Adaptive smoothness constraint image multilevel fuzzy enhancement algorithm

For the problems of poor enhancement effect and long time consuming of the traditional algorithm, an adaptive smoothness constraint image multilevel fuzzy enhancement algorithm based on secondary color-to-grayscale conversion is proposed. By using fuzzy set theory and generalized fuzzy set theory, a new linear generalized fuzzy operator transformation is carried out to obtain a new linear generalized fuzzy operator. By using linear generalized membership transformation and inverse transformation, secondary color-to-grayscale conversion of adaptive smoothness constraint image is performed. Combined with generalized fuzzy operator, the region contrast fuzzy enhancement of adaptive smoothness constraint image is realized, and image multilevel fuzzy enhancement is realized. Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively. The time consuming is short, and it has some advantages

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks

Harmony Search-Based Cluster Initialization For Fuzzy C-Means Segmentation Of MR Images.

We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem

FINGERPRINT ENHANCEMENT USING FUZZY LOGIC AND DEEP NEURAL NETWORKS

Department of Computer Science and EngineeringFingerprint recognition analysis is one of the most leading preferred prodigious biometric advancement which has drawn generous consideration in biometrics. In this work, fingerprint Intensification is performed which is defined by Fuzzy logic technique and recognize the matching image with its unique characteristics extracted and classify the features extracted from a fuzzy enhanced image along with three major types of Neural Networks which are Feed Forward Artificial Neural Network, Neural Network, Recurrent Neural Network in order to classify the unique features extracted from a fingerprint image. This work efficiently expresses the results with Fuzzy logic enhancement and Neural Networks classifiers. Its principle goal is to improve the image using Fuzzy and extricate the spurious minutiae detected and classify the different features generated using GLCM and DWT. This work displays a framework of unique finger impression classification based on particular characteristics for extricating different features and three types of Neural Network for classification. Fuzzy technique is used for the fuzzy based image enhancement to urge the clear see of the unique finger impression. Fingerprint Image Intensification is the procedure to enhance the distorted images to encourage the recognizable proof. The motivation behind the work is to enrich the quality of the distorted condition image generated from any fingerprint sensor, as Images can be corrupted due to various conditions and one of the principal issues is the resolution of the fingerprint sensor generating noisy images. High-quality pictures are vital for the exact coordinating of unique finger impression pictures. But unique mark pictures are seldom of idealizing refinement. As it may be corrupted or debased due to varieties of the skin, impression state and condition. In this way, unique finger impression images must be improved before utilized. The idea behind this work fingerprint image intensification process is to improve the quality of distorted and noisy fingerprint images generated from a low-cost fingerprint sensor. Execution of current ???ngerprint acknowledgment frameworks is vigorously in???uenced by the precision of their characteristic???s extraction evaluation. These days, there are more ways to deal with ???ngerprint analysis with worthy outcomes. Issues begin to emerge in low-quality conditions where the dominant part of the conventional strategies dependent on examining the surface of ???ngerprint can't handle this issue so e???ectively as Neural Networks. Fuzzy logic technique is implemented first to remediate the distorted picture and enhance it with the implementation of GLCM and DWT2 algorithm features of an image is extracted, post to which three types of Neural Network Classification is performed to analyze the accuracy of the image generated from the extracted feature parameters and match the test and trained result with the implementation of Neural Networks and classify the outcome results. The three Neural Network used is Artificial Neural Network (ANN), Neural Network (NN), Recurrent Neural Network (RNN). This algorithm works efficiently to identify the fingerprint matching from the predefined trained images from the fuzzy enhanced image generated. Experiments are performed (in MATLAB 2019 student version) to make sure the extraction process should not get the false minutiae and preserve the true extracted features Fuzzy based Image Enhancement method makes sure the feature traits of the image is intensified. Better improvement proves the quality improvement further incrementing the highest accuracy determined in the classification further. This work can be used in a wide area of applications in biometrics as it is a combined work of distorted fingerprints enhancement, false feature removal, true feature extraction, matching of the images for identification purpose and classification using Neural Networks. Experiments show results which are quite promising and gives a direction of the subsequent further analysis in future work.clos

Tissue segmentation using medical image processing chain optimization

Surveyed literature shows many segmentation algorithms using different types of optimization methods. These methods were used to minimize or maximize objective functions of entropy, similarity, clustering, contour, or thresholding. These specially developed functions target specific feature or step in the presented segmentation algorithms. To the best of our knowledge, this thesis is the first research work that uses an optimizer to build and optimize parameters of a full sequence of image processing chain. This thesis presents a universal algorithm that uses three images and their corresponding gold images to train the framework. The optimization algorithm explores the search space for the best sequence of the image processing chain to segment the targeted feature. Experiments indicate that using differential evolution to build Image processing chain (IPC) out of forty-five algorithms increases the segmentation performance of basic thresholding algorithms ranging from 2% to 78%

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Evolving machine learning and deep learning models using evolutionary algorithms

Despite the great success in data mining, machine learning and deep learning models are yet subject to material obstacles when tackling real-life challenges, such as feature selection, initialization sensitivity, as well as hyperparameter optimization. The prevalence of these obstacles has severely constrained conventional machine learning and deep learning methods from fulfilling their potentials. In this research, three evolving machine learning and one evolving deep learning models are proposed to eliminate above bottlenecks, i.e. improving model initialization, enhancing feature representation, as well as optimizing model configuration, respectively, through hybridization between the advanced evolutionary algorithms and the conventional ML and DL methods. Specifically, two Firefly Algorithm based evolutionary clustering models are proposed to optimize cluster centroids in K-means and overcome initialization sensitivity as well as local stagnation. Secondly, a Particle Swarm Optimization based evolving feature selection model is developed for automatic identification of the most effective feature subset and reduction of feature dimensionality for tackling classification problems. Lastly, a Grey Wolf Optimizer based evolving Convolutional Neural Network-Long Short-Term Memory method is devised for automatic generation of the optimal topological and learning configurations for Convolutional Neural Network-Long Short-Term Memory networks to undertake multivariate time series prediction problems. Moreover, a variety of tailored search strategies are proposed to eliminate the intrinsic limitations embedded in the search mechanisms of the three employed evolutionary algorithms, i.e. the dictation of the global best signal in Particle Swarm Optimization, the constraint of the diagonal movement in Firefly Algorithm, as well as the acute contraction of search territory in Grey Wolf Optimizer, respectively. The remedy strategies include the diversification of guiding signals, the adaptive nonlinear search parameters, the hybrid position updating mechanisms, as well as the enhancement of population leaders. As such, the enhanced Particle Swarm Optimization, Firefly Algorithm, and Grey Wolf Optimizer variants are more likely to attain global optimality on complex search landscapes embedded in data mining problems, owing to the elevated search diversity as well as the achievement of advanced trade-offs between exploration and exploitation