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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]

• An alternative method for unrelated parallel machine scheduling problem. • The proposed method is a modified salp swarm algorithm using firefly algorithm. • The firefly algorithm is applied as local search approach to improve performance of salp swarm algorithm. • The proposed SSAFA approach is evaluated using small and large problem instances. • The results of SSAFA is compared with other state-of-the-art methods and it outperforms these methods. Unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times has received more attention due to its various industrial and scheduling applications. However, the UPMSP is considered an NP-hard problem, even without setup times. Moreover, the sequence-dependent setup times presents more complexity, which makes finding an optimal solution is very hard. In this paper, a modified salp swarm algorithm (SSA) based on the firefly algorithm (FA) is proposed to enhance the quality of the solution of UPMSP. The proposed approach, called SSAFA, uses the operators of FA to improve the exploitation ability of SSA by working as a local search. We evaluate the proposed SSAFA using both small and large problem instances. Furthermore, extensive comparisons to several existing metaheuristic methods used to solve UPMSP problems have been carried out. The evaluation outcomes confirmed the competitive performance of the proposed SSAFA in all problem instances, using different performance measures. [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]

Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO 2) and nitrogen (N 2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO 2 -N 2 mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N 2 -CO 2 mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N 2 , and model fraction of CO 2 , the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets. [Display omitted] • Emphasizes the importance of precise viscosity assessment for CCUS and diverse industries. • Aims to create a smart model for predicting CO 2 -N 2 viscosity. • Focuses on improving the dendritic neural regression (DNR) framework. • Introduces a novel Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for model enhancement. • Offers unprecedented insights into CO2-N2 viscosity prediction. • Promises a breakthrough with great potential for CCUS projects and various industries. [ABSTRACT FROM AUTHOR]

This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have been presented to predict biochar, such as neural network (NN) and least square support vector machine (LS-SVM). However, each of them has its own drawbacks, such as getting stuck in a local optimum, which occurs in NN, and lack of uncertainty and time complexity, as in LS-SVM. Therefore, this paper avoids this limitation by using a hybrid method between the adaptive neuro-fuzzy inference system (ANFIS) and gray wolf optimization (GWO) algorithm. The proposed method is called ANFIS-GWO, which consists of two stages. In the first stage, GWO is used to learn the parameters of ANFIS using the training set. Meanwhile, in the second stage, the testing set is used to evaluate the performance of the proposed ANFIS-GWO method. Three experiments were performed to assess the performance of the proposed method. The first experiment used a set of UCI (University of California, Irvine) benchmark datasets to evaluate the effectiveness of ANFIS-GWO. The aim of the second experiment was to evaluate the performance of the proposed ANFIS-GWO method to predict biochar yield from manure pyrolysis. The third experiment aimed to estimate the values of input parameters of pyrolysis that maximize biochar production. The obtained results were compared to those of other methods, such as ANFIS using gradient descent, practical swarm optimization, genetic algorithm, whale optimization algorithm, sine-cosine algorithm, and LS-SVM. The results of the ANFIS-GWO method were >35% of the standard ANFIS and also better than those of other methods. [ABSTRACT FROM AUTHOR]

This paper presents an alternative early disease detection technique based on the Internet of Medical Things (IoMT) to improve the healthcare system. Since it is necessary to address different aspects to help healthcare organizations to improve their efficiency. Early disease detection using a fast and efficient test, such as chest X-ray images, is one of the most important issues. In this paper, we develop an efficient model to allocate abnormalities in medical chest X-ray images. The developed model consists of a set of stages; the first stage is to segment the images using a multilevel thresholding technique to determine the lung inside the image, then extracting the features from the segmented objects using different extractor methods. Then, we proposed a Fractional-order modified Heterogeneous comprehensive learning particle swarm optimizer (FMHCLPSO) as a feature selection method to determine the relevant features used to improve the detection process. To evaluate the performance of the developed IoMT model, a set of three medical datasets is used, called Covid-19 & Pneumonia, X-raycovid, and Radiography. The results illustrate the high efficiency of the developed model to detect diseases based on performance measures. Furthermore, We compared the proposed method to several existing methods, and it showed significant performance. [ABSTRACT FROM AUTHOR]

