Global journal of computer science and technology 

About: Global journal of computer science and technology is an academic journal. The journal publishes majorly in the area(s): Cloud computing & Wireless sensor network. It has an ISSN identifier of 0975-4350. It is also open access. Over the lifetime, 1190 publications have been published receiving 5849 citations.

A Study of Encryption Algorithms AES, DES and RSA for Security

Abstract: In recent years network security has become an important issue. Encryption has come up as a solution, and plays an important role in information security system. Many techniques are needed to protect the shared data. The present work focus on cryptography to secure the data while transmitting in the network. Firstly the data which is to be transmitted from sender to receiver in the network must be encrypted using the encryption algorithm in cryptography. Secondly, by using decryption technique the receiver can view the original data. In this paper we implemented three encrypt techniques like AES, DES and RSA algorithms and compared their performance of encrypt techniques based on the analysis of its stimulated time at the time of encryption and decryption. Experiments results are given to analyses the effectiveness of each algorithm.

An Analysis of LSB & DCT based Steganography

Abstract: This paper presents analysis of Least Significant Bit (LSB) based Steganography and Discrete Cosine Transform (DCT) based Steganography. LSB based Steganography embed the text message in least significant bits of digital picture. Least significant bit (LSB) insertion is a common, simple approach to embedding information in a cover file. Unfortunately, it is vulnerable to even a small image manipulation. Converting an image from a format like GIF or BMP, which reconstructs the original message exactly (lossless compression) to a JPEG, which does not (lossy compression), and then back could destroy the information hidden in the LSBs. DCT based Steganography embed the text message in least significant bits of the Discrete Cosine (DC) coefficient of digital picture. When information is hidden inside video, the program hiding the information usually performs the DCT. DCT works by slightly changing each of the images in the video, only to the extent that is not noticeable by the human eye. An implementation of both these methods and their performance analysis has been done in this paper.

Drop Out Feature of Student Data for Academic Performance Using Decision Tree Techniques

Abstract: Students’ academic performance is critical for educational institutions because strategic programs can be planned in improving or maintaining students’ performance during their period of studies in the institutions. The academic performance in this study is measured by their cumulative grade point average (CGPA) upon graduating. This study presents the work of data mining in predicting the drop out feature of students. This study applies decision tree technique to choose the best prediction and analysis. The list of students who are predicted as likely to drop out from college by data mining is then turned over to teachers and management for direct or indirect intervention.

Big Data Analysis: Apache Spark Perspective

Abstract: the boom in the technology has resulted in emergence of new concepts and challenges. Big data is one of those spoke about terms today. Big data is becoming a synonym for competitive advantages in business rivalries. Despite enormous benefits, big data accompanies some serious challenges and when it comes to analyzing of big data, it requires some serious thought. This study explores Big Data terminology and its analysis concepts using sample from Twitter data with the help of one of the most industry trusted real time processing and fault tolerant tool called Apache Storm.

Face Recognition Using Local Binary Patterns (LBP)

Abstract: The face of a human being conveys a lot of information about identity and emotional state of the person. Face recognition is an interesting and challenging problem, and impacts important applications in many areas such as identification for law enforcement, authentication for banking and security system access, and personal identification among others. In our research work mainly consists of three parts, namely face representation, feature extraction and classification. Face representation represents how to model a face and determines the successive algorithms of detection and recognition. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database. In our research work, we empirically evaluate face recognition which considers both shape and texture information to represent face images based on Local Binary Patterns for person-independent face recognition. The face area is first divided into small regions from which Local Binary Patterns (LBP), histograms are extracted and concatenated into a single feature vector. This feature vector forms an efficient representation of the face and is used to measure similarities between images.