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Background: The trend of the stock market prediction has always been challenging and confusing for investors There is tremendous growth in stock market prediction with the advancement of technology, machine learning, data science, and big data. The media and entertainment sector is one of the diverse sectors in the stock market. In the Indian stock market, Sensex and Nifty are the two indexes. The 2019 pandemic forced the movie theatres to shut down. As a result, distributors and film directors were not able to release their movies in theatres, and production also was stopped. Consequently, during the lockdown, people spent more time at home watching media. Resulting in a higher degree of media consumption. Objectives: The objective of the research is to predict the performance of the media and entertainment company's stock prices using machine-learning techniques. Investors will be benefited by maximizing the profit and minimizing the loss. Methods: The proposed stock prediction system is used to predict the stock values and find the accuracy of linear regression and logistic regression in machine learning algorithms for data science. Results: The experiments are conducted for the media and entertainment stock price data using Machine-learning algorithms. Media stock prices are considered as the input dataset. The model has been developed using the daily frequency of stock prices with different attributes. Conclusion: Thus, the media and entertainment stocks are predicted using linear regression and logistic regression. Using the above techniques, stock prices are predicted accurately to maximize profits and minimize the loss for the investors.

Employment of robots in manufacturing has been a value-added entity in a manufacturing industry. Robotic simulation is used to visualize entire robotic application system, to simulate the movement of robot arm incorporated with components consist in its environment and to detect collision between the robot and components. This paper presents result of a project in implementing a computer based model to simulate Okura A1600 palletizer robot. The application uses Okura A1600 robot for palletizing bags at the end of the production line and focuses on pick-and-place application. The project objective is to generate a computer simulated model to represent the actual robot model and its environment. The project simulates the robot's first four joints, namely as the Waist, Shoulder, Elbow and Waist and focuses on the position of the robot's end effector, regardless its orientation. Development of the model is using Workspace5 as a simulation tool. Two types of methodology are used, which are the methodology for developing the robotic workcell simulation model and the methodology for executing the robotic simulation. The output of the project will be a three-dimensional view of robot arm movement based on series of predefined Geometry Points, layout checking and robot's reachability by generating working envelope, collision and near miss detection, and monitoring on the cycle time upon completing a task. The project is an offline programming and no robot language is generated.

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