• IoT model-based cloud framework is proposed for COVID-19 prediction. • A new deep COVID-19 model is proposed to detect infected cases. • A complex COVID-19 dataset images are used from different resources. • An end-to-end architecture is designed for a whole platform composed of frontend and backend. • Mobile App is developed to capture the images and pass these images to a centralized backend system. Due to the rapid spread of COVID-19, an urgent need arose for a quick and accurate diagnosis approach. The well-known RT-PCR test is often unavailable and too costly for many suspected cases. In contrast, the chest X-ray scan proves to be a robust and efficient tool for promptly identifying the virus, using various cost-effective tests. This study proposes a hybrid intelligent model for identifying COVID-19 patients based on their chest X-ray scans. The proposed model consists of five phases. Firstly, the X-ray images are preprocessed using a median filter to enhance and remove noise. Secondly, robust invariant and high-level engineering features are extracted using five different feature extraction methods, namely (speeded up robust features (SURF), grey-level co-occurrence matrix (GLCM), histogram of oriented gradients (HoG), segmentation-based fractal texture analysis (SFTA), and local binary pattern (LBP) to help in rapid diagnosis. Then these features are fused to complement the gap of each other and to achieve the best performance. Thirdly, a deep convolutional neural network (DCNN) is used to classify the chest X-ray scans of infected COVID-19 patients. Then, the proposed model is compared with different machine learning techniques under different quality measures. Fourthly, an IoT platform composed of a mobile client application and a backend system is designed. The mobile client application captures the content from the patient side, handles these captured images, and prepares them for rapid sending to the backend side for further processing. The backend system receives the patient images from the client side and applies the proposed model to diagnose the COVID-19 patients. Finally, a cloud-based design for the platform utilizes the modern AWS services characterized by availability, elasticity, coverage, configurability, and reliability. The results show that the proposed model has a good accuracy of around (99.31%) and outperforms the other models. Therefore, the proposed model could be used for real-time automatic early recognition of COVID-19 cases. [ABSTRACT FROM AUTHOR]

The rapid proliferation of news and posts across social media platforms has spawned a concerning wave of misinformation. Disseminating false information and news significantly threatens public health and safety. To address this critical issue, we present an innovative disinformation detection framework, leveraging the power of multi-task learning (MTL) and meta-heuristic techniques. Our framework harnesses the potential of an MTL approach and a state-of-the-art pre-trained Transformer-based model, enabling the extraction of comprehensive contextual features from Arabic social media posts. These contextual are input to an advanced feature selection (FS) model utilizing a modified Nutcracker Optimization Algorithm. Through extensive evaluation of diverse datasets of Arabic social media posts, our proposed framework achieves remarkable results. Notably, our framework attains an accuracy rate of 87% and 69% for binary and multi-classification, respectively. In addition, the developed method outperforms all compared algorithms. Our findings demonstrate the potency of our disinformation detection framework, serving as a robust tool in the battle against misinformation spread. By shedding light on the truth amidst the vast social media content, we can safeguard public health and empower individuals with reliable information. • Propose an alternative Arabic fake news detection framework. • Integrate multi-task learning with a Transformer-based model and Metaheuristic algorithm. • Extracted contextual features using MTL from posts are merged with Twitter-based information to enlarge post representation. • Propose a modified Nutcracker Optimization Algorithm as a feature selection (FS) algorithm. • Apply the developed framework to real-world datasets and compare with other models. [ABSTRACT FROM AUTHOR]

Wind power can be considered one of the most important green sources of electric power. The prediction of wind power is necessary to boost the power grid operations' efficiency and increase power market competitiveness. Artificial neural networks (ANNs) are widely used in prediction applications, including wind power. The Random Vector Functional Link (RVFL) is an efficient ANN model that can be employed in time-series forecasting applications. However, the configuration process of the RVFL needs to be improved. Thus, in this paper, we presented an optimized RVFL network using a new naturally inspired technique called the Capuchin search algorithm (CapSA). The main function of the CapSA is to boost the configuration of the traditional RVFL and enhance its prediction capability. We implement extensive evaluation experiments using public datasets from four wind turbines located in France, using several evaluation measures called RMSE, MAE, MAPE, and R2. The evaluation outcomes reveal that the CapSA-RVFL obtained the best prediction accuracy compared to the original RVFL and several variants of the RVFL model, which verifies that the application of CapSA has a significant contribution to improving the prediction capability of the RVFL. [ABSTRACT FROM AUTHOR]

Social media allows the spread of vital information regarding crises and emergencies. Thus, emergency management systems can benefit from social media because they can be used to inform the public to take the appropriate precautions. However, social media is riddled with irrelevant information. Therefore, researchers have recently focused on developing robust event detection (ED) systems to extract relevant events and to define their types by relying on deep learning techniques (DL). Hence, this paper proposes an event detection model that merges the DL approach (e.g., MobileBERT) and a novel feature selection (FS) method to improve performance. MobileBERT is a transformer-based model designed to extract features from a text dataset, while the FS is used to preserve the relevant features and to reduce feature representation space. The developed FS method depends on improving the sparrow search algorithm (SSA) using manta ray foraging optimization (MRFO) operators. The modification is conducted to enhance the exploitation ability of the SSA using the operators of MRFO as a local search method. To validate the proposed framework, experiments are conducted using real-world datasets, namely Maven, C6, and C36. The results show the ability of the modified FS method to improve the performance of the proposed framework for ED tasks over other existing methods. • Apply MobileBERT to extract features from event detection text data. • Improve efficiency of SSA using MRFO to find relevant features. • Evaluate efficiency of proposed method using various datasets and compare it with other methods. [ABSTRACT FROM AUTHOR]

