CNN 101: Interactive Visual Learning for Convolutional Neural Networks [PDF]
Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020 The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challenging for learners to take the first steps due to the complexity of deep learning models.
Zijie J. Wang +7 more
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Effect of neural network structure in accelerating performance and accuracy of a convolutional neural network with GPU/TPU for image analytics [PDF]
PeerJ Computer Science, 2022 Background In deep learning the most significant breakthrough in the field of image recognition, object detection language processing was done by Convolutional Neural Network (CNN).
Aswathy Ravikumar +4 more
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Cardamom Grading Using Convolutional Neural Network(CNN)
, 2023 <p><strong><em>Abstract— </em>Cardamom grading is a crucial task in the spice industry, and automating it can significantly reduce the cost and time associated with manual grading while improving accuracy.
Dr. Paulin Paul, Afueth Thomas
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Deep Learning: Basics and Convolutional Neural Networks (CNNs)
, 2023 AbstractDeep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components.
Vakalopoulou, Maria +4 more
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Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN
Energies, 2022 The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional ...
Jiaan Zhang, Chenyu Liu, Leijiao Ge
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Case Studies on Neural Networks for Recognition in Biometric Identity Problem [PDF]
International Journal Bioautomation, 2021 Hand-dorsa vein recognition using a convolutional neural network is presented. Our network contains five convolutional layers and three full connected layers, which have high recognition and more robust.
Zhengwen Shen +3 more
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Convolutional Neural Network-Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data [PDF]
, 2023 The accuracy of most SAR-based flood classification and segmentation derived from semi-automated algorithms is often limited due to complicated radar backscatter.
Kithsiri Perera +7 more
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Improved Convolutional Neural Image Recognition Algorithm based on LeNet-5
Journal of Computer Networks and Communications, 2022 Convolutional neural network (CNN) is a very important method in deep learning, which solves many complex pattern recognition problems. Fruitful results have been achieved in image recognition, speech recognition, and natural language processing ...
Lijie Zhou, Weihai Yu
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Jurnal Saintekom, 2023 The increased use of laptops and smartphones during the COVID-19 pandemic has led to an increase in the number of people suffering from nearsightedness. Convolutional Neural Network (CNN) is a class of deep learning that is capable of recognizing images ...
Pramadika Egamo, Arief Hermawan
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A robust deformed convolutional neural network (CNN) for image denoising
CAAI Transactions on Intelligence Technology, 2022 Abstract Due to strong learning ability, convolutional neural networks (CNNs) have been developed in image denoising. However, convolutional operations may change original distributions of noise in corrupted images, which may increase training difficulty in image denoising.
Qi Zhang 0059 +4 more
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