Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution
نویسندگان
چکیده
Air pollution is a pressing concern in urban areas, necessitating the critical monitoring of air quality to understand its implications for public health. Internet Things (IoT) devices are widely utilized due their sensor capabilities and seamless data transmission over Internet. Artificial intelligence (AI) machine learning techniques play crucial role classifying patterns derived from data. Environmental stations offer multitude parameters that can be obtained uncover hidden showcasing impact on surrounding environment. This paper focuses utilizing CO parameter as an indicator two datasets collected wireless environmental greater Port area Town Hall Igoumenitsa City Greece. The normalized facilitate utilization classification algorithms. k-means algorithm applied, elbow method used determine optimal number clusters. Subsequently, introduced grammatical evolution calculate percentage fault. constructs programs human-readable format, making it suitable analysis. Finally, proposed compared against four state-of-the-art models: Adam optimizer optimizing artificial neural network parameters, genetic training network, Bayes model, limited-memory BFGS applied network. comparison reveals GenClass outperforms other approaches terms error.
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ژورنال
عنوان ژورنال: Algorithms
سال: 2023
ISSN: ['1999-4893']
DOI: https://doi.org/10.3390/a16060300