Advanced Machine Learning-Based Analytics on COVID-19 Data Using Generative Adversarial Networks.

The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

Impact of nationwide lockdown on cancer care during COVID-19 pandemic: A retrospective analysis from western India.

BACKGROUND AND AIMS: COVID-19 has impacted healthcare system worldwide including cancer case. Aim of this study was to describe the experience of lockdown on cancer care concerning patient's visit and reception of treatment in western India. METHODS: This is a retrospective observational study conducted in patients with cancer attending a tertiary care center pre-lockdown and during lockdown (from January to May 2020). Data related to demographic parameters, type of tumor, type of treatment received and functional status of patients were retrieved from hospital medical records of patients. RESULTS: Of the 5258 patients included, 4363 visited hospital pre-lockdown (median age, 50 years) and 895 visited during the lockdown period (median age, 47 years). A total of 1168 and 106 patients visiting hospital before and during lockdown, respectively, had comorbidities. Breast cancer (25.6% and 29.7%), head and neck cancer (21.3% and 16.9%) were the most common type of solid tumors; leukemia (58.0% and 73.0%), lymphoma (18.8% and 13.5%) and multiple myeloma (18.6% and 12.2%) were the most common type of hematological malignancies observed in patients visiting pre-lockdown and during lockdown, respectively. Chemotherapy was most commonly received treatment (pre-lockdown, 71.8%; during lockdown, 45.9%). Other therapies reported includes supportive/palliative, targeted, hormonal, and immunotherapy. The majority of patients who visited the hospital pre-lockdown (68.4%) and during lockdown (62.8%) had 0 or 1 Eastern Cooperative Oncology Group (ECOG) score. CONCLUSION: Overall observations highlight a substantial impact of an imposed nationwide lockdown during COVID-19 pandemic on cancer care of patients in terms of reduced patient visits and number of treatments received.

Detection of Virus Using Artificial Intelligence and Machine Learning Concepts (preprint)

This paper is devoted, to study of recent viruses, detection and prediction of viruses using AI and Machine Learning Techniques. Recently in December 2019 a new virus was discovered named coronavirus. So, to detect this type of viruses in the future and present, we can use AI and Machine Learning concepts for the detection of viruses. Artificial intelligence (AI) research is growing rapidly within the medicine research. In 2016, Artificial Intelligence projects on health care attracted more investment than Artificial Intelligence projects within any other sector of the global economy. It is well, known that in feature we may overcome the different types of new viruses across the city. The detection of the viruses is done with the help of the MS Kinect sensor. This sensor analyses human breathing, and if that person is not able to breathe properly then that person must be suffering from coughing, sneezing, etc. This particular sensor will be fixed at the public places if it identifies the person who is suffering from breathing problems, the person will be sanitized or will be provided with the mask or if it is a serious condition that person will be admitted to the hospital for the treatment. So that the spreading of virus decreases at public places. This sensor can also be used for checking the heartbeat pluses of the humans and animals also. Animals should also be monitoring with their health conditions because there are chances of spreading viruses from animals also.