IEEE Organizations related to Convolutional Neural Networks

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Conferences related to Convolutional Neural Networks

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Periodicals related to Convolutional Neural Networks

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No periodicals are currently tagged "Convolutional Neural Networks"


Most published Xplore authors for Convolutional Neural Networks

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Xplore Articles related to Convolutional Neural Networks

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Correction for “3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2D Trained Network” [Jun 18 1522-1534]

IEEE Transactions on Medical Imaging, 2018

In[1], please note the updated figure captions for Figures 5, 6, 7, and 8 as follows:


Comparison of Pooling Methods for Handwritten Digit Recognition Problem

2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018

Convolutional Neural Networks (CNNs) are used as a current approach to the recognition of handwritten digits for the design of pattern recognition systems. The fact that Convolutional neural networks have a multilayered structure and a large number of items in each layer increases the level of complexity. Increasing the level of complexity makes extremely difficult to discover the optimum configuration ...


Handwriting recognition using Deep Learning in Keras

2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018

Nowadays, deep learning is playing an important role in the domain of image classification. In this paper, a Python library known as Keras, is used for classification of MNIST dataset, a database with images of handwritten images. Two architectures - feed forward neural networks and convolutional neural networks are used for feature extraction and training of model, which is optimized ...


Durian Types Recognition Using Deep Learning Techniques

2018 9th IEEE Control and System Graduate Research Colloquium (ICSGRC), 2018

Fruit or plant recognition is a very pragmatic and specific application of deep-learning technique. As compared to conventional method, the technique requires a larger quantity of data for training while at the same time promises a higher level of accuracy. Among various classes of neural network, convolutional neural network (CNN) is arguably the most commonly used method in image classification. ...


Corrections to “Deep Recurrent Neural Networks for Hyperspectral Image Classification” [Jul 17 3639-3655]

IEEE Transactions on Geoscience and Remote Sensing, 2018

Here, we correct some errors caused by a programming bug (a data type error) in overall accuracies (OAs) reported in[1]. The corrected OAs are underlined and shown in bold inTables I–III.


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Educational Resources on Convolutional Neural Networks

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IEEE.tv Videos

Towards On-Chip Optical FFTs for Convolutional Neural Networks - IEEE Rebooting Computing 2017
ICRA 2020 Keynote - Can Deep Reinforcement Learning from pixels be made as efficient as from state?
20 Years of Neural Networks: A Promising Start, A brilliant Future- Video contents
Spike Timing, Rhythms, and the Effective Use of Neural Hardware
Artificial Neural Networks, Intro
Convolutional Drift Networks for Spatio-Temporal Processing: IEEE Rebooting Computing 2017
Emergent Neural Network in reinforcement learning
Lizhong Zheng's Globecom 2019 Keynote
Large-scale Neural Systems for Vision and Cognition
Deep Learning and the Representation of Natural Data
Behind Artificial Neural Networks
ICASSP 2010 - Advances in Neural Engineering
Complex-Valued Neural Networks
Complex Valued Neural Networks: Theory and Applications
A Comparator Design Targeted Towards Neural Net - David Mountain - ICRC San Mateo, 2019
Learning with Memristive Neural Networks: Neuromorphic Computing - Joshua Yang at INC 2019
High Throughput Neural Network based Embedded Streaming Multicore Processors - Tarek Taha: 2016 International Conference on Rebooting Computing
Spiking Network Algorithms for Scientific Computing - William Severa: 2016 International Conference on Rebooting Computing
Active Space-Body Perception and Body Enhancement using Dynamical Neural Systems
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing

IEEE-USA E-Books

  • Correction for “3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2D Trained Network” [Jun 18 1522-1534]

    In[1], please note the updated figure captions for Figures 5, 6, 7, and 8 as follows:

  • Comparison of Pooling Methods for Handwritten Digit Recognition Problem

    Convolutional Neural Networks (CNNs) are used as a current approach to the recognition of handwritten digits for the design of pattern recognition systems. The fact that Convolutional neural networks have a multilayered structure and a large number of items in each layer increases the level of complexity. Increasing the level of complexity makes extremely difficult to discover the optimum configuration for these networks. Modelling the layers of convolutional neural networks independently may be an effective solution to overcome this difficulty and successfully classify images. In this paper, we have investigated the design of the pool layer, which is one of the three basic layers of convoluted neural networks, with optimum techniques. For this purpose primarily a basic convolutional network structure is formed. Then effect of different pooling methods on the classification performance of these networks was investigated using the MNIST dataset. In this study, in addition to the average and maximum pooling methods commonly used in the literature, Mixed pooling, Stochastic pooling, random pooling, Gaussian pooling, median pooling, minimum pooling methods are also used. Experimental results show that the Mixed pooling method has highest accuracy and minimum pooling method has the lowest accuracy .

  • Handwriting recognition using Deep Learning in Keras

    Nowadays, deep learning is playing an important role in the domain of image classification. In this paper, a Python library known as Keras, is used for classification of MNIST dataset, a database with images of handwritten images. Two architectures - feed forward neural networks and convolutional neural networks are used for feature extraction and training of model, which is optimized using Stochastic Gradient Descent. This paper gives an overview of multi-class classification of these images using these models, and their performance evaluation in terms of various metrics. It is observed that convolutional neural networks achieve a greater accuracy as compared to feedforward neural networks for classification of handwritten digits.

