A. K. Sharma

A. K. Sharma, K. R. Tyada

We propose a novel fractal based interpolation scheme termed Rational Cubic Trigonometric Zipper Fractal Interpolation Functions (RCTZFIFs) designed to model and preserve the inherent geometric property, positivity, in given datasets. The method employs a combination of rational cubic trigonometric functions within a zipper fractal framework, offering enhanced flexibility through shape parameters and scaling factors. Rigorous error analysis is presented to establish the convergence of the proposed zipper fractal interpolants to the underlying classical fractal functions, and subsequently, to the data-generating function. We derive necessary constraints on the scaling factors and shape parameters to ensure positivity preservation. By carefully selecting the signature, shape parameters, and scaling factors within these bounds, we construct a class of RCTZFIFs that effectively preserve the positive nature of the data, as compared to a reference interpolant that may violate this property. Numerical experiments and visualisations demonstrate the efficacy and robustness of our approach in preserving positivity while offering fractal flexibility.

Renu Mudgal, Rosy Madaan, A. K. Sharma et al.

Question answering system can be seen as the next step in information retrieval, allowing users to pose question in natural language and receive compact answers. For the Question answering system to be successful, research has shown that the correct classification of question with respect to the expected answer type is requisite. We propose a novel architecture for question classification and searching in the index, maintained on the basis of expected answer types, for efficient question answering. The system uses the criteria for Answer Relevance Score for finding the relevance of each answer returned by the system. On analysis of the proposed system, it has been found that the system has shown promising results than the existing systems based on question classification.

Payal Gulati, A. K. Sharma

Image annotation provides several keywords automatically for a given image based on various tags to describe its contents which is useful in Image retrieval. Various researchers are working on text based and content based image annotations [7,9]. It is seen, in traditional Image annotation approaches, annotation words are treated equally without considering the importance of each word in real world. In context of this, in this work, images are annotated with keywords based on their frequency count and word correlation. Moreover this work proposes an approach to compute importance score of candidate keywords, having same frequency count.

Rosy Madaan, A. K. Sharma, Ashutosh Dixit

The research that has been carried out on blogs focused on blog posts only, ignoring the title of the blog page. Also, in summarization only a set of representative sentences are extracted. Some analysis has been done and it has been found that the blog post contains the content that is likely to be related to the topic of the blog post. Thus, proposed system of summarization makes use of title contained in a blog page. The approach makes use of the Presence factor that indicates the presence of each term of the title in each sentence of the blog post. This is a key feature because it considers those sentences as more relevant for summarization that contain each of the term present in the title. The system has been implemented and evaluated experimentally. The system has shown promising results.