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In the era of rapidly expanding data volumes, the increasing dimensionality of features presents significant computational challenges, often degrading the performance of algorithms. Feature selection has emerged as a critical pre-processing step across various applications, aiming to identify and retain the most relevant features from datasets to enhance efficiency and accuracy. This study introduces an advanced wrapper-based feature selection approach, addressing key limitations of the original Grasshopper Optimization Algorithm (GOA), such as premature convergence and entrapment in local optima. The proposed Grasshopper Optimization Algorithm Harris Hawks Optimizer Lévy Flight (GHL) integrates two strategies: Lévy flight, which enhances the exploration phase by directing GOA toward promising regions of the search space, and Harris Hawks Optimizer techniques, which strengthen the exploitation phase to improve solution quality. Through three comprehensive experiments, the GHL algorithm demonstrated superior performance over nine comparative methods. The first experiment validated its efficacy in solving global optimization problems, achieving the best fitness values in most of the test functions. The second experiment highlighted its ability to effectively select relevant features across twenty benchmark datasets, achieving the best accuracy in 80% of the datasets. The third experiment applied GHL to remote sensing image classification, improving classification accuracy and yielding robust optimization outcomes. [ABSTRACT FROM AUTHOR]

This paper introduces MPAG, a new feature selection method aimed at overcoming the limitations of the conventional Marine Predators Algorithm (MPA). The MPA may experience stagnation and become trapped in local optima during optimization. To address this challenge, we propose a refined version of the MPA, termed MPAG, which incorporates the Local Escape Operator (LEO) from the gradient-based optimizer (GBO). By leveraging the LEO operator, MPAG enhances the exploration ability of the MPA, particularly during the initial one-third of iterations. This enhancement injects more diversity into populations, thereby improving the process of search space discovery and mitigating the risk of premature convergence. The performance of MPAG is evaluated on 14 feature selection benchmark datasets, employing seven performance measures including fitness value, classification accuracy, and selected features. Our findings indicate that MPAG outperforms other algorithms in 86% of the datasets, underscoring its capability to select the most relevant features across various datasets while maintaining stability. Additionally, MPAG is evaluated using two cybersecurity applications, specifically spam detection datasets, where it demonstrates superior performance across most performance measures compared to other methods. [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 the continuous campaign to reduce carbon emissions and mitigate the severe effects of climate change, the strategic use of carbon price forecasting to anticipate future carbon pricing dynamics is a vital strategy. Accurate forecasting of future carbon pricing enables industries to modify their operations, encouraging the adoption of eco-friendly technology and procedures. This coordination of activities, including carbon pricing plans, emission reduction targets, and environmentally friendly behaviors, not only addresses climate change issues but also aligns well with sustainable development goals related to clean energy, climate action, and sustainable economies. To tackle this issue, a hybrid approach is introduced by integrating the Improved Electric Fish Optimization (IEFO) with the Adaptive Neuro-Fuzzy Inference System (ANFIS), creating a robust framework for forecasting the carbon price. The proposed IEFO algorithm, representing an enhanced version of the Electric Fish Optimization (EFO), integrates the searching strategy of the Whale Optimization Algorithm (WOA) to boost exploration capabilities, enabling more effective exploration of new areas and efficient escape from local optima. By strategically combining EFO's active search with WOA's localized exploration strategy, the IEFO-ANFIS model excels in capturing intricate carbon price dynamics and enhances forecasting accuracy. The efficacy of the IEFO-ANFIS model is rigorously demonstrated through comprehensive experiments, showing its superior performance across five datasets, namely BeijingETS, CEHD, GuangzhouETS, HubeiETS, and TianjinETS. The results demonstrated the effectiveness of the proposed IEFO-ANFIS model in accurately forecasting carbon prices. Additionally, the results show remarkable performance of the proposed model compared with state-of-the-art models. Moreover, the results show that the proposed IEFO-ANFIS model can assist policymakers in developing efficient carbon pricing plans, supporting a more seamless shift to a low-carbon economy without disrupting markets. [ABSTRACT FROM AUTHOR]

