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The healthcare industry has witnessed significant interest in applying DTs (DTs), due to technological advancements. DTs are virtual replicas of physical entities that adapt to real‐time data, enabling predictions of their physical counterparts. DT technology enhances understanding of disease occurrence, enabling more accurate diagnoses and treatments. Integrating emerging technologies like big data, cloud computing, Virtual Reality (VR), and internet‐of‐things (IoT) provides a solid foundation for DT implementation in healthcare. However, defining DTs within the healthcare context still has become increasingly challenging. Therefore, exploring the potential of DTs in healthcare contributes to research, emphasizing their transformative impact on personalized medicine and precision healthcare. In this study, we present diverse healthcare applications of DTs, including healthcare 4.0, cardiac analysis, monitoring and management, data privacy, socio‐ethical, and surgical. Moreover, this paper discusses the software and simulations of DTs that can be used in these applications of healthcare, as well as, the future trends of DTs in healthcare. This article is categorized under:Application Areas > Health CareTechnologies > Computational Intelligence [ABSTRACT FROM AUTHOR]

Bone scintigraphy is recognized as an efficient diagnostic method for whole-body screening for bone metastases. At the moment, whole-body bone scan image analysis is primarily dependent on manual reading by nuclear medicine doctors. However, manual analysis needs substantial experience and is both stressful and time-consuming. To address the aforementioned issues, this work proposed a machine-learning technique that uses phases to detect Bone scintigraphy. The first phase in the proposed model is the feature extraction and it was conducted based on integrating the Mobile Vision Transformer (MobileViT) model in our framework to capture highly complex representations from raw medical imagery using two primary components including ViT and lightweight CNN featuring a limited number of parameters. In addition, the second phase is named feature selection, and it is dependent on the Arithmetic Optimization Algorithm (AOA) being used to improve the Growth Optimizer (GO). We evaluate the performance of the proposed FS model, named GOAOA using a set of 18 UCI datasets. Additionally, the applicability of Bone scintigraphy for real-world application is evaluated using 2800 bone scan images (1400 normal and 1400 abnormal). The results and statistical analysis revealed that the proposed GOAOA algorithm as an FS technique outperforms the other FS algorithms employed in this study. [ABSTRACT FROM AUTHOR]

In recent years, several optimization algorithms are proposed, one of them is Multi-Verse Optimizer (MVO). In this paper, a modified version of MVO is proposed, called CMVHHO, which uses the chaos theory and the Harris Hawks Optimization (HHO). The main aim of using the chaotic maps in the proposed method is to determine the optimal value for the parameters of the basic MVO. Besides, the HHO is used as a local search to improve the ability of the MVO to exploit the search space. The performance of the CMVHHO is conducted using a set of chaotic maps to determine the most suitable map, as well as, the different experiments are performed to determine which parameter has the largest effect on the effectiveness of the MVO. Moreover, the performance of the CMVHHO is compared with a set of state-of-the-art algorithms to find the best solution for global optimization problems. Furthermore, the proposed CMVHHO with the best map is applied to solve four well-known engineering problems. The experimental results illustrate that the chaotic Circle map is the best map among all maps because it improved the performance of the CMVHHO, as well as the HHO, affected positively in the behavior of the proposed algorithm. The CMVHHO showed the best results than other algorithms in terms of the performance measures as well as in engineering problems and it outperformed the state-of-the-art algorithms in all problems. [ABSTRACT FROM AUTHOR]

