Academic Data Mining was one of emerging field which comprise procedure of examined students' details by different elements such as earlier semester marks, attendance, assignment, discussion, lab work were of used to improved bachelor academic performance of students, and overcome difficulties of low ranks of bachelor students. It was extracted useful knowledge from bachelor academic students data collected from department of Computing. Subsequently preprocessing data, which was applied data mining techniques to discover classification and clustering. In this study, classification method was described which was based on naïve byes algorithm and used for Academic data mining. It was supportive to students along with to lecturers for evaluation of academic performance. It was cautionary method for student's to progress their performance of study.

Since autistic children suffers from learning disabilities and communication barriers, this research aim to design, develop and evaluate an Android based mobile application (app) providing better learning environment with inclusion of graphical representation in a cost effective manner. This research evaluate various supporting technologies and finds Picture Exchange Communication System (PECS) to be better choice for integrating with the app. Evaluation results reveal that the inclusion of PECS helped the children suffering from Autistic Spectrum Disorder (ASD) to better communicate with others. The study included autistic children who do not speak, who are unintelligible and who are minimally effective communicators with their present communication system. The evolution results showed encouraging impacts of the Autism App in supporting autistic children to adapt to normal life and improve the standard of their life.

The smart meter offered exceptional chances to well comprehend energy consumption manners in which quantity of data being generated. One request was the separation of energy load-profiles into clusters of related conduct. The Research measured the resemblance between groups them together and load-profiles into clusters by k-means clustering algorithm. The cluster met, also called “Gender (Male/Female), House (Rented/Owned) and customers status (Satisfied/Unsatisfied)” display methods of consuming energy. It provided value information aimed at utilities to generate specific electricity charges and healthier aim energy efficiency programs. The results show that 43% extremely dissatisfied of energy customer is achieved by using energy consumption.

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