Support vector machine and neural network for enhanced classification algorithm in ecological data
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2023-06-08Autor(es)
Ravi Prasad, B,
Ashfaq Hussain, Mohammed
Sridharan, K,
Cosio Borda, Ricardo Fernando
Geetha, C
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The current economic scale is bigger and bigger, the social material living standard also along is also getting higher and higher with the rapid economic growth. However, the problems caused by economic development are also increasing, on the one hand, there is the contradiction between supply and demand caused by resource consumption and shortage of resources; on the other hand, there also is contradiction between the great pollution and destruction in the ecological environment and the public’s increasingly demanding ecological environment. Especially, the contradiction between the ecological environment and the social environment has become the focus of attention of the Chinese public. Therefore, the ecological environment protection becomes the current consensus either from the national level or the social level, how to manage and protect the ecological environment is also a question of the current social thinking. The common practice of ecological environment protection is to control and protect, at the same time, governance is to restore the damaged environment. There are many means for protection, such as energy conservation, emission reduction, monitoring and so on. Energy saving and emission reduction not only mean that the consumption of resources is reduced, but also mean that the discharge of pollutants and destroy the ecological environment are reduced. And monitoring refers to the existing ecological environment monitoring; the change of ecological environment is detected by real-time observation, so that counter measures are made according to the changes.
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Cita bibliográfica
Ravi, B., Ashfaq, M., Sridharan, K., Cosio, R. F., & Geetha, C. (2023). Support vector machine and neural network for enhanced classification algorithm in ecological data. Measurement: Sensors, 27, 1-5. https://doi.org/10.1016/j.measen.2023.100780
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