parkinson’s disease (PD), but their limitations have led to the development of lead-free piezoelectric nanoparticle-based DBS (LF-PND-DBS). This novel approach utilizes non-invasive biocompatible piezoelectric nanoparticles to generate electrical stimulation, offering a promising alternative to traditional DBS. In this study, an innovative machine learning (ML)-optimized LF-PND-DBS system for diagnosing and evaluating PD is proposed. By leveraging ML algorithms, the optimized design of LF-PND electrodes and stimulation parameters is derived, ensuring precise and personalized treatment delivery. The ML-optimized LF-PND-DBS system was evaluated in a cohort of PD patients, demonstrating an exceptional diagnostic accuracy with a sensitivity of 99.1% and a specificity of 98.2%. It effectively assessed PD severity and response to DBS treatment, providing valuable guidance for treatment monitoring. The findings highlight the immense potential of the ML-optimized LF-PND-DBS system as a transformative tool for PD diagnosis and evaluation. This novel approach has the potential to enhance DBS efficacy, safety, and personalization, paving the way for improved patient outcomes and quality of life.

Abstract Background Polycystic ovarian syndrome (PCOS) cases undergoing in vitro fertilisation (IVF) are widely at risk of ovarian hyperstimulation; therefore, elective freezing of all embryos to be transferred in a later cycle is preferred. We aimed to compare the pregnancy outcome between the fresh and the frozen embryo transfer (ET) in PCOS cases undergoing IVF with antagonist ovarian induction using human chorionic gonadotropin (HCG) as a trigger. Methods In this prospective randomised study, 110 infertile PCOS women underwent fresh ET (group A) or frozen ET (group B) with GnRH-antagonist protocol. The primary outcome was the chemical and clinical pregnancy rates. The secondary outcomes were the ongoing pregnancy rate, ovarian hyperstimulation syndrome (OHSS) rate, pregnancy loss rate, ectopic pregnancy rate, and congenital anomalies rate. Results There was no significant difference between both groups regarding chemical pregnancy rate (44.23% vs 47.27%, P = 0.752), clinical pregnancy rate (42.31% vs 43.64%, P = 0.89), ongoing pregnancy rate (38.46% vs 41.82%, P = 0.723), pregnancy loss rate (17.39% vs 15.4%, P = 1), ectopic pregnancy rate (1.92% vs 0%, P = 0.486) and anomaly malformation rate (4.35% vs 3.85%, P = 1). On the other hand, the incidence of OHSS was significantly less in group B than in group A (3.64% vs 19.23%, P = 0.011), and the OHSS grade was less severe in group B than in group A (P = 0.033). However, there was no statistically significant difference between both regarding the need for hospitalisation (P = 0.111), ICU admission (P = 0.486), and ascites tapping (P = 0.486). Conclusions Under GnRH-antagonist protocol, frozen ET has the upper hand in PCOS undergoing IVF treatment for infertility, as it protects against OHSS and decreases its severity. Trial registration It was first registered at ClinicalTrials.gov on 22/12/2021 with registration number NCT05167838.

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.

In healthcare, medical image processing is considered one of the most significant procedures used in diagnosing pathological conditions. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and X-ray visualization have been used. Health institutions are seeking to use artificial intelligence techniques to develop medical image processing and reduce the burden on physicians and healthcare workers. Deep learning has occupied an important place in the healthcare field, supporting specialists in analysing and processing medical images. This article will present a comprehensive survey on the significance of deep learning in the areas of segmentation, classification, disease diagnosis, image generation, image transformation, and image enhancement. This survey seeks to provide an overview of the significance of deep learning in the early detection of diseases, studying tumor localization behaviors, predicting malignant diseases, and determining the suitable treatment for a patient. This article concluded that deep learning is of great significance in improving healthcare, enabling healthcare workers to make diagnoses quickly and more accurately, and improving patient outcomes by providing them with appropriate treatment strategies.

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.

