Enhancing Efficiency of Affordable Sensors: An Advanced Neural Network Paradigm with Metaheuristic Optimization Algorithms

Arafin, Tanzila; Mridul, Mahabub Alam; Sadman, Sazid

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dc.contributor.author	Arafin, Tanzila
dc.contributor.author	Mridul, Mahabub Alam
dc.contributor.author	Sadman, Sazid
dc.date.accessioned	2024-01-17T09:30:44Z
dc.date.available	2024-01-17T09:30:44Z
dc.date.issued	2023-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/2045
dc.description	Supervised by Prof. Dr. Ashik Ahmed, Department of Electrical and Electronics Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	This project report gives a thorough investigation into how combining ANN models with metaheuristic optimization methods might improve the performance of low-cost sensors. Although low-cost sensors have a wide range of uses, they frequently lack the accuracy and dependability of their more expensive counterparts. The strength of ANN models is combined with optimization approaches in a novel way to address this restriction. In order to optimize the parameters and design of the neural network, the project focuses on creating an ANN framework that uses metaheuristic techniques like Particle Swarm Optimization (PSO), Osprey Optimization Algorithm(OOA), Driving Training Based Optimization(DTBO), Salp Swarm Algorithm(SSA),Harris Hawk Optimization (HHO. By significantly enhancing the accuracy, precision, and robustness of low-cost sensors, this combination intends to enhance their overall performance.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Metaheuristic, Optimization, PSO, SSA, Artificial Neural Network,HHO,DTBO Hidden Layer, RMSE	en_US
dc.title	Enhancing Efficiency of Affordable Sensors: An Advanced Neural Network Paradigm with Metaheuristic Optimization Algorithms	en_US
dc.type	Thesis	en_US