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DenGue CarB: Mosquito Identification and Classification using Machine Learning

机译:登革热碳:蚊子识别和分类使用机器学习

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This research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.
机译:本研究论文讨论了基于网络的应用程序,协助登革热识别过程中的公共卫生官员。使用图像处理和机器学习技术完成蚊子分类。培训模型是使用卷积神经网络算法开发的,支持向量机算法和k最近邻居算法来验证结果以确定最准确和合适的算法。本文讨论了以前的相关研究在解决方案中突出了设计,方法和实施时的意义和缺点。我们得出结论,CNN算法在所使用的机器学习技术之间提供最高精度。

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