An Ameliorate Analysis of Cryptocurrencies to Determine the Trading Business with Deep Learning Techniques

As the range of COVID-19 sufferers increased, many nations imposed a complete lockdown. As a result, it caused a devastating international financial disaster everywhere in the world. Technical and essential evaluation are two methods for determining future worth. Different strategies use statistics from outside the market, such as monetary conditions, hobby rates, and geopolitical events, to forecast future charge. We use technical evaluation forecasts potential charge using buying and selling statistics from the market, which includes charge and buying and selling volume, whereas other strategies use statistics from outside the market, such as monetary conditions, hobby rates, and geopolitical events. The objective of this project is to give technical and fundamental analysis using machine learning approaches. In business, AI is broadly used to remedy and optimize various problems, including marketing, credit score card fraud detection, algorithmic trading, patron service, portfolio management, and product advice primarily based totally on patron needs. Furthermore, the technology due used in this finished greater to optimize the proposed set of rules to attain the maximum correct result primarily based totally on the current valuation of cryptocurrency. This project is to use machine learning techniques to provide technical analysis. The incorporation of new technology into financial institutions has the potential to propel cryptocurrency values to time highs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

CXNet - A Novel approach for COVID-19 detection and Classification using Chest X-Ray images (preprint)

Preliminary screenings are essential to limit the community propagating nature of COVID-19. COVID-19 patients’ lung health status can be assessed by chest radiographic imaging, such as computed tomography scanning or X-ray images. Therefore, it is preferable to use machine learning approaches to help identify COVID-19 by using chest radiographs. This research presents an image-based diagnosis of COVID-19 disease using Deep Learning. The presented work uses chest CXNet-A Novel approach for COVID-19 detection and Classification X-ray images because the X-ray imaging facility is widely available in almost all healthcare facilities. It is less costly and has a more negligible radiation effect than Computed Tomography (CT) scan images. This study employed the implemented a custom Convolutional Neural Network (CNN) model and pre-trained deep neural architecture such as resnet50, DenseNet121, VGG16, and VGG19 to classify COVID-19 chest X-ray images. The proposed model has been evaluated on the real-life data, the accuracy of 98% has been achieved. We can recommend the proposed model to health care professionals as a trustworthy diagnostic decision-making system for COVID-19 detection.

Deep Learning for COVID-19

Ever since the outbreak in Wuhan, China, a variant of Coronavirus named “COVID 19” has taken human lives in millions all around the world. The detection of the infection is quite tedious since it takes 3–14 days for the symptoms to surface in patients. Early detection of the infection and prohibiting it would limit the spread to only to Local Transmission. Deep learning techniques can be used to gain insights on the early detection of infection on the medical image data such as Computed Tomography (CT images), Magnetic resonance Imaging (MRI images), and X-Ray images collected from the infected patients provided by the Medical institution or from the publicly available databases. The same techniques can be applied to do the analysis of infection rates and do predictions for the coming days. A wide range of open-source pre-trained models that are trained for general classification or segmentation is available for the proposed study. Using these models with the concept of transfer learning, obtained resultant models when applied to the medical image datasets would draw much more insights into the COVID-19 detection and prediction process. Innumerable works have been done by researchers all over the world on the publicly available COVID-19 datasets and were successful in deriving good results. Visualizing the results and presenting the summarized data of prediction in a cleaner, unambiguous way to the doctors would also facilitate the early detection and prevention of COVID-19 Infection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.