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Cuckoo search algorithm is one of the most prominent meta-heuristic optimization algorithms which is applied to various applications. The discovery probability is the one and the only tuning parameter of the cuckoo search algorithm. The physical meaning of this parameter contradicts its implementation in the standard algorithm. Therefore, this study concerns the correction to the definition and implementation of the cuckoo search algorithm to resolve this conflict. Moreover, a novel algorithm called double exponential cuckoo search is proposed, in which the discovery probability became adaptive based on the concept of the double Mersenne numbers. The proposed algorithm is compared to nine other variants to find the best variant that makes the discovery probability adaptive. All the variants are compared and tested on 30 and 50 dimensions of CEC2017 benchmark functions. The results have been statistically proved using the sign test, Wilcoxon signed-rank test, and Friedman test. Moreover, multiple graphical methods are also used to visualize the median performance such as Violin plots and mean convergence graphs. Simulation results prove the superior performance of the proposed algorithm over all other variants. [ABSTRACT FROM AUTHOR]

Abstract Thanks to fewer numbers of control parameters and easier implementation, the Elephant Herding Optimization (EHO) has been gaining research interest during the past decade. In our paper, to understand the impact of the control parameters, a parametric study of the EHO is carried out using a standard test bench, engineering problems, and real-world problems. On top of that, the main aim of this paper is to propose different approaches to enhance the performance of the original EHO, i.e., cultural-based, alpha-tuning, and biased initialization EHO. Acomparative study has been made between these EHO variants and the state-of-the-art swarm optimization methods. Case studies ranging from the recent test bench problems of CEC 2016 to the popular engineering problems of gear train, welded beam, three-bar truss design problem, continuous stirred tank reactor, and fed-batch fermentor are used to validate and test the performances of the proposed EHOs against the existing techniques. Numerical results show that the performances of the three new EHOs are better than or competitive with the population-based optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper presents the dynamic modeling of an interconnected two equal area of conventional combined cycle gas turbine. In addition, fuzzy logic controllers have been designed and applied to improve speed/load control, temperature control, and air flow control. The coordination between fuzzy- controlled speed signal and fuzzy-controlled temperature control signal has been considered in the design process using fuzzy fuel controller in order to compute the accurate control signal to the fuel system for fast response against the system disturbance. On the other hand, the adaptive neuro-fuzzy inference system (ANFIS) controller is investigated for controlling the model parameter and selection of the proper rule base in order to improve the proposed controller performance. The results demonstrate the superiority of the proposed model with fuzzy logic controller and ANFIS over the conventional model under normal and abnormal conditions. Moreover, the proposed controllers improve effectively the system damping characteristics after a load deviation as the settling time is greatly reduced. [ABSTRACT FROM AUTHOR]

Set Partitioning In Hierarchical Tree (SPIHT) is considered one of the most important algorithms for reducing the size of the vision data collected by the sensor node within wireless multimedia sensor network (WMSN). The traditional SPIHT algorithm suffers from image coders complexity due to large memory requirement. This is an essential problem for the implementation on limited resource environments such as WMSN. The main objective of this paper is to introduce a listless pipelined strip based SPIHT for WMSN to reduce system complexity and minimize processing time and memory usage. The proposed algorithm is implemented using discrete wavelet transform (DWT) lifting-based instead of DWT convolution-based filter. The experimental results show the superiority of the proposed algorithm in terms of peak signal-to-noise ratio (PSNR) which reaches 1 dB for all bit rates. In addition, the memory requirement is reduced to 71% with 27% of energy saving. [ABSTRACT FROM AUTHOR]

