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Search Results: 1 - 10 of 71 matches for " Sarawagi Sweta "
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Fine needle aspiration cytology diagnosis of paravertebral extraosseus Ewing′s sarcoma
Buch Archana,Panicker N,Sarawagi Sweta,Anwekar Sampada
Journal of Cytology , 2010,
Abstract: Extraskeletal Ewing′s sarcoma (EES) is a rare tumor. Paravertebral Ewing′s sarcoma requires more extensive therapy as compared to Ewing′s sarcoma of bone. Fine needle aspiration cytology (FNAC) plays an important role in the early diagnosis of these cases. We present a case where paravertebral extraosseous Ewing′s sarcoma was diagnosed on FNAC in a 19-year-old girl.
Framing of Arab Conflicts in India by the Leading Private News Channels NDTV 24*7 and CNN-IBN  [PDF]
Sweta Singh
Advances in Journalism and Communication (AJC) , 2018, DOI: 10.4236/ajc.2018.62003
Abstract: During Arab “Spring”, two broad frames emerged from the coverages by Indian news television: Western and regional. The former caters to the West for its primary viewership whereas the latter is an alternative to CNN for the Arab region. During the conflicts, Indian news television catered to the dominant western perspective due to feeds from western news agencies. So when the Arab Conflicts got mediated to India, a non-participating zone, then it was only likely that the western perspective would dominate the audio-visual narratives because of continued dependency on the western news agencies as has been demonstrated earlier conflicts like invasions of Iraq and Afghanistan. Through a qualitative framing analysis, this research looks at how Arab Conflicts got mediated by NDTV and CNN-IBN, the two leading private English news channels. An analysis of news packages by the two channels during the year 2011 with respect to framing of issues, stakeholders and sources reveals a stronger presence of western perspective in the audio-visual narratives in spite of the fact that channels correspondents contributed almost the same number of stories as those from the western sources.
Knowledge, Attitude and Perception regarding National Health Programmes among villagers of Chauras, Tehri-Garhwal, Uttarakhand
Gupta SK,Sarawagi R
Online Journal of Health & Allied Sciences , 2010,
Abstract: Background and Objective: Since India became independent, several measures have been undertaken by the national government to improve the health of the people. Prominent among these measures are the national health programmes. The main objective of these National Health programmes are protection and promotion of national and individual health. The main objective of this study was to assess the knowledge, attitude and perception regarding various national health programmes among the villagers. Methods: It is a descriptive and observational study. The study subjects comprised 273 respondents belonging to 15 to 64 years age group. The collection tool used was a pre designed questionnaire, which was pre-tested. Results: 60% of respondents were adults, about 16 percent were educated up to primary level and more than 40% belonged to scheduled castes. Nearly 20% were aware about National AIDS Control Programme and 6.59% had clear knowledge about HIV/AIDS. Only 4.02% knew about the national vector borne disease control programme and 24% women clearly knew about exclusive breast feeding. Peripheral health workers were the most common source of information regarding these programmes. 64% of respondents opined that these national health programmes are good. Conclusion: Low level of knowledge was observed among the respondents regarding National Health Programmes.
Answering Table Queries on the Web using Column Keywords
Rakesh Pimplikar,Sunita Sarawagi
Computer Science , 2012,
Abstract: We present the design of a structured search engine which returns a multi-column table in response to a query consisting of keywords describing each of its columns. We answer such queries by exploiting the millions of tables on the Web because these are much richer sources of structured knowledge than free-format text. However, a corpus of tables harvested from arbitrary HTML web pages presents huge challenges of diversity and redundancy not seen in centrally edited knowledge bases. We concentrate on one concrete task in this paper. Given a set of Web tables T1, . . ., Tn, and a query Q with q sets of keywords Q1, . . ., Qq, decide for each Ti if it is relevant to Q and if so, identify the mapping between the columns of Ti and query columns. We represent this task as a graphical model that jointly maps all tables by incorporating diverse sources of clues spanning matches in different parts of the table, corpus-wide co-occurrence statistics, and content overlap across table columns. We define a novel query segmentation model for matching keywords to table columns, and a robust mechanism of exploiting content overlap across table columns. We design efficient inference algorithms based on bipartite matching and constrained graph cuts to solve the joint labeling task. Experiments on a workload of 59 queries over a 25 million web table corpus shows significant boost in accuracy over baseline IR methods.
Joint Structured Models for Extraction from Overlapping Sources
Rahul Gupta,Sunita Sarawagi
Computer Science , 2010,
Abstract: We consider the problem of jointly training structured models for extraction from sources whose instances enjoy partial overlap. This has important applications like user-driven ad-hoc information extraction on the web. Such applications present new challenges in terms of the number of sources and their arbitrary pattern of overlap not seen by earlier collective training schemes applied on two sources. We present an agreement-based learning framework and alternatives within it to trade-off tractability, robustness to noise, and extent of agreement. We provide a principled scheme to discover low-noise agreement sets in unlabeled data across the sources. Through extensive experiments over 58 real datasets, we establish that our method of additively rewarding agreement over maximal segments of text provides the best trade-offs, and also scores over alternatives such as collective inference, staged training, and multi-view learning.