• Developing a new paradigm for the feature selection problem based on the hyperheuristic methodology. • Configuring the combination of the components in to automatically find the most pertinent features for each dataset. • Evaluating the performance of the proposed technique using a comprehensive set of eighteen datasets. • Comparing the results of the best FS combination with the other well-known FS methods. Feature selection (FS) is a crucial step for effective data mining since it has largest effect on improving the performance of classifiers. This is achieved by removing the irrelevant features and using only the relevant features. Many metaheuristic approaches exist in the literature in attempt to address this problem. The performance of these approaches differ based on the settings of a number of factors including the use of chaotic maps, opposition-based learning (OBL) and the percentage of the population that OBL will be applied to, the metaheuristic (MH) algorithm adopted, the classifier utilized, and the threshold value used to convert real solutions to binary ones. However, it is not an easy task to identify the best settings for these different components in order to determine the relevant features for a specific dataset. Moreover, running extensive experiments to fine tune these settings for each and every dataset will consume considerable time. In order to mitigate this important issue, a hyper-heuristic based FS paradigm is proposed. In the proposed model, a two-stage approach is adopted to identify the best combination of these components. In the first stage, referred to as the training stage , the Differential Evolution (DE) algorithm is used as a controller for selecting the best combination of components to be used by the second stage. In the second stage, referred to as the testing stage , the received combination will be evaluated using a testing set. Empirical evaluation of the proposed framework is based on numerous experiments performed on the most popular 18 datasets from the UCI machine learning repository. Experimental results illustrates that the generated generic configuration provides a better performance than eight other metaheuristic algorithms over all performance measures when applied to the UCI dataset. Moreover, The overall paradigm ranks at number one when compared against state-of-the-art algorithms. Finally, the generic configuration provides a very competitive performance for high dimensional datasets. [ABSTRACT FROM AUTHOR]

Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms' performance and help avoid local search problems. Source codes of NO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/156154-the-non-monopolize-search-no. [ABSTRACT FROM AUTHOR]

Highlights • We propose a multi-objective version of the Multiverse Optimizer (MOMVO). • The problem of image thresholding is solved by a multi-objective algorithm. • The Otsu and Kapur's methods are combined in a multi-objective problem. • The MOMVO is tested over different images for thresholding. • Comparisons support the performance of the proposed MOMVO. Graphical abstract Abstract Image segmentation is among the most important techniques in image processing, and many methods have been developed to perform this task. This paper presents a new multi-objective metaheuristic based on a multi-verse optimization algorithm to segment grayscale images via multi-level thresholding. The proposed approach involves finding an approximate Pareto-optimal set by maximizing the Kapur and Otsu objective functions. Both Kapur's and Otsu's methods are highly used for image segmentation performed by means of bi-level and multi-level thresholding. However, each of them has certain characteristics and limitations. Several metaheuristic approaches have been proposed in the literature to separately optimize these objective functions in terms of accuracy, whereas only a few multi-objective approaches have explored the benefits of the joint use of Kapur and Otsu's methods. However, the computational cost of Kapur and Otsu is high and their accuracy needs to be improved. The proposed method, called Multi-objective Multi-verse Optimization, avoids these limitations. It was tested using 11 natural grayscale images and its performance was compared against three of well-known multi-objective algorithms. The results were analyzed based on two sets of measures, one to assess the performance of the proposed method as a multi-objective algorithm, and the other to evaluate the accuracy of the segmented images. The results showed that the proposed method provides a better approximation to the optimal Pareto Front than the other algorithms in terms of hypervolume and spacing. Moreover, the quality of its segmented image is better than those of the other methods in terms of uniformity measures. [ABSTRACT FROM AUTHOR]

This paper presents a machine learning-based technique for interpreting bone scintigraphy images, focusing on feature extraction and introducing a new feature selection method called GJOW. GJOW enhances the effectiveness of the golden jackal optimization (GJO) algorithm by integrating operators from the whale optimization algorithm (WOA). The technique's performance is evaluated through extensive experiments using 18 benchmark datasets and 581 bone scan images obtained from a gamma camera, including 362 abnormal and 219 normal cases. The results highlight the superior predictive effectiveness of the GJOW algorithm in bone metastasis detection, achieving an accuracy of 71.79% and specificity of 91.14%. The contributions of this study include the introduction of a new machine learning-based approach for detecting bone metastasis using gamma camera scans, leading to improved accuracy in identifying bone metastases. The findings have practical implications for early detection and intervention, potentially improving patient outcomes. [ABSTRACT FROM AUTHOR]

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. [ABSTRACT FROM AUTHOR]