The Competitive Swarm Optimizer (CSO) has emerged as a prominent technique for solving intricate optimization problems by updating only half of the population in each iteration. Despite its effectiveness, the CSO algorithm often exhibits a slow convergence rate and a tendency to become trapped in local optimal solutions, as is common among metaheuristic algorithms. To address these challenges, this paper proposes a hybrid approach combining the CSO with the Salp Swarm algorithm (SSA), CL-SSA, to increase the convergence rate and enhance search space exploration. The proposed approach involves a two-step process. In the first step, a pairwise competition mechanism is introduced to segregate the solutions into winners and losers. The winning population is updated through strong exploitation using the SSA algorithm. In the second step, non-winning solutions learn from the winners, achieving a balance between exploration and exploitation. The performance of the CL-SSA is evaluated on various benchmark functions, including the CEC2017 benchmark with dimensions 50 and 100, the CEC2008lsgo benchmark with dimensions 200, 500 and 1000, as well as a set of seven well-known constrained design challenges in various engineering domains defined in the CEC2020 conference. The CL-SSA is compared to other metaheuristics and advanced algorithms, and its results are analyzed through statistical tests such as the Friedman and Wilcoxon rank-sum tests. The statistical analysis demonstrates that the CL-SSA algorithm exhibits improved exploitation, exploration, and convergence patterns compared to other algorithms, including SSA and CSO, as well as popular algorithms. Furthermore, the proposed hybrid approach performs better in solving most test functions. [ABSTRACT FROM AUTHOR]

The oil and gas business has high operating costs and frequently has significant difficulties due to asset, process, and operational failures. Remote monitoring and management of the oil field operations are essential to ensure efficiency and safety. Oil field operations often use SCADA or wireless sensor network (WSN)-based monitoring and control systems; both have numerous drawbacks. WSN-based systems are not uniform or are incompatible. Additionally, they lack transparent communication and coordination. SCADA systems also cost a lot, are rigid, are not scalable, and deliver data slowly. Edge computing and the Industrial Internet of Things (IIoT) help to overcome SCADA's constraints by establishing an automated monitoring and control system for oil and gas operations that is effective, secure, affordable, and transparent. The main objective of this study is to exploit the IIOT and Edge Computing (EC). This study introduces an I2OT-EC framework with flowcharts, a simulator, and system architecture. The validity of the I2OT-EC framework is demonstrated by experimental findings and implementation with an application example to verify the research results as an additional verification and testing that proves the framework results were satisfactory. The significant increase of 12.14% in the runtime for the crude well using the proposed framework, coupled with other advantages, such as reduced operational costs, decentralization, and a dependable platform, highlights the benefits of this solution and its suitability for the automatic monitoring and control of oil field operations. [ABSTRACT FROM AUTHOR]

Abstract: The main weak points in using AI optimization techniques are the possibility of being trapped at local minima, being confined to the population space, difficulty to solve heavily nonlinear problems and to make full use of the historical information beside the lack of prediction about the search space. This paper is concerned with a hybrid optimization technique; namely cultural-based genetic algorithm MCBGA for solving multidimensional and nonlinear applications. The feature of proposed MCBGA technique is enhanced using biased initialization and dynamic parameters setting. Also elitism is carried out. The proposed approach has been carried out on pressure vessel design, fed-batch fermentor and continuous Stirred Tank Reactor CSTR test systems as well as Rastrigin, a well known standard workbench. The proposed MCBGA in terms of the diversity of the optimal solutions are obtained and compared to real coded genetic algorithm as well as other optimization techniques reported in the literature such as binary Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and its variants. The results obtained using the proposed algorithm are more accurate and the fast convergence is obvious. [Copyright &y& Elsevier]

The human brain can effortlessly perform vision processes using the visual system, which helps solve multi-object tracking (MOT) problems. However, few algorithms simulate human strategies for solving MOT. Therefore, devising a method that simulates human activity in vision has become a good choice for improving MOT results, especially occlusion. Eight brain strategies have been studied from a cognitive perspective and imitated to build a novel algorithm. Two of these strategies gave our algorithm novel and outstanding results, rescuing saccades and stimulus attributes. First, rescue saccades were imitated by detecting the occlusion state in each frame, representing the critical situation that the human brain saccades toward. Then, stimulus attributes were mimicked by using semantic attributes to reidentify the person in these occlusion states. Our algorithm favourably performs on the MOT17 dataset compared to state-of-the-art trackers. In addition, we created a new dataset of 40,000 images, 190,000 annotations and 4 classes to train the detection model to detect occlusion and semantic attributes. The experimental results demonstrate that our new dataset achieves an outstanding performance on the scaled YOLOv4 detection model by achieving a 0.89 mAP 0.5. [ABSTRACT FROM AUTHOR]

