In this paper, a new secret data hiding and communication is proposed for natural images.in this proposed methodology. The aim of steganography is to hide an information message within harmless cover medium in such way that it's not possible even to observe that secrete message. It doesn't replace cryptography however rather boosts the security using its obscurity options. In the projected its obscurity features. In the proposed algorithm we have used second order differential equation to hide the data which improve the security level of hidden data. In encryption, information is transformed in such a way that it cannot be detect by hacker. But during encryption, message is changed so it become distorted and intruder may suspect about the presence of important information. For this skin tone detection is performed using HSV (Hue, Saturation and Value) color space. Additionally secret data embedding is performed victimization frequency domain approach-DWT (Discrete wavelet Transform), DWT outperforms than DCT (Discrete cosine Transform). Secret information is hidden in one of the high frequency sub-band of DWT by tracing skin pixels therein sub-band. Totally different steps of data hiding are applied by cropping a picture interactively. The output of our technique provides higher results because with the assistance of cropping an increased security than hiding data while not cropping i.e. in whole image, thus cropped region works as a key at decryption aspect. thus with this object destined steganography we have a tendency to track skin tone objects in image with the higher security and satisfactory PSNR (Peak-Signal-to-Noise Ratio).Modern steganography's goal is to stay its more presence undetectable.

Text mining is a specific method to extract knowledge from structured and unstructured data. This extracted knowledge from text mining process can be used for further usage and discovery. This paper presents the method for extraction information from unstructured text data and the importance of Association Rules Mining, specifically for of Korean language (text) and also, NLP (Natural Language Processing) tools are explained. Association Rules Mining (ARM) can also be used for mining association between itemsets from unstructured data with some modifications. Which can then, help for generating statistical thesaurus, to mine grammatical rules and to search large data efficiently. Although various association rules mining techniques have successfully used for market basket analysis but very few has applied on Korean text. A proposed Korean language mining method calculates and extracts meaningful patterns (association rules) between words and presents the hidden knowledge. First it cleans and integrates data, select relevant data then transform into transactional database. Then data mining techniques are used on data source to extract hidden patterns. These patterns are evaluated by specific rules until we get the valid and satisfactory result. We have tested on Korean news corpus and results have shown that it has worked well, and the results were adequate enough to research further.These processes are done until we get the required result. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. We have used same technique to extract meaningful association rules out of Korean text. First step is to pre-process data (getting rid of un-necessary data), then we transformed process data into transactional database. Grouping this transactional database with other hand-built databases we mined some interesting and useful patterns in given Korean text data collection (corpus).Rest of the paper is structured as follows: Section 2 covers related work; Section 3 describes the association rules mining for Korean language. Experiments and results are in Sections 4, and Sections 5 are conclusion and future work.

Abstract. This paper presents an efficient text mining method focusing on extraction and updating of unknown words (unknown foreign words) to improve data classification and POS tags. Our proposed method used simple but efficient techniques, first it converts the data into structured form, using data preprocessing techniques. In this phase data passes through different stages, such as, cleaning, integration and selection of important data, and then it gets organized into databases for further analysis and processing. These database(s) consists of different kinds of dictionaries, our system heavily based on dictionaries. Our proposed methods for discovering and updating foreign unknown word, first discovers the foreign word using morphological analysis with the help of automatically and manually crated dictionaries, then suffix trimming and word segmentation, next our algorithm checks for its different written pattern using dictionaries according to its spelling and synonym word in native language (Korean) and also, updates the POS tags.

Abstract. This paper explains the importance of Association Rules Mining for of Korean language (text). Association rules mining can also be used for mining association rules from textual data with some modifications. Which can then, help for generating statistical thesaurus, to mine grammatical rules and to search large data efficiently. Although various association rules mining techniques have successfully used for market basket analysis but very few has applied on Korean text. A proposed Korean language mining model calculates and extracts meaningful patterns (association rules) between words and presents the hidden knowledge. First it cleans and integrates data and select relevant data then transform into transactional database. Then data mining techniques are used on data source to extract hidden patterns. These patterns are evaluated by specific rules until we get the valid and satisfactory result. We have tested on Korean news corpus and results have shown that it has worked well, and the results were adequate enough to research further.

It is always a dream to recreate the experience of a particular place, the Panorama Virtual Reality has been interpreted as a kind of technology to create virtual environments and the ability to maneuver angle for and select the path of view in a dynamic scene. In this paper we examined an efficient method for Image registration and stitching of captured imaged. Two approaches are studied in this paper. First, dynamic programming is used to spot the ideal key points, match these points to merge adjacent images together, later image blending is used for smooth color transitions. In second approach, FAST and SURF detection are used to find distinct features in the images and nearest neighbor algorithm is used to match corresponding features, estimate homography with matched key points using RANSAC. The paper also covers the automatically choosing (recognizing, comparing) images to stitching method. 요 약