ENHANCING THE MEDICAL IMAGES QUALITY USING ADAPTIVE GENETIC ALGORITHM

Background/Objective: The objective is to minimize ambiguous test cases by using Adaptive Genetic Algorithm (AGA). Problem description/Proposed Method: Here we are concerned with the problem of randomly generated test cases. It can contain some ambiguous test cases, which lead to problems at the organizational level. A random algorithm will generate random test cases each time it is run, and it will have resemblance each time. Another problem related to random algorithms is that running them can take a lot of time. To minimize these issues we propose a new technique, which will reduce the given drawbacks. We proposed an Adaptive Genetic Algorithm (AGA), which will provide legal input in each case where it applied. Thus the problem of ambiguity will decrease. Results/Findings: In this research, the near optimal inputs will be generated based on the Adaptive Genetic Algorithm (AGA), which will reduce the illegal inputs. The fault detection rate is used as the fitness function in AGA. To remove the fault proneness, our AGA uses the coverage metrics of the test cases. Conclusion: Random algorithms will generate low cost test cases in large number but problem is that it will consists ambiguous test cases ,to reduce these here we are using AGA which will further reduce test cases by moderating the illegal inputs.

In recent years most of researcher’s has done tremendous work in the field of medical image applications such as Magnetic Resonance Imaging (MRI), Ultra Sound, CT scan but still there are many research and experiments in medical imaging field and diagnosing of human health by Health Care Institutes. There is a growing interest for medical imaging de-noising as a hot area of research and also imaging equipment as a device. It is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and Transmission process. This results in blurry image visible in low contrast. Wavelet transforms have effective method to separate the noise from the original medical image by using threshold techniques without affecting the important data of an image. Wavelet transform enables us to use the forward wavelet transform to represent sub-band of the original image in decomposition process then reconstructing this sub band coefficients to original image using inverse wavelet transform. In this work, the quality of medical image has been evaluated using filter assessment parameters like Variance, standard deviation, the squared difference error between original medical image & de-noised image (MSE) and the ratio between original image & noisy image. From numerical results, we can see that the algorithm is efficient de-noising of noisy medical image. When, investigating with Baye’s threshold techniques it achieved the Best value of peak signal to noise ratio (PSNR). For best medical image de-noising, the wavelet based de-noising algorithm has been investigated and results of Baye’s techniques and hard & soft threshold methods have been compared.

The primary objective of this study is to solve the Vehicle Routing Problem with Stochastic Demands (VRPSD) under restocking policy by using adaptive Genetic Algorithm (GA). The problem of VRPSD is one of the most important and studied combinatorial optimization problems, which finds its application on wide ranges of logistics and transportation area. It is a variant of a Vehicle Routing Problem (VRP). The algorithms for stochastic VRP are considerably more intricate than deterministic VRP and very time consuming. This has led us to explore the used of metaheuristics focusing on the permutation-based GA. The GA is enhanced by automatically adapting the mutation probability to capture dynamic changing in population. The GA becomes a more effective optimizer where the adaptive schemes are depend on population diversity measure. The proposed algorithm is compared with standard GA on a set of randomly generated problems following some discrete probability distributions inspired by real case of VRPSD in solid waste collection in Malaysia. The performances of several types of adaptive mutation probability were also investigated. Experimental results show performance enhancements when adaptive GA is used.

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behavior by making use in a direct fashion of the knowledge pertaining to the search landscape. The paper introduces a novel adaptive Genetic Algorithm where the exploration/exploitation is directly controlled during evolution using a Bayesian decision process. Test cases are analyzed as to how parameters affect the search behavior of the algorithm.

