Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data

Big data is the biggest challenges as we need huge processing power system and good algorithms to make an decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar

A Study To Assess The Knowledge And Compliance Of Critical Care Nurses Regarding Ventilator Care Bundle In Prevention Of Ventilator Associated Pnemonia

This study aims to assess the knowledge and compliance of staff nurses regarding ventilator care bundle in prevention of ventilator associated Pneumonia. Knowledge of nurses on ventilator care bundle for the prevention of VAP and adherence to them would reduce the risk of occurrence of VAP and decrease morbidity and mortality of mechanically ventilated patients in the ICU. An exploratory survey research design was adopted and a convenient sampling technique was selected for the study. The findings revealed that more than half of the (56.7%) staff nurses had excellent knowledge regarding ventilator care bundle and 43.3% of them had good knowledge regarding ventilator care bundle. It also showed that knowledge and compliance of staff nurses regarding ventilator care bundle were found to have significant association. More the knowledge, better is the compliance, as the p- value is less than 0.05. The study also revealed that area of work and shift were found to have significant association with the compliances of the staff nurses regarding ventilator care bundle

A Study on the Performances of Representation Strategies Handled For Text Categorization

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Split (n + t)-color partitions and 2-color F-partitions

Andrews [Generalized Frobenius partitions. Memoirs of the American Math. Soc., 301:1{44, 1984] defined the two classes of generalized F-partitions: F-partitions and k-color F-partitions. For many q-series and Rogers-Ramanujan type identities, the bijections are established between F-partitions and (n + t)-color partitions. Recently (n + t)-color partitions have been extended to split (n+t)-color partitions by Agarwal and Sood [Split (n+t)-color partitions and Gordon-McIntosh eight order mock theta functions. Electron. J. Comb., 21(2):#P2.46, 2014]. The purpose of this paper is to study the k-color F-partitions as a combinatorial tool. The paper includes combinatorial proofs and bijections between split (n + t)-color partitions and 2-color F-partitions for some generalized q-series. Our results further give rise to innate three-way combinatorial identities in conjunction with some Rogers-Ramanujan type identities for some particular cases

Prognostic role of immune cells in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), with rising incidence rates, is the most commonly occurring malignancy of the liver that exerts a heavy disease burden particularly in developing countries. A dynamic cross-talk between immune cells and malignant cells in tumor microenvironment governs the hepatocarcinogenesis. Monitoring immune contexture as prognostic markers is quite relevant and essential to evaluate clinical outcomes and to envisage response to therapy. In this review, we present an overview of the prognostic value of various tumor infiltrating immune cells and the continually evolving immune checkpoints as novel biomarkers during HCC. Tumor infiltration by immune cells such as T cells, NK cells and dendritic cells is linked with improved prognosis and favorable outcome, while the intra-tumoral presence of regulatory T cells (Tregs) or myeloid derived suppressor cells (MDSCs) on the other hand is associated with poor clinical outcome. In addition to these, the overexpression of negative regulatory molecules on tumor cells also provides inhibitory signals to T cells and is associated with poor prognosis. The limitation of a single marker can be overcome by advanced prognostication models and algorithms that evaluate multiple prognostic factors and ultimately aid the clinician in improving the disease free and overall survival of HCC patients

Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval

Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm.  This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-art-works

An Enhanced Cluster based Multi-hop Routing Technique in Wireless Sensor Network Using AODV Protocol

Wireless Sensor Network is one of main extent in physical environmental inquiry. The Cluster based multi-hop routing for the capable network to improve the life time data transmission and energy saving for the network progress. In this Research paper based on Flat Multi-Hop Routing Technique is used to LEACH protocol using reduced overall network power utilization. Hierarchical Multi-hop routing Technique is one of the methods using M-LEACH protocol, while using this method user is able to get the large number of data and packet losses also reduced. Hybrid multi-hop routing Technique using PEACH protocol used by the users are able to get Energy saving is high. My Research contribution going to Enhanced Hybrid Multi-hop Routing(EHYMN) for improve the data transmission with less time , reduced the packet losses and also minimum power utilization for this Ad-hoc On Demand Distance Vector (AODV) protocol is used for the implementation in network simulation tool 2.34 version for get a good Results