scholarly journals Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Robert A. Sowah ◽  
Kwaku Apeadu ◽  
Francis Gatsi ◽  
Kwame O. Ampadu ◽  
Baffour S. Mensah
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Convolutional Neural Networks ◽  
Fire Detection ◽  
Web Based ◽  
Notification System ◽  
Multisensor Data Fusion ◽  
Standard Fire ◽  
Area Of Interest ◽  
Detection Technology

This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.

Usage of Machine Learning In Strategy Planning On A Web-Based Business Platforms

2021 ◽  
Vol 12 (1) ◽  
pp. 38-38
Author(s):  
Fedor Zagumennov ◽  
Andrei Bystrov ◽  
Alexey Radaykin ◽  
Paschenko V.V.
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Demand Analysis ◽  
Erp Systems ◽  
Web Based ◽  
Income Data ◽  
Strategy Planning ◽  
Business Functions

This paper describes the practical usage of 1D convolutional neural networks in business platforms for such tasks as income prediction, procurements and order demand analysis. The structure of the CNN model is provided, as well as the results of experiments with real orders, procurements and income data. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Currently web-based platforms featuring advanced business functions are rapidly growing. Their new functions can use classic and modern concepts. The comparison between several approaches, including machine learning and regression are provided. Technologies used in such platforms are provided and analyzed. The structures of a such specific web-platforms frontend and backend systems are observed. Other prospective ideas of usage are formulated. Keywords: Business, Neural, Networks, CNN, Platform

Detection of Carolina Geranium (Geranium carolinianum) Growing in Competition with Strawberry Using Convolutional Neural Networks

Weed Science ◽  
2018 ◽  
Vol 67 (2) ◽  
pp. 239-245 ◽  
Author(s):  
Shaun M. Sharpe ◽  
Arnold W. Schumann ◽  
Nathan S. Boyd
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Future Research ◽  
Study Objective ◽  
Weed Interference ◽  
Detection Technology ◽  
Optimal Approach ◽  
In Situ Detection

AbstractWeed interference during crop establishment is a serious concern for Florida strawberry [Fragaria×ananassa(Weston) Duchesne ex Rozier (pro sp.) [chiloensis×virginiana]] producers. In situ remote detection for precision herbicide application reduces both the risk of crop injury and herbicide inputs. Carolina geranium (Geranium carolinianumL.) is a widespread broadleaf weed within Florida strawberry production with sensitivity to clopyralid, the only available POST broadleaf herbicide.Geranium carolinianumleaf structure is distinct from that of the strawberry plant, which makes it an ideal candidate for pattern recognition in digital images via convolutional neural networks (CNNs). The study objective was to assess the precision of three CNNs in detectingG. carolinianum. Images ofG. carolinianumgrowing in competition with strawberry were gathered at four sites in Hillsborough County, FL. Three CNNs were compared, including object detection–based DetectNet, image classification–based VGGNet, and GoogLeNet. Two DetectNet networks were trained to detect either leaves or canopies ofG. carolinianum. Image classification using GoogLeNet and VGGNet was largely unsuccessful during validation with whole images (Fscore<0.02). CNN training using cropped images increasedG. carolinianumdetection during validation for VGGNet (Fscore=0.77) and GoogLeNet (Fscore=0.62). TheG. carolinianumleaf–trained DetectNet achieved the highestFscore(0.94) for plant detection during validation. Leaf-based detection led to more consistent detection ofG. carolinianumwithin the strawberry canopy and reduced recall-related errors encountered in canopy-based training. The smaller target of leaf-based DetectNet did increase false positives, but such errors can be overcome with additional training images for network desensitization training. DetectNet was the most viable CNN tested for image-based remote sensing ofG. carolinianumin competition with strawberry. Future research will identify the optimal approach for in situ detection and integrate the detection technology with a precision sprayer.

Wildfire detection and monitoring by using PRISMA hyperspectral data and convolutional neural networks

2021 ◽  
Author(s):  
Dario Spiller ◽  
Luigi Ansalone ◽  
Nicolas Longépé ◽  
James Wheeler ◽  
Pierre Philippe Mathieu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Near Infrared ◽  
Fire Detection ◽  
Input Image ◽  
Hyperspectral Data ◽  
Observation Data ◽  
Hyperspectral Camera

&lt;p&gt;Over the last few years, wildfires have become more severe and destructive, having extreme consequences on local and global ecosystems. Fire detection and accurate monitoring of risk areas is becoming increasingly important. Satellite remote sensing offers unique opportunities for mapping, monitoring, and analysing the evolution of wildfires, providing helpful contributions to counteract dangerous situations.&lt;/p&gt;&lt;p&gt;Among the different remote sensing technologies, hyper-spectral (HS) imagery presents nonpareil features in support to fire detection. In this study, HS images from the Italian satellite PRISMA (PRecursore IperSpettrale della Missione Applicativa) will be used. The PRISMA satellite, launched on 22 March 2019, holds a hyperspectral and panchromatic&amp;#160; payload which is able to acquire images with a worldwide coverage. The hyperspectral camera works in the spectral range of 0.4&amp;#8211;2.5 &amp;#181;m, with 66 and 173 channels in the VNIR (Visible and Near InfraRed) and SWIR (Short-Wave InfraRed) regions, respectively. The average spectral resolution is less than 10 nm on the entire range with an accuracy of &amp;#177;0.1 nm, while the ground sampling distance of PRISMA images is about 5 m and 30 m for panchromatic and hyperspectral camera, respectively.&lt;/p&gt;&lt;p&gt;This work will investigate how PRISMA HS images can be used to support fire detection and related crisis management. To this aim, deep learning methodologies will be investigated, as 1D convolutional neural networks to perform spectral analysis of the data or 3D convolutional neural networks to perform spatial and spectral analyses at the same time. Semantic segmentation of input HS data will be discussed, where an output image with metadata will be associated to each pixels of the input image. The overall goal of this work is to highlight how PRISMA hyperspectral data can contribute to remote sensing and Earth-observation data analysis with regard to natural hazard and risk studies focusing specially on wildfires, also considering the benefits with respect to standard multi-spectral imagery or previous hyperspectral sensors such as Hyperion.&lt;/p&gt;&lt;p&gt;The contributions of this work to the state of the art are the following:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Demonstrating the advantages of using PRISMA HS data over using multi-spectral data.&lt;/li&gt; &lt;li&gt;Discussing the potentialities of deep learning methodologies based on 1D and 3D convolutional neural networks to catch spectral (and spatial for the 3D case) dependencies, which is crucial when dealing with HS images.&lt;/li&gt; &lt;li&gt;Discussing the possibility and benefit to integrate HS-based approach in future monitoring systems in case of wildfire alerts and disasters.&lt;/li&gt; &lt;li&gt;Discussing the opportunity to design and develop future missions for HS remote sensing specifically dedicated for fire detection with on-board analysis.&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;To conclude, this work will raise awareness in the potentialities of using PRISMA HS data for disasters monitoring with specialized focus on wildfires.&lt;/p&gt;

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