Comparison of Support Vector Machine and Friis Equation For Identification of Pallet-Level Tagging Using RFID Signal

Choong, Chun Sern; Nasir, Ahmad Fakhri Ab.; Majeed, Anwar P. P. Abdul; Zakaria, Muhammad Aizzat; Razman, Mohd Azraai Mohd; Azmi, Azura

doi:10.1109/iscaie47305.2020.9108834

Cited by 3 publications

(1 citation statement)

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“…The adaptive advantage of SVM is that it can be used for linear as well as nonlinear data classification. To generate a greater accuracy in predicting rainfall precipitation in the City of Kigali, a nonlinear classifier (Sern et al, 2020) was utilized in this study. This classifier operates with a third-order kernel function (Said et al, 2015).…”

Section: Fig4 Support Vector Machine Hyperplanementioning

confidence: 99%

Assessment of Rainfall and Climate Change Patterns in the City of Kigali via Machine Learning and IPCC Codex Models and their Impact on Heavy Storms

Bizimana,

Altunkaynak,

Kalin

et al. 2023

Preprint

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Rwanda has been negatively affected by a changing climate, exacerbated by human impact on land and water resources. In most parts of the country the rainfall pattern has changed over the last decades resulting in both enhanced flooding and water shortage / scarcity in much of the country, especially in the Capital City of Kigali and peripheries which is the main economic hub of the country with strong links to the East African region. Changes in precipitation have affected agricultural production, hydropower production, and water supplies, and has been a result of increased heavy storms, and flash floods in the city. This study developed a new predictive model rainfall patterns in the City of Kigali (CoK) in the Republic of Rwanda using evolutionary methodologies that apply machine learning techniques of Fuzzy Inference Systems (FIS) trained via Genetic Algorithms, Neuro Network Systems and a comparative Support Vector Machine tool, and assessment downscaled climate change combinations with predicted rainfall patterns. The models were calibrated and validated using measured rainfall data in the City of Kigali from 1991 through 2023. The model results show the developed Geno Fuzzy Inference System (GENOFIS) model performed better than the Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) models. The Coefficient of Efficiency (CE), and Root Mean Square Error (RMSE) were used as diagnostic measures for model performance evaluation. Models generated with GENOFIS are therefore recommended for rainfall and related prediction pattern in the City of Kigali for climate change adaptation and resilience policy and planning.

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Section: Fig4 Support Vector Machine Hyperplanementioning

confidence: 99%