Abstract: In this paper, an attempt has been made for the design
of a robotic library using an intelligent system. The robot works on
the ARM microprocessor, motor driver circuit with 5 degrees of
freedom with Wi-Fi and GPS based communication protocol. The
authenticity of the library books is controlled by RFID. The proposed
robotic library system is facilitated with embedded system and ARM.
In this library issuance system, the previous potential readers’
authentic review reports have been taken into consideration for
recommending suitable books to the deserving new users and the
issuance of books or periodicals is based on the users’ decision. We
have conjectured that the Wi-Fi based robotic library management
system would allow fast transaction of books issuance and it also
produces quality readers.
Abstract: Educational data mining is a specific data mining field applied to data originating from educational environments, it relies on different approaches to discover hidden knowledge from the available data. Among these approaches are machine learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.
In our research, we propose a “Student Advisory Framework” that utilizes classification and clustering to build an intelligent system. This system can be used to provide pieces of consultations to a first year university student to pursue a certain education track where he/she will likely succeed in, aiming to decrease the high rate of academic failure among these students. A real case study in Cairo Higher Institute for Engineering, Computer Science and Management is presented using real dataset collected from 2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.
Abstract: Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.