The process of data classification involves determining the optimal number of features that lead to high accuracy. However, feature selection (FS) is a complex task that necessitates robust metaheuristics due to its challenging NP-hard nature. This paper introduces a hybrid algorithm that combines the Artificial Ecosystem Optimization (AEO) operators with the Whale Optimization Algorithm (WOA) to enhance numerical optimization and FS. While the WOA algorithm, inspired by the hunting behavior of whales, has been successful in solving various optimization problems, it can sometimes be limited in its ability to explore and may become trapped in local optima. To address this limitation, the authors propose the use of AEO operators to improve the exploration process of the WOA algorithm. The authors conducted experiments to evaluate the effectiveness of their proposed method, called AEOWOA, using the CEC'20 test suite for numerical optimization and sixteen datasets for FS. They compared the results with those obtained from other optimization methods. Through experimental and statistical analyses, it was observed that AEOWOA delivers efficient search results with faster convergence, reducing the feature size by up to 89% while achieving up to 94% accuracy. These findings shed light on potential future research directions in this field. [ABSTRACT FROM AUTHOR]

Optimization techniques play a crucial role in improving the performance of machine learning applications. However, traditional techniques may not be suitable for addressing complex problems that involve numerous parameters requiring adaptation. This paper presents a novel variant of the gradient-based optimizer (GBO) and investigates the effectiveness of integrating the harmony search (HS) algorithm to enhance the GBO algorithm. The HS algorithm offers several advantages, including fast convergence and fewer adjustable parameters. The proposed method, called GBOHS, is evaluated through three experiments. The first experiment focuses on solving global optimization problems, while the second experiment assesses its ability to select the most relevant features using sixteen benchmark feature selection datasets. The final experiment applies the GBOHS method to solve five real engineering problems. The results are compared against those of well-known optimization algorithms using various performance measures, including fitness function value and classification accuracy. The findings demonstrate that the proposed GBOHS method achieves high accuracy and consistently outperforms the compared methods across all the experiments, demonstrating its promising effectiveness. [ABSTRACT FROM AUTHOR]

In medical imaging, the last decade has witnessed a remarkable increase in the availability and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant advancement in deep learning techniques, noted for their escalating accuracy. These developments have catalyzed a surge in the application of deep learning in various medical studies, particularly in detecting and classifying lung diseases. This study delves into an extensive compilation of over 200 studies from the recent five years (2018–2023), employing advanced machine learning, including deep learning methodologies to analyze CXR images. Our exploration is twofold: it categorizes these studies based on the methods used and the types of lung diseases addressed. It also presents an in-depth examination of the current limitations and prospective trajectories in this rapidly evolving field. Our findings underscore the transformative impact and continual progress of deep learning models in enhancing the accuracy and efficiency of lung disease detection using CXR images. This survey culminates by emphasizing the critical need for further technological advancement in this domain, aiming to bridge gaps in healthcare provision and improve patient outcomes. The overarching goal is to pave the way for more precise, efficient, and accessible diagnostic tools in the battle against lung diseases, reinforcing the indispensable role of technology in modern healthcare. [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]

This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization algorithm, called arithmetic optimization algorithm (AOA), was proposed to address complex optimization tasks. Math operations inspire the AOA, and it showed significant performance in dealing with different optimization problems. However, the traditional AOA faces some limitations in its search process. Thus, we develop a new variant of the AOA, namely, Augmented AOA (AAOA), integrated with the opposition-based learning (OLB) and Lévy flight (LF) distribution. The main idea of applying OLB and LF is to improve the traditional AOA exploration and exploitation trends in order to find the best clusters. To evaluate the AAOA, we implemented extensive experiments using twenty-three well-known benchmark functions and eight data clustering datasets. We also evaluated the proposed AAOA with extensive comparisons to different optimization algorithms. The outcomes verified the superiority of the AAOA over the traditional AOA and several MH optimization algorithms. Overall, the applications of the LF and OLB have a significant impact on the performance of the conventional AOA. [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]

