Steganography is a mean of hiding information within a more obvious form of
communication. It exploits the use of host data to hide a piece of information in such a way
that it is imperceptible to human observer. The major goals of effective Steganography are
High Embedding Capacity, Imperceptibility and Robustness. This paper introduces a scheme
for hiding secret images that could be as much as 25% of the host image data. The proposed
algorithm uses orthogonal discrete cosine transform for host image. A scaling factor (a) in
frequency domain controls the quality of the stego images. Experimented results of secret
image recovery after applying JPEG coding to the stego-images are included.

Hemorrhoids are one of the most common surgical conditions. The hemorrhoid may cause symptoms that are: bleeding, pain, prolapse, itching, spoilage of feces, and psychologic discomfort. There are many methods for treatment of hemorrhoid like, medical therapy, rubber band ligation, electerocoagulation, stapled hemorrhoidpexy, photocoagulation, sclerothereapy, doppler guided artery ligation, Cryosurgery, and surgery. All methods for treatment of hemorrhoids have advantages, disadvantages, and limitations. Conventional haemorrhoidectomy was the traditional operation for the treatment of hemorrhoids. But recently other modalities of treatment had been used as an alternative operations including CO2 laser haemorrhoidectomy. This work aims to

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

In this paper we investigate the automatic recognition of emotion in text. We propose a new method for emotion recognition based on the PPM (PPM is short for Prediction by Partial Matching) character-based text compression scheme in order to recognize Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method is very effective when compared with traditional word-based text classification methods. We have also found that our method works best if the sizes of text in all classes used for training are similar, and that performance significantly improves with increased data.

Various methods are utilized providing complexity for cryptosystem with the aim to increase the security and avoiding hacker attack. Hybrid cryptosystem is one of these cryptosystems which is used two types of cryptosystems and has many applications in data transmitted. This research, proposed a novel method that used power exponent instead of using the prime number directly and also providing complexity of asymmetric cryptosystems. This method has been applied theoretically in two public systems RSA and EL-Gamal. Power RSA and Power EL-Gamal are modified asymmetric cryptosystems, in which the power number is kept by the sender and the receiver. Moreover, we use group theory to prove that these cryptosystems work properly. Our exten