Author: hakankor

Global solar irradiation data is a crucial component to measure solar energy potential when we plan, size, and design solar photovoltaic fields. Often, due to the absence of measuring equipment at meteorological stations, data for the place of interest are not available. However, solar irradiation can be estimated by ordinary meteorological data such as humidity, and air temperature. Herein we propose two different deep learning methods, one based on a deep neural network regression and the other based on multivariate long short term memory unit networks, to estimate solar irradiation at given locations. Validation criteria include mean absolute error, mean squared error, and coefficient of determination (R2 value). According to the simulation results, multivariate long short term memory unit networks performs slightly better than deep neural network. Even though both have very close R2 values, multivariate long short term memory’s R2 values are more consistent. The same is true for mean squared error and mean absolute error. Key words: solar irradiation, deep learning, estimation

Abstract
Coronavirus-caused diseases are common worldwide and might worsen both human health
and the world economy. Most people may instantly encounter coronavirus in their life and
may result in pneumonia. Nowadays, the world is fighting against the new coronavirus:
COVID-19. The rate of increase is high, and the world got caught the disease unprepared. In
most regions of the world, COVID-19 test is not possible due to the absence of the diagnostic kit, even if the kit exists, its false-negative (giving a negative result for a person infected
with COVID-19) rate is high. Also, early detection of COVID-19 is crucial to keep its morbidity and mortality rates low. The symptoms of pneumonia are alike, and COVID-19 is no
exception. The chest X-ray is the main reference in diagnosing pneumonia. Thus, the need
for radiologists has been increased considerably not only to detect COVID-19 but also to
identify other abnormalities it caused. Herein, a transfer learning-based multi-class convolutional neural network model was proposed for the automatic detection of pneumonia and
also for differentiating non-COVID-19 pneumonia and COVID-19. The model that inputs
chest X-ray images is capable of extracting radiographic patterns on chest X-ray images
to turn into valuable information and monitor structural differences in the lungs caused by
the diseases. The model was developed by two public datasets: Cohen dataset and Kermany
dataset. The model achieves an average training accuracy of 0.9886, an average training
recall of 0.9829, and an average training precision of 0.9837. Moreover, the average training false-positive and false-negative rates are 0.0085 and 0.0171, respectively. Conversely,
the model’s test set metrics such as average accuracy, average recall, and average precision are 97.78%, 96.67%, and 96.67%, respectively. According to the simulation results, the
proposed model is promising, can quickly and accurately classify chest images, and helps
doctors as the second reader in their final decision.
Keywords Convolutional neural networks · Transfer learning · Classification ·
Chest X-Rays · Pneumonia · COVID-19

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