Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Analysis of EEG signals and its application to neuromarketing

Abstract: Marketing and promotions of various consumer products through advertisement campaign is a well known practice to increase the sales and awareness amongst the consumers. This essentially leads to increase in profit to a manufacturing unit. Re-production of products usually depends on the various facts including consumption in the market, reviewer's comments, ratings, etc. However, knowing consumer preference for decision making and behavior prediction for effective utilization of a product using unconscious processes is called "Neuromarketing". This field is emerging fast due to its inherent potential. Therefore, research work in this direction is highly demanded, yet not reached a satisfactory level. In this paper, we propose a predictive modeling framework to understand consumer choice towards E-commerce products in terms of "likes" and "dislikes" by analyzing EEG signals. The EEG signals of volunteers with varying age and gender were recorded while they browsed through various consumer products. The experiments were performed on the dataset comprised of various consumer products. The accuracy of choice prediction was recorded using a user-independent testing approach with the help of Hidden Markov Model (HMM) classifier. We have observed that the prediction results are promising and the framework can be used for better business model.

Measurement of the decay B →dℓνℓ in fully reconstructed events and determination of the Cabibbo-Kobayashi-Maskawa matrix element |Vcb |

Abstract: We present a determination of the magnitude of the Cabibbo-Kobayashi-Maskawa matrix element vertical bar V-cb vertical bar using the decay B -> Dl nu(l) (l = e,mu) based on 711 fb(-1) of e(+)e(-) -> Upsilon(4S) data recorded by the Belle detector and containing 772 x 10(6) B (B) over bar pairs. One B meson in the event is fully reconstructed in a hadronic decay mode, while the other, on the signal side, is partially reconstructed from a charged lepton and either a D+ or D-0 meson in a total of 23 hadronic decay modes. The isospin-averaged branching fraction of the decay B -> Dl nu(l) is found to be B(B-0 -> D(-)l(vertical bar)nu(l)) = (2.31 +/- 0.03(stat) +/- 0.11(syst))%. Analyzing the differential decay rate as a function of the hadronic recoil with the parametrization of Caprini, Lellouch, and Neubert and using the form-factor prediction G(1) = 1.0541 +/- 0.0083 calculated by FNAL/MILC, we obtain eta(EW)vertical bar V-cb vertical bar = (40.12 +/- 1.34) x 10(-3), where eta(EW) is the electroweak correction factor. Alternatively, assuming the model-independent form-factor parametrization of Boyd, Grinstein, and Lebed and using lattice QCD data from the FNAL/MILC and HPQCD collaborations, we find eta(EW)vertical bar V-cb vertical bar = (41.10 +/- 1.14) x 10(-3).

A critical review of retrofitting methods for unreinforced masonry structures

Abstract: Unreinforced masonry (URM) buildings are common throughout Latin America, the Himalayan region, Eastern Europe, Indian subcontinent and other parts of Asia. It has been observed that these buildings cannot withstand the lateral loads imposed by an earthquake and often fails, in a brittle manner. Methods for retrofitting URM buildings to increase the time required for collapse and also to improve the overall strength widely vary. This review has collated information on various types of retrofitting methods either under research or early implementation. Furthermore, these methods are categorized and critically analyzed to help further understand which methods are most suitable for future research or application in developing countries. The comparison of the different methods is based on economy, sustainability and buildability and provides a useful insight. The study may provide useful guidance to policy makers, planners, designers, architects and engineers in choosing a suitable retrofitting methodology.

A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems

Abstract: Graphical abstractDisplay Omitted HighlightsProposed a new multi-objective Symbiotic Organisms Search algorithm.Performance is validated on 12 unconstrained and 6 constrained problems.The real life applications are demonstrated on constrained truss design problems. Many real world engineering optimization problems are multi-modal and associated with constrains. The multi-modal problems involve presence of local optima and thus conventional derivative based algorithms do not able to effectively determine the global optimum. The complexity of the problem increases when there is requirement to simultaneously optimize two or more objective functions each of which associated with certain constrains. Recently in 2014, Cheng and Prayogo proposed a new metaheuristic optimization algorithm known as Symbiotic Organisms Search (SOS). The algorithm is inspired by the interaction strategies adopted by the living organisms to survive and propagate in the ecosystem. The concept aims to achieve optimal survivability in the ecosystem by considering the harm and benefits received from other organisms. In this manuscript the SOS algorithm is formulated to solve multi-objective problems (termed as MOSOS). The MOSOS is combined with adaptive penalty function to handle equality and inequality constrains associated with problems. Extensive simulation studies are carried out on twelve unconstrained and six constrained benchmark multi-objective functions. The obtained results over fifty independent runs reveal the superior performance of the proposed algorithm over multi-objective colliding bodies optimization (MOCBO), multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and two gradient based multi-objective algorithms Multi-Gradient Explorer (MGE) and Multi-Gradient Pathfinder (MGP). The engineering applications of the proposed algorithm are demonstrated by solving two constrained truss design problems.