Wavelet Based Image DE-Noising with Optimized Thresholding Using HHO Algorithm

Shahid, Mohammad; Li, Jian Ping; Golilarz, Noorbakhsh Amiri; Addeh, Jalil; Khan, Jalaluddin; Haq, Amin Ul

doi:10.1109/iccwamtip47768.2019.9067590

Cited by 15 publications

(6 citation statements)

References 27 publications

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“…Jia et al [118] employed their algorithm, which is called dynamic HHO, with mutation (DHHO/M) in order to segment satellite images by using three criteria: Kanpur's entropy, Tsallis entropy, and Otsu. Similar work has been conducted by Shahid et al [247], in which denoising of the image was presented in the wavelet domain.…”

Section: Image Processingmentioning

confidence: 70%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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Section: Image Processingmentioning

confidence: 70%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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“…Therefore, the structure of the RBFNN, including center, the number of RBFs and their spread, is of great importance and must be carefully selected. In recent years, meta-heuristic optimization algorithms and machine learning have been successfully applied on engineering problems [28][29][30][31][32][33][34].…”

Section: Proposed Methodsmentioning

confidence: 99%

Orca-RBFNN: A New Machine Learning Method for Control Chart Pattern Recognition

Addeh¹,

Golilarz²,

Gashteroodkhani³

et al. 2022

ENGT

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Supervising the production process in different factories and industries is one of the important and basic measures for the production of high quality goods and is of special importance. This is accomplished by monitoring the behavior of a system. Control chart is one of the most widely used and accurate statistical quality control tools that has been used in recent years in various industries to monitor the production process. In this study, a new method for detecting control chart patterns (CCPs) with the aim of online monitoring of the production process is proposed. In the proposed method, the radial basis function neural network (RBFNN) is used as a classifier of CCPs and a combination of shape and statistical features is used as input. In the proposed method, unlike the conventional methods in the literature, which use a set of shape or statistical features as input, the features are used intelligently and at different steps. In the RBFNN, center of clusters, number of clusters and their spread has a high impact on the network performance. Therefore, their optimal value must be determined correctly. In the proposed method, Orca optimization algorithm (OOA) is used to determine the value of these parameters. The proposed method was tested on a data set containing 800 samples and the simulation results showed that the proposed method is able to identify eight CCPs with 99.41% accuracy.

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“…Making effective decisions is crucial in any field (Khalili et al , 2016; Aghapour et al , 2019). Innovative computational methods particularly machine learning and soft computing techniques have the potential to tackle a wide range of challenging problems (Addeh et al , 2018; Yazdani et al , 2016; Yazdani et al , 2017b; Yazdani et al , 2018; Yazdani and Jolai, 2013; Shahmansouri et al , 2019; Golilarz et al , 2017; Golilarz et al , 2019; Golilarz et al , 2020; Khan et al , 2019; Mirmozaffari, 2020), therefore they have widely been applied in different fields in recent years (Shahid et al , 2019; Yazdani et al , 2019; Azadeh et al , 2016; Jafari Golrokh et al , 2020). Yu et al (2014) addressed the regional distribution of carbon emission reduction goals in China based on the constituent part swarm optimization algorithm, fuzzy c-means clustering algorithm and Shapley decomposition approach.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Mirmozaffari

Shadkam

Khalili

et al. 2021

IJESM

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Purpose Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO2) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO2 consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO2 consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019. Design/methodology/approach This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model. Findings After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process. Originality/value The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

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Wavelet Based Image DE-Noising with Optimized Thresholding Using HHO Algorithm

Cited by 15 publications

References 27 publications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Orca-RBFNN: A New Machine Learning Method for Control Chart Pattern Recognition

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

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