Crime is the factor increases day by day and also needs the solution for identifying these activities in an efficient and quick manner. Many surveillance systems use artificial intelligence and image processing are incorporated with them to implement an intelligent surveillance system. But most of the systems are provided the alarm or identifies the crime after it happens. To solve this problem, camera footage-based theft detection will be used with the help of machine learning to detect theft occurrence. System will detect the human activity with the help of open-pose algorithm and convolution neural networks. Then the footage will be examined based on the pretrained model and it will be classified into three categories namely safe, abnormal or crime. Convolution neural network is used to classify the motion and an alert message will be sent to the owner along with captured image and options such as neglect or call the police.
Predicting the future in all the areas using machine learning techniques was the recent research in the current scenario. Stock market is one among them which needs the prediction future market to invest in the new enterprise or to sell their existing shares to get profit. This need the efficient prediction technique which studies the previous exchanges of stock market and gives the future prediction based on that. This article proposed the prediction system of stock market price based on the exchange takes place in previous scenario. The system studies the diversing effect of market price of product in a particular time gap and analyze its future trend whether it’s loss or gain. During the system of thinking about diverse strategies and variables that should be taken into account, we observed out that strategies like random forest, Support vector machine and regression algorithm. Support vector regression is a beneficial and effective gadget gaining knowledge of approach to apprehend sample of time collection dataset. The data collected for the four years duration which was accumulated to get the expecting prices of the share of the firm. It can produce true prediction end result if the fee of essential parameters may be decided properly. It has been located that the guide vector regression version with RBF kernel indicates higher overall performance while in comparison with different models.
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