Journal ArticleDOI
Early fire detection using convolutional neural networks during surveillance for effective disaster management
TLDR
An early fire detection framework using fine-tuned convolutional neural networks for CCTV surveillance cameras, which can detect fire in varying indoor and outdoor environments is proposed and an adaptive prioritization mechanism for cameras in the surveillance system is proposed to ensure the autonomous response.About:
This article is published in Neurocomputing.The article was published on 2017-12-01. It has received 278 citations till now. The article focuses on the topics: Fire detection & Emergency management.read more
Citations
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Journal ArticleDOI
Multi-grade brain tumor classification using deep CNN with extensive data augmentation
TL;DR: A novel convolutional neural network (CNN) based multi-grade brain tumor classification system that is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.
Journal ArticleDOI
Convolutional Neural Networks Based Fire Detection in Surveillance Videos
TL;DR: Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in CCTV surveillance systems compared to state-of-the-art methods.
Journal ArticleDOI
Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications
TL;DR: This paper proposes an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire, which uses smaller convolutional kernels and contains no dense, fully connected layers.
Journal ArticleDOI
A review of machine learning applications in wildfire science and management
Piyush Jain,Piyush Jain,Sean C. P. Coogan,Sriram Subramanian,Mark Crowley,Steve W. Taylor,Mike D. Flannigan +6 more
TL;DR: Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems as discussed by the authors, and it has rapidly accelerated the field's development.
Journal ArticleDOI
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey
TL;DR: This survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations, and investigates the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Posted Content
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
Proceedings ArticleDOI
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.