Divya Bhatnagar

An Efficient Approach for Inverted Index Pruning Based on Document Relevance

Information Retrieval deals with retrieving documents from a large collection that matches the information need of a user. Efficient retrieval is based on the proper storage of the inverted index. There have been many techniques for reducing the size of the inverted index. Static index pruning is one such technique, which is used to reduce the index size. This paper investigates a static index pruning approach which is useful to reduce the index size. The proposed approach prunes the entire document from the index based on its importance and relevance of top-k results. The elimination takes place on the basis of the score of the individual document. Experiments have been conducted on the FIRE text collection. Based on the results, it was found that for specific collections, the proposed model gives better precision values for the retrieval of top 30 and above documents.

A New Approach for Compression on Textual Data

Data compression algorithms are used to reduce the redundancy, storage requirement and efficiently reduce communication costs. Data Encryption is used to protect our data from unauthorized users. Due to the unprecedented explosion in the amount of digital data transmitted via the Internet, it has become necessary to develop a compression algorithm that can effectively use available network bandwidth and also taking into account the security aspects of the compressed data transmitting over Internet. This paper presents an encoding technique that offers high compression ratios. An intelligent and reversible transformation technique is applied to source text to improve the capability of algorithms to compress the transmitted data. The results prove that the proposed method performs better than many other popular techniques with respected to compression ratio and the speed of compression and requires some additional processing on the server/nodes.

Analysis of quantitative autistic traits while teaching social skills to ASD children using multimedia tool

This analysis evaluates a technology-based training (TBT) for emergent of cognitive skills in ASD children. It primarily explores social competence in learning environment and attempts to enhance behavioral performances in social and cognitive interactions. To study the Autistic traits, 54 individuals suffering from ASD aged from 3 to 29 year were selected for experimental observation. Autistic kids were taught using multimedia tools like mobile applications and short stories. The Autistic traits like medical history and responses to prompts along with the results of discrete trials were recorded and analyzed using appropriate tools. It was observed that ASD children after randomized trials became less impetuous, less restless and trivial. The rise in mood constancy was also observed. Multimedia intrusion is found to be greatly successful in treating disorders like anxiety and autism.

Analyzing the Social Awareness in Autistic Children Trained through Multimedia Intervention Tool using Data Mining

This study focuses on creating a guideline for the ASD children by simulating the situation and analyzing the understanding of ASD (Asperger Syndrome) children over social skills by using a multimedia intervention tool designed for this purpose. 84 ASD individuals belonging to NGOs and clinics were selected for studying their social and cultural awareness. Autistic kids were taught social skills using specially designed multimedia intervention tool, in a controlled environment under the supervision of special educators or parents. Data mining technique was used to extract knowledge from the data collected after intervention. The results were analyzed to understand the impact of the designed multimedia intervention tool and share with special educators and parents of autistic children. The proposed multimedia intervention tool is inexpensive and user friendly. Integration of this tool has been observed to improve the quality of training an individual with autism traits. The overall growth in social communication of the ASD children under observation was observed to be 26.19%. There were substantial variances between age groups, training set and behavior parameters on any of the measures at follow-up. It was considered that an intervention starts at early age and proves beneficiary to ASD children. The study is establishing the remarkable benefits of designed multimedia intervention tool to train the ASD children.

On Using Chi Square Based Term Scoring for Static Index Pruning

In this study, a novel technique for static index pruning based on document relevance with chi square scoring is presented. The term presents in the document are score using the chi-square statistical method. It takes into account the terms occurrences and the expected frequency in the document. The expected frequency is estimated by the terms entropy and the dispersion value of the document. During the computation of document score, the associated terms are averaged by their chi-square based score. The performance of the proposed algorithm is tested on the FIRE 2010 dataset. Experimental results show that the proposed approach increases the Precision for pruning level 60 and above for the top-30 documents retrieval.

Phrase Term Static Index Pruning Based on the Term Cohesiveness

This paper proposes a static index pruning method for phrase queries which is based on the cohesive similarity between terms. The co-occurrence between terms is model by terms cohesiveness within document. The less relevant terms gets pruned away while assuring that there is no change in the top-k results. The proposed method creates an effective pruned index. This method also considers the term proximity based on the term frequency and the terms informative ness. The experiments were conducted on a number of different standard text collections, and analysis of the results shows promising results and is comparable with the existing static pruning method. It is an advantage of the proposed approach that it can be applies to standard inverted index for phrase queries also.

Performance Evaluation of Optimized Mobile IP Protocol Vis-à-vis Bit Map Indexing Method

Mobile IP is such a protocol that provides the mobility support in the Internet. However, even with some proposed optimization techniques, there are still some drawbacks in regard to improve its performance. In this paper, a novel bit map indexing scheme has been represented to improve the performance. In this scheme, each mobile node can be shifting to a foreign network which is associated with the Register Buffer by using bit map indexing method. A sender sends packets to the receiver which is stored further in the Register Buffer so that it could reach to the destination. During handoff, a mobile node could decide whether to move its buffer and report the handoff to the home agent, or, simply report the handoff to the buffer. In this way, this scheme optimizes the burden on the home agent so that the total cost of message delivery and mobility management can be mitigated. To evaluate the performance of the proposed scheme, a performance model has been developed, considering two way models for mobile nodes along with a proposed iterative algorithm for deriving a minimal point where the minimum cost parameter has been achieved. The results show that the new scheme can enable Mobile IP route optimization with proper handoff extension, irrespective of number of packets received during each shift. General Terms Data Transfer and performance evaluation for Mobile IPv4 by novel Bit Map Indexing Method along with algorithm.