In recent time, there is an increasing growth in the amount of trading taking place in the currency exchange market. However, effective analysis and simulation tools for performing accurate prediction of these exchange rates are lacking. To alleviate this challenge, this work presents an hybrid machine learning and prediction model by suitably combining the Sample Mean Estimator (SME) simulation architecture with the multiple linear regression technique based training of feed-forward parameters. The developed model has the capability to overcome prediction inaccuracy, inconsistent forecasting, slow response due to computational complexity and scalability problems. The SME method is used to overcome the problems of uncertainty and non-linearity nature of the predictive variable as it’s always affected by economic and political factors. The implementation of the proposed currency exchange rate forecasting system is achieved through the use of a developed in-house Java program with Net Beans as the editor and compiler. Performance comparison between the present system and two baseline methods which are the Autoregressive Moving Average and the Deep Belief network techniques demonstrates that the present forecasting model out-performed the baseline methods studied. The experimental result shows that the precision rate of the present system are equal to or greater than 70%. Therefore, the present foreign exchange predictive system is capable of providing usable, consistent, efficient, faster and accurate prediction to the users consistently at any-time.Keywords- currency exchange,, feed-forward. Forecasting, Sample Mean Estimator, multiple linear regressions, prediction
There has been a global upsurge of interest in the topic of citizenship identity over the past decades, specifically in the world dominated by profound insecurity, inequalities, proliferation of identities, and rise of identity politics,engendered by capitalism. However finding effective solution to these problems has been rendered difficult. To alleviate these problems, this paper presents an analytical Machine learning model that suitably combined the graph signature with random forest techniques. This study presents the design and realization of a novel Intelligent Citizenship Identity through family pedigree using Graph Signature based random forest (GSB-RF) model. The study also showcases the development of a novel graph signature technique referred to as Canonical Code Signature(CCS) method. The CCS method is used at the pre-processing stage of the identification process to build signature for any given tuple. Performance comparisim between the present system and the baseline techniques which includes: the K-Nearest Neighbour and the traditional Random Forest shows that the present system outperformed the baseline method studied. The proposed system shows capability to perform continuous re-identification of Citizens based on their family pedigree with ability to select best sample with low computational complexity, high identification accuracy and speed. Our experimental result shows that the precision rate and identification quality of our system in most cases are equal to or greater than 70%. Therefore, the proposed Citizenship Identification machine is capable of providing usable, consistent, efficient, faster and accurate identification, to the users, security agents, government agents and institutions on-line, real-time and at any-time.Keywords- Canonical code,Citizenship Identity, Family pedigree,Graph-Signature,Machine learning, Random-forest
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