1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data

Meliboev, Azizjon; Alikhanov, Jumabek; Kim, Wooseong

doi:10.48550/arxiv.2003.00476

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…BN enables us to use much higher learning rates and to take less care of initialization. It also works as a regularizer and eliminates the need for dropout in some cases and increases a neural network's stability [52].…”

Section: Batch Normalizationmentioning

confidence: 99%

A Novel One-Dimensional CNN with Exponential Adaptive Gradients for Air Pollution Index Prediction

et al. 2020

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Air pollution is one of the world’s most significant challenges. Predicting air pollution is critical for air quality research, as it affects public health. The Air Pollution Index (API) is a convenient tool to describe air quality. Air pollution predictions can provide accurate information on the future pollution situation, effectively controlling air pollution. Governments have expressed growing concern about air pollution due to its global effect on human health and sustainable growth. This paper proposes a novel forecasting model using One-Dimensional Deep Convolutional Neural Network (1D-CNN) and Exponential Adaptive Gradients (EAG) optimization to predict API for a selected location, Klang, a city in Malaysia. The proposed 1D-CNN–EAG exponentially accumulates past model gradients to adaptively tune the learning rate and converge in both convex and non-convex areas. We use hourly air pollution data over three years (January 2012 to December 2014) for training. Parameter optimization and model evaluation was accomplished by a grid-search with k-folds cross-validation. Results have confirmed that the proposed approach achieves better prediction accuracy than the benchmark models in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and the Correlation Coefficient (R-Squared) with values of 2.036, 2.354, 4.214 and 0.966, respectively, and time complexity.

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Section: Batch Normalizationmentioning

confidence: 99%