A New Disaster Recognition Algorithm Based on SVM for ERESS: Buffering and Bagging-SVM

Möri, K.; Wada, Tomotaka; Ohtsuki, Kazuhiro

doi:10.1109/icppw.2016.19

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…The regression technique is a predictive learning feature that maps a data object to a predictive variable with real meaning [25] . Following are a few common regression techniques: Logistic Regression: Logistic regression is based on probability and uses sigmoid as its cost function.…”

Section: Machine Learning Overviewmentioning

confidence: 99%

Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

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This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

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Section: Machine Learning Overviewmentioning

confidence: 99%

Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…When the number of features increases, it gets very difficult to picture the hyperplane. Mori et al, (Mori et al, 2016), used SVM to predict the outbreak of disaster for a specific location. SVM was used with IoT (Internet of Things) and CNN (Convolutional Neural Networks) for the diagnosis of COVID-19 (Le et al, 2021).…”

Section: Classificationmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also

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“…In addition, the researchers of [23] propose using SVM to divide aerial photos into a massive flood as well as non-flood-affected regions. In addition, the researchers of [24] employed SVM to predict a disaster epidemic in a particular region.…”

Section: State-of-the-artmentioning

confidence: 99%

Machine Learning Based Real Time-Heuristic Sensor Data Analytics For Early Warning Prediction

Pal¹,

Mishra²,

Haldorai³

et al. 2021

Preprint

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Landslides have the potential to cause significant property damage as well as fatalities. Landslides are identified by real-time heuristic data analysis acquired using wireless sensor networks (WSNs) in changing environments. People can abandon dangerous locations earlier when landslides are forecasted. In this paper, the early warning prediction system developed using machine learning; Artificial Neural Networks (ANNs) provide precise predictions. The weight coefficients of ANN can be adjusted exactly enough by network functional training. In the case of unbalanced data distribution, the proposed ANN model is unable to learn the sample data pattern. This results in incorrect prediction and therefore, a switching method is utilized to switch between alternative predictors based on the current environmental state. Furthermore, the proposed model has been developed to forecast and compensate for errors during the prediction phase. Thus, the proposed model can enhance precise prediction, and an early warning prediction system of landslides can issue warnings 44.2 minutes before a landslide occurrence.

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A New Disaster Recognition Algorithm Based on SVM for ERESS: Buffering and Bagging-SVM

Cited by 9 publications

References 11 publications

Disaster and Pandemic Management Using Machine Learning: A Survey

Disaster and Pandemic Management Using Machine Learning: A Survey

Battling COVID-19 using machine learning: A review

Machine Learning Based Real Time-Heuristic Sensor Data Analytics For Early Warning Prediction

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