This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO’s tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of shell-and-tube heat exchangers (STHEs) and photovoltaic module parameters. The CP-EODE algorithm leverages DE's exploration strengths to enhance EO’s performance adopting a cooperative parallel approach that divides the population into EO and DE sub-populations for a more effective search space exploration. The validation of CP-EODE’s effectiveness commenced with its application to 29 benchmark functions, where a Friedman test yielded an Fr\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${F}_{r}$$\end{document} value of 22.19, significantly surpassing the critical value of 7.81, and a Wilcoxon signed rank test confirmed statistical improvements with a z-score beyond − 1.96. Four quality metrics were discussed to provide a detailed assessment of CP-EODE’s performance highlighting its robust exploration and exploitation capabilities. In practical applications, CP-EODE demonstrated significant advancements in STHE design achieving up to 19.706% reduction in heat exchanger surface area compared to traditional and other algorithmic designs, showcasing its potential to enhance engineering design efficiency and cost-effectiveness. A critical examination of the algorithm's convergence rate revealed its swift optimization capability, with rapid attainment of optimal solutions in various test cases, further establishing CP-EODE’s advantage in handling complex optimization challenges. Through rigorous statistical analysis and performance metrics, CP-EODE emerges as a superior solution for complex optimization challenges, underscoring the value of hybrid algorithmic strategies in engineering optimizations. [ABSTRACT FROM AUTHOR]

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substantially improve the independence and safety of visually impaired individuals in various environments. [ABSTRACT FROM AUTHOR]

In recent years, the increasing prevalence of credit card usage has raised concerns about accurately predicting and managing credit card defaults. While machine learning and deep learning methods have shown promising results in default prediction, the black-box nature of these models often limits their interpretability and practical adoption. This study presents a new method for predicting credit card default using a combination of deep learning and explainable artificial intelligence (XAI) techniques. Integrating these methods aims to improve the interpretability of the decision-making process involved in credit card default prediction. The proposed approach is evaluated using a real-world dataset and compared to existing state-of-the-art models. Results show that the proposed approach achieves competitive prediction accuracy while providing meaningful insights into the factors driving credit card default risk. The present investigation adds to the increasing body of literature on explainable artificial intelligence (AI) in the realm of finance. Besides, it provides a pragmatic approach to assessing credit risk, balancing precision and comprehensibility. In conclusion, the model demonstrates strong potential as a credit risk assessment tool, with an accuracy of 0.8350, sensitivity of 0.8823, and specificity of 0.9879. Among the most important features identified by the model are payment delays and outstanding bill amounts. This study is a step toward more interpretable and transparent credit scoring models. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) exhibit hysteresis in their J-V characteristics, complicating the identification of appropriate electrical models and the determination of the maximum power point. Given the rising prominence of PSCs due to their potential for superior performance, there is a pressing need to address this challenge. Existing solutions in the literature have not fully addressed the hysteresis issue, especially in the context of dynamic modeling. To bridge this gap, this study introduces Artificial Rabbits Optimization (ARO) as an innovative method for optimizing the parameters of an enhanced PSC dynamic model. The proposed model is constructed based on experimental J-V data sets of PSCs, ensuring that it accounts for the hysteresis characteristics observed in both forward and backward scans. The study conducted a rigorous statistical analysis to validate the Modified Two-Diode Model performance with that of the Energy Balance (MTDM_E) optimized using the innovative ARO algorithm. The performance metric utilized for validation was the Root mean square error (RMSE), a widely recognized degree of the differences between values predicted by a model and the values observed. The statistical analysis encompassed 30 independent runs to ensure the robustness and reliability of the results. The summary statistics for the MTDM_E model under the ARO algorithm demonstrated a minimum RMSE of 4.84E−04, a maximum of 6.44E−04, and a mean RMSE of 5.14E−04. The median RMSE was reported as 5.07E−04, with a standard deviation of 3.17E−05, indicating a consistent and tight clustering of results around the mean, which suggests a high level of precision in the model's performance. Validated using root mean square error (RMSE) across 30 runs, the ARO algorithm showcased superior precision in parameter determination for the MTDM_E model, with a mean RMSE of 5.14E−04, outperforming other algorithms like GWO, PSO, SCA, and SSA. This affirms ARO's robustness in optimizing solar cell models. [ABSTRACT FROM AUTHOR]

