Enhancement of Convolutional Neural Networks Classifier Performance in the Classification of IoT Big Data

Abstract

Current developments in technologies occupy a central role in weather forecasting and the Internet-of-Things for both organizations and the IT sector. Big-data analytics and the classification of data (derived from many sources including importantly the Internet-of-Things) provides significant information on which organizations can optimize their current and future business planning. This paper considers convolutional neural networks and data classification as it relates to big-data and presents a novel approach to weather forecasting. The proposed approach targets the enhancement of convolutional neural networks and data classification to enable improved classification performance for big-data classifiers. Our contribution combines the positive benefits of convolutional neural networks with expert knowledge represented by fuzzy rules for prepared data sets in time series, the aim being to achieve improvements in the predictive quality of weather forecasting. Experimental testing demonstrates that the proposed enhanced convolutional network approach achieves a high level of accuracy in weather forecasting when compared to alternative methods evaluated.