The importance of using renewable energy systems (RESs) worldwide has been consolidated. Moreover, connecting more RESs to the utility grid will lead to more technical problems. Photovoltaic (PV) and wind turbine (WT) based power plants are the most nonlinear sources of renewable energies contributing to the energy mix Electronic ballast and switching mode power supply in energy conservation of the PV and WT have caused power quality problems and less reliable output voltage. PV power plants are becoming increasingly integrated with the utility grid by onboarding certain superior power quality features. This grid integration drastically reduces the use of fossil fuels and prevents environmental hazards. This article presents the design of a 26 MWp grid-connected PV power plant, which is already tied to the Egyptian electrical network in Fares City, Kom Ombo Center, Aswan Governorate, Egypt The 26 MWp PV power plant consists of (11) blocks and the utility grid, which are simulated using Matlab/Simulink. Every block contains 2,376 kWp PV arrays connected directly to DC-DC boost converters to regulate the output DC power generated by each PV array. This output DC power is fed into a particular type of inverter called a “central inverter”, which converts it to AC power. In some cases, higher harmonic distortion at the grid and a greater negative impact on the power system performance occur when using this type of inverter. To optimize the gains of the proportional-integral (PI) controller for both the voltage and current regulators of this central inverter, meta-heuristic optimization techniques (MOTs) are used. During this article, Gray Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA) are applied as MOTs to enhance the quality of the power and voltage in addition to limiting the total harmonic distortions (THD) under the effect of different sunlight conditions and partial shading. As a result, the AOA-based controllers are found to show outstanding results and superior performance compared to GWO and HHO regarding solution quality and computational efficiency. Finally, MOTs are the best solution to most electrical problems regarding controlling nonlinear and high-penetration systems, such as PV power plants connected to the utility grid.

Artificial intelligence and machine learning are used to optimize the design parameters of renewable energy sources, which are now regarded as vital components in current clean energy sources. As a result, system requirements can be reduced, and a well-designed system can improve performance. Artificial intelligence approaches in renewable energy sources and system design would significantly cut optimization time while maintaining high modeling accuracy and optimum performance. This study examines machine learning in depth, emphasizing how it can be used in developing renewable energy sources because of the vast range of technologies it can use. This paper approximates the hourly tilted solar irradiation using climate factors. The irradiance is estimated using a hybrid ensemble-learning approach. This approach combines a proposed adaptive dynamic squirrel search optimization algorithm (ADSSOA) with long short-term memory (LSTM) methods. To the best of our knowledge, this combination has not been used for solar radiation. The results are analyzed and contrasted with the outcomes of several recent swarm intelligence algorithms, such as the genetic algorithm, particle swarm optimization, and gray wolf optimizer. The binary ADSSOA approach performed as expected, with an average error of 0.1801 and a standard deviation of 0.0656. The ADSSOA–LSTM model had the lowest root mean square error (0.000388) compared to LSTM’s (0.001221). In addition, the statistical analysis uses 10 iterations of each presented and evaluated method to provide accurate comparisons and reliable results.

Solar-powered water electrolysis can produce clean hydrogen for sustainable energy systems. Accurate solar energy generation forecasts are necessary for system operation and planning. Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) are used in this paper to ensemble forecast solar hydrogen generation. The suggested method optimizes the dynamic hyperparameters of the deep learning model of recurrent neural network (RNN) using the BER metaheuristic search optimization algorithm and PSO algorithm. We used data from the HI-SEAS weather station in Hawaii for 4 months (September through December 2016). We will forecast the level of solar energy production next season in our simulations and compare our results to those of other forecasting approaches. Regarding accuracy, resilience, and computational economy, the results show that the BER-PSO-RNN algorithm has great potential as a useful tool for ensemble forecasting of solar hydrogen generation, which has important ramifications for the planning and execution of such systems. The accuracy of the proposed algorithm is confirmed by two statistical analysis tests, such as Wilcoxon’s rank-sum and one-way analysis of variance (ANOVA). With the use of the proposed BER-PSO-RNN algorithm that excels in processing and forecasting time-series data, we discovered that with the proposed algorithm, the Solar System could produce, on average, 0.622 kg/day of hydrogen during the season in comparison with other algorithms.

