Highlights Deep Learning based time series forecasting and comparative case study of Covid-19 confirmed and death cases in India and USA. Recurrent neural network (RNN) based variants of long short term memory (LSTM) are being used to design proposed models. Convolutional LSTM based model outperform other models with high accuracy and very less error. One of the unique studies providing state-of-the-art results to help both countries to recede Covid-19 impact.

The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading all over the world. Medical health care systems are in urgent need to diagnose this pandemic with the support of new emerging technologies like artificial intelligence (AI), internet of things (IoT) and Big Data System. In this dichotomy study, we divide our research in two waysfirstly, the review of literature is carried out on databases of Elsevier, Google Scholar, Scopus, PubMed and Wiley Online using keywords Coronavirus, Covid-19, artificial intelligence on Covid-19, Coronavirus 2019 and collected the latest information about Covid-19. Possible applications are identified from the same to enhance the future research. We have found various databases, websites and dashboards working on real time extraction of Covid-19 data. This will be conducive for future research to easily locate the available information. Secondly, we designed a nested ensemble model using deep learning methods based on long short term memory (LSTM). Proposed Deep-LSTM ensemble model is evaluated on intensive care Covid-19 confirmed and death cases of India with different classification metrics such as accuracy, precision, recall, f-measure and mean absolute percentage error. Medical healthcare facilities are boosted with the intervention of AI as it can mimic human intelligence. Contactless treatment is possible only with the help of AI assisted automated health care systems. Furthermore, remote location self treatment is one of the key benefits provided by AI based systems.

Many countries around the world have been influenced by Covid-19 which is a serious virus as it gets transmitted by human communication. Although, its syndrome is quite similar to the ordinary flu. The critical step involved in Covid-19 is the initial screening or testing of the infected patients. As there are no special detection tools, the demand for such diagnostic tools has been increasing continuously. So, it is eminently admissible to find out positive cases of this disease at the earliest so that the spreading of this dangerous virus can be controlled. Although, some methods for the detection of Covid-19 patients are available, which are performed upon respiratory based samples and among them, a critical approach for treatment is radiologic imaging or X-ray imaging. The latest conclusions obtained from X-ray digital imaging based algorithms and techniques recommend that such type of digital images may consist of significant facts regarding the SARS-CoV-2 virus. The utilization of Deep Neural Networks based methodologies clubbed with digital radiological imaging has been proved useful for accurately identifying this disease. This could also be adjuvant in conquering the problem of dearth of competent physicians in far-flung areas. In this paper, a CheXImageNet model has been introduced for detecting Covid-19 disease by using digital images of Chest X-ray with the help of an openly accessible dataset. Experiments for both binary class and multi-class have been performed in this work for benchmarking the effectiveness of the proposed work. An accuracy of 100 is reported for both binary classification (having cases of Covid-19 and Normal X-Ray) and classification for three classes (including cases of Covid-19, Normal X-Ray and, cases of Pneumonia disease) respectively.

The catastrophic phase of Covid-19 turns the table over with the spread of its disastrous transmission network throughout the world. Covid-19 associated with mucormycosis fungal infection accompanied by opportunistic comorbidities have emerged the myriad of complications and manifestations. We searched the electronic databases of Google Scholar, PubMed, Springer, and Elsevier until June 05, 2021, using keywords. We retrieved the details of confirmed and suspected mucormycosis patients associated with Covid-19. We analyzed the case reports, treatment given for Covid-19, steroids used, associated comorbidities, mucormycosis site involved, and patients survived or dead. Overall, 102 patients of mucormycosis associated with Covid-19 have been reported from India. Mucormycosis was predominant in males (69.6%) rather than females (19.6%), and most of the patients were active Covid-19 cases (70.5%). Steroids were mostly used (68.6%) for the treatment of Covid-19 followed by remdesivir (10.7%). Patients were suffering from diabetes mellitus (88.2%) and severe diabetic ketoacidosis (11.7%). Mucormycosis affects the sino-nasal (72.5%), orbit (24.5%), central nervous system (18.6%), and maxillary necrosis (13.7%) of the patients. The Mortality rate was recorded as 23.5%, and recovery rate was 2.9%. Diabetes mellitus cases are highest in India as compared to other countries, and prevalent use of steroids with the background of Covid-19 becomes an opportunistic environment for mucormycosis fungal infection to survive.

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Data Mining is a method for extracting patterns from historical data. In this paper, we forecast the number of tourists in J&K state for the next five years that should totally depend upon the historical time series data of tourists in J&K state. For this, Exponential Smoothing model and IBM SPSS Modeler 16.0 data mining tool are used. Exponential Smoothing is a popular forecasting method that is used to predict the immediate future for time series data. The purpose of this paper is to forecast the number of tourists in advance for the J&K state so that the tourism department shall be prepared in advance to provide essential services to the forthcoming tourists.

The syndrome called COVID‐19 which was firstly spread in Wuhan, China has already been declared a globally “Pandemic.” To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence‐based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network‐based “LiteCovidNet” model is proposed that detects COVID‐19 cases as the binary class (COVID‐19, Normal) and the multi‐class (COVID‐19, Normal, Pneumonia) bifurcated based on chest X‐ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi‐class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID‐19 infected patients at an early stage with convenient cost and better accuracy.

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