Optimization algorithms have shown significant advantages in solving diverse several real-world problems, especially where are limitations in computations and hardware requirements. However, they might get trapped in local optima or fast convergence. To solve these challenges, the exploration ability of an optimization algorithm should be evaluated and improved. This paper proposes a new approach for solving global optimization and feature selection problems to overcome these drawbacks and balance exploitation and exploration abilities. We propose a hybrid approach combining both, Gradient-Based Optimizer (GBO) using Slime Mould Algorithm (SMA), which exploits the strength factors of Gradient-Based Optimizer and Slime Mould Algorithm. The SMA is applied as a local search for the GBO to enhance its ability to explore more regions in the search space. This strategy is employed to achieve a balance between both exploration and exploitation. Two main experiments are applied to evaluate the performance of the GBOSMA. The first one solves a well-known global optimization problem (CEC2017), whereas the second one solves feature selection problems using several benchmark datasets. The GBOSMA is compared to the standard GBO, SMA, and the most recent optimization algorithms in both experiments. The results also show that the GBOSMA approach obtains promising results in both experiments outperforming other algorithms. Comparing with eight well-known optimization algorithms, the results (for both global optimization and feature selection problems) demonstrate that the GBOSMA overcomes others in terms of performance, speed and stability. • Improving the search ability of the GBO optimizer using the SMA algorithm. • Evaluating the performance of the proposed method using CEC2017 functions. • Selecting the most relative features to increase the classification accuracy. [ABSTRACT FROM AUTHOR]

Precise streamflow prediction is necessary for better planning and managing available water and future water resources, especially for high altitude mountainous glacier melting affected basins in the climate change context. In the current study, a novel hybridized machine learning method, extended marine predators algorithm (EMPA)-based ANN (ANN-EMPA), is developed for streamflow estimation in the Upper Indus Basin, a key mountainous glacier melt affected basin of Pakistan. The prediction accuracy of the novel metaheuristic algorithm (EMPA) was also compared with several benchmark metaheuristic algorithms, including the marine predators algorithm (MPA), particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimization (GWO). The results revealed that the newly developed hybridized ANN-EMPA outperformed the other hybrid ANN methods in streamflow prediction. ANN-EMPA improved the root mean square error, mean absolute error and Nash–Sutcliffe efficiency of ANN-PSO by 4.8, 4.1 and 0.5%, ANN-GA by 6.2, 5.6 and 0.6%, ANN-GWO by 3.7, 4.4 and 0.5%, and ANN-MPA by 3.2, 7.5 and 0.3%, respectively. Month number (MN) was also examined as input to the best models to assess its impact on the prediction precision. Obtained results showed that MN generally slightly improved the models' accuracy. Results also showed that temperature-based inputs provided better prediction accuracy than only streamflow as inputs. Therefore, the ANN-EMPA model can be used for streamflow estimation from temperature data only when long-term streamflow data is unavailable. [Display omitted] • Streamflow prediction was conducted using novel hybrid artificial intelligence model. • Extended marine predator's algorithm (EMPA) was hybridized ANN model. • The proposed model was validated with several hybrid models for verification. • Statistical metrics confirmed the ANN-EMPA potential against the benchmark models. • The research was provided a reliable alternative AI model for streamflow prediction. [ABSTRACT FROM AUTHOR]

In this paper, an improved Barnacles Mating Optimizer (BMO) is proposed to deal with optimization problems and develop a new automatic clustering approach. BMO is a well-established optimization technique inspired by the mating behavior of barnacles in real-life. The exploratory trends of BMO are influential and can maintain the right balance among exploration and exploitation. However, this population-based method can be improved further to reduce the probability of potential drawbacks for any optimization technique. As such, we revised the core searching phased of BMO based on a sine–cosine algorithm (SCA) and disruption operators (DO). The proposed method is named BMSCD, which updates the current solution by switching between the mechanisms of the BMO and SCA based on a probability calculated using the fitness value of the current solution. The experiments results on various benchmark cases for global optimizations demonstrate the improved performance of the proposed BMSCD in terms of quality of solutions, the balance of the exploration–exploitation, and convergence rates. Besides, the proposed BMSCD is evaluated by nine measures in solving different clustering problems. The results show that the BMSCD can effectively and powerfully address the tested problems and provide excellent performance compared to the state-of-the-art methods. • Developed an improved Sine–Cosine-Barnacles Algorithm Optimizer. • Evaluate the proposed method against benchmark functions and data clustering problems. • Compared the proposed method to other well-known methods. • Demonstrated effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]