A flexible tolerance genetic algorithm for optimal problems with nonlinear equality constraints

Contrast enhancement methods based on reversible data hiding (RDHCE) can be used for contrast enhancement of medical images, which is a hot research topic in recent years. However, the region of interest (ROI) of medical images cannot be accurately segmented using the current RDHCE algorithms and histogram pixels clustering in medical images results in incorrect localization of longer intervals, which affects the contrast enhancement effect of images. In this paper, the Unet3+ network model is used, which makes the segmented ROI region and ROI histogram clearer and more accurate than those obtained by the traditional segmentation methods and the algorithm integrates a larger embedding capacity and a better visual quality of the image. It adaptively determines and stretches the interval of the ROI greyscale histogram and at the same time enlarges the embedding capacity of the ROI to enhance the contrast of the image. The proposed algorithm improves the visual quality of medical images by 20% and enhances ROI embedding capacity by 25% compared to existing methods.

PurposeThe Hermite collocation method of discretization can be used to determine highly accurate solutions to the steady‐state one‐dimensional convection‐diffusion equation (which can be used to model the transport of contaminants dissolved in groundwater). This accuracy is dependent upon sufficient refinement of the finite‐element mesh as well as applying upstream or downstream weighting to the convective term through the determination of collocation locations which meet specified constraints. Owing to an increase in computational intensity of the application of the method of collocation associated with increases in the mesh refinement, minimal mesh refinement is sought. Very often this optimization problem is the one where the feasible region is not connected and as such requires a specialized optimization search technique. This paper aims to focus on this method.Design/methodology/approachAn original hybrid method that utilizes a specialized adaptive genetic algorithm followed by a hill‐climbing approach is used to search for the optimal mesh refinement for a number of models differentiated by their velocity fields. The adaptive genetic algorithm is used to determine a mesh refinement that is close to a locally optimal mesh refinement. Following the adaptive genetic algorithm, a hill‐climbing approach is used to determine a local optimal feasible mesh refinement.FindingsIn all cases the optimal mesh refinements determined with this hybrid method are equally optimal to, or a significant improvement over, mesh refinements determined through direct search methods.Research limitationsFurther extensions of this work could include the application of the mesh refinement technique presented in this paper to non‐steady‐state problems with time‐dependent coefficients with multi‐dimensional velocity fields.Originality/valueThe present work applies an original hybrid optimization technique to obtain highly accurate solutions using the method of Hermite collocation with minimal mesh refinement.

In this paper we propose an adaptive genetic algorithm (AGA) to optimize wind farm layout in order to achieve a highest aggregate wind power conversion efficiency with the presence of wake effect impacts. Instead of using random “crossover” in each iteration as in conventional GA, our proposed adaptive algorithm introduces novel relocation of “bad” turbines so that turbines experiencing worst wake effect impacts in a layout will be relocated to some new and more efficient positions. Therefore each iteration of the AGA can more effectively improve the aggregate wind power conversion efficiency and greatly accelerate the algorithm convergence. We experiment the proposed AGA and compare its performance with conventional GA based on a number of scenarios such as multi-directional wind distribution, offshore and inland wind distribution, with sparse and crowded farm settings. Numerical results verify the effectiveness of the proposed AGA algorithm which is able to locate an optimal layout at a much faster convergence speed and achieve a higher aggregate wind power output from a farm.

GPU acceleration of liver enhancement for tumor segmentation

A new approach based on masking technique for contrast enhancement of medical image is presented due to the low contrast characteristics. In medical image, preserving mean brightness, average information and noise factor reduction are essential to make input image more appealing visually. The proposed method incorporates spatial and frequency domain techniques to enhance the contrast of the medical image. The mask is formulated effectively between reconstructed approximation coefficients and inverse singular value decomposition (ISVD) to obtain contrast residual. Discrete wavelet transformation (DWT) and singular value decomposition (SVD) have used to decompose the input image. At last maximum contrast enhancement achieved by adding mask with the image obtained through intensity exposure histogram equalization (IEHE). The proposed approach is tested for medical images by comparing its peak signal to noise Ratio (PSNR), absolute mean brightness error (AMBE) and entropy with some existing methods and also measured in terms of visual quality.

