Performance analysis of cloud-based cve communication architecture in comparison with the traditional client server, p2p and hybrid models

Gital et al. (2014) proposed a cloud based communication architecture for improving efficiency of collaborative virtual environment (CVE) systems in terms of Scalability and Consistency requirements. This paper evaluates the performance of the proposed CVE architecture. The metrics use for the evaluation is response time. We compare the cloud-based architecture to the traditional client server and peer-2–peer (P2P) architecture. The comparison was implemented in the CVE systems. The comparative simulation analysis of the results suggested that the CVE architecture based on cloud computing can significantly improve the performance of the CVE system

Modified low energy adaptive clustering hierarchy protocol for efficient energy consumption in wireless sensor networks

In this paper, we propose a Modified Low-Energy Adaptive Clustering Hierarchy (MoLEACH) protocol to improve energy consumption in in Wireless Sensor Networks. The novelty of MoLEACH is that, unlike the original LEACH that uses the residual energy of the network, it considers the residual energy of each node for calculation of the threshold value for the next round in cluster head selection. We make comparative simulation analysis between the MoLEACH and LEACH in testing different parameters such as first node dead, half node dead, and the effect of the number of nodes to the network lifetime. The simulation results show that the number of nodes affects the network lifetime in which increments of number of nodes decrease the network lifetime. In small area, minimum number of nodes is better for network lifetime in both MoLEACH and LEACH protocols. Hence, MoLEACH shows improvement of energy efficiency over the LEAC

Comparing performances of Markov Blanket and Tree Augmented Naïve-Bayes on the IRIS dataset

This research investigates the performances of the Markov Blanket (MB) and Tree Augmented Naïve-Bayes Network (TAN) of the Bayesian Network structure of the IRIS dataset. For evaluation purposes, the performances of the TAN, and MB classifiers were measured using statistical indices. Experimental results strongly suggested that the TAN is better than MB on training dataset and vise vasa in the test dataset. In the other hand, time computational complexity of both the classifiers was found to be equal. The result obtained in this research is of significance to researchers intending to use Bayesian Network to create a classifier for enhancing the performance of biometrics system

Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories

This paper present a novel approach to crude oil price prediction based on co-active neuro-fuzzy inference systems (CANFIS) instead of the commonly use fuzzy neural network and adaptive network-based fuzzy inference systems due to superiority and robustness of the CANFIS model. Monthly data of West Texas Intermediate crude oil price and organization for economic co-operation and development (OECD) inventories, obtained from US Department of Energy were used to built the propose model. The CANFIS prediction model was trained, validated and tested. The performance of our approach is measured using mean square error, root mean square error, mean absolute error and regression. Suggestion from the results shows that the CANFIS demonstrated a high level of generalization capability with relatively very low error and high correlation which exhibited successful prediction performance of the proposal. The model has the potential of being developed into real life systems for use by both government and private businesses for making strategic planning that can boost economic activities

Intelligent decision support systems for oil price forecasting

This research studies the application of hybrid algorithms for predicting the prices of crude oil. Brent crude oil price data and hybrid intelligent algorithm (time delay neural network, probabilistic neural network, and fuzzy logic) were used to build intelligent decision support systems for predicting crude oil prices. The proposed model was able to predict future crude oil prices from August 2013 to July 2014. Future prices can guide decision makers in economic planning and taking effective measures to tackle the negative impact of crude oil price volatility. Energy demand and supply projection can effectively be tackled with accurate forecasts of crude oil prices, which in turn can create stability in the oil market. The future crude oil prices predict by the intelligent decision support systems can be used by both government and international organizations related to crude oil such as organization of petroleum exporting countries (OPEC) for policy formulation in the next one year