Detection of weather images by using spiking neural networks of deep learning models
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ORIGINAL ARTICLE
Detection of weather images by using spiking neural networks of deep learning models Mesut Tog˘açar1
•
Burhan Ergen2
•
Zafer Co¨mert3
Received: 29 April 2020 / Accepted: 24 September 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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
1 Introduction Weather information given over a certain time is important for people. People shape their daily lives and behaviors directly or indirectly according to weather conditions. For example, cycling, traveling by plane, going on vacation, etc. Besides, business plans, driving systems, sports activities, and sightseeing tours, such as the weather of the places where events are taken into account [1]. & Mesut Tog˘ac¸ar [email protected] Burhan Ergen [email protected] Zafer Co¨mert [email protected] 1
Department of Computer Technology, Technical Sciences Vocational School, Fırat University, Elazig, Turkey
2
Department of Computer Engineering, Faculty of Engineering, Fırat University, Elazig, Turkey
3
Department of Software Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey
The weather is local to a region and is usually detected by sensors or by human observations. The cost of sensors with cameras has a negative impact on the economy of the region. Nowadays, it is foreseen that artificial intelligence technology will take place in embedded systems and analysis will be performed more accurately and hardware costs will decrease [1, 2]. Recently, the concept of artificial intelligence has started to take place in people’s lives and has facilitated their lives. Today, large g
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