Dynamic Artificial Bee Colony Algorithm with Hybrid Initialization Method

Raheem, Sabreen Fawzi; Alabbas, Maytham

doi:10.31449/inf.v45i6.3652

Cited by 8 publications

(6 citation statements)

References 34 publications

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“…According to Eq. ( 7) [28], a random sample of food source solutions (SN) is used to create the initial population.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

2024

IJIES

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As a result of the widespread adoption of hovercraft systems, the path finding of such systems has become an essential rule for locating the target with the shortest distance and avoiding collision between the starting point and the target locations. The purpose of this paper is to develop a method for path finding by proposing a hybrid method of intelligent optimization techniques, including two stochastic approaches. The first one is called the Artificial Bee Colony (ABC) algorithm, and it aims to direct the hovercraft toward the target utilizing the behaviour of honey bees to reduce the path length required to reach the target. The second approach makes use of the Self Perception Particle Swarm Optimization (SP-PSO) algorithm, which is designed to improve particle swarm optimization in order to obtain a path that is more effective, smoother, and shorter for the hovercraft to travel in a complicated environment. The developed hybrid method is called the Artificial Bee Colony Self Perception Particle Swarm Optimization (ABC-SPPSO), which enhances the convergence speed and achieves a trade-off between exploration and exploitation in order to generate the shortest path while simultaneously avoiding collisions. The simulation results indicate that the performance of the proposed hybrid algorithm surpasses those of the original techniques. Moreover, the results demonstrate that the proposed hybrid algorithm outperforms the hybrid Firefly Algorithm Modified Chaotic Particle Swarm (HFAMCPSO) by (11.90%) in terms of distance and (56%) in terms of iterations. In addition, it outperforms the Quarter Orbit combined with the Particle Swarm Optimization (QOPSO) algorithm by (1.89%) in terms of distance and (16.66%) in terms of iterations. Furthermore, compared to the Rapid Random Tree Star Particle Swarm Optimization (RRT*PSO), the proposed method achieved enhancement of (0.82%) in terms of distance and (33.33%) in terms of iterations in providing the shortest path while avoiding collisions and reducing the trajectory tracking error to approximately zero. All the considered approaches were simulated in a global environment, utilizing the MATLAB 2022 package.

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“…According to Eq. ( 7) [28], a random sample of food source solutions (SN) is used to create the initial population.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

2024

IJIES

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“…It calculates the harmonic mean of the precision and the recall [36], [37]. It is denoted mathematically as [38]:…”

Section: F1-scorementioning

confidence: 99%

Epileptic Seizures Detection from EEG Recordings Based on a Hybrid system of Gaussian Mixture Model and Random Forest Classifier

Ohannesian

Harfash

2022

IJCAI

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Epilepsy is the most common neurological disease defined as a central nervous system disorder that is characterized by recurrent seizures. While electroencephalography (EEG) is an essential tool for monitoring epilepsy patients' brain activity and diagnosing epilepsy, Visual detection of the EEG signal to identify epileptic seizures is a time-consuming approach that might result in human error. Therefore, an early and precise epilepsy diagnosis is critical to reducing the risk of future seizures. This paper aims to increase epileptic seizure detection accuracy in a balanced dataset while reducing the execution time.To address this, we proposed a hybrid system of supervised and unsupervised machine learning algorithms to construct a computationally efficient and scalable model for the early detection of epileptic seizures from two-class EEG datasets. First, Discrete Wavelet Transform (DWT) was applied to the EEG signal to decompose it into frequency sub-bands. Then these EEG extracted features were fed into the Gaussian Mixture Model (GMM) for partitioning these features into two clusters: epilepsy or not. Lastly, the clusters' output was evaluated with the random forest classifier. In addition, Principal Component Analysis (PCA) was used to reduce the EEG features and to reduce further the features obtained after conducting DWT on the EEG signal to determine the impacts of dimension reduction on this system performance. The experimental results show that the highest accuracy was achieved by the hybrid system of GMM with random forest with DWT features with an accuracy of 93.62 %. Povzetek: Razvita je bila izvirna metoda strojnega učenja za zaznavanje epileptičnih napadov iz EEG signalov.

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“…The field of artificial intelligence is a widespread field, especially after the development of ML and DL technologies. These technologies have been used in wide fields and these technologies have been developed [16], these techniques were used to distinguish writing, for example [17] [18]. The development of ML recently, especially after the emergence of DL, has achieved impressive results in the medical field [19] .One of the most common uses of ML is in the fields of image processing, to get rid of the deterioration of the problem of blur due to the atmosphere .The technique of removing dehazing from images has entered many important horizons [20].…”

Section: Applications Of Dehazing Imagesmentioning

confidence: 99%

Review about applications of Remove haze from images

.Karoon,

Nemer

2022

J. Al-Qadisiyah Comp. Sci. Math.

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The problem of removing haze from images is a problem that researchers have been interested in, because of its importance in various fields in computer vision and image processing. The photos are usually taken in nature, like forests, cities, streets, etc., and are generally accepted in terrible weather conditions, especially fog. This is because the light is a critical factor in obtaining clear images, assuming the cameras' quality. The presence of various types of haze (smoke, fog, and dust) reduces the light rays that are reflected from the scenes to be photographed, so removing the haze was the researchers' primary concern. Moreover, many computerized devices use these cameras and imaging devices in various fields. Through this research, we will mention a number of those applications that use this biotechnology "dehazing."

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Dynamic Artificial Bee Colony Algorithm with Hybrid Initialization Method

Cited by 8 publications

References 34 publications

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

Epileptic Seizures Detection from EEG Recordings Based on a Hybrid system of Gaussian Mixture Model and Random Forest Classifier

Review about applications of Remove haze from images

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