A Hybrid Machine Learning Approach for Classifying Aerial Images of Flood-Hit Areas

Jothi, Akshya; Priyadarsini, P. L. K.

doi:10.1109/iccids.2019.8862138

Cited by 32 publications

(12 citation statements)

References 24 publications

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“…Also, authors of [22] have combined Deep Belief Networks (DBN) with naive Bayes to detect the user’s location. In models mentioned in [23] and [24] , naive Bayes outperformed all other classifiers to classify tweets.…”

Section: Machine Learning Overviewmentioning

confidence: 97%

“…Following are a few common regression techniques: Logistic Regression: Logistic regression is based on probability and uses sigmoid as its cost function. Authors of [23] and [24] have used logistic regression to extract useful post-disaster information from tweets. Furthermore, authors of [26] have used logistic regression to determine the survival rate of people during a disaster.…”

Section: Machine Learning Overviewmentioning

confidence: 99%

See 1 more Smart Citation

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: 97%

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

“…In addition, [22] discovered that SVM seems to have a higher precision for user position confirmation compared to wireless networks which rely on channel features information to function. 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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“…As the world is moving towards an era of minimizing the use of human intervention and increasing the usage of autonomous machines, UAVs are not an exception. Autonomous UAVs are being deployed in various applications as their design allows them to visit all the locations where the reach of humans remains impossible or would require more manpower [1][2][3]. The Coverage Path Planning (CPP) problem is one of the most important challenges that need to be focused upon while deploying a UAV in any real-time surveillance application.…”

Section: Introductionmentioning

confidence: 99%

Optimal Path Planning for Intelligent UAVs Using Graph Convolution Networks

Jothi¹,

Priyadarsini²

2022

Intelligent Automation &Amp; Soft Computing

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Unmanned Aerial Vehicles (UAVs) are in use for surveillance services in the geographic areas, that are very hard and sometimes not reachable by humans. Nowadays, UAVs are being used as substitutions to manned operations in various applications. The intensive utilization of autonomous UAVs has given rise to many new challenges. One of the vital problems that arise while deploying UAVs in surveillance applications is the Coverage Path Planning(CPP) problem. Given a geographic area, the problem is to find an optimal path/tour for the UAV such that it covers the entire area of interest with minimal tour length. A graph can be constructed from the map of the area under surveillance, using computational geometric techniques. In this work, the Coverage Path Planning problem is posed as a Travelling Salesperson Problem(TSP) on these graphs. The graphs obtained are large in number of vertices and edges and the real-time applications require good computation speed. Hence a model is built using Graph Convolution Network (GCN). The model is effectively trained with different problem instances such as TSP20, TSP50, and TSP100. Results obtained from the Concorde Benchmark Dataset were used to analyze the optimality of the predicted tour length by the GCN. The model is also evaluated against the performance of evolutionary algorithms on several self-constructed graphs. Particle Swarm Optimization, Ant Colony Optimization, and Firefly Algorithm are used to find optimal tours and are compared with GCN. It is found that the proposed GCN framework outperforms these evolutionary algorithms in optimal tour length and also the computation time.

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A Hybrid Machine Learning Approach for Classifying Aerial Images of Flood-Hit Areas

Cited by 32 publications

References 24 publications

Disaster and Pandemic Management Using Machine Learning: A Survey

Disaster and Pandemic Management Using Machine Learning: A Survey

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

Optimal Path Planning for Intelligent UAVs Using Graph Convolution Networks

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