In the previous era, a computer is programmed for some specific task. An electronic device is programmed to do its function electronically. It was done with a target device, the programming environment and the system. We get the necessary intermediate code by running the program with the above said environment and committed into the target device. Thus the device performs the task it was intended to do. In case if we need to change the functionality of the device by the learning experience of the vendor and users, the vendor will upgrade the product. Nowadays in this machine learning era, the devices are programmed in such a way it can learn by its own experience and with the available data it collected it can even manipulate the algorithm by itself with the provided data set. Thus machine learning is ruling this era. We are going to discuss the machine learning algorithms here which was used to predict by itself with the data set collected. Therefore, machine learning is all about learning about computer algorithms that progress its potential through the experience. Thus, Machine learning is presently highly regarded analysis topic and applied to all told application in day to day life. In this paper we have a tendency to extract the knowledge of machine learning algorithms like decision tree, Naive Bayes and enforce the algorithms with sample dataset of weather prognostication.
Meta-patterns are a sort of basic object-oriented constructs that have been used to design an object-oriented framework. It has been used to precisely describe possible design pattern of a framework at meta-level to manifest framework hot-spots and its corresponding adaptability. The present study is an attempt to develop a genetic algorithm approach for detecting the types and numbers of meta-patterns. For this purpose we have converted the UML class diagram of object-oriented framework and meta-patterns into directed graph and applied hybrid genetic algorithm. The obtained results from the proposed algorithm are further validated manually with the recall and precision percentage of 86.20 and 80.64 respectively. Overall the study demonstrates the utility of the uniquely proposed algorithm for the near accurate identification of meta-patterns for high reusability. This can be applied on frameworks for assessing the evolution process, documentation of hot-spots and reducing the customization effort.
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