A disaster classification application using convolutional neural network by performing data augmentation

Sobhana, M.; Ch, Smitha Chowdary; Indira, D. N. V. S. L. S.; Kumar, K. Kranthi

doi:10.11591/ijeecs.v27.i3.pp1712-1720

Cited by 8 publications

(3 citation statements)

References 22 publications

(22 reference statements)

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“…The purpose of the work is not only to determine the optimal methods for solving this problem but also to develop recommendations for their practical application. The importance of this work can hardly be overestimated, given the possible consequences of fires [13] and the need to increase the efficiency of early warning systems [14], [15]. The research results are expected to make significant contributions to the fields of remote sensing, machine learning, and environmental security.…”

Section: Introductionmentioning

confidence: 96%

Predicting and detecting fires on multispectral images using machine learning methods

Aitimov,

Kaldarova,

Kassymova

et al. 2024

IJECE

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In today's world, fire forecasting and early detection play a critical role in preventing disasters and minimizing damage to the environment and human settlements. The main goal of the study is the development and testing of machine learning algorithms for automated detection of the initial stages of fires based on the analysis of multispectral images. Within the framework of this study, the capabilities of three popular machine learning methods: extreme gradient boosting, logistic regression, and vanilla convolutional neural network (vanilla CNN), are considered in the task of processing and interpreting multispectral images to predict and detect fires. XGBoost, as a gradient-boosted decision tree algorithm, provides high processing speed and accuracy, logistic regression stands out for its simplicity and interpretability, while vanilla CNN uses the power of deep learning to analyze spatial and spectral data. The results of the study show that the integration of these methods into monitoring systems can significantly improve the efficiency of early fire detection, as well as help in predicting potential fires.

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Section: Introductionmentioning

confidence: 96%

Predicting and detecting fires on multispectral images using machine learning methods

Aitimov,

Kaldarova,

Kassymova

et al. 2024

IJECE

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“…RSI often has a high spatial resolution and a poor spectral resolution, or vice versa, as a result of technology compromises linked to data quantity and signal-to-noise ratio (SNR) constraints. The classification procedure is used to identify various feature types on the surface of the earth based on the notion that each has a unique spectral reflectance and emission property [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach to remote sensing image classification based on pixel density distribution and adaptive neural networks

Singh

2024

IJ-AI

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<span lang="EN-US">The proposed study employed a method for identifying the main contents (category/class) that a Remote Sensing Image (RSI) belongs to, as well as the percentage contribution if the image comprises a significant number of different content types. Histogram based approach has been used to extract the pixel density distribution and its normalized form helps to make solution independent from image physical characteristics. A multilayer feedforward Artificial Neural Network (ANN) design has been used to address the classification problem. The architecture included an adaptive form of transfer function, whose slope characteristics changes along with weights as learning progresses. The approach of solution design is computation efficient because it doesn’t apply extensive pre-processing.</span>

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“…This method has a list of words, and finds other words in a huge corpus. Negation is analyzed on eight prominent corpus ranging six different natural language understanding tasks [16]- [18]. This has few negations which are not important to English language, and right predictions can be made by omitting such words or phrases.…”

Section: Introductionmentioning

confidence: 99%

Enhanced negation handling for sentiment analysis on Twitter using deep neural networks

Mylarappa,

Kumar B. N,

J. Gowda

et al. 2023

IJEECS

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<span>Sentiment analysis is a tool to identify and measure the emotion in a piece of text. Negation handling is an important aspect of natural language processing (NLP) for Twitter data. It is a process of correctly interpreting the sentences containing negation words, such as, "never", "no", "neither" and so on. Negation words are used in machine learning to express negative sentiment or indicate the absence of something. In this article, a negation handling technique using deep learning models. Artificial neural networks (ANNs) and convolutional neural networks (CNNs) for classification is proposed. The system is evaluated on SemEval-2017 dataset. The classification performance is improved by using ANN and CNN on the negative tweets. The study aims to improve the classification accuracy by considering negation words in the text. The paper compares the performance of ANNs and CNNs in handling negation words and evaluates them on the tweets data. This study provides insights into the effectiveness of using deep learning techniques for negation handling in sentiment analysis and highlights the importance of considering negation words in the text for improved sentiment analysis performance. The proposed negation strategy attains a superior performance accuracy over machine learning models by preventing misclassified tweets.</span>

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A disaster classification application using convolutional neural network by performing data augmentation

Cited by 8 publications

References 22 publications

Predicting and detecting fires on multispectral images using machine learning methods

Predicting and detecting fires on multispectral images using machine learning methods

A hybrid approach to remote sensing image classification based on pixel density distribution and adaptive neural networks

Enhanced negation handling for sentiment analysis on Twitter using deep neural networks

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