With the development of hyperspectral sensors, more and more hyperspectral images can be acquired, and the pixel-oriented classification of hyperspectral images has attracted the attention of many researchers. However, accurately classifying pixels is challenging due to the limited labeling data and the high feature dimension of hyperspectral images. Graph Convolutional Network (GCN) based methods offer a new research direction for hyperspectral image classification due to their exceptional ability to handle irregular data. GCN uses nodes to represent samples and edges to represent relationships between nodes. Graph convolution operations enable information propagation between nodes, capturing complex associations and facilitating node classification. The GCN-based approach has demonstrated significant potential in hyperspectral image classification due to its ability to effectively extract spectral features from such images. This paper presents a comprehensive review of GCN-based hyperspectral image classification methods. The review covers five aspects: research background, traditional hyperspectral classification methods, mainstream GCN-based hyperspectral classification methods, challenges and limitations, and future developments. With the continuous development of GCN methods, hyperspectral image classification is expected to achieve higher accuracy and wider application in various fields. • Present the first comprehensive review on hyperspectral image classification using Graph Convolutional Network (GCN). • Describe the basic concepts of the two most basic GCNs, including spectral-domain GCNs and spatial-domain GCNs. • Analyze the mainstream of the GCNs used for HSI classification and compare their characteristics. • Discuss current challenges and limitations of HSI classification using GCN and highlight future directions. [ABSTRACT FROM AUTHOR]

The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis. [Display omitted] • Develop a new breast cancer detection method using CrossViT and a modified GOA. • Apply CrossViT to extract features and to learn new representations from images. • Enhance feature selection using modified GOA based on Adaptive Gaussian distribution. • Apply proposed technique to enhance breast cancer detection using different datasets. [ABSTRACT FROM AUTHOR]

In this study, we propose an improved version of the adaptive neuro-fuzzy inference system (ANFIS) for forecasting the air quality index in Wuhan City, China. We propose a hybrid optimization method to improve ANFIS performance, called PSOSMA, using a new modified meta-heuristics (MH) algorithm, Slime mould algorithm (SMA), which is improved by using the particle swarm optimizer (PSO). The proposed PSOSMA-ANFIS has been trained with air quality index time series data of three years and has been applied to forecast the fine particulate matter (PM2.5), sulfur dioxide (SO2), carbon dioxide (CO2), and nitrogen dioxide (NO2) for one year. We also compared the proposed PSOSMA to other MH algorithms used to train ANFIS. We found that the modified ANFIS using PSOSMA achieved better performance than compared algorithms. Moreover, we analyzed the impacts of the lockdown of Wuhan City on the concentrations of PM2.5, NO2, CO2, and SO2. We compared the correspondence period with previous years, and we concluded that there are significant decreases in the concentrations of PM2.5, CO2, SO2, and NO2. • To provide efficient time-series forecasting model for air quality index based on modified ANFIS using SMA and PSO algorithms. • The developed PSOSMA improves the performance of the traditional ANFIS model and produces significant results. • We implement extensive comparisons with several existing MH algorithms to verify the efficiency of the PSOSMA-ANFIS model. • Study the impacts of lockdown on air quality of Wuhan City, the first lockdown city due to the spread of COVID-19. [ABSTRACT FROM AUTHOR]

• Brazil and Russia become hotspots of the COVID-19, and they suffer from high increase of the daily confirmed cases. • Proposed an Artificial intelligence based forecasting model to forecast the number of confirmed cases of COVID-19 in Brazil and Russia. • Enhancing the performance of the ANFIS using an improved marine predators algorithm (MPA), called chaotic MPA (CMPA). • Compare the proposed CMPA-ANFIS model to three models, the original ANFIS, PSO-ANFIS, and the original MPA-ANFIS. COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models. [ABSTRACT FROM AUTHOR]

