Informatics Publishing Limited

K. Kiran Kumar ¹, K. Indira Priyadarshini ²

Affiliations
1 Department of Information Technology, Bapatla Engineering College, Bapatla, AndhraPradesh, IN
2 Department of Computer Science & Engineering, Veltech Multitech Dr. Rangarajan Dr. Shakuntala Engineering College, Chennai, Tamilnadu, IN

Abstract

This project is a solution for improving the security of web forms from web bots (robots). The greatest danger from bots is the unmonitored used of forms. Mostly these are forms to enter some data (user registration, giving comments). For the protection of such forms web programmers apply several techniques. Each of these techniques is based on a single fact: humans can read, the PC cannot. There are different types of form protection like the random numbers method, the contact form method, the security code method and the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) method. This project delivers the best result than the existing system. The emphasis is on the CAPTCHA method that is most often used method. The main goal of CAPTCHA is preventing automated software or bots from performing certain actions on a site. Sure, the automated code may access the site, but we don‟t want it posting comments (spam), creating user accounts, or placing orders. I am implementing CAPTCHA security method in java web programming. I am implementing different methods of CAPTCHA like NORMAL CAPTCHA IMAGE, MULTIPLE CAPTCHA IMAGE and AUDIO CAPTCHA IMAGE. To increase the security to the extent possible I am using DES algorithm in order to encrypt the plain text.

Keywords

CAPTCHA, Encryption, Security, Web Bots.

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M. Suneel ¹, K. Kiran Kumar ¹, P. Uday Bhaskar ²

Affiliations
1 Department of ECE, Bapatla Engineering College, IN
2 Department of ECE, Nova College of Engineering, IN

Abstract

The theory of fuzzy sets has been used to deal with image enhancement problems for degraded images in which the image edges and some fine details of image are uncertain and inaccurate. For those kinds of images, to some extent, the good enhancement effect can be obtained using the fuzzy sets-based image enhancement method instead of the traditional image enhancement approaches. This proposed fuzzy image enhancement method is also gives good results of degraded images with less gray levels and low contrasts. This proposed fuzzy color image enhancement method gives better results than the traditional image enhancement technique like histogram equalization. This method is an extension of the generalized fuzzy image enhancement method. By using the similarity methods we can compare the original images with the proposed fuzzy image enhancement and histogram equalization methods, and then the proposed method based on fuzzy set theory gives best results.

Keywords

Image Enhancement, Fuzzy Set, Fuzzy Enhancement, Degraded Image.

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K. Srinivasa Rao ¹, K. Kiran Kumar ¹, P. Srinivasa Rao ¹

Affiliations
1 Department of Information Technology, Bapatla Engineering College, Bapatla, Andhra Pradesh, IN

Abstract

Cluster analysis is one of the major data analysis methods for clustering the large data sets. The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on some similarity. K-means is one of the most popular data partitioning algorithms that solve the well known clustering problem. Performance of the k-means clustering greatly depends upon the correctness of the initial centroids. Typically the initial centroids for the original k-means clustering are determined randomly. So, the clustering result may reach the local optimal solutions, not the global optimum. Several improvements have been proposed to improve the performance of k-means algorithm. This paper proposes an Efficient k-means algorithm for finding the better initial centroids and an efficient way for assigning data points to appropriate clusters. The proposed algorithm is tested with six bench mark datasets, which are taken from UCI machine learning data repository and found that the proposed algorithm gives better result than the existing.

Keywords

Clustering, Data Partitioning, Data Mining, Heuristic K-Means, K-Means Algorithm.

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K. Kiran Kumar ¹, V. A. P. Sarma ¹, N. Balashanmugam ¹, K. B. Vinaya Kumar ², G. M. Hegde ³, K. Rajanna ², M. M. Nayak ³, N. S. Dinesh ⁴

Affiliations
1 Nano Manufacturing Technology Centre, CMTI, Bangalore, IN
2 Department of Instrumentation and Applied Physics, IISc, Bangalore, IN
3 Centre for Nano Science and Engineering, IISc, Bangalore, IN
4 Department of Electronic Systems Engineering, IISc, Bangalore, IN

Abstract

In this work, we present the fabrication of hollow steel microneedles for transdermal drug delivery. Hollow steel microneedles are fabricated by laser ablation technique using femto second laser micromachining. A 4x3 array of rectangular microneedles with circular holes are fabricated. The processed hollow steel microneedles are 700 μm in height with 60 μm and 150 μm inner and outer diameters respectively.

Keywords

Micro-Needles, Steel, Femto Second Laser.

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K. R. R. Mohan Rao ¹, K. Kiran Kumar ¹, P. Subba Rao ¹

Affiliations
1 Department of Computer Science & Engineering, Chalapathi Institute of Engineering and Technology, Guntur-522034, IN

Abstract

In the fund world stock exchanging is one of the most significant exercises. Securities exchange expectation is a demonstration of attempting to decide the future estimation of a stock other money related instrument exchanged on a monetary trade. This paper clarifies the expectation of a stock utilizing Machine Learning[6]. The specialized and central or the time arrangement examination is utilized by the a large portion of the stockbrokers while making the stock forecasts. The programming language is utilized to anticipate the securities exchange utilizing AI is Python. Right now propose a Machine Learning[10] (ML) approach that will be prepared from the accessible stocks information and increase insight and afterward utilizes the gained information for a precise forecast. Right now study utilizes an AI system called Support Vector Machine (SVM)[1] to anticipate stock costs for the enormous and little capitalizations and in the three distinct markets, utilizing costs with both every day and regularly updated frequencies.

Keywords

Machine Learning, Predictions, Stock Market, Support Vector Machine.

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