Manoj Kumar Kandala’s research while affiliated with Sri Venkateswara College of Engineering and other places

Handwriting is a skill developed by humans from the very early stage in the order to represent his/her thoughts visually using letters and making meaningful words and sentences. Every person improves this skill by practicing and develops his/her own style of writing. Because of the distinctiveness of handwriting style, it is frequently used as a measure to identify a forgery. Even though the applications of synthesizing of handwriting is less, this problem can be generalized and can be functionally applied to other more practical problems. Synthesizing the handwriting is a quite complicated task to achieve. Deep recurrent neural networks specifically Deep LSTM cells can be used along with a Mixture Density Network to generate artificial handwriting data. But using this model we can only generate random handwriting styles which are being hallucinated by the model. Mimicking a specific handwriting style is not so efficient with this model. Mimicking or imitating a specific handwriting style can have an extensive variety of applications like generating personalized handwritten documents, editing a handwritten document by using the similar handwriting style and also it is extended to compare handwriting styles to identify a forgery. A web prototype is developed along with the model to test the results where the user can enter the text input and select handwriting style to be used. And the application will return the handwritten document containing input text mimicking the selected handwriting style. The application will also provide a way to fine-tune the handwriting styles by changing few parameters.

In all the modern browsers, maintaining user’s web history is one of the primary tasks. Browser history will help to summarize the activity of the user during a certain period. However, current browser history is not so efficient to visualize in a user-friendly manner and also doesn’t provide enough information for personalized recommendations. One of the key reason is that browsers never maintain any inter-connection between history items. Overall history is maintained in a linear fashion with no information about how the user reached to a particular state. Another issue is that it is not possible to calculate how much time the user spent on any particular website using current history system. This paper provides a conceptual idea of solving these issues by providing a framework that solves this issue by introducing linked data and also describes how this can benefit in improving user experience and quality of recommendations.

This book deals about the privacy concerns facing by the users in web search engines. Now-a-Days, Privacy is a major constraint in internet. Different Techniques for preserving the user's privacy is demonstrated and briefly explained in this book. Research methodologies related to enhancing privacy are introduced. Those concepts are very good reference for researchers and students who are working on search engines.

Cloud computing is a thriving technology that relies on sharing computing resources rather than having a local server in order to handle the application. Cloud usually uses the phrase of " pay as you go " to describe its functionality. The security issue is the major concern in data interchange since the entire user cannot pertain to access all the data from the server. In this paper, Cost Effective and dynamic Security Provision strategy of Staging Data items in the cloud has been proposed. The concept of utilizing staging data items rather than direct cloud access is taken as the core idea to reduce the cost of utilization as well as the concept of Anonymization is discussed that reduces the size of data utilization and behaves as the security provision strategy according to the severity of the data to per users. The effective check taken in the projected approach is a flow time monitoring to provide the cost reduction and security enhancing scheme in a dynamic way.

Personalized web search (PWS) has incontestable its effectiveness in rising the standard of assorted search services on the web. However, evidences show that users' reluctance to disclose their non-public data throughout search has become a significant barrier for the wide proliferation of PWS. Having a tendency to study privacy protection in PWS applications that model user preferences as hierarchic user profiles and also tendency to propose a PWS framework referred to as UPS which will adaptively generalize profiles by queries whereas respecting user specified privacy needs. Runtime generalization aims at gaining a balance between 2 prophetical metrics that appraise the utility of personalization and also the privacy risk of exposing the generalized profile. Having a tendency to gift 2 greedy algorithms, specifically Greedy IL for runtime generalization.It will also conjointly give a web prediction mechanism for deciding whether or not personalizing a question is helpful.MD5 Algorithm is used for generating a unique identity & it is stored in the records instead of user sensitive data. Privacy can be obtained without revealing user profile.