Trapping in local solutions is the main issue in several metaheuristic techniques. To solve such drawbacks by enhancing the search agents, a modified search strategy becomes a more attractive tactic. In this paper, an innovative version of Manta Ray Foraging Optimization (MRFO) is proposed to solve its crucial drawbacks while handling global and engineering optimization problems. The proposed version presents an integrated variant of MRFO with the triangular mutation operator and orthogonal learning strategy, called MRTMO. The two approaches are considered to achieve a robust equipoise between algorithm cores and provide a reliable mechanism to guide the search agents during the optimization process. The proposed MRTMO was tested with challenging CEC2005 and CEC2017 functions and six engineering problems to show its performance. Additionally, several evaluation metrics were employed to ensure the efficiency and robustness of the proposed MRTMO. Furthermore, extensive comparisons with existing optimization algorithms were carried out to ensure the superiority of MRTMO. The numerical experiments proved the competitive performance of the proposed MRTMO in solving all tested CEC optimization and engineering problems. [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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Several metaheuristic methods have been applied to tackling various global and engineering optimization problems. However, this method still needs more improvement since they require a suitable balance between exploration and exploitation. Therefore, this study presents an enhancement of the arithmetic optimization algorithm (AOA) as a global optimization method. The developed method, named AOASC, depends on using the sine-cosine algorithm's operators to enhance the exploitation ability of AOA during the searching process. This leads to improving the convergence rate of the developed method toward the optimal solution. Besides, improve the process of avoiding the attraction toward the local point. Besides these behaviors, the quality of the final solution (best one) is improved. To validate the efficiency of the developed method, a set of experiments is conducted, including various optimization problems, such as ten benchmark functions and five engineering optimization problems. Besides, the results of the developed method are compared with other well-known metaheuristic methods. The results showed the high efficiency of the developed method over other methods in terms of performance measures. [ABSTRACT FROM AUTHOR]

Seagull optimization algorithm (SOA) is a recent bio-inspired technique utilized to improve the constrained large-scale problems in low computational cost and quick convergence speed. However, the globally optimized search space for the SOA is linear, which means that the SOA's global search capability could not be fully utilized. Thus, we propose an improved SOA algorithm (ISOA) using Lévy flight and mutation operators. The ISOA obtains some Lévy flight features, which improves the original SOA by performing large jumps, making the search escape from the local optima and begin at a different search space region. The mutation operator, which improves the exploration–exploitation trade-off, allows the catch of the optimal solution quickly and accurately. In order to examine the performance of the proposed ISOA approach, three experiments were conducted. The first one evaluates the ISOA in solving the global optimization problem. The second one is a comparative study based on twenty benchmark datasets to evaluate the general capability of ISOA in feature selection, compared to ten recent and well-established algorithms constructed using the other meta-heuristics methods. Furthermore, the third experiment is conducted using a real dataset with various face poses to investigate the efficiency of the ISOA in pose-variation recognition. Compared to the other meta-heuristics methods, the results show that the proposed model is more accurate and efficient in global optimization, feature selection purposes, and pose variation recognition. Furthermore, the ISOA approach outperforms the other methods proposed in the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a necessary process applied to reduce the high dimensionality of the dataset. It is utilized to obtain the most relevant information and reduce the computational efforts of the classification process. Recently, metaheuristics methods have been widely employed for various optimization problems, including FS. In the current study, we present an FS method based on a new modified version of the marine predators algorithm (MPA). In the developed MPASCA model, the sine–cosine algorithm (SCA) is utilized to improve the search ability, which works as a local search of the MPA. To evaluate the performance of the MPASCA algorithm, extensive experiments were carried out using 18 UCI datasets. More so, the metabolomics dataset is used to test the proposed method as a real-world application. Furthermore, we implemented extensive comparisons to several state-of-art methods to verify the efficiency of the MPASCA. The evaluation outcomes showed that the MPASCA has significant performance, and it outperforms the compared methods in terms of classification measures. [ABSTRACT FROM AUTHOR]

