SMART OPTIMIZER SELECTION TECHNIQUE: A COMPARATIVE STUDY OF MODIFIED DENSNET201 WITH OTHER DEEP LEARNING MODELS

Kamaran Manguri; Aree A. Mohammed

doi:10.35784/iapgos.5332

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Published: Dec 20, 2023

DOI: https://doi.org/10.35784/iapgos.5332

Issue Vol. 13 No. 4 (2023)

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DOI

https://doi.org/10.35784/iapgos.5332

Authors

Kamaran Manguri

kamaran@uor.edu.krd

1Erbil Polytechnic University, Erbil Technical Engineering College, Department of Technical Information System Engineering, 2University of Raparin, Department of Software and Informatics Engineering, Iraq

https://orcid.org/0000-0001-8567-3367

Aree A. Mohammed

kamaran@uor.edu.krd

University of Sulaimani, College of Science, Computer Science Department, Iraq

https://orcid.org/0000-0001-9710-4559

Abstract

The rapid growth and development of AI-based applications introduce a wide range of deep and transfer learning model architectures. Selecting an optimal optimizer is still challenging to improve any classification type's performance efficiency and accuracy. This paper proposes an intelligent optimizer selection technique using a new search algorithm to overcome this difficulty. A dataset used in this work was collected and customized for controlling and monitoring roads, especially when emergency vehicles are approaching. In this regard, several deep and transfer learning models have been compared for accurate detection and classification. Furthermore, DenseNet201 layers are frizzed to choose the perfect optimizer. The main goal is to improve the performance accuracy of emergency car classification by performing the test of various optimization methods, including (Adam, Adamax, Nadam, and RMSprob). The evaluation metrics utilized for the model’s comparison with other deep learning techniques are based on classification accuracy, precision, recall, and F1-Score. Test results show that the proposed selection-based optimizer increased classification accuracy and reached 98.84%.

Keywords:

deep learning, optimization technique, transfer learning, customized dataset, modified DenseNet201

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Manguri, K., & Mohammed, A. A. (2023). SMART OPTIMIZER SELECTION TECHNIQUE: A COMPARATIVE STUDY OF MODIFIED DENSNET201 WITH OTHER DEEP LEARNING MODELS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(4), 39–43. https://doi.org/10.35784/iapgos.5332

SMART OPTIMIZER SELECTION TECHNIQUE: A COMPARATIVE STUDY OF MODIFIED DENSNET201 WITH OTHER DEEP LEARNING MODELS

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