Fire Detection with Video Using Fuzzy c-Means and Back-Propagation Neural Network

2011 ◽  
pp. 373-380 ◽  
Author(s):  
Tung Xuan Truong ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Fire Detection ◽  
Back Propagation Neural Network ◽  
Fuzzy C Means

scholarly journals Brain Tumor Classification and Segmentation using DTCW Transform, Back Propagation Neural Network and Spatial Fuzzy C-Means Clustering

2020 ◽  
Vol 9 (5) ◽  
pp. 2073-2079
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Mri ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Mr Images ◽  
Data Set ◽  
Fuzzy C Means ◽  
Tumor Identification ◽  
Fuzzy C Means Clustering

A novel method is presented in this paper for finding brain tumor and classifying it using the back-propagation neural network is proposed. Spatial Fuzzy C-Means clustering is utilized for the segmentation of image to identify the influenced area of brain MRI picture. Automated detection of tumors in brain MR images is urgent in many diagnosis processes. Because of noise, blurred edges, the detection, and classification of brain tumor are very difficult. This paper presents one programmed brain tumor identification strategy to expand the exactness and yield and diminishing the determination time. The objective is ordering the tissues to three classes of typical, start and malignant. The size and the location tumor is very important for doctors for defining the treatment of tumor. The proposed determination strategy comprises of four phases, pre-processing of MR images, feature extraction, and classification. The features are extracted using Dual-Tree Complex wavelet transformation (DTCWT). Back Propagation Neural Network (BPN) is employed for finding brain tumor in MRI images. In the last stage, a productive scheme is proposed for segmentation depends on the Spatial Fuzzy C-Means Clustering. The performance analysis clearly proves that the proposed scheme is more efficient and the efficiency of the scheme is measured with sensitivity and specificity. The evaluation is performed on the image data set of 15 MRI images of brain.

scholarly journals Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 59
Author(s):  
Lesong Wu ◽  
Lan Chen ◽  
Xiaoran Hao
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Back Propagation ◽  
Fire Detection ◽  
Back Propagation Neural Network ◽  
Standard Test ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Trend Extraction ◽  
Fire Scenarios

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.

scholarly journals Random Forest Feature Selection and Back Propagation Neural Network to Detect Fire Using Video

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Jin-Xing Liang ◽  
Jian-Fu Zhao ◽  
Ning Sun ◽  
Bao-Jun Shi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Random Forest ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Fire Detection ◽  
Back Propagation Neural Network ◽  
Detection Algorithm ◽  
Low Sensitivity ◽  
Fire Recognition

As the most common serious disaster, fire may cause a lot of damages. Early detection and treatment of fires are of great significance to ensure public safety and to reduce losses caused by fires. However, traditional fire detectors are facing some focus issues such as low sensitivity and limited detection scenes. To overcome these problems, a video fire detection hybrid method based on random forest (RF) feature selection and back propagation (BP) neural network is proposed. The improved flame color model in RGB and HSI space and the visual background extractor (ViBe) in moving target detection algorithm are used to segment the suspected flame regions. Then, multidimensional features of flames are extracted from the suspected regions, and these extracted features are combined and selected according to the RF feature importance analysis. Finally, a BP neural network model is constructed for multifeature fusion and fire recognition. The test results on several experimental video sets show that the proposed method can effectively avoid feature interference and has an excellent recognition effect on fires in a variety of scenarios. The proposed method is applicable for fire recognition applied in video surveillance and detection robots.

scholarly journals Incorporating FCM and Back Propagation Neural Network for Image Segmentation

2014 ◽  
pp. 16-21
Author(s):  
Ebrahim. Aghajari ◽  
Dr.Mrs. Gharpure Damayanti
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Input Image ◽  
Discrete Wavelet ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm ◽  
The Membership Function

Hybrid image segmentation is proposed in this paper. The input image is firstly preprocessed in order to extract the features using Discrete Wavelet Transform (DWT) .The features are then fed to Fuzzy C-means algorithm which is unsupervised. The membership function created by Fuzzy C-means (FCM) is used as a target to be fed in neural network. Then the Back Propagation Neural network (BPN) has been trained based on targets which is obtained by (FCM) and features as input data. Combining the FCM information and neural network in unsupervised manner lead us to achieve better segmentation .The proposed algorithm is tested on various Berkeley database gray level images.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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