The total number of pilgrims for the Hajj Season of 1438H reached 2,352, 122 — according to the General Authority for statistics Kingdom of Saudi Arabia. Pilgrims data analysis and prediction help concerned entities of the country in the future planning programs for the purpose of ensuring the necessary services — social, health, security, food and transportation services to name a few. Predictive analytics is the process of using data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. Predictive analytics is often discussed in the context of big data as businesses apply algorithms to derive insights from large datasets using a framework like Hadoop, HDFS, and Spark. Building MATLAB-based framework for data analytics applied to Hajj dataset is the main aim of this research paper. The proposed framework is mainly relying on four main concepts; namely the cloud-based Internet of things (IoT), fog, Edge-of-Things (EoT), and predictive analytics. This proposed framework helps in reducing the amount of data sent, lowering network traffic, increasing bandwidth, and reducing power energy consumption. On top that, the framework including regression has the potential to predict how likely Hajj is susceptible to illness or even death. [ABSTRACT FROM AUTHOR]

The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with 'ImageNet' weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a 'normal' class with 2494 images and an 'OSCC' (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis. [ABSTRACT FROM AUTHOR]

Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency and subjectivity. The advent of machine learning heralds a transformative era, offering data-driven solutions that transcend these limitations. This research delves into a comprehensive analysis of various machine learning algorithms, emphasizing their mathematical underpinnings and their applicability in credit score classification. A comprehensive evaluation is conducted on a range of algorithms, including logistic regression, decision trees, support vector machines, and neural networks, using publicly available credit datasets. Within the research, a unified mathematical framework is introduced, which encompasses preprocessing techniques and critical algorithms such as Particle Swarm Optimization (PSO), the Light Gradient Boosting Model, and Extreme Gradient Boosting (XGB), among others. The focal point of the investigation is the LIME (Local Interpretable Model-agnostic Explanations) explainer. This study offers a comprehensive mathematical model using the LIME explainer, shedding light on its pivotal role in elucidating the intricacies of complex machine learning models. This study's empirical findings offer compelling evidence of the efficacy of these methodologies in credit scoring, with notable accuracies of 88.84%, 78.30%, and 77.80% for the Australian, German, and South German datasets, respectively. In summation, this research not only amplifies the significance of machine learning in credit scoring but also accentuates the importance of mathematical modeling and the LIME explainer, providing a roadmap for practitioners to navigate the evolving landscape of credit assessment. [ABSTRACT FROM AUTHOR]

In the evolving landscape of targeted marketing, integrating deep learning (DL) and explainable AI (XAI) offers a promising avenue for enhanced customer segmentation. This paper introduces a groundbreaking approach, DeepLimeSeg, which synergizes DL methodologies with Lime-based Explainability to segment customers effectively. The approach employs a comprehensive mathematical model to harness demographic data, behavioral patterns, and purchase histories, categorizing customers into distinct clusters aligned with their preferences and needs. A pivotal component of this research is the mathematical underpinning of the DeepLimeSeg approach. The Lime-based Explainability module ensures that the segmentation results are accurate and interpretable. The mathematical rigor facilitates businesses tailoring their marketing strategies with precision, optimizing sales outcomes. To validate the efficacy of DeepLimeSeg, we employed two real-world datasets: Mall-Customer Segmentation Data and an E-Commerce dataset. A comparative analysis between DeepLimeSeg and the traditional Recency, Frequency, and Monetary (RFM) analysis is presented. The RFM analysis, grounded in its mathematical modeling, segments customers based on purchase recency, frequency, and monetary value. Our preprocessing involved computing RFM scores for each customer, followed by K-means clustering to delineate customer segments. Empirical results underscored the superiority of DeepLimeSeg over other models in terms of MSE, MAE, and R 2 metrics. Specifically, the model registered an MSE of 0.9412, indicative of its robust predictive accuracy concerning the spending score. The MAE value stood at 0.9874, signifying minimal deviation from actual values. This paper accentuates the importance of mathematical modeling in enhancing customer segmentation. The DeepLimeSeg approach, with its mathematical foundation and explainable AI integration, paves the way for businesses to make informed, data-driven marketing decisions. [ABSTRACT FROM AUTHOR]

Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model's predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life. [ABSTRACT FROM AUTHOR]

Recently, the concept of green building has become popular, and various renewable energy systems have been integrated into green buildings. In particular, the application range of fuel cells (FCs) has become widespread due to the various government plans regarding green hydrogen energy systems. In particular, proton exchange membrane fuel cells (PEMFCs) have proven superiority over other existing FCs. However, the uniqueness of the operating maximum power point (MPP) of PEMFCs represents a critical issue for the PEMFC control systems. The perturb and observe, incremental conductance/resistance, and fuzzy logic control (FLC) represent the most used MPP tracking (MPPT) algorithms for PEMFC systems, among which the FLC-based MPPT methods have shown improved performance compared to the other methods. Therefore, this paper presents a modified FLC-based MPPT method for PEMFC systems in green building applications. The proposed method employs the rate of change of the power with current ( d P / d I ) instead of the previously used rate of change of power with voltage ( d P / d V ) in the literature. The employment of d P / d I in the proposed method enables the fast-tracking of the operating MPP with low transient oscillations and mitigated steady-state fluctuations. Additionally, the design process of the proposed controller is optimized using the enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE algorithm. The design optimization of the proposed method is advantageous for increasing the adaptiveness, robustness, and tracking of the MPP in all the operating scenarios. Moreover, the proposed MPPT controller can be generalized to other renewable energy and/or FCs applications. The proposed method is implemented using C-code with the PEMFC model and tested in various operating cases. The obtained results show the superiority and effectiveness of the proposed controller compared to the classical proportional-integral (PI) based d P / d I -based MPPT controller and the classical FLC-based MPPT controller. Moreover, the proposed controller achieves reduced output waveforms ripple, fast and accurate MPPT operation, and simple and low-cost implementation. [ABSTRACT FROM AUTHOR]

Recently, order picking routing (OPR) for robots inside modern warehouses have become one of the most challenging problems. The process of OPR can be formulated as a Travelling Salesman Problem (TSP). Traditional techniques used to solve this problem usually require a long execution time and are problem-specific. Meta-heuristic optimisation techniques have been applied to solve this problem and have shown outstanding results. In this study, we solve the OPR problem using a newly proposed discrete variant of the cuckoo search algorithm. Five modifications were made to the current discrete cuckoo search algorithm. The proposed variant was applied to a traditional TSP problem. Then, the proposed algorithm was customised to solve the OPR problem in a warehouse environment. Finally, the proposed algorithm was applied to a physical prototype. It was then compared with genetic, particle swarm optimisation, and ant colony optimisation algorithms. Simulation and practical results proved the significant performance of the proposed algorithm over all other algorithms, especially in solving complex problems. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a unique swarm intelligent algorithm widely used for various practical applications due to its simple framework and good optimization performance. However, like other swarm-based algorithms, SSA yields local optimal solutions and has a slow convergence rate and low solution accuracy when dealing with high-dimensional global optimization problems. Based on quadratic interpolation and a local escape operator (LEO), a salp swarm optimization algorithm (QSSALEO) is proposed to address these issues. Quadratic interpolation around the best search agent aids QSSALEO's exploitation ability and solution accuracy, whereas the local escaping operator employs random operators to escape local optima. These tactics complement one another to help SSA promote convergence performance. Furthermore, the algorithm strives for a balance of exploitation and exploration. The proposed QSSALEO method was tested using the CEC 2017 benchmark with 50 and 100 decision variables, as well as seven CEC2008lsgo test functions with 200, 500, and 1000 decision variables, and its performance was compared to that of other metaheuristic algorithms and advanced algorithms, including seven salp swarm variants. The experimental results reveal that QSSALEO outperforms SSA and other population-based algorithms regarding convergence rate and solution correctness. The QSSALEO was then evaluated as a feature selection algorithm on 19 datasets (including three high-dimensional datasets). Friedman and Wilcoxon rank-sum statistical tests are also used to analyze the results. According to experimental data and statistical tests, the QSSALEO algorithm is very competitive and often superior to the algorithms employed in research. Therefore, the proposed method can also be considered a specialized large-scale global optimization optimizer whose performance surpasses state-of-the-art algorithms such as CMA-ES and SHADE. The algorithm source code is available at https://github.com/MohammedQaraad/An-Innovative-Quadratic-interpolation-Salp-Swarm. [ABSTRACT FROM AUTHOR]

