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Over the past years music has been continually evolving through the its tempos and beats as well as it’s melody. Traditionally, music is produced by a group of musicians with different instruments combining together to create a final synchronised product. In recent years, harmonies and beats were always considered to be generated manually. However, with the advent of digital technologies and software, it has become possible for machines to generate music automatically at an alarming pace. The purpose of this research is to propose a method for creating musical notes using Recurrent Neural Networks (RNN), specifically Long Short-Term Memory (LSTM)networks. To implement this algorithm, a model is created and the data is represented in the form of a percussive instrument digital interface (MIDI) file for easy access and interpretation. The process of preparing the data for input into the model isalso discussed, as well as techniques for receiving, processing, and storing MIDI files for use as input.To enhance its learning capabilities, the model should be able to remember previous details of a musical sequence and its structure. This paper discusses the use of a layered architecture in the LSTM model and how its connections interweave to create a neural network.

Game Theory concepts have been successfully applied in a wide variety of domains over the past decade. Sports and games are one of the popular areas of game theory application owing to its merits and benefits in solving complex scenarios. With recent advancements in technology, the technical and analytical assistance available to players before the match, during game-play and after the match in the form of post-match analysis for any kind of sport has improved to a great extent. In this paper, we propose three novel approaches towards the development of a tool that can assist the players by providing detailed analysis of optimal decisions so that the player is well prepared with the most appropriate strategy which would produce a favourable result for a given opponent's strategy. We also describe how the system changes when we consider real-time game-play wherein the history of the opponent's strategies in the current rally is also taken into consideration while suggesting.