Analysis of Student Performance by using Data Mining Concept

Data mining methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. Its related with developing and improving methods for discovering knowledge from data that extract from educational domain. This paper represents to categorize the students into grade order in all their education studies and it helps to improve ‘students’ academic performance. In this format of paper we used educational data mining techniques to improve ‘students’ performance, and detects the problem of low academic performance of students. For evaluation of better performance of students we use some interesting techniques like association rules, clustering, classification and outlier detection. DOI: 10.17762/ijritcc2321-8169.15059

Improving Time and Position Resolution of RPC detectors using Time Over Threshold Information

INO-ICAL is a proposed underground particle physics experiment to study the neutrino oscillation parameters by detecting neutrinos produced in the atmospheric air showers. Iron CALorimeter (ICAL) is to have 151 layers of iron stacked vertically, with active detector elements in between the iron layers. The iron layers will be magnetized to enable the measurement of momentum and charge of the $\mu^-$ (or $\mu^+$) produced by $\nu_\mu$ (or $\bar{\nu}_\mu$) interactions. Resistive Plate Chambers (RPCs) have been chosen as the active detector elements due to their large area coverage, uncompromised sensitivity, consistent performance for decades, as well as cost effectiveness. The major factors that decide the physics potential of the ICAL experiment are efficiency, position resolution and time resolution of the large area RPCs. A prototype detector called miniICAL (with 11 iron layers) was commissioned to understand the engineering challenges in building the large scale magnet and its ancillary systems, and also to study the performance of the RPC detectors and readout electronics developed by the INO collaboration. As part of the performance study of the RPC detectors, an attempt is made to improve the position and time resolution of them. Even a small improvement in the position and time resolution will help to improve the measurements of momentum and directionality of the neutrinos in ICAL. The Time-over-Threshold (ToT) of the RPC pulses (signals) is recorded by the readout electronics. ToT is a measure of the pulse width and consequently the amplitude. This information is used to improve the time and position resolution of the RPCs and consequently INO physics potential

Magnetic field simulations and measurements on the mini-ICAL detector

The ICAL (Iron Calorimeter) is a 51 kTon magnetized detector proposed by the INO collaboration. It is designed to detect muons with energies in the 1-20 GeV range. A magnetic field of about 1.5 T in the ICAL detector will be generated by passing a DC current through suitable copper coils. This will enable it to distinguish between muons and anti-muons that will be generated from the interaction of atmospheric muon neutrinos and anti-neutrinos with iron. This will help in resolving the open question of mass ordering in the neutrino sector. Apart from charge identification, the magnetic field will be used to reconstruct the muon momentum (direction and magnitude). Therefore it is important to know the magnetic field in the detector as accurately as possible. We present here an (indirect) measurement of the magnetic field in the 85 ton prototype mini-ICAL detector working in Madurai, Tamil Nadu, for different coil currents. A detailed 3-D finite element simulation was done for the mini-ICAL geometry using Infolytica MagNet software and the magnetic field was computed for different coil currents. This paper presents, for the first time, a comparison of the magnetic field measured in the air gaps with the simulated magnetic field, to validate the simulation using real time data. Using the simulations the magnetic field inside the iron is estimated.Comment: 20 pages, 22 figures, latex sourc

A Learning Automata Based Solution to Service Selection in Stochastic Environments

With the abundance of services available in today’s world, identifying those of high quality is becoming increasingly difficult. Reputation systems can offer generic recommendations by aggregating user provided opinions about service quality, however, are prone to ballot stuffing and badmouthing . In general, unfair ratings may degrade the trustworthiness of reputation systems, and changes in service quality over time render previous ratings unreliable. In this paper, we provide a novel solution to the above problems based on Learning Automata (LA), which can learn the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational complexity. In additional to its computational simplicity, unlike most reported approaches, our scheme does not require prior knowledge of the degree of any of the above mentioned problems with reputation systems. Instead, it gradually learns which users provide fair ratings, and which users provide unfair ratings, even when users unintentionally make mistakes. Comprehensive empirical results show that our LA based scheme efficiently handles any degree of unfair ratings (as long as ratings are binary). Furthermore, if the quality of services and/or the trustworthiness of users change, our scheme is able to robustly track such changes over time. Finally, the scheme is ideal for decentralized processing. Accordingly, we believe that our LA based scheme forms a promising basis for improving the performance of reputation systems in general

Magnetic study of some silver salts

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