FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This paper discussed the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This paper proposed and developed fog computing based framework i.e. FogLearn for application of K-means clustering in Ganga River Basin Management and realworld feature data for detecting diabetes patients suffering from diabetes mellitus. Proposed architecture employed machine learning on deep learning framework for analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results showed that fog computing hold an immense promise for analysis of medical and geospatial big data

Clustering and 5G-enabled smart cities: a survey of clustering schemes in VANETs

This chapter highlights the importance of Vehicular Ad-hoc Networks (VANETs) in the context of the 5Genabled smarter cities and roads, a topic that attracts significant interest. In order for VANETs and its associated applications to become a reality, a very promising avenue is to bring together multiple wireless technologies in the architectural design. 5G is envisioned to have a heterogeneous network architecture. Clustering is employed in designing optimal VANET architectures that successfully use different technologies, therefore clustering has the potential to play an important role in the 5G-VANET enabled solutions. This chapter presents a survey of clustering approaches in the VANET research area. The survey provides a general classification of the clustering algorithms, presents some of the most advanced and latest algorithms in VANETs, and it is among the fewest works in the literature that reviews the performance assessment of clustering algorithms

Measurement of the charge ratio of atmospheric muons with the CMS detector

This is the pre-print version of this Article. The official published version can be accessed from the link below - Copyright @ 2010 ElsevierWe present a measurement of the ratio of positive to negative muon fluxes from cosmic ray interactions in the atmosphere, using data collected by the CMS detector both at ground level and in the underground experimental cavern at the CERN LHC. Muons were detected in the momentum range from 5 GeV/c to 1 TeV/c. The surface flux ratio is measured to be 1.2766 \pm 0.0032(stat.) \pm 0.0032 (syst.), independent of the muon momentum, below 100 GeV/c. This is the most precise measurement to date. At higher momenta the data are consistent with an increase of the charge ratio, in agreement with cosmic ray shower models and compatible with previous measurements by deep-underground experiments

Measurements of inclusive W and Z cross sections in pp collisions at root s=7 TeV

This is the pre-print version of the Published Article, which can be accessed from the link below - Copyright @ 2011 Springer VerlagMeasurements of inclusive W and Z boson production cross sections in pp collisions at sqrt(s)=7 TeV are presented, based on 2.9 inverse picobarns of data recorded by the CMS detector at the LHC. The measurements, performed in the electron and muon decay channels, are combined to give sigma(pp to WX) times B(W to muon or electron + neutrino) = 9.95 \pm 0.07(stat.) \pm 0.28(syst.) \pm 1.09(lumi.) nb and sigma(pp to ZX) times B(Z to oppositely charged muon or electron pairs) = 0.931 \pm 0.026(stat.) \pm 0.023(syst.) \pm 0.102(lumi.) nb. Theoretical predictions, calculated at the next-to-next-to-leading order in QCD using recent parton distribution functions, are in agreement with the measured cross sections. Ratios of cross sections, which incur an experimental systematic uncertainty of less than 4%, are also reported

Observation of a new Xi(b) baryon

The first observation of a new b baryon via its strong decay into Xi(b)^- pi^+ (plus charge conjugates) is reported. The measurement uses a data sample of pp collisions at sqrt(s) = 7 TeV collected by the CMS experiment at the LHC, corresponding to an integrated luminosity of 5.3 inverse femtobarns. The known Xi(b)^- baryon is reconstructed via the decay chain Xi(b)^- to J/psi Xi^- to mu^+ mu^- Lambda^0 pi^-, with Lambda^0 to p pi^-. A peak is observed in the distribution of the difference between the mass of the Xi(b)^- pi^+ system and the sum of the masses of the Xi(b)^- and pi^+, with a significance exceeding five standard deviations. The mass difference of the peak is 14.84 +/- 0.74 (stat.) +/- 0.28 (syst.) MeV. The new state most likely corresponds to the J^P=3/2^+ companion of the Xi(b).Comment: Submitted to Physical Review Letter

Measurement of dijet angular distributions and search for quark compositeness in pp collisions at √s=7TeV

Dijet angular distributions are measured over a wide range of dijet invariant masses in pp collisions at root s = 7 TeV, at the CERN LHC. The event sample, recorded with the CMS detector, corresponds to an integrated luminosity of 36 pb(-1). The data are found to be in good agreement with the predictions of perturbative QCD, and yield no evidence of quark compositeness. With a modified frequentist approach, a lower limit on the contact interaction scale for left-handed quarks of Lambda(+) = 5.6 TeV (Lambda(-) = 6.7 TeV) for destructive (constructive) interference is obtained at the 95% confidence level