Accurate forecasting of wind speed is crucial for power systems stability. Many machine learning models have been developed to forecast wind speed accurately. However, the accuracy of these models still needs more improvements to achieve more accurate results. In this paper, an optimized model is proposed for boosting the accuracy of the prediction accuracy of wind speed. The optimization is performed in terms of a new optimization algorithm based on dipper-throated optimization (DTO) and genetic algorithm (GA), which is referred to as (GADTO). The proposed optimization algorithm is used to optimize the bidrectional long short-term memory (BiLSTM) forecasting model parameters. To verify the effectiveness of the proposed methodology, a benchmark dataset freely available on Kaggle is employed in the conducted experiments. The dataset is first preprocessed to be prepared for further processing. In addition, feature selection is applied to select the significant features in the dataset using the binary version of the proposed GADTO algorithm. The selected features are utilized to learn the optimization algorithm to select the best configuration of the BiLSTM forecasting model. The optimized BiLSTM is used to predict the future values of the wind speed, and the resulting predictions are analyzed using a set of evaluation criteria. Moreover, a statistical test is performed to study the statistical difference of the proposed approach compared to other approaches in terms of the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. The results of these tests confirmed the proposed approach’s statistical difference and its robustness in forecasting the wind speed with an average root mean square error (RMSE) of 0.00046, which outperforms the performance of the other recent methods.

Abstract Objective This study aims to assess delayed versus early umbilical cord clamping in preeclamptic mothers undergoing scheduled caesarean delivery regarding the maternal intra-operative blood loss and neonatal outcomes. Methods A clinical trial was conducted on 62 near-term preeclamptic mothers (36-38+6 weeks) who were planned for caesarean delivery. They were randomly assigned into two groups. The first group was the early cord clamping (ECC) group (n= 31), in which clamping the umbilical cord was within 15 seconds, while the second group was the delayed cord clamping (DCC) group (n= 31), in which clamping the umbilical cord was at 60 seconds. All patients were assessed for intra-operative blood loss and incidence of primary postpartum haemorrhage (PPH). Otherwise, all neonates were assessed for APGAR scores, the need for the neonatal intensive care unit (NICU) admission due to jaundice, and blood tests (haemoglobin, haematocrit. and serum bilirubin). Results There was not any significant difference between the two groups regarding the maternal estimated blood loss (P=0.673), the rates of PPH (P=0.1), post-delivery haemoglobin (P=0.154), and haematocrit values (P=0.092). Neonatal outcomes also were showing no significant difference regarding APGAR scores at the first minute (P=1) and after 5 minutes (P=0.114), day 1 serum bilirubin (P=0.561), day 3 serum bilirubin (P=0.676), and the rate of NICU admission (P=0.671). However, haemoglobin and haematocrit values were significantly higher in the DCC group than in the ECC group (P

The most common complication of burn injuries is scar formation, which causes structural and cosmetic impairment. Between 1 and 3 months after an injury, hypertrophic scarring develops. Hypertrophic scarring caused by surgical operations, trauma, and particularly burns is a source of worry for patients and a difficult problem for practitioners. Burn scars can cause aesthetic and functional problems, resulting in limitations in activities of daily living. They can substantially impact one's quality of life, both functionally and cosmetically. Despite using many therapeutic techniques, scar control remains a significant concern. Pharmacological and physical therapy, as well as surgical procedures, are used with varying degrees of success and efficacy. New treatments for scar prevention are currently making their way into normal practice, due to the implementation of novel procedures for treating hypertrophic scars and keloids, as are new insights into the pathophysiology of excessive scarring. The purpose of this study was to review different physical therapy modalities that might be effective for the management of hypertrophic scars.

This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner’s search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners’ improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development.

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model’s accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system’s efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.

Accurate cyclone intensity prediction is crucial for smart cities to effectively prepare and mitigate the potential devastation caused by these extreme weather events. Traditional meteorological models often face challenges in accurately forecasting cyclone intensity due to cyclonic systems’ complex and dynamic nature. Predicting the intensity of cyclones is a challenging task in meteorological research, as it requires expertise in extracting spatio-temporal features. To address this challenge, a new technique, called linear support vector regressive gradient descent Jaccardized deep multilayer perceptive classifier (LEGEMP), has been proposed to improve the accuracy of cyclone intensity prediction. This technique utilizes a dataset that contains various attributes. It employs the Herfindahl correlative linear support vector regression feature selection to identify the most important characteristics for enhancing cyclone intensity forecasting accuracy. The selected features are then used in conjunction with the Nesterov gradient descent jeopardized deep multilayer perceptive classifier to predict the intensity classes of cyclones, including depression, deep depression, cyclone, severe cyclone, very severe cyclone, and extremely severe cyclone. Experimental results have demonstrated that LEGEMP outperforms conventional methods in terms of cyclone intensity prediction accuracy, requiring minimum time, error rate, and memory consumption. By leveraging advanced techniques and feature selection, LEGEMP provides more reliable and precise predictions for cyclone intensity, enabling better preparedness and response strategies to mitigate the impact of these destructive storms. The LEGEMP technique offers an improved approach to cyclone intensity prediction, leveraging advanced classifiers and feature selection methods to enhance accuracy and reduce error rates. We demonstrate the effectiveness of our approach through rigorous evaluation and comparison with conventional prediction methods, showcasing significant improvements in prediction accuracy. Integrating our enhanced prediction model into smart city disaster management systems can substantially enhance preparedness and response strategies, ultimately contributing to the safety and resilience of communities in cyclone-prone regions.

