Detection of weather images by using spiking neural networks of deep learning models - Togacar, Mesut; Ergen, Burhan; Comert, Zafer (Springer London Ltd, 2021)
The transmission of weather information of a location at certain time intervals affects the living conditions of the people there directly or indirectly. According to weather information, people shape their behavior in daily life. Besides, agricultural activities are carried out according to the weather conditions. Considering the importance of this subject, it is possible to make weather predictions based on the weather images in today's technology exploiting the computer systems. However, the recent mention of the name of artificial intelligence technology in every field has made it compulsory for computer systems to benefit from this technology. The dataset used in the study has four classes: cloudy, rain, shine, and sunrise. In the study, GoogLeNet and VGG-16 models and the spiking neural network (SNN) were used together. The features extracted from GoogLeNet and VGG-16 models were combined and given to the SNNs as the input. As a result, the SNNs contributed to the success of classification with the proposed approach. The classification accuracy rates of cloudy, rain, shine, and sunrise classes were 98.48%, 97.58%, 97%, and 98.48%, respectively, together with SNN. Also, the use of SNNs in combination with deep learning models to obtain a successful result is proved in this study.
Keywords: Spiking neural network; Deep networks; Weather images; Feature extraction and combination