Spectral CG Algorithm for Solving Fuzzy Nonlinear Equations

Abed, Mezher M.; Öztürk, Ufuk; Khudhur, Hisham M.

doi:10.52866/ijcsm.2022.01.01.001

Cited by 6 publications

(2 citation statements)

References 17 publications

(32 reference statements)

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“…The KNN [1], [2], [44], [50], [54]- [56], [86]- [88], [4], [26], [27], [32], [37]- [42] is a non-parametric pattern recognition approach that is used for classification and regression in pattern recognition. In our example, the algorithm is utilized for classification, with the outcome being a class membership [89].…”

Section: Methodsmentioning

confidence: 99%

Big data classification based on improved parallel k-nearest neighbor

Ali

Mohammed²,

Hasan³

et al. 2023

TELKOMNIKA

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In response to the rapid growth of many sorts of information, highway data has continued to evolve in the direction of big data in terms of scale, type, and structure, exhibiting characteristics of multi-source heterogeneous data. The k-nearest neighbor (KNN) join has received a lot of interest in recent years due to its wide range of applications. Processing KNN joins is time-consuming and inefficient due to the quadratic structure of the join method. As the number of applications dealing with vast amounts of data develops, KNN joins get more sophisticated. The authors seek to save money on computer resources by leveraging a large number of threads and multiprocessors. Six popular datasets are used to apply the method and evaluate the sequential and parallel performance of the KNN technique. These datasets are used to compare the sequential and parallel performance of the KNN method. When compared to a matching multi-core solution, the final implementation saves computing resources. It has been optimized to utilize as little RAM as possible, allowing it to manage high-resolution photo data without sacrificing efficiency. The authors will use the technique they presented using Spark Radoop. Our performance research validates the supplied method's efficacy and scalability.

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Section: Methodsmentioning

confidence: 99%

Big data classification based on improved parallel k-nearest neighbor

Ali

Mohammed²,

Hasan³

et al. 2023

TELKOMNIKA

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“…One of the disadvantages of the method is that it does not guarantee reaching a solution. Spectral CG algorithm for solving fuzzy nonlinear equations was developed in [30]. This method does not reach a global solution.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Optimization algorithm based on the Euler method for solving fuzzy nonlinear equations

Ibraheem

Khudhur

2022

EEJET

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In a variety of engineering, scientific challenges, mathematics, chemistry, physics, biology, machine learning, deep learning, regression classification, computer science, programming, artificial intelligence, in the military, medical and engineering industries, robotics and smart cars, fuzzy nonlinear equations play a critical role. As a result, in this paper, an Optimization Algorithm based on the Euler Method approach for solving fuzzy nonlinear equations is proposed. In mathematics and computer science, the Euler approach (sometimes called the forward Euler method) is a first-order numerical strategy for solving ordinary differential equations (ODEs) with a specified initial value. The local error is proportional to the square of the step size, while the general error is proportional to the step size, according to the Euler technique. The Euler method is frequently used to create more complicated algorithms. The Optimization Algorithm Based on the Euler Method (OBE) uses the logic of slope differences, which is computed by the Euler approach for global optimizations as a search mechanism for promising logic. Furthermore, the mechanism of the proposed work takes advantage of two active phases: exploration and exploitation to find the most important promising areas within the distinct space and the best solutions globally based on a positive movement towards it. In order to avoid the solution of local optimal and increase the rate of convergence, we use the ESQ mechanism. The optimization algorithm based on the Euler method (OBE) is very efficient in solving fuzzy nonlinear equations and approaches the global minimum and avoids the local minimum. In comparison with the GWO algorithm, we notice a clear superiority of the OBE algorithm in reaching the solution with higher accuracy. We note from the numerical results that the new algorithm is 50 % superior to the GWO algorithm in Example 1, 51 % in Example 2 and 55 % in Example 3.

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