Traditionally, the Economic Dispatch Model (EDM) integrating Combined Heat and Power (CHP) units aims to reduce fuel costs by managing power-only, CHP, and heat-only units. Today, reducing pollutant emissions to the environment is of paramount concern. This research presents a novel honey badger optimization algorithm (HBOA) for EDM-integrated CHP units. HBOA is a novel meta-heuristic search strategy inspired by the honey badger's sophisticated hunting behavior. In HBOA, the dynamic searching activity of the honey badger, which includes digging and honing, is separated into exploration and exploitation phases. In addition, several modern meta-heuristic optimization algorithms are employed, which are the African Vultures Algorithm (AVO), Dwarf Mongoose Optimization Algorithm (DMOA), Coot Optimization Algorithm (COA), and Beluga Whale Optimization Algorithm (BWOA). These algorithms are applied in a comparative manner considering the seven-unit test system. Various loading levels are considered with different power and heat loading. Four cases are investigated for each loading level, which differ based on the objective task and the consideration of power losses. Moreover, considering the pollutant emissions minimization objective, the proposed HBOA achieves reductions, without loss considerations, of 75.32%, 26.053%, and 87.233% for the three loading levels, respectively, compared to the initial case. Moreover, considering minimizing pollutant emissions, the suggested HBOA achieves decreases of 75.32%, 26.053%, and 87.233%, relative to the baseline scenario, for the three loading levels, respectively. Similarly, it performs reductions of 73.841%, 26.155%, and 92.595%, respectively, for the three loading levels compared to the baseline situation when power losses are considered. Consequently, the recommended HBOA surpasses the AVO, DMOA, COA, and BWOA when the purpose is to minimize fuel expenditures. In addition, the proposed HBOA significantly reduces pollutant emissions compared to the baseline scenario. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a recently developed meta-heuristic swarm intelligence optimization algorithm based on simulating the chain movement behavior of salps sailing and foraging in the sea. In this paper, a novel hybrid meta-heuristic algorithm called SSA-FGWO is proposed to overcome the shortcomings of the original SSA, including slow convergence speed in dealing with high-dimensional and multimodal landscapes, low precision, and global optimization problems. The essential idea of SSA-FGWO is to improve the salp swarm optimization algorithm (SSA) by utilizing the Grey Wolf Algorithm (GWO) strategy. The hybridization mechanism includes two steps: First, the strong exploitation of SSA is applied to update the leaders' position of the chain population. Second, the strong exploration strategy of GWO is used to update the followers' position to increase the population variance of the proposed optimizer. The proposed algorithm is expected to enhance exploration and exploitation ability significantly by the hybrid design. The effectiveness of SSA-FGWO is investigated through CEC2020, 23 representative benchmark cases, and feature selection problems (18 data sets) are solved. The algorithm has been examined for its computational complexity and convergent behavior. In addition to GWO, SSA and other swarm optimization algorithms are employed to compare with the proposed optimizer. The obtained experimental results show that the exploitation, exploration tendencies, and convergence patterns of SSA-FGWO have significantly improved. The results show that the proposed SSA-FGWO algorithm is a promising one that outperforms the basic SSA, GWO, and other algorithms in terms of efficacy. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer's disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer's patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A 3 C-TL-GTO framework for MRI image classification and AD detection. The A 3 C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer's Dataset (four classes of images) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer's disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer's Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters. [ABSTRACT FROM AUTHOR]

Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public's concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios. [ABSTRACT FROM AUTHOR]

Optimizing, regulating, and simulating photovoltaic systems are crucial for producing solar energy. The performance of PV systems is greatly affected by model parameters, which can be variable and not always easily accessible. As a result, finding these model parameters is a constant goal. Current-voltage data is needed to extract characteristics of solar modules and construct high-accuracy models for the modeling, control, and optimization of photovoltaic systems. This paper introduces an Improved Moth Flame algorithm with Local escape operators (IMFOL). The LEO technique improves the MFO algorithm's efficiency and the results' precision. Furthermore, the LEO mechanism enhances both the diversity of the population and the MFO's exploration efficiency. This keeps the exploration and exploitation rates in equilibrium. The IMFO achieved the lowest RMSE for a double diode module, 9.8252542E-04, while IMFOL and JADE had the best RMSE for a single diode module, 9.8602E-04. Additionally, IMFOL obtained 2,42521000E-03 for the Photowatt-PWP 201 model and 1,72981457E-03 for the STM6-40/36 model. The proposed method surpasses the state-of-the-art in terms of accuracy, reliability, and output. IMFOL is a reliable tool for evaluating solar cell and PV module data. [ABSTRACT FROM AUTHOR]

The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting the curse of dimensionality and making classifying data from microarrays challenging. In most of the examined studies, cancer classification (Malignant and benign) accuracy was examined without disclosing biological information related to the classification process. A new approach was proposed to bridge the gap between cancer classification and the interpretation of the biological studies of the genes implicated in cancer. This study aims to develop a new hybrid model for cancer classification (by using feature selection mRMRe as a key step to improve the performance of classification methods and a distributed hyperparameter optimization for gradient boosting ensemble methods). To evaluate the proposed method, NB, RF, and SVM classifiers have been chosen. In terms of the AUC, sensitivity, and specificity, the optimized CatBoost classifier performed better than the optimized XGBoost in cross-validation 5, 6, 8, and 10. With an accuracy of 0.91 ± 0.12 , the optimized CatBoost classifier is more accurate than the CatBoost classifier without optimization, which is 0.81 ±   0.24 . By using hybrid algorithms, SVM, RF, and NB automatically become more accurate. Furthermore, in terms of accuracy, SVM and RF ( 0.97 ± 0.08 ) achieve equivalent and higher classification accuracy than NB ( 0.91 ± 0.12 ). The findings of relevant biomedical studies confirm the findings of the selected genes. [ABSTRACT FROM AUTHOR]

There is a crucial need to process patient's data immediately to make a sound decision rapidly; this data has a very large size and excessive features. Recently, many cloud-based IoT healthcare systems are proposed in the literature. However, there are still several challenges associated with the processing time and overall system efficiency concerning big healthcare data. This paper introduces a novel approach for processing healthcare data and predicts useful information with the support of the use of minimum computational cost. The main objective is to accept several types of data and improve accuracy and reduce the processing time. The proposed approach uses a hybrid algorithm which will consist of two phases. The first phase aims to minimize the number of features for big data by using the Whale Optimization Algorithm as a feature selection technique. After that, the second phase performs real-time data classification by using Naïve Bayes Classifier. The proposed approach is based on fog Computing for better business agility, better security, deeper insights with privacy, and reduced operation cost. The experimental results demonstrate that the proposed approach can reduce the number of datasets features, improve the accuracy and reduce the processing time. Accuracy enhanced by average rate: 3.6% (3.34 for Diabetes, 2.94 for Heart disease, 3.77 for Heart attack prediction, and 4.15 for Sonar). Besides, it enhances the processing speed by reducing the processing time by an average rate: 8.7% (28.96 for Diabetes, 1.07 for Heart disease, 3.31 for Heart attack prediction, and 1.4 for Sonar). [ABSTRACT FROM AUTHOR]