Tropical cyclone intensity detection by geometric features of cyclone images and multilayer perceptron

Kar, Chinmoy; Kumar, Ashirvad; Banerjee, Sreeparna

doi:10.1007/s42452-019-1134-8

Cited by 26 publications

(10 citation statements)

References 19 publications

(34 reference statements)

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“…In addition, a series of Artificial Neural Network (ANN) layers [17] have been used to predict cyclone occurrences with 98% detection accuracy by using NOAA-AVHRR satellite images. Tropical cyclone intensity detection through geometric features of cyclone images with the help of multilayer perception is proven to be a great predictor with 84% detection accuracy [26] . ANN approach [27] also has a high potential for modeling rainfall due to typhoons in Taiwan, China.…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

Jena

Bhoi

Nayak

et al. 2023

Big Data Min. Anal.

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As a huge number of satellites revolve around the earth, a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis.Therefore, classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones. In this article, a classification approach is proposed using Deep Convolutional Neural Network (DCNN), comprising numerous layers, which extract the features through a downsampling process for classifying satellite cloud images. DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy.Delivery time decreases for testing images, whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances. The satellite images are taken from the Meteorological & Oceanographic Satellite Data Archival Centre, the organization is responsible for availing satellite cloud images of India and its subcontinent. The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.

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Section: Related Workmentioning

confidence: 99%

Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

Jena

Bhoi

Nayak

et al. 2023

Big Data Min. Anal.

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“…The CNN is composed of different types of convolutional layers. Similar to the multilayer neural network [ 27 ], there are fully connected (FC) layers after these convolutional layers. A CNN is built in such a way as to take advantage of the 2D input image structure.…”

Section: Modelmentioning

confidence: 99%

Attention based automated radiology report generation using CNN and LSTM

et al. 2022

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The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients. A new research direction is gaining increasing attention that involves the use of hybrid approaches based on natural language processing and computer vision techniques to create auto medical report generation systems. The auto report generator, producing radiology reports, will significantly reduce the burden on doctors and assist them in writing manual reports. Because the sensitivity of chest X-ray (CXR) findings provided by existing techniques not adequately accurate, producing comprehensive explanations for medical photographs remains a difficult task. A novel approach to address this issue was proposed, based on the continuous integration of convolutional neural networks and long short-term memory for detecting diseases, followed by the attention mechanism for sequence generation based on these diseases. Experimental results obtained by using the Indiana University CXR and MIMIC-CXR datasets showed that the proposed model attained the current state-of-the-art efficiency as opposed to other solutions of the baseline. BLEU-1, BLEU-2, BLEU-3, and BLEU-4 were used as the evaluation metrics.

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“…The eye, or central section of a cyclone, is an important landmark for detecting and tracking its path. Researchers' ongoing efforts to automate intensity measurement using satellite photos aim to eliminate the need for human involvement [1] [2]. The early stages of tropical cyclones are extremely dangerous, with accompanying hazards such as floods and tornadoes having disastrous results.…”

Section: Introductionmentioning

confidence: 99%

Cyclone Intensity Estimation Using Deep Learning

D. Yella Krishna

2024

jes

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Cyclone Intensity Estimation based on Deep Learning focuses on cyclone intensity estimation, enhancing early warning systems and empowering decision-makers to take proactive measures to safeguard vulnerable communities and infrastructure, which aims to minimize the devastating impacts of cyclones worldwide. The challenge in cyclone intensity prediction persists due to the complex and dynamic nature of these storms. Traditional methods fall short of capturing rapid changes. This research project leverages the power of deep learning to enhance the accuracy of cyclone intensity prediction by utilizing both satellite images and grayscale representations as input datasets. It involves preprocessing and feature extraction from satellite images captured during cyclonic events. Convolutional Neural Networks (CNN) are employed to automatically learn and extract relevant patterns from these complex datasets. In parallel, grayscale images derived from the original satellite images are utilized to capture essential structure information that contributes to cyclone intensity. The fusion of information from both modalities is achieved through a novel deep learning patterns that go beyond the capabilities of traditional intensity estimation methods, and architecture, fostering a comprehensive understanding of cyclonic patterns that go beyond the capabilities of traditional intensity estimation methods. A new approach seeks to automate cyclone estimation, streamlining timelines and increasing efficiency by merging deep learning with hurricane-focused satellite data. This approach might result in more precise forecasts, Reducing the amount of casualties and property damage in prone areas.

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Tropical cyclone intensity detection by geometric features of cyclone images and multilayer perceptron

Cited by 26 publications

References 19 publications

Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

Attention based automated radiology report generation using CNN and LSTM

Cyclone Intensity Estimation Using Deep Learning

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