Association Rule Based Flexible Machine Learning Module for Embedded System Platforms like Android

Abstract—The past few years have seen a tremendous growth in the popularity of smartphones. As newer features continue to be added to smartphones to increase their utility, their significance will only increase in future. Combining machine learning with mobile computing can enable smartphones to become ‘intelligent ’ devices, a feature which is hitherto unseen in them. Also, the combination of machine learning and context aware computing can enable smartphones to gauge users’ requirements proactively, depending upon their environment and context. Accordingly, necessary services can be provided to users. In this paper, we have explored the methods and applications of integrating machine learning and context aware computing on the Android platform, to provide higher utility to the users. To achieve this, we define a Machine Learning (ML) module which is incorporated in the basic Android architecture. Firstly, we have outlined two major functionalities that the ML module should provide. Then, we have presented three architectures, each of which incorporates the ML module at a different level in the Android architecture. The advantages and shortcomings of each of these architectures have been evaluated. Lastly, we have explained a few applications in which our proposed system can be incorporated such that their functionality is improved. Keywords—machine learning; association rules; machine learning in embedded systems; android, ID3; Apriori; Max-Miner I

International Journal of Electronics and Computer Science Engineering 861 Available Online at www.ijecse.org ISSN- 2277-1956 Automated Robot with Object Recognition and Handling Features

Abstract- With the advent of new technologies, every industry is moving towards automation. A large number of jobs in industries, such as Manufacturing, are performed repeatedly. These jobs require a lot of human effort. In such cases, there is a need of an automated robot which can perform the repetitive task more efficiently. This paper is about a robot which has object recognition and handling features. The robot will optically recognize the objects and pick and place them as per the hand gestures given by the user. It will have a camera to capture image of the objects and one arm to perform the pick and place function