Student Dropout Risk Assessment in Undergraduate Course at Residential University
Sweta Rai
Computer Science , 2014,
Abstract: Student dropout prediction is an indispensable for numerous intelligent systems to measure the education system and success rate of any university as well as throughout the university in the world. Therefore, it becomes essential to develop efficient methods for prediction of the students at risk of dropping out, enabling the adoption of proactive process to minimize the situation. Thus, this research work propose a prototype machine learning tool which can automatically recognize whether the student will continue their study or drop their study using classification technique based on decision tree and extract hidden information from large data about what factors are responsible for dropout student. Further the contribution of factors responsible for dropout risk was studied using discriminant analysis and to extract interesting correlations, frequent patterns, associations or casual structures among significant datasets, Association rule mining was applied. In this study, the descriptive statistics analysis was carried out to measure the quality of data using SPSS 20.0 statistical software and application of decision tree and association rule were carried out by using WEKA data mining tool.
User Informatics Optimized Search and Retrieval-Congestion Avoidance Scheme for 4G Networks  [PDF]
Pushpa Pushpa, Sweta Sneha, Rajeev Agrawal
Communications and Network (CN) , 2012, DOI: 10.4236/cn.2012.43026
Abstract: The objective of 4G network is to provide best services to the users which in turn made the performance of existing network more critical. Further, the large traffic generated in such networks creates congestion resulting in overloading of the system. Frequent delays, loss of packets, and in addition the number of retransmission/paging also increases the computational cost of the system. This paper proposes a novel way to reduce overloading and retrieval mechanism for VLR through optimized search, based on the information of users mobility pattern (User profiles based (UPB)) to track the user. This not only improves the overall performance of the system, especially in the events when the visitor location register (VLR) is overloaded due to heavy traffic and congestion of the network. It was also established through simulation studies that the proposed UPB scheme optimizes the search and reduces the average waiting time in a queue. In addition, the provision of VLRW (waiting visitor location register) avoids the overloading of main VLR and provides a recovery/retrieval mechanism for VLR failure.
Spontaneous Regression of a Histologically Proven Cutaneous Squamous Cell Carcinoma  [PDF]
Sweta Sengupta, James Arocho, T. Christopher Windham
Journal of Cancer Therapy (JCT) , 2013, DOI: 10.4236/jct.2013.42068

Introduction: The rate of squamous cell carcinoma spontaneous regression (SR) remains unknown because incidences are rare and underreported. Case Report: We present a case of a 92-year-old Caucasian female who was found to have a 1 cm lesion on her nose. Pathologic evaluation supported the diagnosis of a moderately-differentiated squamous cell carcinoma. The patient refused treatment and returned with no clinical evidence of disease several months later. The lesion spontaneously regressed without excision. Discussion: Frequency of SR of malignancies has been estimated to occur 1 in 80,000 to 100,000 cases [1]. The biologic mechanisms of SR in malignancies remain unclear. Further investigations into the mechanisms of SR may identify potential treatment strategies for cancer.

Fibrolipomatous hamartoma of the median nerve presenting with carpal tunnel syndrome
Sarawagi Radha,Anderson G,Cherian Rekha
Neurology India , 2009,
Generalized Collective Inference with Symmetric Clique Potentials
Rahul Gupta,Sunita Sarawagi,Ajit A. Diwan
Computer Science , 2009,
Abstract: Collective graphical models exploit inter-instance associative dependence to output more accurate labelings. However existing models support very limited kind of associativity which restricts accuracy gains. This paper makes two major contributions. First, we propose a general collective inference framework that biases data instances to agree on a set of {\em properties} of their labelings. Agreement is encouraged through symmetric clique potentials. We show that rich properties leads to bigger gains, and present a systematic inference procedure for a large class of such properties. The procedure performs message passing on the cluster graph, where property-aware messages are computed with cluster specific algorithms. This provides an inference-only solution for domain adaptation. Our experiments on bibliographic information extraction illustrate significant test error reduction over unseen domains. Our second major contribution consists of algorithms for computing outgoing messages from clique clusters with symmetric clique potentials. Our algorithms are exact for arbitrary symmetric potentials on binary labels and for max-like and majority-like potentials on multiple labels. For majority potentials, we also provide an efficient Lagrangian Relaxation based algorithm that compares favorably with the exact algorithm. We present a 13/15-approximation algorithm for the NP-hard Potts potential, with runtime sub-quadratic in the clique size. In contrast, the best known previous guarantee for graphs with Potts potentials is only 1/2. We empirically show that our method for Potts potentials is an order of magnitude faster than the best alternatives, and our Lagrangian Relaxation based algorithm for majority potentials beats the best applicable heuristic -- ICM.
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