Abstract: This paper describes the problem of building secure
computational services for encrypted information in the Cloud
Computing without decrypting the encrypted data; therefore, it meets
the yearning of computational encryption algorithmic aspiration
model that could enhance the security of big data for privacy,
confidentiality, availability of the users. The cryptographic model
applied for the computational process of the encrypted data is the
Fully Homomorphic Encryption Scheme. We contribute a theoretical
presentations in a high-level computational processes that are based
on number theory and algebra that can easily be integrated and
leveraged in the Cloud computing with detail theoretic mathematical
concepts to the fully homomorphic encryption models. This
contribution enhances the full implementation of big data analytics
based cryptographic security algorithm.
Abstract: In the oil and gas industry, energy prediction can help
the distributor and customer to forecast the outgoing and incoming
gas through the pipeline. It will also help to eliminate any
uncertainties in gas metering for billing purposes. The objective of
this paper is to develop Neural Network Model for energy
consumption and analyze the performance model. This paper
provides a comprehensive review on published research on the
energy consumption prediction which focuses on structures and the
parameters used in developing Neural Network models. This paper is
then focused on the parameter selection of the neural network
prediction model development for energy consumption and analysis
on the result. The most reliable model that gives the most accurate
result is proposed for the prediction. The result shows that the
proposed neural network energy prediction model is able to
demonstrate an adequate performance with least Root Mean Square
Error.