Journal of Information

Published by: Conscientia Beam
Online ISSN: 2520-7652
Print ISSN: Pending
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No. 1

Real-Time Workload Scheduling (RTWS) Algorithm for Cloud

Pages: 36-52
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Real-Time Workload Scheduling (RTWS) Algorithm for Cloud

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DOI: 10.18488/journal.104/2015.1.1/104.1.36.52

Citation: 1

Sabout Nagaraju , Latha Parthiban

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Sabout Nagaraju , Latha Parthiban (2015). Real-Time Workload Scheduling (RTWS) Algorithm for Cloud. Journal of Information, 1(1): 36-52. DOI: 10.18488/journal.104/2015.1.1/104.1.36.52
Cloud computing is the revenue gain and most advanced technology that has tremendous advantages over other technologies. It can be used as a utility for executing large size of real-time programs. These programs are decomposed into multiple inter-dependent tasks and executed on the multiple virtual processors where the open research issue is to be minimized make-span of the scheduling tasks. Our research aims to address this issue and degenerate the schedule length approximately equal to the available number of virtual processors. We proposed a real-time workload scheduling algorithm that does very well in reducing the number of initial clusters.  The experimental results show that the execution times for various kinds of the DAGs can be reduced as much as possible and improves the performance of the early load scheduling algorithms for distributed cloud environment.
Contribution/ Originality

RLS Fixed-Lag Smoother Using Covariance Information Based on Innovation Approach in Linear Continuous Stochastic Systems

Pages: 23-35
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RLS Fixed-Lag Smoother Using Covariance Information Based on Innovation Approach in Linear Continuous Stochastic Systems

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DOI: 10.18488/journal.104/2015.1.1/104.1.23.35

Citation: 2

Seiichi Nakamori

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  1. S. Nakamori, A. Hermoso-Carazo, and J. Linares-Pérez, "Design of RLS fixed-lag smoother using covariance information in linear discrete stochastic systems," Applied Mathematical Modelling, vol. 34, pp. 1093-1106, 2010.
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  3. S. Nakamori, A. Hermoso-Carazo, and J. Linares-P'erez, "Design of RLS wiener fixed-lag smoother using covariance information in linear discrete stochastic systems," Applied Mathematical Modelling, vol. 32, pp. 1338–1349, 2008.
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  5. S. Nakamori, "RLS fixed-lag smoother using covariance information in linear continuous stochastic systems," Applied Mathematical Modelling, vol. 33, pp. 242-255, 2009.
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Seiichi Nakamori (2015). RLS Fixed-Lag Smoother Using Covariance Information Based on Innovation Approach in Linear Continuous Stochastic Systems. Journal of Information, 1(1): 23-35. DOI: 10.18488/journal.104/2015.1.1/104.1.23.35
This paper newly designs the RLS (recursive least-squares) fixed-lag smoother and filter, based on the innovation theory, in linear continuous-time stochastic systems. It is assumed that the signal is observed with additive white noise and the signal is uncorrelated with the observation noise. It is a characteristic that the estimators use the covariance information of the signal, in the form of the semi-degenerate kernel, and the observation noise. With respect to the RLS fixed-lag smoother, the algorithm for the estimation error variance function is developed to guarantee the stability of the fixed-lag smoother. The proposed estimators have the recursive property in calculating the fixed-lag smoothing and filtering estimates. Also, this paper proposes the Chandrasekhar-type RLS Wiener filter in linear wide-sense stationary stochastic system. Unlike the usual filter including the Riccati-type equations, the Chandrasekhar-type filter does not contain the Riccati-type differential equations and has an advantage of eliminating the possibility of the covariance matrix becoming nonnegative.
Contribution/ Originality

Information about Simulation Software for Testing of Wireless Network

Pages: 12-22
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Information about Simulation Software for Testing of Wireless Network

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DOI: 10.18488/journal.104/2015.1.1/104.1.12.22

Kalpana Chaudhari , P.T. Karule

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Kalpana Chaudhari , P.T. Karule (2015). Information about Simulation Software for Testing of Wireless Network. Journal of Information, 1(1): 12-22. DOI: 10.18488/journal.104/2015.1.1/104.1.12.22
Actual network testing is  time consuming and costly  for researcher  so  Solution for this problem is that to  test  wireless network using open source  and free software   is available for that .The paper  reflects  the  number of software  availability to  network  model design and its performance . Many authors have worked on various QoS parameters using different service classes in WiMAX. WiMAX is wireless technology for fixed and mobile access and also known by IEEE802.16 standard .Now a day’s WiMAX is  fast and suitable wireless technology  for urban and rural area which can cover maximum area with minimum infrastructure. IEEE 802.16e-2005 has been developed for mobile wireless communication which is based on OFDM technology and this enables going towards the 4G mobile in the future. In this paper, simulation model based on 802.16e OFDM-PHY baseband (WIMAX) and demonstrated in simulation scenarios with QPSK to find out the best performance of physical layer for WiMAX Mobile. All the necessary conditions were implemented in the simulation according to the 802.16e OFDMA-PHY specification. The study is conducted on various quality parameters impacting the WiMAX service performance of a WiMAX network and also introduces NS3 simulation software to  test WiMAX  network.  


Contribution/ Originality
This paper contributes the literature study on network simulators availability and   network model design and performance analysis   using NS3 software. The contribution in the paper   is based on number of nodes and their   parameter which are (Design and simulation parameter, QOS) taken in to the consideration. 

Predict Survival of Patients with Lung Cancer Using an Ensemble Feature Selection Algorithm and Classification Methods in Data Mining

Pages: 1-11
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Predict Survival of Patients with Lung Cancer Using an Ensemble Feature Selection Algorithm and Classification Methods in Data Mining

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DOI: 10.18488/journal.104/2015.1.1/104.1.1.11

Citation: 2

Mahdis Dezfuly , Hedieh Sajedi

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  1. L. GloecklerRies, A. M. Reichman, D. Lewis, R. B. F. Hankey, and B. K. Edwards, "Cancer survival and incidence from the surveillance, epidemiology, and end results (SEER) Program," Oncologist, 2003.
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Mahdis Dezfuly , Hedieh Sajedi (2015). Predict Survival of Patients with Lung Cancer Using an Ensemble Feature Selection Algorithm and Classification Methods in Data Mining. Journal of Information, 1(1): 1-11. DOI: 10.18488/journal.104/2015.1.1/104.1.1.11
This research proposes an efficient model for predicting the survival rate of patients affected by lung cancer. The researchers collected data from four feature categories (population, recognition, treatment, and result) of cancer patients based on the importance of the survival of patients with lung cancer. Analyses of the predicted survival rates of the patients indicate that, among the classification algorithms, Decision Tree C5.0 results the highest accuracy. The models were created using algorithms based on the  level of death risk in five stages: six months, nine months, one year, two years, and five years. In this paper, we proposed a mechanism for feature selection. Our mechanism combines the results of some feature section algorithm. The results illustrate that out mechanism outperform other feature selection algorithms. After applying the proposed mechanism for feature selection, the accuracy of the C5.0 algorithm was equivalent to 97.93%.


Contribution/ Originality
This study proposes an Ensemble feature selection algorithm for predict survival of patients with lung cancer.