A new Watermarking System based on Discrete Cosine Transform (DCT) in color biometric images.

This paper recommend a biometric color images hiding approach An Watermarking System based on Discrete Cosine Transform (DCT), which is used to protect the security and integrity of transmitted biometric color images. Watermarking is a very important hiding information (audio, video, color image, gray image) technique. It is commonly used on digital objects together with the developing technology in the last few years. One of the common methods used for hiding information on image files is DCT method which used in the frequency domain. In this study, DCT methods in order to embed watermark data into face images, without corrupting their features.

A robust multi-class feature selection strategy based on Rotation Forest Ensemble algorithm for diagnosis of Erythemato-Squamous diseases.

In biomedical studies, accuracy of classification algorithms used in disease diagnosis systems is certainly an important task and the accuracy of system is strictly related to extraction of discriminatory features from data. In this paper, we propose a new multi-class feature selection method based on Rotation Forest meta-learner algorithm. The feature selection performance of this newly proposed ensemble approach is tested on Erythemato-Squamous diseases dataset. The discrimination ability of selected features is evaluated by the use of several machine learning algorithms. In order to evaluate the performance of Rotation Forest Ensemble Feature Selection approach quantitatively, we also used various and widely utilized ensemble algorithms to compare effectiveness of resultant features. The new multi-class or ensemble feature selection algorithm exhibited promising results in eliminating redundant attributes. The Rotation Forest selection based features demonstrated accuracies between 98% and 99% in various classifiers and this is a quite high performance for Erythemato-Squamous Diseases diagnosis.

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems.

Assessing effects of pre-processing mass spectrometry data on classification performance.

Disease prediction through mass spectrometry (MS) data is gaining importance in medical diagnosis. Particularly in cancerous diseases, early prediction is one of the most life saving stages. High dimension and the noisy nature of MS data requires a two-phase study for successful disease prediction; first, MS data must be pre- processed with stages such as baseline correction, normalizing, de-noising and peak detection. Second, a dimension reduction based classifier design is the main objective. Having the data pre-processed, the prediction accuracy of the classifier algorithm becomes the most significant factor in the medical diagnosis phase. As health is the main concern, the accuracy of the classifier is clearly very important. In this study, the effects of the pre- processing stages of MS data on classifier performances are addressed. Three pre-processing stages--baseline correction, normalization and de-noising--are applied to three MS data samples, namely, high-resolution ovarian cancer, low-resolution prostate cancer and a low-resolution ovarian cancer. To measure the effects of the pre-processing stages quantitatively, four diverse classifiers, genetic algorithm wrapped K-nearest neighbor (GA-KNN), principal component analysis-based least discriminant analysis (PCA-LDA), a neural network (NN) and a support vector machine (SVM) are applied to the data sets. Calculated classifier performances have demonstrated the effects of pre-processing stages quantitatively and the importance of pre-processing stages on the prediction accuracy of classifiers. Results of computations have been shown clearly.