93 research outputs found

    Sentiment analysis by deep learning approaches

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    We propose a model for carrying out deep learning based multimodal sentiment analysis. The MOUD dataset is taken for experimentation purposes. We developed two parallel text based and audio basedmodels and further, fused these heterogeneous feature maps taken from intermediate layers to complete thearchitecture. Performance measures–Accuracy, precision, recall and F1-score–are observed to outperformthe existing models

    A Review on Optimizing Radial Basis Function Neural Network using Nature Inspired Algorithm

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    Radial Basis Function (RBF) is a type of feed forward neural network .This function can be applied to interpolation, chaotic time-series modeling, control engineering, image restoration, data fusion etc. In RBF network, parameters of basis functions (such as width, the position and number of centers) in the nonlinear hidden layer have great influence on the performance of the network. Common RBF training algorithms cannot possibly find the global optima of nonlinear parameters in the hidden layer, and often have too many hidden units to reach certain approximation abilities, which will lead to too large a scale for the network and decline of generalization ability. Also, RBF neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Secondly, the Swarm Intelligence Algorithms are (Meta-Heuristic) development Algorithms, which attracted much attention and appeared its ability in the last ten years within many applications such as data mining, scheduling, improve the performance of artificial neural networks (ANN) and classification. So, in this paper the work of Artificial Bee Colony (ABC), Genetic algorithm(GA), Particle swarm optimization(PSO) and Bat algorithm(BA) have been reviewed, which optimized the RBF neural network in their own terms

    Gravitational and electromagnetic fields of a charged tachyon

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    An axially symmetric exact solution of the Einstein-Maxwell equations is obtained and is interpreted to give the gravitational and electromagnetic fields of a charged tachyon. Switching off the charge parameter yields the solution for the uncharged tachyon which was earlier obtained by Vaidya. The null surfaces for the charged tachyon are discussed.Comment: 8 pages, LaTex, To appear in Pramana- J. Physic

    Neutron total cross sections on nuclei at Fermilab energies

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    We have measured total cross sections for neutrons on protons, deuteriom, beryllium, carbon, aluminium, iron, copper, cadmium, tungsten, lead, and uranium for momenta between 30 and 300 GeV/c. The measurements were carried out in a small-angle neutral beam at Fermilab. Typical accuracy of the data is 0.5 to 1%. The cross sections are consistent with an A0.77+/-0.01 dependence over the entire momentum range. The cross sections are compared with theoretical predictions. Agreement is found only if inelastic screening is included. Nuclear radii obtained from our data are in good agreement with previous determinations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/22036/1/0000454.pd

    Neutron-proton and neutron-deuteron total cross sections at 4.0 and 5.7 GeV/c

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    The np and nd total cross sections have been measured directly with a neutron beam with momenta of 4.0 +/- 0.6 and 5.7 +/- 0.6 GeV/c. The data are compared with the previous nucleon-nucleon and nucleon-deuteron results, and the deuteron screening term was also evaluated. The measured total cross section are 43.1 +/- 0.6 and 80.3 +/- 1.9 mb at 4.0 GeV/c and 42 +/- 0.6 and 77.8 +/- 1.3 mb at 5.7 GeV/c.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32799/1/0000172.pd

    Neutron-proton differential cross sections in the range 70 to 400 GeV/c

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    We report the results of an experiment which measured np elastic scattering differential cross sections over a range in -t from 0.15 to ~3.6 (GeV/c)2 for incident neutron momenta from 70 to 400 GeV/c. We find the logarithmic slope parameter, evaluated at -t = 0.2 (GeV/c)2, to be consistent with existing proton-proton parametrizations. The data exhibit a dip in the cross section near -t = 1.4 (GeV/c)2 for incident neutron momenta above 200 GeV/c. For neutron momenta less than 280 GeV/c, the neutron-proton cross sections are found to be higher than existing proton-proton data in the range 0.7 [les] -t [les] 1.3 (GeV/c)2 which is in contradiction to most Regge predictions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/23627/1/0000590.pd

    The properties of proton-proton interactions between 100 and 1000 GeV from a cosmic-ray experiment

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    Proton-proton interactions above 100 GeV have been studied in an experiment using cosmic-ray protons interacting in a liquid hydrogen target. From several hundred hydrogen interactions, it has been learned that: (a) the total inelastic pp cross section is not changing significantly with energy above 30 GeV; (b) the multiplicity distributions of charged prongs agree with a Poisson distribution in charged-particle pairs; (c) the average charged prong multiplicity increases as 1 n s; (d) the angular distribution of charged particles agrees with a c.m. momentum distribution of charged particles that varies as exp exp [-8 pT2-8x2] d3p/E (where X = pL/po) and a nucleon distribution that varies as 10x exp [-3 pT2] d3p/E for 0.05 < x < 0.85. Further properties of the angular distributions, characteristics of the ionization calorimeter, the cross section in iron, and other features of the data are reported.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34084/1/0000363.pd

    IMB Detector‐The first 30 Days

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    A large water Chernekov detector, located 2000 feet below ground, has recently been turned on. The primary purpose of the device is to measure nucleon stability to limits 100 times better than previous measurements. The properties of the detector are described along with its operating characteristics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87428/2/138_1.pd
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