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Digital image watermarking is a technique adopted to get rid of the increasing piracies in digital images. Computerized information can be effectively duplicated, altered and falsifications be made by anybody having a PC. Most inclined to such vindictive assaults are the watermarked pictures distributed in the Internet. Advanced Watermarking can be utilized as a device for finding unapproved information reuse and furthermore for copyright security. In the existing method, texturization dependant image watermarking methodology is performed which involves the embedding and extraction of a logo image to and from an original image respectively. After finding out the texture regions of host image, the logo image is embedded into the identified texture regions by Discrete Wavelet Transform. Before embedding, according to the textual characteristics of the host image analyzed, texturization of a logo is done by using Arnold transform and a rotation. It is effective for attaining a similar texture for both logo image and host image. Later the logo image is extracted back. Discrete Wavelet Transform results in degradation of quality and robustness of watermarked image. Also it is not a difficult task for an attacker to compromise the Arnold transform and rotation performed. In this work, Lifting Wavelet Transform technique is used instead of the Discrete Wavelet Transform as it overcome the above mentioned drawback. In addition, Arnold transform and rotation is replaced with circular shift method for enhancing security.

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Data security is an important issue in the age of big data. The existing data security approaches should be improved to cover inactive databases, i.e. the databases with existing information only, and suit the requirements of big data mining. Therefore, this paper proposes framework to protect the data anonymity in big data environment. The framework is mainly implemented in three steps: mining the association rules, computing the confidence of each rule, and determining the sensitivity of each rule using fuzzy logic. To process massive data, the authors paid attention to enhance the parallelism and scalability of the proposed framework. The proposed framework was verified through experiments on two datasets. Judging by metrics like lost, ghost and false rules, it is confirmed that our framework can protect the association rules efficiently in the big data environment.

An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper is aimed at building an Artificial Intelligence (AI) framework that has dual goal of “monitoring and regulating power usage” and facilitating autonomous driving with technology-driven and real time knowledge required. A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. To facilitate real time knowledge discovery from given scenarios, both edge and cloud resources are appropriately exploited to benefit the vehicle as driving safety is given paramount importance. There is power management module where modular Recurrent Neural Network is used. Another module known as speed control is used to have real time control over the speed of the vehicle. The usage of AI framework makes the electronic and autonomous vehicles realize unprecedented possibilities in power management and safe autonomous driving. Key words: Artificial Intelligence Autonomous Driving Recurrent Neural Network Transfer Learning

Computer Aided Software Engineering (CASE) has been growing faster in software industry. As part of it Model Driven Engineering (MDE) has been around for focusing on models and transforming them from one model to other model. The tool named Extensible Real Time Software Design Inconsistency Checker (XRTSDIC) proposed by us in previous paper supports UML modelling, design inconsistency checking and model transformation from UML to ERD to SQL. In this paper it is extended further to facilitate model transformation from PIM (UML class diagram) to PSM (source code). We proposed an algorithm and defined model transformation and consistency rules. The extended framework has provision for class relationship analysis and support for choosing different object oriented languages like C#, C++ and Java. While transforming the model, we used the concept of dialects. Dialect is the class with transformation functionality which has ability to adapt to syntax and semantics of chosen language. Different dialects are made available for different languages. Thus the proposed system is capable of transforming models and the prototype application we built and extended demonstrates the proof of concept. The empirical results revealed that the model transformation is consistent and accurate.Keywords: Model Driven Engineering; Model Driven Engineering; Platform Independent Model (PIM); Platform Specific Model (PSM).