Parallel population-based algorithm portfolios::An empirical study

Although many algorithms have been proposed, no single algorithm is better than others on all types of problems. Therefore, the search characteristics of different algorithms that show complementary behavior can be combined through portfolio structures to improve the performance on a wider set of problems. In this work, a portfolio of the Artificial Bee Colony, Differential Evolution and Particle Swarm Optimization algorithms was constructed and the first parallel implementation of the population-based algorithm portfolio was carried out by means of a Message Passing Interface environment. The parallel implementation of an algorithm or a portfolio can be performed by different models such as master-slave, coarse-grained or a hybrid of both, as used in this study. Hence, the efficiency and running time of various parallel implementations with different parameter values and combinations were investigated on benchmark problems. The performance of the parallel portfolio was compared to those of the single constituent algorithms. The results showed that the proposed models reduced the running time and the portfolio delivered a robust performance compared to each constituent algorithm. It is observed that the speedup gained over the sequential counterpart changed significantly depending on the structure of the portfolio. The portfolio is also applied to a training of neural networks which has been used for time series prediction. Result demonstrate that, portfolio is able to produce good prediction accuracy. (C) 2017 Elsevier B.V. All rights reserved

A simple generalized neuro-fuzzy operator for efficient removal of impulse noise from highly corrupted digital images

A generalized neuro-fuzzy (NF) operator for removing impulse noise from highly corrupted digital images is presented. The fundamental building block of the operator is a simple 3-input 1-output NF filter. The operator is constructed by combining a desired number of NF filters with a postprocessor. Each NF filter in the structure evaluates a different pixel neighborhood relation. Hence, the number of NF filters in the structure can be varied to obtain the desired filtering performance. Internal parameters of the NF filters are adaptively optimized by training by using a simple artificial training image that can easily be generated in a computer. Simulation results indicate that the proposed operator outperforms popular conventional as well as state-of-the-art impulse noise removal operators and offers superior performance in removing impulse noise from highly corrupted images while efficiently preserving image details and texture. (c) 2004 Elsevier GmbH. All rights reserved

Efficient removal of impulse noise from highly corrupted digital images by a simple neuro-fuzzy operator

A new neuro-fuzzy operator for removing impulse noise from highly corrupted digital images is presented. The proposed operator is very simple and comprises two identical neuro-fuzzy filters combined with a postprocessor. The internal parameters of the filters are adaptively adjusted by training. Training of the filters is easily accomplished by using a simple computer generated artificial image. The fundamental advantage of the proposed operator over other operators is that it offers superior noise removal performance while at the same time efficiently preserving details and texture in the noisy input image. Experiments prove that the proposed operator may be used for efficient removal of impulse noise from highly corrupted images without distorting the useful information in the image

Performance of LDPC coded image transmission over realistic PLC channels for smart grid applications

WOS: 000339601500058Power line communication (PLC) systems are known as the most rapidly enhancing technologies within the smart grid scope due to offering remarkable cost efficiency by decreasing the transmission device requirement. In this study, image transmission performance of low-density parity-check (LDPC) coded communication systems that is based on a realistic PLC channel model is proposed. Very long-term experimental measurements are carried out to acquire the most accurate PLC channel characteristics from a practical grid. A mathematical PLC channel model is derived and the parameters of this model are optimized by using genetic algorithm. The simulation results obtained over the modeled PLC channel have shown that perfect image transmission can be provided by the help of the LDPC coding. The results of this study also emphasize that the proposed system can be employed for image transmission over power lines in the smart grids for security or other purposes. (C) 2014 Elsevier Ltd. All rights reserved.Scientific and Technological Research Council of Turkey (TUBITAK) [113E425]; Research Fund of Erciyes University [FBD-12-3986]This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 113E425, and by the Research Fund of Erciyes University under Grant FBD-12-3986

Detail-preserving restoration of impulse noise corrupted images by a switching median filter guided by a simple neuro-fuzzy network

A new operator for the restoration of digital images corrupted by impulse noise is presented. The proposed operator is a simple recursive switching median filter guided by a neuro-fuzzy network functioning as an impulse detector. The internal parameters of the neuro-fuzzy impulse detector are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over other operators is that it offers excellent detail- and texture-preservation performance, while effectively removing noise from the input image. Extensive experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image

Classification of high resolution hyperspectral remote sensing data using deep neural networks

The high resolution hyperspectral remote sensing data collected from urban and landscape areas have been extensively studied over the past decades. Recent applications pose an emerging need of analyzing the land cover types based on high resolution hyperspectral remote sensing data originating from remote sensory devices. Toward this goal, we propose a deep neural network (DNN) classifier in this paper. The DNN is constructed by combining a stacked autoencoder with desired numbers of autoencoders and a softmax classifier. Our experimental results based on the hyperspectral remote sensing data demonstrate that the presented DNN classifier can accurately distinguish different land covers including the mixed deciduous broadleaf natural forest and different land covers such as agriculture, roads, buildings, etc. We test the proposed method by using three different benchmark data sets. The proposed method showcases the huge potential of deep neural networks for hyperspectral data analysis

How to Affect the Number of Images on the Success Rate for Detection of Weeds with Deep Learning

The detection of weeds with computer vision without the help of an expert is important for scientific studies and other purposes. The images used for the detection of weeds are recorded under controlled conditions and used in image processing-deep learning methods. In this study, the images of 3-4-leaf (true-leaf) periods of the wild mustard (Sinapis arvensis) plant, which is the critical process for chemical control, were recorded from its natural environment by a drone. The datasets were included 50-100-250-500 and 1 000 raw images and were augmented by image preprocessing methods. Totally 12 different augmentation methods used and datasets were examined for understand how to affects the numbers of images on training-validation performance. YOLOv5 was used as a deep learning method and results of the datasets were evaluated with the Confusion Matrix, Metrics-Precision, and Train-Object Loss. For results of Confusion Matrix where 1 000 images gave the highest results with TP (True Positive) 80% and FP (False Positive) 20%. The TP-FP ratios of 500, 250, 100 and 50 image numbers were respectively; 65%-35%, 43%-57%, 0%-100% and 0%-100%. With 100 and 50 images, the system did not show any TP success. The highest metrics-precision ratio was found 92.52% for 1 000 images set and for 500 and 250 image sets respectively; 88.34% and 79.87%. The 100 and 50 images datasets did not show any metrics-precision ratio. The minimum object loss ratio was 5% at 50th epochs in the 100 images dataset. This dataset was followed by other 50, 250, 500, and 1 000 images respectively; 5.4%, 6.14%, 6.16%, and 8.07%.</jats:p