A novel methodology to manipulate wave file and create a feature array for each wave file will be introduced, this array can be used later on to recognize the voice file. A set of experiments will be performed in order to prove the uniqueness of the calculated feature array, and that the created feature array for a certain wave file does not match any other feature array for other wave files. The proposed methodology will minimize the efforts of voice recognition by mean of minimizing the time of feature array creation and minimizing the size of the calculated array. General TermsVoice recognitions, artificial intelligence KeywordsWave file, feature array, histogram.

Steganography is the art of hiding secret data within other information (such as wave file) that it cannot be detected, but only by its intended recipient. Embedding secret text in wave file is a difficult process. There are varying techniques for embedding information in wave files. In this research a new simple technique of hiding secret information using wave files were produced, regardless the simplicity this technique it will be accurate and high confident.This paper features a new technique that suggests that the secrete text is encoded through the use position vector into wave file. The position vector (PV) is to be initialized randomly and to be kept confidential between the sender and the receiver. The security level of this technique will be high and it can be increased by encrypting the secrete text or/and encrypt the wave file including the text.

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Retinopathy is an eye disease caused by diabetes, and early detection and treatment can potentially reduce the risk of blindness in diabetic retinopathy sufferers. Using retinal Fundus images, diabetic retinopathy can be diagnosed, recognized, and treated. In the current state of the art, sensitivity and specificity are lacking. However, there are still a number of problems to be solved in state-of-the-art techniques like performance, accuracy, and being able to identify DR disease effectively with greater accuracy. In this paper, we have developed a new approach based on a combination of image processing and artificial intelligence that will meet the performance criteria for the detection of disease-causing diabetes retinopathy in Fundus images. Automatic detection of diabetic retinopathy has been proposed and has been carried out in several stages. The analysis was carried out in MATLAB using software-based simulation, and the results were then compared with those of expert ophthalmologists to verify their accuracy. Different types of diabetic retinopathy are represented in the experimental evaluation, including exudates, micro-aneurysms, and retinal hemorrhages. The detection accuracies shown by the experiments are greater than 98.80 percent.