The design of complex mechanical components is a time-consuming process which involves many design variables with multiple interacted objectives and constraints. Traditionally, the design process of mechanical components is performed manually depending on the intuition and experience of the designer. In recent decades, automatic methods have been proposed to effectively search diverse and large parameter spaces. There is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. Nowadays, there is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. This review article demonstrates the applications of different metaheuristic algorithms in enhancing the design process of different mechanical systems. First, the basic concepts of common used metaheuristic algorithms are introduced. Then the applications of theses algorithms in optimization of different mechanical systems are discussed. [ABSTRACT FROM AUTHOR]

Unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times is considered a hot topic among the researchers, as it presents more complexity to be able to find an optimal solution. Many efforts have been made to solve UPMSP problems and established their performances. Therefore, in this study, a new method is introduced to address UPMSP problems with sequence-dependent and machine-dependent setup time. Our proposed method utilizes two meta-heuristic techniques, the whale optimization algorithm (WOA) and the firefly algorithm (FA), by combining their features to perform this task. The hybrid model is called WOAFA. For more detail, the operators of the FA are employed to improve the exploitation ability of the WOA by serving as a local search. Moreover, the quality of the proposed WOAFA method is tested by comparing with well-known meta-heuristic algorithms over six machines and six jobs, namely (2, 4, 6, 8, 10, and 12 machines) and (20, 40, 60, 80, 100, and 120 jobs). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A personalized system of instruction is one of the strategies to personalize instruction. It is a technique that allows the student to move from one unit to another according to his own pace and his potential. Although this system is distinguished with activity and effectiveness to master the instructional subject, it lacks evaluation of the essay questions automatically. Automated essay scoring is the operation of scoring written essays by computer programs. It has been widely used in recent years. In this paper, a proposed method is presented to automatically grade students' Arabic essays to support personalized systems of instruction. It uses the elitist-Jaya (e-Jaya) optimization algorithm to train the classic artificial neural network (called eJaya-NN). The proposed method is tested over 240 student's essays. The essays are graded by two human experts in the fields then they are fed to a pre-processing phase to be converted to a digit's matrix. The results are evaluated using different measures and it is compared with some optimization algorithms. The eJaya-NN outperformed all compared algorithms and achieved the best values. Its correlation with the scores of the human experts equals 0.92 which indicates that the proposed method produces acceptable scores for the Arabic essay compared to the human experts and can effectively increase the features of personalized systems of instruction. [ABSTRACT FROM AUTHOR]

This paper presents a novel forecasting model for crude oil price which has the largest effect on economies and countries. The proposed method depends on improving the performance of the adaptive neuro-fuzzy inference system (ANFIS) using a modified salp swarm algorithm (SSA). The SSA simulates the behaviors of salp swarm in nature during searching for food, and it has been developed as a global optimization method. However, SSA still has some limitations such as getting trapped at a local point. Therefore, this paper uses the genetic algorithm to improve the behavior of SSA. The proposed model (GA-SSA-ANFIS) aims to determine the suitable parameters for the ANFIS by using the GA-SSA algorithm since these parameters are considered as the main factor influencing the ANFIS's prediction process. The results of the GA-SSA-ANFIS are compared to other models, including the traditional ANFIS model, ANFIS based on GA (GA-ANFIS), ANFIS based on SSA (SSA-ANFIS) ANFIS based on particle swarm optimization (PSO-ANFIS), and ANFIS based on grey wolf optimization (GWO-ANFIS). The results show the superiority and high performances of the GA-SSA-ANFIS over the other models in predicting crude oil prices. [ABSTRACT FROM AUTHOR]

Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. The overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters that dominate the outbreak incidence of VDPV. The proposed method is based on the integration of random vector functional link (RVFL) networks with a robust optimization algorithm called whale optimization algorithm (WOA). WOA is applied to improve the accuracy of the RVFL network by finding the suitable parameter configurations for the algorithm. The classification performance of the WOA-RVFL method is successfully validated using a number of datasets from the UCI machine learning repository. Thereafter, the method is implemented to track the VDPV outbreak incidences recently occurred in several provinces in Lao People's Democratic Republic. The results demonstrate the accuracy and efficiency of the WOA-RVFL algorithm in detecting the VDPV outbreak incidences, as well as its superior performance to the traditional RVFL method. [ABSTRACT FROM AUTHOR]

