A Multilevel Image Thresholding Based on Hybrid Salp Swarm Algorithm and Fuzzy Entropy

Alwerfali, Husein S. Naji; Elaziz, Mohamed Abd; Al-qaness, Mohammed A. A.; Abbasi, Aaqif Afzaal; Lu, Songfeng; Liu, Fang; Li, Li

doi:10.1109/access.2019.2959325

Cited by 31 publications

(24 citation statements)

References 72 publications

(77 reference statements)

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“…Moreover, SSA is also an optimization algorithm proposed by Mirjalili et al [48] inspired by the behavior of salp chains. In recent years, the SSA was utilized to solve different optimization problems, such as feature selection [49,50], data classification [51], image segmentation [52], and others [53,54].…”

Section: Introductionmentioning

confidence: 99%

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness

Ewees

Fan

et al. 2020

JCM

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365

316

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In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

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Section: Introductionmentioning

confidence: 99%

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness

Ewees

Fan

et al. 2020

JCM

Self Cite

365

316

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“…The second step was performed based on the line symmetry to directly obtain the upper-right and the bottom-left quarters of the elements according to the mapping functions in (11), (12), as shown at the bottom of the next page. The last step was based on the point symmetry, namely to directly obtain the bottom-right quarters of the elements by using (13). The number of basic computations was R 2 × C 2 × (R × C − 1) with this improved method (e.g., 0.3240×10 11 for a 600 × 600 image).…”

Section: Symmetry-based Methods For Efficiently Constructing a Loomentioning

confidence: 99%

“…efficient method for this assessment is to employ information entropy (IE, also called Shannon entropy) [5]- [9], which is an information-theoretic metric that quantifies the information content of a dataset [10]. This method has been extensively used because of its theoretical elegance and practical simplicity, which helped create numerous image processing algorithms [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Efficient approach for computing the discrimination ratio-based variant of information entropy for image processing

et al. 2020

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Information content is an important criterion for many image processing algorithms such as band selection and image fusion. Usually, information content is quantified by using information entropy (i.e., Shannon entropy); however, this is not a suitable measure because information entropy is independent of the spatial distribution of image pixels. Thus, improved information entropies and variants of information entropy have been developed. Among all the entropic measures, the discrimination ratio-based variant of information entropy (hereinafter DVIE) has recently been demonstrated to be the most effective. On the other hand, DVIE is the most inefficient measure in terms of computation time, which severely restricts its applications. To solve this problem, we present a three-strategy approach to efficiently compute the DVIE of an image. The first strategy is to use a simplified equation for DVIE. The second strategy is to selectively compute the two computationally intensive components of DVIE-intra-distance and extra-distance-based on the computational complexity. Only one distance was computed, and the other distance was derived based on the lookup table of average distances. The third strategy was to efficiently construct the lookup table based on geometric symmetry. We performed both validation and evaluation experiments to demonstrate that the proposed approach was not only valid for accurately computing DVIE, but it was also highly efficient. Our proposed approach saved more than 99% of the time taken for the original approach, without compromising the accuracy; therefore, DVIE was made applicable for processing images.

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“…The hybrid method is based on the gravitational search algorithm and genetic algorithm. In Reference [ 27 ], a hybrid multi-level thresholding method is proposed based on an improved salp swarm optimizer and Fuzzy entropy. The MFO is used to overcome the limitation of the salp swarm algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…However, in such hybrid methods, one MH algorithm is needed to improve the local search for the other MH algorithm, such as, in Reference [ 27 ], the MFO is used as a local search for SSA. These hybrid MH methods can solve optimization problems efficiently.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

Alwerfali

Al-qaness

Elaziz

et al. 2020

Entropy

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Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

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A Multilevel Image Thresholding Based on Hybrid Salp Swarm Algorithm and Fuzzy Entropy

Cited by 31 publications

References 72 publications

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Efficient approach for computing the discrimination ratio-based variant of information entropy for image processing

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

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