Medical image enhancement is an effective tool to improve visual quality of digital medical images. In this paper, we propose a new unsharp masking scheme for medical image enhancement. It embeds the PLIP multiplication into the unsharp masking framework. Experimental results demonstrate that the proposed method can effectively enhance the overall contrast and edges of medical images while suppressing background noise.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Flexible manipulators have received wide attention because they are more realistic than their rigid counterparts in many practical conditions. The control of the motion and vibration of an elastic model has inspired many studies in the past decades. In this paper, a finite element model is developed and used for the modeling of the single-link flexible arm. A new adaptive genetic algorithm (AGA) is proposed to optimize the feedback gains of a proportional derivative (PD) controller for the control of the motion and vibration of the flexible arm. The proposed technique is based on a new adaptive mutation operator to adjust the probability of mutation of each chromosome based on the average fitness value at each generation. The results are compared with those obtained by applying simple GA and a direct evaluation of the objective function in the domain. It has been shown that the new developed adaptive mechanism is faster in convergence and the obtained solutions are of higher fitness values. Compared analysis shows the effectiveness of the proposed adaptive GA over traditional genetic algorithms that have the defects of premature convergence and stagnation when applied in optimization problems. Three different objective functions based on minimizing the error function are tested and compared. Multi-objective error fitness function combined with AGA has given the best system response. Simulation results demonstrate the effectiveness of the proposed technique which encourages for further research into the application of the technique in controlling multiple link flexible robots for further applications.

The linear motor feeding system is a typical electromechanical coupling system. Conventional characteristic analyses of electromechanical coupling often overlook the influence of flexible deformation in critical components of the linear motor feeding system. Moreover, when employing genetic algorithms to optimize servo system PID control parameters, slow convergence, nonconvergence, or premature convergence problems may arise. To address these issues, this paper proposes a new performance optimization method for a linear motor feeding system. The method uses a combination of “multi-body theory + finite element” to accurately account for the flexible deformation of critical components of the feeding system, establishes a rigid–flexible electromechanical coupling model of the linear motor feeding system, and optimizes the PID parameters of the established model with an improved adaptive genetic algorithm. Simulation results demonstrate that, when utilizing an adaptive genetic algorithm to optimize the rigid–flexible electromechanical coupling model and a control system model that disregards flexible body deformation, the system achieves stability in 0.02 s and 0.027 s with overshoots of 13% and 27%, respectively. These outcomes confirm the accuracy and importance of considering flexible body deformation in the optimization performance of a linear motor feeding system. At the same time, the time required to reach the steady state of the rigid–flexible electromechanical coupling model optimized by the adaptive genetic algorithm is shortened from 0.035 s to 0.02 s. The sinusoidal signal response curve of the optimized system does not exhibit any peak overshoot compared with that of the nonoptimized system, and the response speed is also faster. These results demonstrate the effectiveness of the rigid–flexible electromechanical coupling model optimized by the nonlinear adaptive genetic algorithm. The displacement response curves of the linear motor feeding system under different workbench loads are obtained through experiments and compared with those obtained from simulations to verify the established model and the correctness of the proposed method.

Distribution is the challenging and interesting problem to be solved. Distribution problems have many facets to be resolved because it is too complex problems such as limited multi-level with one product, one-level and multi-product even desirable in terms of cost also has several different versions. In this study is proposed using an adaptive genetic algorithm that proved able to acquire efficient and promising result than the classical genetic algorithm. As the study and the extension of the previous study, this study applies adaptive genetic algorithm considering the problems of multi-level distribution and combination of various products. This study considers also the fixed cost and variable cost for each product for each level distributor. By using the adaptive genetic algorithm, the complexity of multi-level and multi-product distribution problems can be solved. Based on the cost, the adaptive genetic algorithm produces the lowest and surprising result compared to the existing algorithm