White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks. [ABSTRACT FROM AUTHOR]

An accurate forecasting model for the price volatility of minerals plays a vital role in future investments and decisions for mining projects and related companies. In this paper, a hybrid model is proposed to provide an accurate model for forecasting the volatility of copper prices. The proposed model combines the adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). Genetic algorithms are used for estimating the ANFIS model parameters. The results of the proposed model are compared to other models, including ANFIS, support vector machine (SVM), generalized autoregressive conditional heteroscedasticity (GARCH), and autoregressive integrated moving average (ARIMA) models. The empirical results confirm the superiority of the hybrid GA–ANFIS model over other models. The proposed model also improves the forecasting accuracy obtained from the ANFIS, SVM, GARCH, and ARIMA models by a 62.92%, 36.38%, 91.72%, and 42.19% decrease in mean square error, respectively. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets. This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification. This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization. The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved. To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions. Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets. Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy. The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort. [ABSTRACT FROM AUTHOR]

The multi-verse optimizer (MVO) is a new evolutionary algorithm inspired by the concepts of multi-verse theory namely, the white/black holes, which represents the interaction between the universes. However, the MVO has some drawbacks, like any other evolutionary algorithms, such as slow convergence and getting stuck in local optima (maximum or minimum). This paper provides a novel chaotic MVO algorithm (CMVO) to avoid these drawbacks, where chaotic maps are used to improve the performance of MVO algorithm. The CMVO algorithm is applied to solve the feature selection problem, in which five benchmark datasets are used to evaluate the performance of CMVO algorithm. The results of CMVO is compared with standard MVO and two other swarm algorithms. The experimental results show that logistic chaotic map is the best chaotic map that increases the performance of MVO, and also the MVO is better than other swarm algorithms. [ABSTRACT FROM AUTHOR]

In the recent years, there are massive digital images collections in many fields of our life, which led the technology to find methods to search and retrieve these images efficiently. The content-based is one of the popular methods used to retrieve images, which depends on the color, texture and shape descriptors to extract features from images. However, the performance of the content-based image retrieval methods depends on the size of features that are extracted from images and the classification accuracy. Therefore, this problem is considered as a multi-objective and there are several methods that used to manipulate it such as NSGA-II and NSMOPSO. However, these methods have drawbacks such as their time and space complexity are large since they used traditional non-dominated sorting methods. In this paper, a new non-dominated sorting based on multi-objective whale optimization algorithm is proposed for content-based image retrieval (NSMOWOA). The proposed method avoids the drawbacks in other non-dominated sorting multi-objective methods that have been used for content-based image retrieval through reducing the space and time complexity. The results of the NSMOWOA showed a good performance in content-based image retrieval problem in terms of recall and precision. [ABSTRACT FROM AUTHOR]

The computer-aided diagnosis system is used to reduce the high mortality rate among heart patients through detecting cardiac diseases at an early stage. Since the process of detecting the cardiac heartbeat is a hard task because of the human eye cannot be distinguished between the variations in electrocardiogram (ECG) signals due to they are very small. There are several machine learning approaches are applied to improve the performance of detecting the heartbeats, however, these methods suffer from some limitations such as high time computational and slow convergence. To avoid these limitations, this paper proposed an ECG heartbeat classification approach, called Swarm-TWSVM, that combined twin support vector machines (TWSVMs) with the hybrid between the particle swarm optimization with gravitational search algorithm (PSOGSA). Also, the empirical mode decomposition (EMD) has been applied for the ECG noise removing, and feature extraction, then PSOGSA was used to find the optimal parameters of TWSVM to improve the classification process. The experiments were performed using the MIT-BIH arrhythmia database and results show that the Swarm- TWSVM gives better accuracy than TWSVM 99.44 and 85.87%, respectively. [ABSTRACT FROM AUTHOR]

Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [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]