Decision trees classifiers are simple and prompt data classifiers as supervised learning means with the potential of generating comprehensible output, usually used in data mining to study the data and generate the tree and its rules that will be used to formulate predictions. One of the major challenges for knowledge discovery and data mining systems stands in developing their data analysis capability to discover out of the ordinary models in data. The excellence of a university is specified among other concerns by its adapting competence to the constant changing needs of the socio-economic background, the quality of the managerial system based on a high level of professionalism and on applying the latest technologies. This article represents an implementation of a J48 algorithm analysis tool on data collected from surveys on different specialization students of my faculty, with the purpose of differentiating and predicting their choice in continuing their education with post university studies (master degree, Ph.D. studies) through decision trees.
Nowadays, Information Technology (IT) is part of virtually every business, and companies that cannot keep the pace with new technologies will disappear over time. Due to their nature of specific activities and exceeding other areas, professional accounting and auditing services can improve their performance through Robotic Process Automation (RPA). Furthermore, RPA can contribute to increasing the credibility of the accounting profession, as well as streamlining the activity in order to comply with the requirements imposed by professional standards but with much lower costs. This study is based on a review of the literature and through an exploratory approach opens a discussion on the concept of RPA and customises it in the field of professional accounting services by analysing robotics models specific to accounting and audit.
Due to increased competition in higher education environments, the universities adopted modern Information and Communication Technologies (ICT) with the aim of completing quality educational processes. They plan to use more efficiently the collected data, develop tools so that to collect and direct management information, in order to support managerial decision making. The collected data could be utilized to evaluate quality, perform analyses and diagnoses, evaluate dependability to the standards and practices of curricula and syllabi, and suggest alternatives in decision processes.Data mining (DM) and Decision Support Systems (DSS) are well suited technologies to provide decision support in the higher education environments, by generating and presenting relevant information and knowledge towards quality improvement of education processes and management.
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