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

Structure of Complexes of 2-Phenylazopyridine with Perchlorate Halides of Ni(II) & with Ferrous Iodide

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Superconductivity in doped FeTe1-xSx (x= 0.00 to 0.25) single crystals

We report self flux growth and characterization of FeTe1-xSx (x= 0.00 to 0.25) single crystal series. Surface X-ray diffraction (XRD) exhibited crystalline nature with growth in (00l) plane. Micro-structural (electron microscopy) images of representative crystals showed the slab-like morphology and near stoichiometric composition. Powder XRD analysis (Rietveld) of single crystals exhibited tetragonal structure with P4/nmm space group and decreasing a and c lattice parameters with increase in x. Electrical resistivity measurements (R-T) showed superconductivity with Tconset at 9.5K and 8.5K for x =0.10 and x =0.25 respectively. The un-doped crystal exhibited known step like anomaly at around 70K. Upper critical field Hc2(0), as calculated from magneto transport for x =0.25 crystal is around 60Tesla and 45Tesla in H//ab and H//c directions. Thermal activation energy [U0(H)] calculated for x =0.10 and 0.25 crystals followed weak power law, indicating single vortex pinning at low fields. Mossbauer spectra for FeTe1-xSx crystals at 300K and 5K are compared with non superconducting FeTe. Both quadrupole splitting (QS) and isomer shift (IS) for S doped crystals were found to decrease. Also at 5K the hyperfine field for x =0.10 superconducting crystal is decreased substantially from 10.6Tesla (FeTe) to 7.2Tesla. For x =0.25 crystal, though small quantity of un-reacted Fe is visible at room temperature, but unlike x =0.10, the low temperature (5K) ordered FeTe hyperfine field is nearly zero.Comment: 20 Pages Text + Figs: Accepted Mat. Res. Exp, Mat. Rex. Exp. (2018

Analytical Modelling of Power Efficient Reliable Operation of Data Fusion in Wireless Sensor Network

Irrespective of inclusion of Wireless Sensor Network (WSN) in majority of the research proposition for smart city planning, it is still shrouded with some significant issues. A closer look into problems in WSN shows that energy parameter is the origination point of majority of the other problems in resource-constrained sensors as well as it significant minimizes the reliability in standard sensory operation in adverse environment. Therefore, this manuscript presents a novel analytical model that is meant for establishing a well balance between energy efficiency over multi-path data forwarding and reliable operation with improved network performance. The complete process is emphasized during data fusion stage to ensure data quality too. A simulation study has been carried out using benchmarked test-bed of MEMSIC nodes to find that proposed system offers good energy conservation process during data fusion operation as well as it also ensure good reliable operation in comparison to existing system

Study on serum iron profile in COVID-19 associated mucormycosis patients

Background: Mucormycosis is an angio-invasive disease caused by fungi prevalence of which in India is approximately 0.14 cases per 1000 population. The incidence of mucor in COVID 19 patients has increased to greater extent. Probable cause of which is increased serum ferritin among these patients and Iron is required by virtually all microbial pathogens for growth and virulence. Hence, we had conducted a study to estimate serum iron profile and association of iron profile with mucor mycosis in covid-19 associated mucormycosis.Methods: Cross sectional study conducted from May 2021 to July 2021 by the department of general medicine, Banglore medical college and research centre, Karnataka. The data collected was analyzed statistically using descriptive statistics.Results: We observed increased prevalence of mucor cases among the patients aged between 41 to 60 years and those who were not vaccinated. Hyperglycaemia had strong correlation with development of mucor. There was lower UIBC, lower TIBC, high ferritin and serum Iron levels among those who had developed mucormycosis.Conclusions: By our observations, we concluded that the increased serum iron, ferritin, transferrin and reduced TIBC and UIBC are the associated risk factor in the development of COVID 19 associated invasive mucor mycosis. Patients with HbA1c >7 are at higher risk of developing COVID 19 associated mucor mycosis

Lung Segmentation from Chest X-rays using Variational Data Imputation

Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.Comment: Accepted to be presented at the first Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37th International Conference on Machine Learning (ICML). Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE

New Binuclear Nickel(II) Species

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Crystal structure of 1-benzylsulfonyl-1,2,3,4-tetrahydroquinoline

SJ thanks the Vision Group on Science and Technology, Government of Karnataka, for the award of a major project under the CISE scheme (reference No. VGST/CISE/GRD192/2013-14). BSPM thanks Rajegowda, Department of Studies and Research in Chemistry, UCS, Tumkur University, Karnataka 572 103, India, for his support.Peer reviewedPublisher PD

First report of the occurrence of Myrothecium verrucaria in watermelon seeds from India

Watermelon (Citrullus lanatus) is known to be affected by a variety of both seed-borne and soil-borne fungi. In routine screening of watermelon seed samples, sporodochia of Myrothecium verrucaria were observed. The fungus was isolated and the spore suspension was inoculated onto healthy seedlings of watermelon. The resulting symptoms confirmed Koch's postulates

Crystal structure of 1-methanesulfonyl-1, 2, 3, 4-tetrahydroquinoline

SJ thanks Vision Group on Science and Technology, Government of Karnataka, for awarding a major project under CISE scheme (reference No. VGST/CISE/GRD-192/ 2013–14). BSP thanks Rajegowda, Department of Studies and Research in Physics, UCS, Tumkur University, Karnataka 572103, India, for his support.Peer reviewedPublisher PD