COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

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In this paper, a new neuro-based approach using a feed-forward neural network is presented to design a Wilkinson power divider. The proposed power divider is composed of symmetrical modified T-shaped resonators, which are a replacement for quarter-wave transmission lines in the conventional structure.The proposed technique reduces the size of the power divider by 45% and suppresses unwanted bands up to the fifth harmonics. To verify the concept, a prototype of the power divider has been fabricated and tested, exhibiting good agreement between the predicted and measured results. The results show that the insertion loss and the isolation at the center frequency are about 3.3 ± 0.1 dB and 23 dB, respectively. K E Y W O R D Sartificial intelligence, couplers, evolutionary optimization, harmonic suppression, lumpedequivalent circuit, microstrip technology, neural network, Wilkinson power divider

A dual-band bandpass filter (BPF) composed of a coupling structure and a bent T-shaped resonator loaded by small L-shaped stubs is presented in this paper. The first band of the proposed BPF covers 4.6 to 10.6 GHz, showing 78.9% fractional bandwidth (FBW) at 7.6 GHz, and the second passband is cantered at 11.5 GHz with a FBW of 2.34%. The bent T-shaped resonator generates two transmission zeros (TZs) near the wide passband edges, which are used to tune the bandwidth of the first band, and the L-shaped stubs are used to create and control the narrow passband. The selectivity performance of the BPF is analyzed using the transfer function extracted from the lumped circuit model verified by a detailed even/odd mode analysis. The BPF presents a flat group delay (GD) of 0.45 ns and an insertion loss (IL) less than 0.6 dB in the wide passband and a 0.92 IL in the narrow passband. A prototype of the proposed BPF is fabricated and tested, showing very good agreement between the numerically predicted and measured results.

In the design of a microstrip power divider, there are some important factors, including harmonic suppression, insertion loss, and size reduction, which affect the quality of the final product. Thus improving each of these factors contributes to a more efficient design. In this respect, a hybrid technique to reduce the size and improve the performance of a Wilkinson power divider (WPD) is introduced in this paper. The proposed method includes a typical series LC circuit, a miniaturizing inductor, and two transmission lines, which make an LC branch. Accordingly, two quarter-wavelength branches of the conventional WPD are replaced by two proposed LC branches. Not only does this modification lead to a 100% size reduction, an infinite number of harmonics suppression, and high-frequency selectivity theoretically, but it also results in a noticeable performance improvement practically compared to using quarter-wavelength branches in the conventional microstrip power dividers. The main important contributions of this technique are extreme size reduction and harmonic suppression for the implementation of a filtering power divider (FPD). Furthermore, by tuning the LC circuit, the arbitrary numbers of unwanted harmonics are blocked while the operating frequency, the stopband bandwidth, and the operating bandwidth are chosen optionally. The experimental result verifies the theoretical and simulated results of the proposed technique and demonstrates its potential for improving the performance and reducing the size of other similar microstrip components.

Most common machine learning (ML) algorithms usually work well on balanced training sets, that is, datasets in which all classes are approximately represented equally. Otherwise, the accuracy estimates may be unreliable and classes with only a few values are often misclassified or neglected. This is known as a class imbalance problem in machine learning and datasets that do not meet this criterion are referred to as imbalanced data. Most datasets of soil classes are, therefore, imbalanced data. One of our main objectives is to compare eight resampling strategies that have been developed to counteract the imbalanced data problem. We compared the performance of five of the most common ML algorithms with the resampling approaches.The highest increase in prediction accuracy was achieved with SMOTE (the synthetic minority oversampling technique). In comparison to the baseline prediction on the original dataset, we achieved an increase of about 10, 20 and 10% in the overall accuracy, kappa index and F-score, respectively. Regarding the ML approaches, random forest (RF) showed the best performance with an overall accuracy, kappa index and F-score of 66, 60 and 57%, respectively. Moreover, the combination of RF and SMOTE improved the accuracy of the individual soil classes, compared to RF trained on the original dataset and allowed better prediction of soil classes with a low number of samples in the corresponding soil profile database, in our case for Chernozems. Our results show that balancing existing soil legacy data using synthetic sampling strategies can significantly improve the prediction accuracy in digital soil mapping (DSM). Highlights• Spatial distribution of soil classes in Iran can be predicted using machine learning (ML) algorithms. • The synthetic minority oversampling technique overcomes the drawback of imbalanced and highly biased soil legacy data. • When combining a random forest model with synthetic sampling strategies the prediction accuracy of the soil model improves significantly.

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