Periocular Region based Gender Identification using Transfer Learning

Kumar, Aishwarya; Seeja, K. R.

doi:10.1016/j.ijcce.2023.07.003

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Transfer learning is a widely recognized and influential methodology [4]. Transfer learning [5] involves utilizing a pre-trained artificial neural network model trained explicitly for a particular task as a foundational framework for a similar study. In this specific instance, the decision was made to employ the ResNet artificial neural network architecture, demonstrating its resilience across various computer vision applications.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Detection With Transfer Learning Architecture Restnet18, Restnet50, Restnet101 on Corn Leaf Disease

Hindarto

2023

JTekInfULM

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The occurrence of diseases that impact the leaves of corn plants presents a substantial obstacle in agriculture, leading to a reduction in the overall yield of crops. This study aims to perform a comparative analysis of transfer learning methodologies by employing three distinct ResNet architectures: ResNet18, ResNet50, and ResNet101. The dataset utilized by the author consists of a compilation of images portraying corn leaves that demonstrate varying levels of disease severity. Transfer learning refers to leveraging a pre-existing ResNet model and retraining the network by employing the corn leaf dataset. The experimental results demonstrate that the ResNet18, ResNet50, and ResNet101 models achieved accuracy rates of 96.68%, 95.73%, and 95.26%, respectively. The ResNet101 model shows superior performance in terms of precision and recall metrics. This research indicates that utilizing a more complex and sophisticated network structure can improve the effectiveness of disease identification in corn plant leaves. The result above is essential in promoting sustainable agricultural methodologies and efficiently managing corn plant diseases.

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Section: Introductionmentioning

confidence: 99%

Comparison of Detection With Transfer Learning Architecture Restnet18, Restnet50, Restnet101 on Corn Leaf Disease

Hindarto

2023

JTekInfULM

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“…ResNet101, and ResNet50, respectively (Kumar & Seeja, 2023). Due to the COVID-19 pandemic, this study aims to address the problem of gender identification when wearing face masks.…”

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confidence: 99%

PyTorch Deep Learning for Food Image Classification with Food Dataset

Iswahyudi,

Hindarto,

Santoso

2023

SinkrOn

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Classification of food images is crucial in today's increasingly digitally connected world. In the rapidly evolving mobile applications and social media era, the demand for an automated system that can recognize food types from an image is intensifying. This study employs deep learning and the PyTorch framework to develop a dependable and efficient solution for classifying food images. This research is motivated by the growing complexity of food introduction challenges. The primary challenge is improving the accuracy of food type recognition and overcoming variations in the visual presentation of food, such as lighting, shooting angles, and proportional and textural differences. Convolutional Neural Networks (CNN) are effective for image classification and are incorporated into the methods utilized. In addition, we employ ResNet101 transfer learning techniques to capitalize on the knowledge of trained models for large image datasets. The primary objective of this study is to develop a food image classification model that is accurate, training-efficient, and capable of accurately recognizing various types of food. In testing and evaluation, the developed model could realize multiple types of food with satisfactory accuracy. The accuracy of training reached 99.35%, while the accuracy of testing reached 94.65%. This study also reveals how Resnet101 transfer learning is utilized by deep learning technology.

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Region based medical image encryption using advanced zigzag transform and 2D logistic sine map (2DLSM)

M B,

Ravi

2023

International Journal of Cognitive Computing in Engineering

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Periocular Region based Gender Identification using Transfer Learning

Cited by 4 publications

References 15 publications

Comparison of Detection With Transfer Learning Architecture Restnet18, Restnet50, Restnet101 on Corn Leaf Disease

Comparison of Detection With Transfer Learning Architecture Restnet18, Restnet50, Restnet101 on Corn Leaf Disease

PyTorch Deep Learning for Food Image Classification with Food Dataset

Region based medical image encryption using advanced zigzag transform and 2D logistic sine map (2DLSM)

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