A Comparative Analysis of AlexNet and GoogLeNet with a Simple DCNN for Face Recognition

Kalaiarasi, P; Rani, P. Esther

doi:10.1007/978-981-15-5029-4_54

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…AlexNet has eight layers whereas GoogleNet is deeper as it has 22 layers. GoogleNet is a promising deep CNN that can avoid the problem of overfitting due to many deep layers with the use of multiple-size filters at the same level of operation [147]. These image recognition networks are used on the ImageNet dataset for various applications.…”

Section: ) Deep Learning Algorithmsmentioning

confidence: 99%

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

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Section: ) Deep Learning Algorithmsmentioning

confidence: 99%

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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“…During the feature extraction process, the AlexNet model is used to generate feature vectors. AlexNet is a commonly employed model, which contains eight layers: five convolutional layers, two fullyconnected hidden layers, and one fully-connected output layer [16]. Second, AlexNet used the ReLU instead of the sigmoid as its activation function.…”

Section: Alexnet-based Feature Extractionmentioning

confidence: 99%

Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification

Fatma¹,

Abdelaziz²

2022

IJNS

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Early detection and classification of skin lesions using dermoscopic images have attracted significant attention in the healthcare sector. Automated skin lesion segmentation becomes tedious owing to the presence of artifacts like hair, skin line, etc. Earlier works have developed skin lesion detection models using clustering approaches. The advances in neutrosophic set (NS) models can be applied to derive effective clustering models for skin lesion segmentation. At the same time, artificial intelligence (AI) tools can be developed for the identification and categorization of skin cancer using dermoscopic images. This article introduces a Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification (NCCOML-SKSC) model. The proposed NCCOML-SKSC model derives a NCC-based segmentation approach to segment the dermoscopic images. Besides, the AlexNet model is exploited to generate a feature vector. In the final stage, the optimal multilayer perceptron (MLP) model is utilized for the classification process in which the MLP parameters are chosen by the use of a whale optimization algorithm (WOA). A detailed experimental analysis of the NCCOML-SKSC model using a benchmark dataset is performed and the results highlighted the supremacy of the NCCOML-SKSC model over the recent approaches.

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