• Use the ETR method to increase the data stationariness. • Propose a new hybrid model for energy forecasting. • Use six different energy time series datasets. • The hybrid model with the ETR method provides the best results. • The proposed methods yield the best results for lengthy and short data periods. Due to weather and political fluctuations that significantly impact the production and price of energy sources, enhancing data distribution and reducing data complexity is crucial to achieving accurate forecasting. Additionally, it is essential to provide a flexible forecasting model capable of handling rapid changes in the energy market and effectively anticipating energy supplies and demands. This study introduces a novel method to deal with energy market fluctuations in the long and short term and provide highly accurate forecasts for various energy data. It uses the Enhancing Transformation Reduction (ETR) method to improve the stationarity of the data, reduce seasonality and trend, and resolve rapid fluctuations. The output of ETR is then passed into a hybrid forecasting model referred to as " Time-Series Forecasting Model using Long Short-Term Memory integrated with Artificial Neural Networks" (TLIA). The TLIA model benefits from transfer learning, which transmits the output of the LSTM layers into the ANN layers, enabling TLIA to base its work on the best performance and continue improving it. The study evaluates and tests its methods using six different datasets, including the electricity dataset of Victoria State, the oil price for the West Texas Intermediate, the Elia Grid load dataset, and wind power production. In addition to its characteristics, ETR accelerates and enhances the TLIA processing to achieve the highest accuracy compared to seven forecasting models in all six datasets. The TLIA is often 40 times or more superior to competing models. Compared to another model, the Mean Absolute Error (MAE) results of TLIA range between (0.008 and 0.088) versus (0.77 and 4318.544). [ABSTRACT FROM AUTHOR]

The Artificial Gorilla Groups Optimizer (GTO) is a novel metaheuristic algorithm that takes its cues from the collective intelligence of wild gorilla troops. Although it has shown promise in solving various real-world applications, it may still get stuck at local optima and premature convergence when dealing with more complex optimization tasks. Because of these shortcomings, this research proposes a new modified Gorilla Groups Optimizer (mGTO) method, employing a set of operators to achieve a more stable balance between exploitation and exploration. These operators are the elite Opposition Based-Learning (EOBL), Cauchy Inverse Cumulative (CICD) Distribution Operator, and tangent Flight (TFO). Each operator is used to achieve a specific task during the search process. The EOBL aims to enhance the diversity of the population, which leads to discovering the feasible region. Whereas the integration between CICD and TFO is used to improve the population's exploitation ability, which leads to an increase in the convergence rate. To validate the efficiency of the presented method, called mGTO, a set of experimental series is conducted using the CEC2020 benchmark and constraint design engineering problems. In addition, its applicability is assessed by implementing mGTO as a feature selection technique and applying it to improve the classification accuracy of sixteen datasets. The results of mGTO are also compared with those produced by other well-known meta-heuristic techniques. The statistical validity of the performance is also verified using Wilcoxon's rank-sum test. The experimental results and comparison analysis reveal the consistent and better performance of the proposed mGTO method to solve optimization problems. [ABSTRACT FROM AUTHOR]

Text document clustering is to divide textual contents into clusters or groups. It received wide attention due to the vast amount of daily data from the Web. In the last decade, Meta-Heuristic (MH) techniques have been adopted to solve clustering problems. Motivated by that, the authors introduce a reliable version of the newly developed MH algorithm called Arithmetic Optimization Algorithm (AOA). Math arithmetic operators inspire the AOA: multiplication, subtraction adding, and division. The AOA showed good performance in several global problems; nonetheless, it suffers from entrapment in local optima in complicated and high dimensional problems. Therefore, this paper proposes an improved version of AOA for the text document clustering problem. The Improved AOA (IAOA) introduces an integration between Opposition-based learning (OBL) and Levy flight distribution (LFD) with AOA to tackle the limitations of the traditional AOA. The IAOA is examined with different UCI datasets for the text clustering problems and assessed with a set of CEC2019 benchmark functions as a global optimization algorithm with extensive comparison to existing optimization algorithms. Overall, experimental results show the superiority of the proposed IAOA compared to several optimization algorithms. Moreover, the proposed IAOA is compared with twenty-one state-of-the-art methods using thirty-one benchmark text datasets, and the results proved the superiority of the proposed IAOA. [ABSTRACT FROM AUTHOR]