Artificial intelligence (AI) systems are increasingly used in corporate security measures to predict the status of assets and suggest appropriate procedures. These programs are also designed to reduce repair time. One way to create an efficient system is to integrate physical repair agents with a computerized management system to develop an intelligent system. To address this, there is a need for a new technique to assist operators in interacting with a predictive system using natural language. The system also uses double neural network convolutional models to analyze device data. For fault prioritization, a technique utilizing fuzzy logic is presented. This strategy ranks the flaws based on the harm or expense they produce. However, the method’s success relies on ongoing improvement in spoken language comprehension through language modification and query processing. To carry out this technique, a conversation-driven design is necessary. This type of learning relies on actual experiences with the assistants to provide efficient learning data for language and interaction models. These models can be trained to have more natural conversations. To improve accuracy, academics should construct and maintain publicly usable training sets to update word vectors. We proposed the model dataset (DS) with the Adam (AD) optimizer, Ridge Regression (RR) and Feature Mapping (FP). Our proposed algorithm has been coined with an appropriate acronym DSADRRFP. The same proposed approach aims to leverage each component’s benefits to enhance the predictive model’s overall performance and precision. This ensures the model is up-to-date and accurate. In conclusion, an AI system integrated with physical repair agents is a useful tool in corporate security measures. However, it needs to be refined to extract data from the operating system and to interact with users in a natural language. The system also needs to be constantly updated to improve accuracy.

This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission’s stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic.

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study’s overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.

Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson’s correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results.

Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.

According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women’s death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments.

Biometric data has gain great interests as being precise authenticators. Saving and transmitting biometric data in the form of images over public networks needs a fast, lossless and secure cryptosystem for keeping biometric images details while securing it. In this paper, a novel encryption scheme is presented for securing palm print images based on hybrid chaotic maps. The proposed hybrid scheme is designed to fit the challenges of diffusion, confusion, and to provide large key space. The scheme applies a mixture of different chaos maps for a particular selection of the control parameters. Both the encryption and decryption processes are explained, formulated, and graphically presented. The results of security analysis verified the strengths of the proposed cryptosystem against statistical, brute force and differential attacks. The evaluated running time, for both encryption and decryption processes, guarantee that the cryptosystem can work effectively in real-time applications compared to current available techniques.

The performance of a hybrid solar desalination system is predicted in this work using an enhanced prediction method based on a supervised machine-learning algorithm. A humidification–dehumidification (HDH) unit and a single-stage flashing evaporation (SSF) unit make up the hybrid solar desalination system. The Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) algorithms serve as the foundation for the suggested algorithm. Using experimental data, the BER–PSO algorithm is trained and evaluated. The cold fluid and injected air volume flow rates were the algorithms’ inputs, and their outputs were the hot and cold fluids’ outlet temperatures as well as the pressure drop across the heat exchanger. Both the volume mass flow rate of hot fluid and the input temperatures of hot and cold fluids are regarded as constants. The results obtained show the great ability of the proposed BER–PSO method to identify the nonlinear link between operating circumstances and process responses. In addition, compared to the other analyzed models, it offers better statistical performance measures for the prediction of the outlet temperature of hot and cold fluids and pressure drop values.

