Search Results (29)

Many Chinese cities have severe air pollution due to the rapid development of the Chinese economy, urbanization, and industrialization. Particulate matter (PM2.5) is a significant component of air pollutants. It is related to cardiopulmonary and other systemic diseases because of its ability to penetrate the human respiratory system. Forecasting air PM2.5 is a critical task that helps governments and local authorities to make necessary plans and actions. Thus, in the current study, we develop a new deep learning approach to forecast the concentration of PM2.5 in three major cities in China, Beijing, Shijiazhuang, and Wuhan. The developed model is based on the Informer architecture, where the attention distillation block is improved with a residual block-inspired structure from efficient networks, and we named the model ResInformer. We use air quality index datasets that cover 98 months collected from 1 January 2014 to 17 February 2022 to train and test the model. We also test the proposed model for 20 months. The evaluation outcomes show that the ResInformer and ResInformerStack perform better than the original model and yield better forecasting results. This study’s methodology is easily adapted for similar efforts of fast computational modeling. Full article

This paper proposes an improved method for solving diverse optimization problems called EGBO. The EGBO stands for the extended gradient-based optimizer, which improves the local search of the standard version of the gradient-based optimizer (GBO) using expanded and narrowed exploration behaviors. This improvement aims to increase the ability of the GBO to explore a wide area in the search domain for the giving problems. In this regard, the local escaping operator of the GBO is modified to apply the expanded and narrowed exploration behaviors. The effectiveness of the EGBO is evaluated using global optimization functions, namely CEC2019 and twelve benchmark feature selection datasets. The results are analyzed and compared to a set of well-known optimization methods using six performance measures, such as the fitness function’s average, minimum, maximum, and standard deviations, and the computation time. The EGBO shows promising results in terms of performance measures, solving global optimization problems, recording highlight accuracies when selecting significant features, and outperforming the compared methods and the standard version of the GBO. Full article

The exponential spread of news and posts related to the COVID-19 pandemic on social media platforms led to the emergence of the disinformation phenomenon. The phenomenon of spreading fake information and news creates significant concern for the public health and safety of the population. In this paper, we propose a disinformation detection framework based on multi-task learning (MTL) and meta-heuristic algorithms in the context of the COVID-19 pandemic. The developed framework uses an MTL and a pre-trained transformer-based model to learn and extract contextual feature representations from Arabic social media posts. The extracted contextual representations are fed to an alternative feature selection technique which depends on modified version of the Fire Hawk Optimizer. The proposed framework, which aims to improve the disinformation detection rate, was evaluated on several datasets of Arabic social media posts. The experimental results show that the proposed framework can achieve accuracy of 59%. It obtained, at best, precision, recall, and F-measure of 53%, 71%, and 53%, respectively, on all datasets; and it outperformed the other algorithms in all measures. Full article

It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA’s search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction model, called DNR-SOAAO, using different performance indicators. We also assessed the quality of the SOAAO with extensive comparisons to the original versions of the SOA and AO, as well as several other optimization methods. The developed model achieved excellent results in the evaluation. For example, the SOAAO achieved high $R^2$ results of 0.95, 0.96, 0.95, and 0.91 on the four datasets. Full article

Feature selection (FS) methods play essential roles in different machine learning applications. Several FS methods have been developed; however, those FS methods that depend on metaheuristic (MH) algorithms showed impressive performance in various domains. Thus, in this paper, based on the recent advances in MH algorithms, we introduce a new FS technique to modify the performance of the Dwarf Mongoose Optimization (DMO) Algorithm using quantum-based optimization (QBO). The main idea is to utilize QBO as a local search of the traditional DMO to avoid its search limitations. So, the developed method, named DMOAQ, benefits from the advantages of the DMO and QBO. It is tested with well-known benchmark and high-dimensional datasets, with comprehensive comparisons to several optimization methods, including the original DMO. The evaluation outcomes verify that the DMOAQ has significantly enhanced the search capability of the traditional DMO and outperformed other compared methods in the evaluation experiments. Full article

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine–cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods. Full article

To meet the needs of Egypt’s rising population, more land must be cultivated. Land evaluation is vital to achieving sustainable agricultural production. To determine the soil capability in the northeast Nile Delta region of Egypt, the present study introduces a new form of integration between the Agriculture Land Evaluation System (ALES Arid) model and the machine learning (ML) approach. The soil capability indicators required for the ALES Arid model were determined for the 47 collected soil profiles covering the study area. These indicators include soil pH, soil salinity, the sodium adsorption ratio (SAR), the exchangeable sodium percentage (ESP), the organic matter (OM) content, the calcium carbonate (CaCO₃) content, the gypsum content, the clay percentage, and the slope. The ALES Arid model was run using these indicators, and soil capability indexes were obtained. Using GIS, these indexes helped to classify the study area into four capability classes, ranging from good to very poor soils. To predict the soil capability, three machine learning algorithms named traditional RVFL, sine cosine algorithm (SCA), and AFO were also applied to the same soil criteria. The developed ML method aims to enhance the prediction of soil capability. This method depends on improving the performance of Random Vector Functional Link (RVFL) using an optimization technique named Aptenodytes Forsteri Optimization (AFO). The operators of AFO were used to determine the best parameters of RVFL since traditional RVFL is sensitive to parameters. To assess the performance of the developed AFO-RVFL method, a set of real collected data was used. The experimental results illustrate the high efficacy of AFO-RVFL in the spatial prediction of soil capability. The correlations found in this study are critical for understanding the overall techniques for predicting soil capability. Full article

Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been applied to enhance the efficiency of FS algorithms. These approaches reduce the processing time and improve the accuracy of the learning models. In this paper, a developed method called MPAO based on the marine predators algorithm (MPA) and the “narrowed exploration” strategy of the Aquila optimizer (AO) is proposed to handle FS, global optimization, and engineering problems. This modification enhances the exploration behavior of the MPA to update and explore the search space. Therefore, the narrowed exploration of the AO increases the searchability of the MPA, thereby improving its ability to obtain optimal or near-optimal results, which effectively helps the original MPA overcome the local optima issues in the problem domain. The performance of the proposed MPAO method is evaluated on solving FS and global optimization problems using some evaluation criteria, including the maximum value (Max), minimum value (Min), and standard deviation (Std) of the fitness function. Furthermore, the results are compared to some meta-heuristic methods over four engineering problems. Experimental results confirm the efficiency of the proposed MPAO method in solving FS, global optimization, and engineering problems. Full article

Personalized travel recommendations propose locations of interest (LOIs) for users. The LOI sequence suggestion is more complicated than a single LOI recommendation. Only a few studies have considered LOI sequence recommendations. Creating a reliable succession of LOIs is difficult. The two LOIs that follow each other should not be identical or from the same category. It is vital to examine the types of subsequent LOIs when designing a sequence of LOIs. Another issue is that providing precise and accurate location recommendations bores users. It can be tedious and monotonous to look at the same types of LOIs repeatedly. Users may want to change their plans in the middle of a trip. The trip must be dynamic rather than static. To address these concerns in the recommendations, organize a customized journey by looking for continuity, implications, innovation, and surprising (i.e., high levels of amusement) LOIs. We use LOI-likeness and category differences between subsequent LOIs to build sequential LOIs. In our travel recommendations, we leveraged luck and dynamicity. We suggest a fortunate refining trip recommendation (FRTR) to address the issues of identifying and rating user pleasure. An algorithm oof compelling recommendation should offer what we are likely to enjoy and provide spontaneous yet objective components to maintain an open doorway to new worlds and discoveries. In addition, two advanced novel estimations are presented to examine the recommended precision of a sequence of LOIs: regulated precision (RP) and pattern precision (PP). They consider the consistency and order of the LOIs. We tested our strategy using data from a real-world dataset and user journey records from Foursquare dataset. We show that our system outperforms other recommendation algorithms to meet the travel interests of users. Full article

Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although the AO has a significant performance in various problems, like other optimization algorithms, the AO suffers from certain limitations in its search mechanism, such as local optima stagnation and convergence speed. This is a general problem that faces almost all optimization problems, which can be solved by enhancing the search process of an optimizer using an assistant search tool, such as using hybridizing with another optimizer or applying other search techniques to boost the search capability of an optimizer. Following this concept to address this critical problem, in this paper, we present an alternative version of the AO to alleviate the shortcomings of the traditional one. The main idea of the improved AO (IAO) is to use the search strategy of the Whale Optimization Algorithm (WOA) to boost the search process of the AO. Thus, the IAO benefits from the advantages of the AO and WOA, and it avoids the limitations of the local search as well as losing solutions diversity through the search process. Moreover, we apply the developed IAO optimization algorithm as a feature selection technique using different benchmark functions. More so, it is tested with extensive experimental comparisons to the traditional AO and WOA algorithms, as well as several well-known optimizers used as feature selection techniques, like the particle swarm optimization (PSO), differential evaluation (DE), mouth flame optimizer (MFO), firefly algorithm, and genetic algorithm (GA). The outcomes confirmed that the using of the WOA operators has a significant impact on the AO performance. Thus the combined IAO obtained better results compared to other optimizers. Full article

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, is currently developing into a rapidly disseminating and an overwhelming worldwide pandemic. In severe COVID-19 cases, hypercoagulability and inflammation are two crucial complications responsible for poor prognosis and mortality. In addition, coagulation system activation and inflammation overlap and produce life-threatening complications, including coagulopathy and cytokine storm, which are associated with overproduction of cytokines and activation of the immune system; they might be a lead cause of organ damage. However, patients with severe COVID-19 who received anticoagulant therapy had lower mortality, especially with elevated D-dimer or fibrin degradation products (FDP). In this regard, the discovery of natural products with anticoagulant potential may help mitigate the numerous side effects of the available synthetic drugs. This review sheds light on blood coagulation and its impact on the complication associated with COVID-19. Furthermore, the sources of natural anticoagulants, the role of nanoparticle formulation in this outbreak, and the prevalence of thrombosis with thrombocytopenia syndrome (TTS) after COVID-19 vaccines are also reviewed. These combined data provide many research ideas related to the possibility of using these anticoagulant agents as a treatment to relieve acute symptoms of COVID-19 infection. Full article

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO’s premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally. Full article

Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques. Full article

Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE. Full article

Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature. Full article