A Comparative Study Of Classification Techniques For Fire

2 hours ago A Comparative Study of Classification Techniques for Fire Data Set Rachna Raghuwanshi M.Tech CSE Gyan Ganga Institute of Technology & Science, Jabalpur Abstract:Classification of data has become an important research area. The process of classifying documents into predefined categories based on their content is Text classification.

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6 hours ago 1 A Comparative Study of Classification Techniques for Fire Data Set . By Rachna Raghuwanshi. Abstract. Abstract:Classification of data has become an important research area. The process of classifying documents into predefined categories based on their content is Text classification. we study classification techniques and use of them in
1. Author: Rachna Raghuwanshi

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1 hours ago Prajapati H., Raval H., Joshi H. (2021) A Comparative Study of Classification Techniques in Context of Microblogs Posted During Natural Disaster. In: Kotecha K., Piuri V., Shah H., Patel R. (eds) Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 52.

Author: Harshadkumar Prajapati, Hitesh Raval, Hardik Joshi
Publish Year: 2021

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4 hours ago ISO 5658-2 is a method for assessing the fire performance of vertically oriented materials, and it is already used by the ship [12] and train [13] industries. In this study we have compared the three test methods (ISO 3795, ISO 6941 and ISO 5658-2) for 11 materials commonly used in bus passenger areas. 2 Materials and Methods 2.1 Tested products

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8 hours ago A comparative study of test methods for assessment of fire safety performance of bus interior materials Michael Försth*,†, Håkan Modin and Björn Sundström SP Technical Research Institute of Sweden, Fire Technology, Box 857, S-501 15, Borås, Sweden SUMMARY This work concerns the assessment of fire performance of interior materials in buses.

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1 hours ago A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA among all the classification methods. This study would be helpful speed detection devices, fire and burglary alarms, has helped various monitoring and tracking

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3 hours ago A Comparative Study of Traffic Classification Techniques for Smart City Networks. Razan M. AlZoman 1, 2 Some studies have shown a comparative analysis of traffic classification based on machine learning utilizing different datasets, such as the backbone network, while others have used machine learning for traffic classification or

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5 hours ago Smart city networks involve many applications that impose specific Quality of Service (QoS) requirements, thus representing a challenging scenario for network management. Solutions aiming to guarantee QoS support have not been deployed in large-scale networks. Traffic classification is a mechanism used to manage different aspects, including QoS …

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8 hours ago A Comparative Study of Different Classification Techniques for Sentiment Analysis 2018) contributed some methods which we have applied in our work. computation of weight and classification. The research study (Barbosa L., & Feng J., 2010) designed a two-step analysis method which was an automatic sentiment analysis for

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Just Now Also, the data set was not evaluated for outlier or entry errors. A. Gupta, et al, [9] undertook a comparative study of the classification of algorithms on accidents and crime in Denver city of

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2 hours ago Comparative study on machine learning algorithms for early fire forest detection system using geodata. October 2020 International Journal of Electrical and Computer Engineering 10(5):5507

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4 hours ago A Comparative Study of Classification Methods in Financial Risk Detection Abstract: Early detection of financial risks can help credit grantors and institutions to establish appropriate policies for credit products, reduce losses and increase revenue. In recent years, the application of data mining techniques, such as classification and

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4 hours ago methods provide very high classification accuracy and CNN outperformed the comparative methods. This study concludes that thermal infrared hyperspectral emissivity data has the potential to discern vegetation species using state of the art machine learning and deep learning methods. 1. INTRODUCTION 1.1 Background . Identifying tree species

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5 hours ago CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In fire risk, correct description of topographic and fuel properties is critical to improve fire danger assessment and fire behaviour modelling. Many rural areas are now scanned using LIDAR sensors. In some of these areas the information registered by the sensor includes not only the geometric …

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3 hours ago Different test methods were used to predict [7] S. García, J. Luengo and F. Herrera, Data preprocessing in data mining. the outcomes for same classification methods. This study has Switzerland: Springer, 2015. found that various crime patterns have heightened in particular [8] R. Deb, A. W. C. Liew, Incorrect attribute value detection for seasons.

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2 hours ago This study aims to predict forest fires in North Lebanon in order to reduce fire occurrence based on 4 meteorological parameters (Temperature, Humidity, Precipitation and Wind speed) using different data mining techniques: Neural networks, decision tree (J48), fuzzy logic, support vector machine (SVM) and linear discriminant analysis (LDA).

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4 hours ago A Comparative Study of Data Mining In this section, we focus on the classification techniques used in this study and also their related work that has done in IDS. Dickerson et al. [9]designed FIRE Intrusion Detection System. To procedure network stimulus data,

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Frequently Asked Questions

Which decision tree based classifiers are best for spatial classification of wildfires?

In this study, we analyzed the spatial pattern of wildfires with five decision tree based classifiers, including alternating decision tree (ADT), classification and regression tree (CART), functional tree (FT), logistic model tree (LMT), and Naïve Bayes tree (NBT).

Which machine learning methods are used to classify vegetation species?

Three different machine learning methods including support vector machine (SVM), artificial neural network (ANN) and convolutional neural network (CNN) are used to classify thirteen vegetation species and their performance is assessed based on their overall accuracy. The accuracy obtained by CNN, ANN and SVM is 99%, 94% and 91%, respectively.

What are the kappa indices for the different classifiers?

The Kappa indices for the various classifiers ranged from 0.886 (ADT) to 0.684 (NBT) for the training dataset and from 0.757 (ADT) to 0.609 (NBT) for the validation dataset ( Table 3 ), indicating a substantial (Kappa > 0.6) to almost perfect (Kappa > 0.8) agreement between predicted and observed fire events. Table 3.

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