Grayscale Image Authentication using Neural Hashing

Many different approaches for neural network based hash functions have been proposed. Statistical analysis must correlate security of them. This paper proposes novel neural hashing approach for gray scale image authentication. The suggested system is rapid, robust, useful and secure. Proposed hash function generates hash values using neural network one-way property and non-linear techniques. As a result security and performance analysis are performed and satisfying results are achieved. These features are dominant reasons for preferring against traditional ones.Comment: international journal of Natural and Engineering Sciences (NESciences.com) : Image Authentication, Cryptology, Hash Function, Statistical and Security Analysi

Patient Specific Congestive Heart Failure Detection From Raw ECG signal

In this study; in order to diagnose congestive heart failure (CHF) patients, non-linear second-order difference plot (SODP) obtained from raw 256 Hz sampled frequency and windowed record with different time of ECG records are used. All of the data rows are labelled with their belongings to classify much more realistically. SODPs are divided into different radius of quadrant regions and numbers of the points fall in the quadrants are computed in order to extract feature vectors. Fisher's linear discriminant, Naive Bayes, Radial basis function, and artificial neural network are used as classifier. The results are considered in two step validation methods as general k-fold cross-validation and patient based cross-validation. As a result, it is shown that using neural network classifier with features obtained from SODP, the constructed system could distinguish normal and CHF patients with 100% accuracy rate. KeywordsComment: Congestive heart failure, ECG, Second-Order Difference Plot, classification, patient based cross-validatio

PERFORMANCE COMPARISON OF DIFFERENT SIZED REGIONS OF INTEREST ON FISH CLASSIFICATION

In this study, different sized regions of interest were obtained from fish images and these were used for fish species classification. A previously proposed region of interest obtaining method was upgraded in order to acquire wider regions of interest. Depending on general accuracies of classification performances, comparison between these regions of interest was made. According to comparison results the effects of the different sized regions of interest were discussed for classification purposes of fish species. This study was performed by using a database which consists of 1321 fish images. These fish images include fish samples from 16 fish families and 35 fish species. All images were colored in RGB color space. But two different feature sets were extracted for fishes by examining images both in RGB and HSV color spaces. Feature extraction was performed by using a color based method. For each color space, seven statistical features were extracted from each component of the color space. Two feature sets were acquired for each fish sample by combining the extracted statistical features according to color spaces. The obtained feature sets from RGB and HSV color spaces were used separately for classification purposes. Classification was performed according to families and species by using Nearest Neighbor algorithm as classifier. According to classification results, the best performances on general accuracies were achieved as 93.5% and 91% for fish families and species classification respectively

Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis

Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using at least two hidden layers on the proposed structures by fully connected layers depending on of the artificial neural networks. The optimization of the predefined classification parameters for the supervised models eases reaching the global optimality with exact zero training error. The autoencoder (AE) models are the highly generalized ways of the unsupervised stages for the DL to define the output weights of the hidden neurons with various representations. As alternatively to the conventional Extreme Learning Machines (ELM) AE, Hessenberg decomposition-based ELM autoencoder (HessELM-AE) is a novel kernel to generate different presentations of the input data within the intended sizes of the models. The aim of the study is analyzing the performance of the novel Deep AE kernel for clinical availability on electroencephalogram (EEG) with stroke patients. The slow cortical potentials (SCP) training in stroke patients during eight neurofeedback sessions were analyzed using Hilbert-Huang Transform. The statistical features of different frequency modulations were fed into the Deep ELM model for generative AE kernels. The novel Deep ELM-AE kernels have discriminated the brain activity with high classification performances for positivity and negativity tasks in stroke patients

Classification of Serranidae Species Using Color Based Statistical Features

In this study 6 species (Epinephelus aeneus, Epinephelus caninus, Epinephelus costae, Epinephelus marginatus, Hyporthodus haifensis, Mycteroperca rubra) of Serranidae family were classified by using a color based feature extraction method. A database which consists of 112 fish images was used in this study. In each image, a fish was located on a white background floor with the same position and the images were taken from different distances. A combination of manual processes and automatic algorithms were applied on images until obtaining colored fish sample images with a black background. Since the presented color based feature extraction method avoids including background, these images were processed by using an automatic algorithm in order to obtain a solid texture image from the fish and extract features. The obtained solid texture image was in HSV color space and used due to extract meaningful information about fish sample. Each of the hue, saturation and value components of the HSV color space was used separately in order to extract 7 statistical features. Hence, totally 21 features were extracted for each fish sample. The extracted features were used within Nearest Neighbor algorithm and 112 fish samples from 6 species were classified with an overall accuracy achievement of 86%