COVID-19 Pandemic: A Comparative Prediction Using Machine Learning

Coronavirus Disease 2019 or COVID-19 is an infectious disease which is declared as a pandemic by the World Health Organization (WHO) have a noxious effect on the entire human civilization. Each and every day the number of infected people is going higher and higher and so the death toll. Many of country Italy, UK, USA was affected badly, yet since the identification of the first case, after a certain number of days, the scenario of infection rate has been reduced significantly. However, a country like Bangladesh couldn't keep the infection rate down. A number of algorithms have been proposed to forecast the scenario in terms of the number of infection, recovery and death toll. Here, in this work, we present a comprehensive comparison based on Machine Learning to predict the outbreak of COVID-19 in Bangladesh. Among Several Machine Learning algorithms, here we used Polynomial Regression (PR) and Multilayer Perception (MLP) and Long Short Term Memory (LSTM) algorithm and epidemiological model Susceptible, Infected and Recovered (SIR), projected comparative outcomes

A Multidimensional Trust Evaluation Model for MANETs

Effective trust management can enhance nodes’ cooperation in selecting trustworthy and optimal paths between the source and destination nodes in mobile ad hoc networks (MANETs). It allows the wireless nodes (WNs) in a MANET environment to deal with uncertainty about the future actions of other participants. The main challenges in MANETs are time-varying network architecture due to the mobility of WNs, the presence of attack-prone nodes, and extreme resource limitations. In this paper, an energy-aware and social trust inspired multidimensional trust management model is proposed to achieve enhanced quality of service (QoS) parameters by overcoming these challenges. The trust management model calculates the trust value of the WNs through peer to peer and link evaluations. Energy and social trust are utilized for peer to peer evaluation, while an optimal routing path with a small number of intermediate nodes with minimum acceptable trust value is used for evaluation of the link. Empirical analysis reveals that the proposed trust model is robust and accurate in comparison to the state-of-the-art model for MANETs

An Approach to Improve the Performance of Mobile Computation Technology Using Data Offloading

Modern mobile phones have a powerful processing unit that can perform multiple operations simultaneously. But the main constrain is the processing power and energy required to drive it. Cloud computing has come as a blessing for the mobile computing as it possesses a vast resource, high storage capacity and high processing power. In Mobile Cloud Computing (MCC), computation is offloaded to the cloud, cloud processes the data and generates information; and sends it back to the mobile device. Probability of offloading depends on line bandwidth, line length, size of data, processing speed of mobile, processing speed of cloud, storage and energy capacity of mobile etc. This paper presents a model for efficient data offloading decision, depending on the total execution time of a task when the data computation happens in the mobile and when it is offloaded to the cloud. An algorithm has been proposed here on data offloading decision. As thus an equation has been proposed to observe the probability of offloading process

iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems

To meet the demand of the world's largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols – that demand considerably high computational power and memory – can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Measurement of the cross-section for b-jets produced in association with a Z boson at root s=7 TeV with the ATLAS detector ATLAS Collaboration

A measurement is presented of the inclusive cross-section for b-jet production in association with a Z boson in pp collisions at a centre-of-mass energy of root s = 7 TeV. The analysis uses the data sample collected by the ATLAS experiment in 2010, corresponding to an integrated luminosity of approximately 36 pb(-1). The event selection requires a Z boson decaying into high P-T electrons or muons, and at least one b-jet, identified by its displaced vertex, with transverse momentum p(T) > 25 GeV and rapidity vertical bar y vertical bar < 2.1. After subtraction of background processes, the yield is extracted from the vertex mass distribution of the candidate b-jets. The ratio of this cross-section to the inclusive Z cross-section (the average number of b-jets per Z event) is also measured. Both results are found to be in good agreement with perturbative QCD predictions at next-to-leading order

Measurement of charged-particle event shape variables in inclusive root(s)=7 TeV proton-proton interactions with the ATLAS detector

The measurement of charged-particle event shape variables is presented in inclusive inelastic pp collisions at a center-of-mass energy of 7 TeV using the ATLAS detector at the LHC. The observables studied are the transverse thrust, thrust minor, and transverse sphericity, each defined using the final-state charged particles' momentum components perpendicular to the beam direction. Events with at least six charged particles are selected by a minimum-bias trigger. In addition to the differential distributions, the evolution of each event shape variable as a function of the leading charged-particle transverse momentum, charged-particle multiplicity, and summed transverse momentum is presented. Predictions from several Monte Carlo models show significant deviations from data

Search for the standard model Higgs boson in the diphoton decay channel with 4.9fb -1 of pp collision data at √s=7TeV with atlas

A search for the standard model Higgs boson is performed in the diphoton decay channel. The data used correspond to an integrated luminosity of 4.9  fb-1 collected with the ATLAS detector at the Large Hadron Collider in proton-proton collisions at a center-of-mass energy of √s=7  TeV. In the diphoton mass range 110–150 GeV, the largest excess with respect to the background-only hypothesis is observed at 126.5 GeV, with a local significance of 2.8 standard deviations. Taking the look-elsewhere effect into account in the range 110–150 GeV, this significance becomes 1.5 standard deviations. The standard model Higgs boson is excluded at 95% confidence level in the mass ranges of 113–115 GeV and 134.5–136 GeV

Search for the standard model Higgs boson in the diphoton decay channel with 4.9fb -1 of pp collision data at √s=7TeV with atlas

A search for the standard model Higgs boson is performed in the diphoton decay channel. The data used correspond to an integrated luminosity of 4.9  fb-1 collected with the ATLAS detector at the Large Hadron Collider in proton-proton collisions at a center-of-mass energy of √s=7  TeV. In the diphoton mass range 110–150 GeV, the largest excess with respect to the background-only hypothesis is observed at 126.5 GeV, with a local significance of 2.8 standard deviations. Taking the look-elsewhere effect into account in the range 110–150 GeV, this significance becomes 1.5 standard deviations. The standard model Higgs boson is excluded at 95% confidence level in the mass ranges of 113–115 GeV and 134.5–136 GeV