In past decades, the structured and consistent data analysis has seen huge success. It is a challenging task to analyse the multimedia data which is in unstructured format. Here the big data defines the huge volume of data that can be processed in distributed format. The big data can be analysed by using the hadoop tool which contains the Hadoop Distributed File System (HDFS) storage space and inbuilt several components are there. Hadoop manages the distributed data which is placed in the form of cluster analysis of data itself. In this, it shows the working of Sqoop and Hive in hadoop. Sqoop (SQL-to-Hadoop) is one of the Hadoop component that is designed to efficiently imports the huge data from traditional database to HDFS and vice versa. Hive is an open source software for managing large data files that is stored in HDFS. To show the working, here we are taking the application Instagram which is a most popular social media. In this analyze the data that is generated from Instagram that can be mined and utilized by using Sqoop and Hive. By this, prove that sqoop and hive can give results efficiently. This paper gives the details of sqoop and hive working in hadoop.

No abstract

No abstract

Machine learning has been leveraged in the digital era, resulting in an increasing desire for computers to perform human-like tasks. Text classification is rapidly becoming one of the most significant applications of machine learning. However, the manual reading and classification of books based on genre requires substantial time and effort. As a result, machine learning methods are critical for enabling automated classification. In this study, a book description-based text classification framework was proposed, utilizing a wealth of information about book contents. The automated classification of books was achieved through the implementation of supervised machine learning. A variety of classifiers were employed, including Multinomial Naive Bayes, Gradient Boosting, and Random Forest, to categorize book genres. According to the results, the Naive Bayes classifier outperformed the other two techniques in classification accuracy, while comparable performance was achieved with Gradient Boosting and Random Forest. The comprehensive machine learning framework efficiently and accurately categorized books by extracting information from book descriptions. The proposed methodology has the potential to facilitate large-scale book classification for both academic and industrial purposes. Overall, this study provided an automated solution to relieve the burden of manual classification while achieving high accuracy.

Due to swift improvement of information innovation in recent times, providing security to data has become major concern and threat to Information Privacy has become inevitable. Data Hiding technology is an efficient way to solve the problems of data leakage and loss of information. Data hiding called steganography is a security method to provide security to secret data which is transferred from sender to receiver from harmful attacks. Steganography is an interaction of concealing a mysterious message inside a cover object which is not secret. There are many cover media like images, audio, video, text files etc. There are many ways to approach steganography like spatial domain, transformation domain, masking and filtering. This technique is helpful because the human eye is quite insensitive to the minute changes that help the embedded data stay safe and secure. The main motive of steganography is to get high stego image quality, low computational complexity, more embedding capability, visually unnoticeable, invisibility, and improved security. A capable steganographic technique must be resistant to any steganalysis approach the secret data is prone to. In this proposed system, implement the GUI implementation image steganography in spatial domain using Least Significant Bit (LSB) where the modified high capacity cover image undergoes the Discrete Wavelet Transformation (DWT) and propose an Advanced Encryption Standard (AES) secret key stego system such that the data is secure. The distortion between the two images in identified with the help of MSE and Histogram analysis.