A hybrid neural network Imperialist Competitive Algorithm for skin color segmentation

Razmjooy, Navid; Mousavi, B. Somayeh; Soleymani, Fazlollah

doi:10.1016/j.mcm.2012.09.013

Cited by 62 publications

(30 citation statements)

References 18 publications

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“…The ICA is a derivative‐free optimization algorithm that is inspired by political science and, as with all of the abovementioned optimization algorithms, can be categorized as metaheuristic. More information about the procedure of applying the ICA as an optimizer of the ANN can be found elsewhere . Appendix A demonstrates the procedure of how the ICA connects with the ANN and optimizes the ANN parameters.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

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Machine learning models to predict bottom hole pressure in multi‐phase flow in vertical oil production wells

Ahmadi

Chen

2019

Can J Chem Eng

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The precise estimation of a pressure drop in vertical multiphase flowing oil wells plays a crucial role in designing robust production facilities and evaluating optimum production plans. A significant amount of research has been conducted on determining a pressure drop via calculating the bottom hole pressure (BHP); these different methods as well as numerical, analytical, semi‐analytical, and empirical correlations can be used for doing this task. Unfortunately, most of those correlations are unable to provide reasonable precision when calculating BHP and, consequently, improvements are still required. To predict BHP in vertical wells, several hybrids of meta‐heuristic optimization methods and a bio‐inspired connectionist approach, i.e., artificial neural network (ANN), are employed. The main goal of these optimization algorithms is to optimize the parameters of the ANN models, i.e., weights and biases, to improve their performance. Based on the obtained outputs and various robustness indexes, a hybrid genetic algorithm and particle swarm optimization (HGAPSO) is highly precise and has a maximum of 10 % error compared to measured pressure data. The results of this work reveal the capability of those hybrid connectionist models for predicting the BHP of multi‐phase flow in vertical wells; these results demonstrate the promising use of connectionist methods in commercial production software in the near future.

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Section: Evolutionary Algorithmsmentioning

confidence: 99%

“…More information about the procedure of applying the ICA as an optimizer of the ANN can be found elsewhere. [59][60][61] Appendix A demonstrates the procedure of how the ICA connects with the ANN and optimizes the ANN parameters.…”

Section: Imperialist Competitive Algorithm (Ica)mentioning

confidence: 99%

Machine learning models to predict bottom hole pressure in multi‐phase flow in vertical oil production wells

Ahmadi

Chen

2019

Can J Chem Eng

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“…Face detection/recognitions datasets are also utilized for skin detection purposes. Razmjooy et al [68] used Bao test bed for training and evaluation. Ding et al [69] used Caltech with their own ground truth to measure the effectiveness of their classifier.…”

Section: Standard Datasets and Evaluation Metricsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

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Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

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“…With this motivation, in this section we use an ICA for clustering. ICA is a new population based metaheuristic which introduced by Atashpaz- Gargari & Lucas (2007) for solving continuous optimization problems and in recent years many researcher modify it to overcome the wide range of problems problem (Afonso, Mariani, & Coelho, 2013;Anjomshoa, Mahani, & Sadeghifard, 2014;Azadeh, Seif, Sheikhalishahi, & Yazdani, 2015;Duan, Xu, Liu, & Shao, 2010;Kaveh & Talatahari, 2010;Mahari & Zare, 2014;Moghaddam, 2013;Moradi & Zandieh, 2013;Razmjooy, Mousavi, & Soleymani, 2013;Shamshirband et al, 2014;Yazdani, Fariborz, Khalili, & Shiripour, 2014;Yazdani & Jolai, 2013). Atashpaz-Gargari & Lucas (2007) proposed and described the algorithm as Like other evolutionary ones, this algorithm starts with an initial population.…”

Section: Imperialist Competitive Algorithm For Clusteringmentioning

confidence: 99%

Strategic Financial Performance Evaluation of the Iranian automotive industry Using Imperialist competitive Algorithm

Kaviani

Yazdani

2015

JFI

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Purpose. This paper evaluates strategic financial performance of 10 Iranian stock exchange listed automotive companies over the period 2005-2009 at the hand of value-creating performance indicators and the Free Cash Flow derived value indicators.Design. To this effect, profiting from the Imperialist Competitive Algorithm (ICA), the understudy companies were assigned to three clusters in terms of debt structure, firm size, and growth opportunitiesFindings. The results, in general, indicate a significant correlation between value-based indicators Economic Value Added (EVA) and True Value Added (TVA) and the FCF-derived indicators Created Value from Free Cash Flow to Firm (CVFCFF) and Created Value from Free Cash Flow to Equity (CVFCFE), and between Market Value Added (MVA) and CVFCFF (one of the two FCF-derived indicators), while, according to the results, there is no significant correlation between the value-driven performance indicators Refined Economic Value Added (REVA) and Equity Economic Value Added (EEVA) and either of the FCF-derived indicators CVFCFF and CVFCFE.Originality. Present Paper, by company clustering in ICA environment, the companies are clustered based on their close similarity in all three criteria and subsequently for examination of each strategic performance indicator were subjected to correlation and Fisher (F) tests.

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A hybrid neural network Imperialist Competitive Algorithm for skin color segmentation

Cited by 62 publications

References 18 publications

Machine learning models to predict bottom hole pressure in multi‐phase flow in vertical oil production wells

Machine learning models to predict bottom hole pressure in multi‐phase flow in vertical oil production wells

A Comprehensive Survey on Human Skin Detection

Strategic Financial Performance Evaluation of the Iranian automotive industry Using Imperialist competitive Algorithm

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