The Ability of Ultra Wideband to Differentiate Between Hematoma and Tumor Occur in The Brain

Al-Qerem, Ahmad; Attar, Hani; Alomoush, Waleed; Deif, Mohand

doi:10.1109/eiceeai56378.2022.10050471

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…The experiments were implemented using Python 3.8 on an HP system with an i5 Intel Core processor working at 1.60 GHz and Windows10 operating system. Several Python open-source modules are used to analyze the machine learning models and optimization strategies involved (sklearn, optunity, hyperband, DEAP ) [51][52][53]…”

Section: Methodsmentioning

confidence: 99%

Hyperparameter Optimization of Regression Model for Electrical Load Forecasting During the COVID-19 Pandemic Lockdown Period

2023

IJIES

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Due to global lockdown policies implemented against COVID-19, there has been an impact on electricity consumption. Several countries have emphasized the significance of ensuring electricity supply security during the pandemic to maintain the livelihood of people. Accurate forecasting of electricity demand plays a crucial role in ensuring energy security across all nations; accordingly to achieve this objective, this study employs metaheuristics optimization algorithms to enhance the prediction model's operation, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), at an optimized level to minimize errors. Two metaheuristics optimization methods, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), are utilized. The suggested prediction models are trained using daily power usage data from three US urban regions. In terms of prediction accuracy, the findings show that KNN with PSO surpasses the other models. The COVID-19 pandemic reduced power usage by 20% relative to pre-pandemic levels.

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Section: Methodsmentioning

confidence: 99%