  • Durian Types Recognition Using Deep Learning Techniques

    Fruit or plant recognition is a very pragmatic and specific application of deep-learning technique. As compared to conventional method, the technique requires a larger quantity of data for training while at the same time promises a higher level of accuracy. Among various classes of neural network, convolutional neural network (CNN) is arguably the most commonly used method in image classification. The aim of this research work is to develop an effective method to classify the various cultivars of Durio zibethinus (or commonly known as durian) based on the crop's visual features via the application of CNN to improve the accuracy and speed of the cultivars recognition. Meanwhile, a reliable database consisting of labelled durian cultivars has been created. A total of 800 images consisting of the bottom view of 3 classes of cultivars and non-durian images are used during the training process of the neural network. The research work starts with the pre- processing and conversion of the images then followed by one-hot labelling of the data, construction of the network architecture, training and validation of the model then lastly exporting the trained model for general application. Important system parameters and prediction accuracy are obtained, including the graphs of loss function and accuracy against the number of epochs, confusion matrix, miss-classified images, the effect of network architecture on prediction performance, etc. The prediction accuracy of the trained model on the perfect bottom-view images of Durio zibethinus is 82.50%. With the addition of non-durian images, the prediction accuracy is slightly dropped to 81.25%.

  • Corrections to “Deep Recurrent Neural Networks for Hyperspectral Image Classification” [Jul 17 3639-3655]

    Here, we correct some errors caused by a programming bug (a data type error) in overall accuracies (OAs) reported in[1]. The corrected OAs are underlined and shown in bold inTables I–III.

  • Neural network model of pumping units in oil preparation and pumping complex

    Methods of improving the efficacy of oil extraction are highly sought after. One way to achieve such an improvement is to reduce the downtime of equipment and to implement measures to increase oil recovery for the entire life cycle of the well based on analysis of operational data. The work we present in this paper is aimed at improving a model of pumping units of the oil preparation and pumping complex. For this purpose, we employ an approach based on computational intelligence techniques and in particular an approach that is based on recurrent neural networks in combination with convolutional neural networks to address the problem of operative analysis of telemetric data from pumping units and to forecast the state of technological equipment. Our proposed approach provides an attractive model of optimizing the parameters of pumping equipment and thus a useful avenue of improving the efficacy of oil extraction.

  • A new stochastic mutiplier for deep neural networks

    An XNOR gate is the most commonly used multiplier in bipolar encoded stochastic deep neural networks, but it is not suitable due to the inaccuracy in processing near-zero values. In this paper, we introduce a novel circuit that multiplies near-zero values more accurately and assess its performance with MNIST and CIFAR-10. For the CIFAR-10 dataset, the use of the proposed multipliers gives accuracy of 60.59%, improving by 11.64%p over the XNOR multiplier implementation.

  • Review on fraud detection methods in credit card transactions

    Cashless transactions such as online transactions, credit card transactions, and mobile wallet are becoming more popular in financial transactions nowadays. With increased number of such cashless transaction, number of fraudulent transactions are also increasing. Fraud can be distinguished by analyzing spending behavior of customers (users) from previous transaction data. If any deviation is noticed in spending behavior from available patterns, it is possibly of fraudulent transaction. To detect fraud behavior, bank and credit card companies are using various methods of data mining such as decision tree, rule based mining, neural network, fuzzy clustering approach, hidden Markov model or hybrid approach of these methods. Any of these methods is applied to find out normal usage pattern of customers (users) based on their past activities. The objective of this paper is to provide comparative study of different techniques to detect fraud.

  • Handwritten digit recognition based on depth neural network

    Neural network and depth learning have been widely used in the field of image processing. Good recognition results are often required for complex network models. But the complex network model makes training difficult and takes a long time. In order to obtain a higher recognition rate with a simple model, the BP neural network and the convolutional neural network are studied separately and verified on the MNIST data set. In order to improve the recognition results further, a combined depth network is proposed and validated on the MNIST dataset. The experimental results show that the recognition effect of the combined depth network is obviously better than that of a single network. A more accurate recognition result is achieved by the combined network.

  • Classification of Metaphase Chromosomes Using Deep Learning Neural Network

    Karyotyping of Banded Metaphase Chromosomes is one of the preliminary steps used in cytogenetics to analyze the chromosomes for diagnostic purposes. Deep learning is a subfield of machine learning concerned with structure and function of brain. It exploits a way to automate predictive analysis. The key aspect of deep learning is that the layers of features are not designed by human engineers. They are learned from data using a general purpose learning procedure. This paper proposes a convolution based deep learning to classify the chromosomes for automated karyotyping. The developed architecture allows us to train and test images that helps in predicting the chromosome abnormality. The performance analysis is based on loss and accuracy curves and the graphical representation clearly exhibits better classification results for this architecture.



Standards related to Convolutional Neural Networks

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No standards are currently tagged "Convolutional Neural Networks"