A CNN methodology can yield pretty accurate results on stock prices if we look at day-to-day fluctuation in stock prices, but where this method fails is in anticipating big changes in prices that are not based on trends. In technical analysis, price patterns are used to identify transitions between rising and falling trends. A price movement pattern that may be calculated using a series of trend lines and/or curves is known as a cost sequence. Finding patterns in high-dimensional data might be difficult since it is difficult to visualize. Many different machine learning algorithms can match this high-dimensional data to predict and classify future events, but having the computer learn the match for a specific area of the dataset might be expensive. Using deep learning, this study proposes a way for identifying various stock market pricing styles. A CNN is used to find the pattern in stock market data, and projections are made based on it. The stock pattern is divided into five pieces. Price stability, stock value fall (quick decline, moderate decline), and stock value increase (rapid increase, gradual increase). The accuracy of our system is 98.17 %.
The Covid Techniques for predicting have demonstrated their value in anticipating perioperative outcomes for the objective of improve future decision-making activities. The designs have existed for a long time. Utilized in a large number of possible uses where unfavorable variables for a danger required be identifying and prioritizing. To take care of forecasting challenges, a large number of prediction approaches are widely utilized. This study demonstrates the model’s ability to estimate how many patients will be COVID-19 is a virus that affects people. Currently assumed to be a possibility danger to the human race. In this case, study; four conventional forecasting models were put to use to foresee the hazardous elements of COVID-19: LR, least LASSO, SVM, and ES.Each of the models makes 3 sorts of forecasts for the next ten days: the no. of freshly infected cases, the no.of newly infected cases, the no. of newly infected cases, the no. of newly fatalities, and the no. of recoveries. The study’s findings show that using these strategies in the present situation COVID-19 pandemic scenario is a promising mechanism.
Although the majority of persons who get COVID-19 recover completely, current evidence suggests that 10% to 20% of those who recover experience a variety of mid- and long-term symptoms after their initial sickness. In the system Medbot: Artificial Intelligence based Interactive Chatbot for assisting with Telephonic Health Checkup Service after COVID-19, we use the NLP technique. Patients who use this system after finishing the Covid-19 must log in whenever they have symptoms. Patients use this system to get therapy at home, and if their symptoms are too severe, the system will refer them to a doctor. Patients can book appointments with doctors following Covid-19 if the chatbot gives a list of doctors. In this system, we use the FL approach to get accurate results. We guarantee 98.47 % while utilizing our technology.
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