Enhancing the exploration and exploitation phases of the metaheuristic (MH) optimization algorithms is the key to avoiding local optima. The Manta ray foraging optimizer is a recently proposed MH optimizer. The MRFO showed a good performance in the simple optimization problems. However, it is trapped into the local optimum in the more elaborated ones due to the original algorithm's low capability in exploiting the optimal solutions and its convergence. From this principle, in this work, a novel variant of the Manta ray foraging optimizer has been proposed for global optimization problems, engineering design optimization problems, and multi-threshold segmentation. In the proposed approach, the fractional calculus (FC) using Caputo fractional differ-sum operator has been adopted to enhance the manta rays movement in the exploitation phase via utilizing history dependency of FC to boost exploiting the optimal solutions via sharing the past knowledge over the optimization process. Moreover, to avoid premature convergence, the somersault factor has been adaptively tuned. The fractional-order Caputo Manta-Ray Foraging Optimizer (FCMRFO) has been proposed. The proposed algorithm's sensitivity for the FC coefficients has been tested with ten-dimensional CEC2017 benchmarks. The scalability test of the proposed algorithm has been performed with 30, 50 and 100-dimensional CEC2017. Moreover, CEC2020 benchmarks with dimensions 5 and 20 have been applied for providing an extensive investigation, and the FCMRFO has been compared with recent state-of-the-art algorithms. Through utilizing the non-parametric statistical analysis and ranking test, the FCMRFO confirms its superiority and ability to avoid the local optimum in several cases. For the second part of the study, three constrained engineering design problems have been investigated; then, numerous natural images are applied to appraise the FCMRFO for multilevel threshold image segmentation. By performing several metrics, the FCMRFO proves its quality and efficiency compared to recent well-regarded algorithms in engineering applications and image segmentation. • Novel memory-based fractional-order Caputo Manta ray foraging optimizer is proposed. • Adaptively tuned somersault factor is used for balancing memory window and solutions. • The proposed optimizer validated with CEC2017 and CEC2020 with several dimensions. • The FCMRFO applicability is confirmed with engineering applications and segmentation. • Comparison of the state-of-the-art techniques using non-parametric tests is performed. [ABSTRACT FROM AUTHOR]

This paper presents a modified version of Manta ray foraging optimizer (MRFO) algorithm to deal with global optimization and multilevel image segmentation problems. MRFO is a meta-heuristic technique that simulates the behaviors of manta rays to find the food. MRFO established its ability to find a suitable solution for a variant of optimization problems. However, by analyzing its behaviors during the optimization process, it is observed that its exploitation ability is less than exploration ability, which makes MRFO more sensitive to attractive to a local point. Therefore, we enhanced MRFO by using the fractional-order (FO) calculus during the exploitation phase. We used the heredity and non-locality properties of the Grunwald–Letnikov fractional differ-integral operator to simulate the after effect of the previous locations of manta rays on their future movement directions. The proposed Fractional-order MRFO (FO-MRFO) quality is confirmed using a set of two experimental series. Firstly, it is applied to find the solution for CEC2017 benchmark functions with different dimensions of 10, 30, and 50. Through performing the non-parametric statistical analysis, the FO-MRFO shows its superiority in comparison with the basic MRFO. For the second series of experiments, the developed algorithm is implemented as a multilevel threshold image segmentation technique. In this experiment, a variant of natural images is used to assess FO-MFRO. According to different performance measures, the FO-MRFO outperforms the compared algorithms in the global optimization and image segmentation. [ABSTRACT FROM AUTHOR]

The first issue in the optimal photovoltaic system design is providing an accurate PV model that emulates the system behaviour under several environmental conditions. The accuracy of the photovoltaic model stands on its identified parameters that are mainly based on the executed optimization technique and the employed objective function. As per the literature, two methodologies have been applied for computing the estimated current in the objective function, detecting the most efficient one is the first step for achieving high qualified and precise solutions. Motivated by that, we investigate the two objective functions with considering several novel optimization algorithms. The implemented algorithms are marine predators algorithm, Slime mould algorithm, atom search optimization, Political Optimizer, Parasitism–Predation algorithm as well as harris hawk optimizer and salp swarm algorithm. The Lambert function forms have been used for validating the results. Several profiles of the experimental datasets are measured under different levels of temperature and irradiation conditions to identify the single, double and three diode models parameters of the RTC France solar cell and Canadian-Solar-(CS6P-240P) multi-crystalline solar panel. The main findings show that, applying Newton–Raphson while computing the estimated current in the objective function enhances the algorithms performance to provide the more precise and accurate parameters in comparison with using the measured current and solve the photovoltaic model equation linearly. Moreover, the marine predators algorithm confirms the quality of its solutions and provides a better representation for the photovoltaic datasets with high stability based on the lambert forms and the statistical analyses. • Investigate two objective functions to determine the more accurate to estimate PV parameters. • Propose a new five techniques, including MPA, SMA, ASO, PO, and PPA, as PV parameter estimation methods. • Evaluate the tested algorithms and two objectives using different diode models. • Apply the Lambert function to report the accuracy of the identified parameters and reliability of the objective function. • Assess the quality of the proposed five algorithms through five experimentally measured datasets. [ABSTRACT FROM AUTHOR]

