Search results for: S. Rathee

Authors: R. Komsa-Penkova, G. Golemanov, G. Georgieva, K. Popovski, N. Slavov, P. Ivanov, K. Kovacheva, S. Rathee, E. Konova, A. Blajev

Abstract:

Leptin and PAI-1 are important cytokines and may play a role in the regulation of PCOS development. PCOS is frequently associated with obesity, high BMI index and consequently with increased risk of metabolic disorders. The aim of the present study was to evaluate PAI-1 levels, genetic influence of the carriage of 675 4G/5G polymorphism in PAI-1 gene and leptin as a marker of obesity in the development of PCOS. Methods: Genotyping in 84 patients with PCOS and PCO and 100 healthy control subjects to detect single nucleotide deletion 675 G in the promoter of PAI-1 gene. The present study provides evidence that SNP 4G in the PAI-1 gene is associated with early pregnancy losses in patients with polycystosis. Further to this, there is a correlation between leptin levels, PAI-1 levels and BMI in the patients with PCOS, which confirms the role of obesity as a risk factor for PCOS.

Keywords: carriage of 675 4G/5G polymorphism, PCOS, early pregnancy losses, PAI-1 gene

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Authors: Sharmila Rathee

Abstract:

Individuals with disability/ies often face negative attitudes, discrimination, exclusion, and inequality of treatment due to stigmatization and stigmatized treatment. While a significant number of studies in field of stigma suggest that group-identification has positive consequences for stigmatized individuals, ironically very miniscule empirical work in sight has attempted to investigate in-group identification as a coping measure against stigma, humiliation and related experiences among disability group. In view of death of empirical research on in-group identification among disability group, through present work, an attempt has been made to examine the experiences of stigma, humiliation, and in-group identification among disability group. Results of the study suggest that use of in-group identification as a coping strategy is not uniform across members of disability group and degree of in-group identification differs across different sub-groups of disability groups. Further, in-group identification among members of disability group depends on variables like degree and impact of disability, factors like onset of disability, nature, and visibility of disability, educational experiences and resources available to deal with disabling conditions.

Keywords: disability, stigma, in-group identification, social identity

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Authors: Sanjay Rathee, Arti Kashyap

Abstract:

Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

Procedia PDF Downloads 250