Component-based software development (CBSD) has become one of the preferred streams for developing large and complex systems by integrating prefabricated software components that not only facilitates the process of software development but is also changing the ways for software professionals to develop software applications. Till today, numerous attempts have been made by several organizations, software development teams, developers as well as researchers to improve component-oriented software systems (COSS) through improved measurement tools and techniques i.e. through an effective metrics. Our paper is a simple attempt to work for the demand of an appropriate and relevant integration metrics for the measurement of complexity of a software component that could be used as one of the approaches for further guidance in component complexity measurement and problem reduction. We represented a component metrics as an instantiation of the integration complexity measurement which can then be evaluated using appropriate metric tools. The work presented in this paper introduces a perception of component-oriented software systems complexity and also defines some new complexity metrics.

Computers are being utilized in field in education for many years. In last few decades, research within the field of artificial intelligence (AI) is positively affecting educational application. Advanced machine learning and deep learning techniques could be used for extracting knowledgeable information from crude information. In this chapter, the authors have analysed the impact of artificial intelligence in the education domain. The authors will discuss how with the development of machine learning techniques in last few decades, machine learning models can anticipate student performance. By learning about every student, models can identify the shortcomings. Then the authors will propose different approaches to improve student performance. Teachers can also use this model to understand student perception levels in a better way so that they can modulate their lectures according to student perception levels.

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