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All Journal Bulletin of Electrical Engineering and Informatics
Bouraya, Sara
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Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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A comparative analysis of activation functions in neural networks: unveiling categories Bouraya, Sara; Belangour, Abdessamad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7274

Abstract

Activation functions (AFs) play a critical role in artificial neural networks, allowing for the modeling of complex, non-linear relationships in data. In this review paper, we provide an overview of the most commonly used AFs in deep learning. In this comparative study, we survey and compare the different AFs in deep learning and artificial neural networks. Our aim is to provide insights into the strengths and weaknesses of each AF and to provide guidance on the appropriate selection of AFs for different types of problems. We evaluate the most commonly used AFs, including sigmoid, tanh, rectified linear units (ReLUs) and its variants, exponential linear unit (ELU), and SoftMax. For each activation category, we discuss its properties, mathematical formulation (MF), and the benefits and drawbacks in terms of its ability to model complex, non-linear relationships in data. In conclusion, this comparative study provides a comprehensive overview of the properties and performance of different AFs, and serves as a valuable resource for researchers and practitioners in deep learning and artificial neural networks.
Dissecting of the two-stages object detection models architecture and performance Bouraya, Sara; Belangour, Abdessamad
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6424

Abstract

Artificial intelligence (AI) is the discipline focused on enabling computers to operate autonomously without explicit programming. Within AI, computer vision is an emerging field tasked with endowing machines with the ability to interpret visual data from images and videos. Over recent decades, computer vision has found applications in diverse fields such as autonomous vehicles, information retrieval, surveillance, and understanding human behavior. Object detection, a key aspect of computer vision, employs deep neural networks to continually advance detection accuracy and speed. Its goal is to precisely identify objects within images or videos and assign them to specific classes. Object detection models typically consist of three components: a backbone network for feature extraction, a neck model for feature aggregation, and a head for prediction. The focus of this study lies on two stage detectors. This study aims to provide a comprehensive review of two stage detectors in object detection, followed by benchmarking to offer insights for researchers and scientists. By analyzing and understanding the efficacy of these models, this research seeks to guide future developments in the field of object detection within computer vision.
Co-Authors Belangour, Abdessamad