Smart grids evolution is ramping up in the global energy scenario by offering deregulated markets, demandside management, prosumer culture, demand response, contingency forecasting, outage management, etc., functionalities. These functionalities help to manage the grid effectively by taking informed decisions timely. Further, the progressive developments in information and communication technologies improve smartness in the power grids. Especially, smart homes are playing a key role, which possesses the communication between various devices/appliances and collect their functional data in terms of energy consumption readings, timestamp, etc. However, the availability of high-quality data is always desired to achieve superior benefits with respect to all the above-mentioned functionalities. But, the failures of communication networks, metering devices, server station issues, etc., create anomalies in the data collection. Hence, there is a dire need of identifying the ways of analyzing the smart home data to find the irregularities that occurred because of aforesaid failures. Especially, it has been a common problem to see missing data at some particular instants in the overall database captured. In this view, this paper proposes a simple and effective descriptive analysis to find missing data anomalies in smart home energy consumption data. A real-time dataset is used to execute the proposed method. For which, a clear enumeration of missing data is visualized using comprehensive simulation results. This helps to realize the actual problems that are hidden in the energy consumption data.

This paper aims to calculate the factors and build prediction models for the persuasive message changing student's attitude by applying classification techniques. We used a questionnaire to collect data such as gender, age and their satisfaction with persuasive messages, obtained from students at other country Universities. The classification rule generation process is based on the decision tree as a classification method where the generated rules are studied and evaluated. We compared the results obtained from three algorithms.

Mathematical Modelling and Big-data Analytics are playing a vital role in educational databases. The result of integrating technology to predict student performance along with Mathematical Modelling and Big-Data Analytics helps us to make better decisions about teaching and learning. Modelling involves formulating real-life situations or to convert the problems in mathematical explanations to a real or believable situation. However, Mathematical modelling are an essential enabler in Big-data and Developments in Big-data analytics require not only more computing, but also new advanced mathematical approaches. In this paper, our main aim to see how Mathematical Modelling and Big-Data analytics help in students’ learning and how they relate to each other.