Work place: Department of Statistics, Kogi State Poly Technique, Lokoja, Nigeria
Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computer Architecture and Organization, Solid Modeling, Data Structures and Algorithms
Johnson Oladipupo Samuel is a Mathematics PhD research student of Federal University Lokoja, Kogi State, Nigeria. He obtained B.Sc and M.Sc Mathematics in the year 2006 and 2017 respectively from the Federal University of Abuja, Nigeria. His area of specialization is Mathematical modeling and optimization.
DOI: https://doi.org/10.5815/ijmsc.2023.02.02, Pub. Date: 8 May 2023
This research paper presents a Gaussian process regression (GPR) model for predicting path loss signal in an urban environment. The Gaussian process regression model was developed using a dataset of path loss signal measurements acquired in two urban environments in Nigeria. Three different kernel functions were selected and compared for their performance in the Gaussian process regression model, including the squared exponential kernel, the Matern kernel, and the rotational quadratic kernel. The GPR model was validated and evaluated using various performance metrics and compared with different regression models. The results show that the Gaussian process regression model with the Matern kernel outperforms the linear regression and the support vector regression, but the decision tree and the random forest regression did better than the GPR in both cities. In the city of Port Harcourt, the GPR has a RMSE value of 3.0776 dB, the DTR has 2.0005 dB, the SVR has 3.6047 dB, the RFR has 1.0459 dB, and the LR 3.5947dB. The proposed GPR model provides more accurate and efficient approach to predict path loss compared to traditional methods. The extensive data collection and analysis conducted has resulted in a well-developed and accurate model.[...] Read more.
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