This paper presents the classification of benign and malignant breast tumor based on fine needle aspiration cytology (FNAC) and probabilistic neural network (PNN). Five hundred and sixty nine sets of cell nuclei characteristics obtained by applying image analysis techniques to microscopic slides of FNAC samples of breast biopsy have been used in this study. These data were obtained from the University of Wisconsin Hospitals, Madison. The dataset consist of thirty features which represent the input layer to the PNN. The PNN will classify the input features into benign and malignant. The sensitivity, specificity and accuracy were found to be equal 97.5%, 92.5% and 96.2% respectively. It can be concluded that PNN gives fast and accurate classification and it works as promising tool for classification of breast cell nuclei.

SECURITY RISKS FROM THE MODERN MAN-IN-THE-MIDDLE ATTACKS

Steganography is the art and science of concealing information in unremarkable cover media so as not to arouse an eavesdropper's suspicion. It is an application under information security field. Being classified under information security, steganography is characterized by having set of measures that rely on strengths and counter-measures (attacks) that are driven by weaknesses and vulnerabilities. Today, computer and network technologies provide easy-to-use communication channels for steganography. The aim of this paper is to propose a modified high-capacity image steganography technique based on curvelet transform with acceptable levels of imperceptibility and distortion in the cover image and high level of overall security.

This paper shows an approach for ECG signal processing based on artificial neural networks ANN and transform domains (discrete wavelet transform DWT and Fourier transform FT). The neural networks NNs are introduced to solve different pattern recognition problems associated with ECG analysis. A Multi-Layer Perceptron Neural Network MLP-NN is used in the present work with Back Propagation BP algorithm to train the proposed network. The operation of a classification system based on DWT and FT was analyzed for diagnosing a set of real patterns (QRS interval of normal and MI disease) that were taken from many patients of MAC-1200 12 channel ECG system from two hospitals in Baghdad. An off-line method was then used for the extraction of ECG signals from ECG images papers by using special image processing techniques. The obtained accuracy for the WT-NN was 90%, whereas that for the FT-NN was 85%. The results showed WT-NN to be better for analyzing the nonstationary signals.

Because of the technical and environmental advantages of many solar energy sources, their use has recently been rapidly rising. The extraction of the unknown parameters in photovoltaic models is one of the key challenges in the modeling and simulation of solar energy sources. To satisfy the behavior of the solar photovoltaic (SPV) cells, the Single-Diode Model (SDM) is recommended as a more dependable modeling method. In this study, we applied the recently introduced meta-heuristic optimization method that inspires the behavior of the swarm of birds called Coot. This Coot Algorithm is used to estimate the unknown parameters of an SPV cell/module at 33 ºC. Simulation results of this study were compared with other nine different previous Optimization Algorithms, which are Moth Flame Optimization (MFO), Dragonfly Algorithm (DA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Ant Lion Optimization (ALO), Harris Hawk Optimization (HHO), Hybrid of Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), Marine Predator Algorithm (MPA), and African Vultures Optimization Algorithm (AVOA). The obtained results of this comparison showed that the Coot algorithm outperformed the previous algorithms in terms of the root mean square error (RMSE) and the degree of convergence between the power versus voltage curve and the current versus voltage curve compared with the measured data. Moreover, the results confirm that the Coot optimization algorithm is favorable in reducing time and improving accuracy.