A sustainable environment by decreasing fossil fuel utilization and anthropogenic greenhouse gases is a globally main goal due to climate change and serious air pollution. Carbon dioxide (CO2) is a heat-trapping (greenhouse) that is released into the earth's atmosphere from natural processes, such as volcanic respiration and eruptions, as well as human activities, such as burning fossil fuels and deforestation. Due to this fact, underground carbon storage (UCS) is a promising technology to cut carbon emissions. However, there are some barriers to prevent UCS from applying globally. One of them is evaluating the feasibility of storage projects. Thus, the prediction accuracy of CO2 storage efficiencies may promote the attention of the community for UCS. In this study, we utilize the recent advances of swarm intelligence to develop a hybrid algorithm called AOSMA, employed to train the long short-term memory (LSTM). The developed swarm intelligence method (AOSMA) is an enhanced Aquila optimizer (AO) using the search mechanism of the slime mould algorithm (SMA). It is used to boost the prediction capability of the LSTM by optimizing its parameters. We considered two CO2 trapping indices, called residual trapping index (RTI) and solubility trapping index (STI). The evaluation experiments have shown that the AOSMA achieved significant results compared to the original AO and SMA and several swarm intelligence and optimization algorithms. The developed smart tools could use as a game changer to provide fast and accurate storage efficiency for projects that have similar parameters falling within the range of the database., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Feature selection techniques are considered one of the most important preprocessing steps, which has the most significant influence on the performance of data analysis and decision making. These FS techniques aim to achieve several objectives (such as reducing classification error and minimizing the number of features) at the same time to increase the classification rate. FS based on Metaheuristic (MH) is considered one of the most promising techniques to improve the classification process. This paper presents a modified method of the Slime mould algorithm depending on the Marine Predators Algorithm (MPA) operators as a local search strategy, which leads to increasing the convergence rate of the developed method, named SMAMPA and avoiding the attraction to local optima. The efficiency of SMAMPA is evaluated using twenty datasets and compared its results with the state-of-the-art FS methods. In addition, the applicability of SMAMPA to work with real-world problems is evaluated by using it as a quantitative structure-activity relationship (QSAR) model. The obtained results show the high ability of the developed SMAMPA method to reduce the dimension of the tested datasets by increasing the prediction rate. In addition, it provides results better than other FS techniques in terms of performance metrics., Competing Interests: Conflict of interestThe authors declare that there is no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Oil production forecasting is an important task to manage petroleum reservoirs operations. In this study, a developed time series forecasting model is proposed for oil production using a new improved version of the adaptive neuro-fuzzy inference system (ANFIS). This model is improved by using an optimization algorithm, the slime mould algorithm (SMA). The SMA is a new algorithm that is applied for solving different optimization tasks. However, its search mechanism suffers from some limitations, for example, trapping at local optima. Thus, we modify the SMA using an intelligence search technique called opposition-based learning (OLB). The developed model, ANFIS-SMAOLB, is evaluated with different real-world oil production data collected from two oilfields in two different countries, Masila oilfield (Yemen) and Tahe oilfield (China). Furthermore, the evaluation of this model is considered with extensive comparisons to several methods, using several evaluation measures. The outcomes assessed the high ability of the developed ANFIS-SMAOLB as an efficient time series forecasting model that showed significant performance., Competing Interests: Conflict of interestAll authors declare that they have no conflict of interest, (© The Author(s) 2021.)

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.)