Background and aim: Machine learning methods are examined by many researchers to identify weeds in crop images captured by drones. However, metaheuristic optimization is rarely used in optimizing the machine learning models used in weed classification. Therefore, this research targets developing a new optimization algorithm that can be used to optimize machine learning models and ensemble models to boost the classification accuracy of weed images. Methodology: This work proposes a new approach for classifying weed and wheat images captured by a sprayer drone. The proposed approach is based on a voting classifier that consists of three base models, namely, neural networks (NNs), support vector machines (SVMs), and K-nearest neighbors (KNN). This voting classifier is optimized using a new optimization algorithm composed of a hybrid of sine cosine and grey wolf optimizers. The features used in training the voting classifier are extracted based on AlexNet through transfer learning. The significant features are selected from the extracted features using a new feature selection algorithm. Results: The accuracy, precision, recall, false positive rate, and kappa coefficient were employed to assess the performance of the proposed voting classifier. In addition, a statistical analysis is performed using the one-way analysis of variance (ANOVA), and Wilcoxon signed-rank tests to measure the stability and significance of the proposed approach. On the other hand, a sensitivity analysis is performed to study the behavior of the parameters of the proposed approach in achieving the recorded results. Experimental results confirmed the effectiveness and superiority of the proposed approach when compared to the other competing optimization methods. The achieved detection accuracy using the proposed optimized voting classifier is 97.70%, F-score is 98.60%, specificity is 95.20%, and sensitivity is 98.40%. Conclusion: The proposed approach is confirmed to achieve better classification accuracy and outperforms other competing approaches.

Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework’s efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.

Recent technologies such as artificial intelligence, machine learning, and big data are essential for supporting healthcare monitoring systems, particularly for monitoring Monkeypox confirmed cases. Infected and uninfected cases around the world have contributed to a growing dataset, which is publicly available and can be used by artificial intelligence and machine learning to predict the confirmed cases of Monkeypox at an early stage. Motivated by this, we propose in this paper a new approach for accurate prediction of the Monkeypox confirmed cases based on an optimized Long Short-Term Memory (LSTM) deep network. To fine-tune the hyper-parameters of the LSTM-based deep network, we employed the Al-Biruni Earth Radius (BER) optimization algorithm; thus, the proposed approach is denoted by BER-LSTM. Experimental results show the effectiveness of the proposed approach when assessed using various evaluation criteria, such as Mean Bias Error, which is recorded as (0.06) using BER-LSTM. To prove the superiority of the proposed approach, six different machine learning models are included in the conducted experiments. In addition, four different optimization algorithms are considered for comparison purposes. The results of this comparison confirmed the superiority of the proposed approach. On the other hand, several statistical tests are applied to analyze the stability and significance of the proposed approach. These tests include one-way Analysis of Variance (ANOVA), Wilcoxon, and regression tests. The results of these tests emphasize the robustness, significance, and efficiency of the proposed approach.

The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%.

Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers’ ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults’ diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon’s rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases.

Personal Identification Numbers (PIN) and unlock patterns are two of the most often used smartphone authentication mechanisms. Because PINs have just four or six characters, they are subject to shoulder-surfing attacks and are not as secure as other authentication techniques. Biometric authentication methods, such as fingerprint, face, or iris, are now being studied in a variety of ways. The security of such biometric authentication is based on PIN-based authentication as a backup when the maximum defined number of authentication failures is surpassed during the authentication process. Keystroke-dynamics-based authentication has been studied to circumvent this limitation, in which users were categorized by evaluating their typing patterns as they input their PIN. A broad variety of approaches have been proposed to improve the capacity of PIN entry systems to discriminate between normal and abnormal users based on a user’s typing pattern. To improve the accuracy of user discrimination using keystroke dynamics, we propose a novel approach for improving the parameters of a Bidirectional Recurrent Neural Network (BRNN) used in classifying users’ keystrokes. The proposed approach is based on a significant modification to the Dipper Throated Optimization (DTO) algorithm by employing three search leaders to improve the exploration process of the optimization algorithm. To assess the effectiveness of the proposed approach, two datasets containing keystroke dynamics were included in the conducted experiments. In addition, we propose a feature selection algorithm for selecting the proper features that enable better user classification. The proposed algorithms are compared to other optimization methods in the literature, and the results showed the superiority of the proposed algorithms. Moreover, a statistical analysis is performed to measure the stability and significance of the proposed methods, and the results confirmed the expected findings. The best classification accuracy achieved by the proposed optimized BRNN is 99.02% and 99.32% for the two datasets.