Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

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Applications of artificial intelligence in battling against covid-19: A literature review.

Particle swarm optimization performance improvement using deep learning techniques

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12759

Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.

Long Short-Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China

Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification.

/pdf/analysis-prediction-and-evaluation-of-covid-19-datasets-1kyi9ot9y2.pdf

Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms

Prominent advances in the field of artificial intelligence during the past decade and the breakthrough of deep learning would be useful for investigating ionospheric weather using ground and space-based ionospheric sensors data. The significance of deep learning algorithms needs to be assessed in forecasting the low latitude ionospheric disturbances (delays) for the global positioning system (GPS) signals. Total electron content (TEC) data sets prepared by taking advantage of GPS satellite radio frequency (RF) signals. This letter provides the application of deep learning models, long short-term memory (LSTM), gated recurrent unit (GRU), and a hybrid model that consists of LSTM combined with convolution neural network (CNN) to forecast the ionospheric delays for GPS signals. The deep learning models implemented using the vertical TEC (VTEC) time-series data estimated from GPS measurements over Bengaluru, Guntur, and Lucknow GPS stations. The LSTM-CNN model performs well when compared to other ionospheric deep learning forecasting algorithms with minimum root-mean-square error (RMSE) of 1.5 TEC units (TECUs) and a high degree of $R^{2} = 0.99$ .

Implementation of Hybrid Deep Learning Model (LSTM-CNN) for Ionospheric TEC Forecasting Using GPS Data

Support vector machine (SVM) is one of the most widely used classification algorithms. It uses supervised learning method (Aizerman et al., Auto Remote Cont 25:821–837, 1964) for training. The SVM classifier is mostly used in multi-classification problems. SVM differs from the traditional classifiers as it uses “decision boundary,” which separates the classes. The decision boundary maximizes distances of data points belongs to different classes .in this; decision boundary is the optimum that is Most optimal (Baron and Ensley, Opportunity recognition as the detection of meaningful patterns: evidence from the prototypes of novice and experienced entrepreneurs. Manuscript under review, 2005) decision boundary has maximum margin. The data points which are nearer to the boundary are called support vectors. The most important thing in SVM is its hyper plane, where for a N-dimensional space it is an (N-1)-dimensional subspace. To better understand, the hyper plane is just a line in one dimension for a two-dimensional space. It is a two-dimensional plane that separates the classes for a three-dimensional space.

Pattern Recognition and Machine Learning

Developed by Google Brain team, tensor flow in an open source library package originally created for the tasks in heavy numerical computations (Learning TensorFlow [Authors: Tom Hope, Yehezkel S. Resheff & Itay Lieder]). Its main application is machine learning and deep learning where a computer learns from experience and the world is understood in the form of hierarchy of concepts, each concept defining its relation to simpler concepts (Deep Learning Pipeline: Building A Deep Learning Model With TensorFlow [Authors: Hisham El-Amir, Mahmoud Hamdy]). Drawing the hierarchy graph built on top of each other, the graph is deep with many layers and computation costly. Tensorflow provides faster computation and an excellent functionality compared to other popular deep learning frameworks/library. It also supports Central Processing Unit (CPUs), Graphics Processing Unit (GPUs) and distributed processing in a cluster.

Kolla Bhanu Prakash

Papers

Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms

Implementation of Hybrid Deep Learning Model (LSTM-CNN) for Ionospheric TEC Forecasting Using GPS Data

Pattern Recognition and Machine Learning

Introduction to Tensorflow Package

Quantum inspired meta-heuristic approach for optimization of genetic algorithm