Pattern Recognition: Analysis and Applications by S. Ramakrishnan

By S. Ramakrishnan

Trend popularity persevered to be one of many vital examine fields in machine technology and electric engineering. plenty of new purposes are rising, and for that reason trend research and synthesis develop into major subfields in development acceptance. This booklet is an edited quantity and has six chapters prepared into sections, specifically, trend attractiveness research and development reputation purposes. This publication may be beneficial for graduate scholars, researchers, and practising engineers operating within the box of desktop imaginative and prescient and laptop technology and engineering.

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By S. Ramakrishnan

Trend popularity persevered to be one of many vital examine fields in machine technology and electric engineering. plenty of new purposes are rising, and for that reason trend research and synthesis develop into major subfields in development acceptance. This booklet is an edited quantity and has six chapters prepared into sections, specifically, trend attractiveness research and development reputation purposes. This publication may be beneficial for graduate scholars, researchers, and practising engineers operating within the box of desktop imaginative and prescient and laptop technology and engineering.

Show description

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291–294. [35] Zahran B M, and Kanaan G. Text feature selection using particle swarm optimization algorithm. World Applied Sciences Journal. 2009; 7: pp. 69–74. [36] Jacob M, and Vishwanath N. Multi-objective evolutionary PSO algorithm for matching surgically altered face images. International Journal of Emerging Trends in Engineering and Development. 2014; 2(4): 640–648. [37] Yan Y, Kamath G, and Osadciw L. Feature selection optimized by discrete particle swarm optimization for face recognition.

In Figure 2, the reference objects fn (n = 1, 2, 3, 4) are presented. Figure 2. Objects: (a) f1, (b) f2, (c) f3, (d) f4. For all of them, we calculated the autocorrelation functions fn ⊗ fn. The SP-objects φn were calculated by the iteration scheme (Figure 1a) with the initial distribution of phases ϕ0 = const. In order to find the degree of connection of φn with fn, which determines the degree of suitability of the use of φn instead of fn, we obtained φn for different numbers of iterations N, by gradually increasing N.

Figure 6. The control strategy in hybrid metaheuristics [4]. Figure 7. The order of executing the combined algorithms in hybrid metaheuristics [4]. 4. Designing a hybrid metaheuristic The main motivation behind combining various algorithmic ideas from different metaheuristics is to get better performing system that exploits and includes advantages of the combined algorithms [3, 4]. These advantages should be complementary to each other so that the resulting hybrid metaheuristic can benefit from them [2, 3, 23].

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