Sliding window and dual-channel CNN (SWDC-CNN): A novel method for synchronous prediction of coal and electricity consumption in cement calcination process
14
Citation
44
Reference
10
Related Paper
Citation Trend
Keywords:
Sliding window protocol
Deep learning has been very successful in dealing with big data from various fields of science and engineering. It has brought breakthroughs using various deep neural network architectures and structures according to different learning tasks. An important family of deep neural networks are deep convolutional neural networks. We give a survey for deep convolutional neural networks induced by 1‐D or 2‐D convolutions. We demonstrate how these networks are derived from convolutional structures, and how they can be used to approximate functions efficiently. In particular, we illustrate with explicit rates of approximation that in general deep convolutional neural networks perform at least as well as fully connected shallow networks, and they can outperform fully connected shallow networks in approximating radial functions when the dimension of data is large.
Deep Neural Networks
Cite
Citations (9)
A method which we call support vector machine with graded resolution (SVM-GR) is proposed in this paper. During the training of the SVM-GR, we first form data granules to train the SVM-GR and remove those data granules that are not support vectors. We then use the remaining training samples to train the SVM-GR. Compared with the traditional SVM, our SVM-GR algorithm requires fewer training samples and support vectors, hence the computational time and memory requirements for the SVM-GR are much smaller than those of a conventional SVM that use the entire dataset. Experiments on benchmark data sets show that the generalization performance of the SVM-GR is comparable to the traditional SVM.
Ranking SVM
Benchmark (surveying)
Sequential minimal optimization
Cite
Citations (30)
Fire detection using computer vision techniques and image processing has been a topic of interest among the researchers. Indeed, good accuracy of computer vision techniques can outperform traditional models of fire detection. However, with the current advancement of the technologies, such models of computer vision techniques are being replaced by deep learning models such as Convolutional Neural Networks (CNN). However, many of the existing research has only been assessed on balanced datasets, which can lead to the unsatisfied results and mislead real-world performance as fire is a rare and abnormal real-life event. Also, the result of traditional CNN shows that its performance is very low, when evaluated on imbalanced datasets. Therefore, this paper proposes use of transfer learning that is based on deep CNN approach to detect fire. It uses pre-trained deep CNN architecture namely VGG, and MobileNet for development of fire detection system. These deep CNN models are tested on imbalanced datasets to imitate real world scenarios. The results of deep CNNs models show that these models increase accuracy significantly and it is observed that deep CNNs models are completely outperforming traditional Convolutional Neural Networks model. The accuracy of MobileNet is roughly the same as VGGNet, however, MobileNet is smaller in size and faster than VGG.
Transfer of learning
Deep Neural Networks
Cite
Citations (55)
Cite
Citations (5)
Purpose The paper aims to address the tracking algorithm based on deep learning and four deep learning tracking models developed. They compared with each other to prevent collision and to obtain target tracking in autonomous aircraft. Design/methodology/approach First, to follow the visual target, the detection methods were used and then the tracking methods were examined. Here, four models (deep convolutional neural networks (DCNN), deep convolutional neural networks with fine-tuning (DCNNFN), transfer learning with deep convolutional neural network (TLDCNN) and fine-tuning deep convolutional neural network with transfer learning (FNDCNNTL)) were developed. Findings The training time of DCNN took 9 min 33 s, while the accuracy percentage was calculated as 84%. In DCNNFN, the training time of the network was calculated as 4 min 26 s and the accuracy percentage was 91%. The training of TLDCNN) took 34 min and 49 s and the accuracy percentage was calculated as 95%. With FNDCNNTL, the training time of the network was calculated as 34 min 33 s and the accuracy percentage was nearly 100%. Originality/value Compared to the results in the literature ranging from 89.4% to 95.6%, using FNDCNNTL, better results were found in the paper.
Transfer of learning
Tracking (education)
Cite
Citations (16)
Mining frequent patterns over data streams is an interesting problem due to its wide application area. The researchers in this field have been facing two key challenges, namely reduction in runtime and memory usage. In this study, a novel method for
Sliding window protocol
Cite
Citations (23)
Real time object detection in traffic surveillance is one of the latest topics in today's world using Region based Convolutional Neural Networks algorithm in comparison with Convolutional Neural Networks. Real-Time Object Detection is performed using Regional Convolutional Neural Networks (N=78) over Convolutional Neural Networks (N=78) with the split size of training and testing dataset 70% and 30% respectively. Regional Convolutional Neural Networks had significantly better accuracy (75.6%) compared to Convolutional Neural Networks (47.7%) and attained significance value of p=0.041. Regional Convolutional Neural Networks achieved significantly better object detection than Convolutional Neural Networks for identifying real-time objects in traffic surveillance.
Cite
Citations (8)
The suggested study's objectives are to develop an unique criterion-based method for classifying RBC pictures and to increase classification accuracy by utilizing Deep Convolutional Neural Networks instead of Conventional CNN Algorithm. Materials and Procedures A dataset-master image dataset of 790 pictures is used to apply Deep Convolutional Neural Network. Convolutional Neural Network and Deep Convolutional Neural Network comparison using deep learning has been suggested and developed to improve classification accuracy of RBC pictures. Using Gpower, the sample size was calculated to be 27 for each group. Results: When compared to Convolutional Neural Network, Deep Convolutional Neural Network had the highest accuracy in classifying blood cell pictures (95.2%) and the lowest mean error (85.8 percent). Between the classifiers, there is a statistically significant difference of p=0.005. The study demonstrates that Deep Convolutional Neural Networks perform more accurately than Conventional Neural Networks while classifying photos of blood cells[1].
Convolution (computer science)
Cite
Citations (2)
Deep learning is now an active research area. Deep learning has done a success in computer vision and image recognition. It is a subset of the Machine Learning. In Deep learning, Convolutional Neural Network (CNN) is popular deep neural network approach. In this paper, we have addressed that how to extract useful leaf features automatically from the leaf dataset through Convolutional Neural Networks (CNN) using Deep Learning. In this paper, we have shown that the accuracy obtained by CNN approach is efficient when compared to accuracy obtained by the traditional neural network.
Deep Neural Networks
Cite
Citations (7)
According to the updating granularity,two kinds of sliding window are defined: continuously updating sliding window and periodically updating sliding window. All the existed sliding window algorithms over data streams are designed for continuously updating sliding window, which are not suitable for periodically updating sliding window. To address this problem, three effective join algorithms for periodically updated sliding window, BSHJ, BSNLJ and BSNHJ, are proposed. Theoretical analysis and experiment results show that BSNHJ achieves optimal performance.
Sliding window protocol
Join (topology)
Granularity
Cite
Citations (1)