首页|Study Findings from Egypt-Japan University of Science and Technology Provide New Insights into Machine Learning (Classification and Detection of Natural Disasters Using Machine Learning and Deep Learning Techniques: a Review)
Study Findings from Egypt-Japan University of Science and Technology Provide New Insights into Machine Learning (Classification and Detection of Natural Disasters Using Machine Learning and Deep Learning Techniques: a Review)
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NETL
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Springer Nature
Investigators discuss new findings in Machine Learning. According to news reporting originating from Alexandria, Egypt, by NewsRx correspondents, research stated, “For efficient disaster management, it is essential to identify and categorize natural disasters. The classical approaches and current technological advancements for identifying, categorizing, and reducing the harmful effects of natural catastrophes are discussed in this review article.” Funders for this research include Science, Technology, and Innovation Funding Authority, JICA. Our news editors obtained a quote from the research from the Egypt-Japan University of Science and Technology, “They include human observation and reporting, satellite images, seismology, radar, infrared imagery, and sonar. The article explores natural disasters’ challenges and harmful effects and their mitigation measures. The article explains the benefits and drawbacks of published approaches and emphasizes how they may be used to identify many kinds of natural catastrophes, including earthquakes, floods, wildfires, and hurricanes. Discussions on current technological advancements, including machine and deep learning applications, that can potentially increase the precision and efficiency of natural disaster detection and classification are presented. Overall, the review article emphasizes the significance of continuing research and improving current techniques to increase communities’ and countries’ resilience and preparedness for natural disasters.”
AlexandriaEgyptAfricaCyborgsEmerging TechnologiesMachine LearningEgypt-Japan University of Science and Technology
NETL
NSTL
Springer Nature
