Data mining tasks such as clustering and classification have proved to highly impact various fields such as business, including the banking sector, as well as medicine, including the radiology sector. As the decision‐making process is critically dependent on the availability of high‐quality information presented in a timely and easily understood manner, the successful application of efficient data mining approaches is a great support for achieving the required target in the available time. This study presents an enhancement for the Iterative Dichotomiser 3 (ID3) classification decision tree algorithm based on two related approaches, namely, data partitioning and parallelism. The study applied the proposed algorithm in the banking and radiology sectors; as data have been classified to the defined fields’ clusters, the processing time and the results’ accuracy parameters have been compared with the ID3 algorithm and have proved an enhancement in both parameters. WIREs Data Mining Knowl Discov 2016, 6:70–79. doi: 10.1002/widm.1177 This article is categorized under: Algorithmic Development > Hierarchies and Trees Application Areas > Government and Public Sector Application Areas > Health Care Technologies > Classification

Educational data mining is concerned with the development methods for exploring the unique types of data that come from the educational context. Furthermore, educational data mining is an emerging discipline that concerned with the developing methods for exploring the unique types of data that come from the educational context. This study focuses on the way of applying data mining techniques for higher education system by using the most common techniques on most common application called Moodle system in education system. There are an increasing numbers of researches that interest in using data mining in education system. The proposed system for Higher Educational Data Mining System (HEDMS) is concerned with the developing methods that discover useful knowledge from data that extracted from educational system. The data collated form historical and usage data reside in the databases of educational institutes. The proposed system helps to get sufficient results which consist of several steps in our case study starting with collected data, pre-processing, applying data mining techniques and visualization results. We collected students' data from Moodle database.

No abstract

The Demand for healthcare IT and its analytics increases in the last few years. To improve quality of care (e.g., ensuring that patients receive the correct medication) which will help to improve the efficiency of clinical quality and safety, operations.The Nature of the medical field is rich with information where there's a variety and abundance of data but untapped in a correct and effective manner to get the right knowledge. and therefore, the most serious challenge facing this area is the quality of service provided which means to make the diagnose in a proper manner at a timely manner and provide appropriate medications to patients because Poor diagnosing can lead to serious consequences which are unacceptable. And because there is a lack of effective analysis tools to discover hidden relationships and trends in data, so Health information technology has emerged as a new technology in health care sector in a short period by utilizing Business Intelligence 'BI' which is a data-driven Decision Support System. Which Was developed from 1990s to now, and gradually become one of the most important information systems applied in any sector. BI enables to deal with huge amount of data and extract useful knowledge to support decision making. Data mining 'DM' is a kind of data processing technology which can be regarded as a part of the BI system, but it can be also considered as an independent and integrated technology which can treat mass data and extract hidden relationships from it.This introduction highlights the main importance of how to apply the business intelligence applications using data mining techniques to help medical professionals in healthcare sector rapidly diagnosing and predicting diseases of any patients not only this but also detecting the disease complications on the patient which will decrease the overall cost of expenditure that the country paid, briefly this is the central research idea which address the motivation for doing this research.

No abstract

It has become an essential element for all organizations to be engaged in a more deliberate transform for prevention, protection, preparedness, mitigation and reaction to Business Continuity Management (BCM) and recovery. It is becoming risky for the organization to reply of disaster plan which targets to minimize the causes of the disruption. An organization should take a proactive approach to reduce the likelihood as well as the probability of the disruptions. It involves the development of advanced measures and actions that enable the organization to take action in such a way that the critical and essential functions of business could continue according to the predefined level of change and disruption. Recovering from disruptions is a challenge; it relies on an emergency response plan or uses disaster management strategies that were previously used. BCM is considered mandatory to provide a systematic approach towards crisis response in order to increase organizational resilience capability. It is necessarily required by the organization to protect its reputation, brand, value adding activities and stakeholders' interests. This paper proposes an intelligent framework that applies the business continuity management process on an educational institute which reveals the stakeholder awareness of the business continuity level in their organization. In order to successfully formulate the proposed framework, the research went through a set of phases. The research presented the whole process phases for developing the proposed framework which is based on applying the suitable technique in each phase to guarantee a suitable performance with ensuring the results' validity. The research used 93% confidence, Z = 1.98 with error margin E = 0.03 for sampling selection and the determined association were 94% confidence for 93 rules. Finally, the research discussed the recommendations for successful crisis management process with the support of intelligent techniques.

Urbanization growth is creeping, and the number of cities will increase each day more than before. Predictions say that approximately 70% of the world population will live in urban areas by 2050. Hence, new approaches must be considered while designing/upgrading/enhancing urban places. This article describes a new theoretical model to enhance the urban design process through Knowledge Discovery techniques based on Artificial Intelligence, data mining, machine learning, 5G, and Micro-World. The research findings enable assessing the whole process of Urban Design phases by proposing new techniques using the Knowledge Discovery by combining the traditional research methods used frequently in the Urban Design Process with the Knowledge Discovery Techniques. Through reviewing and analyzing the urban design and its relation to urban behavior, the authors deduct the correlations between the urban design process and the knowledge discovery techniques that can build solutions based on objective output, not only the traditional subjective ones. The new research results explain the exact application relevant to each step in the urban design process. Also, the research will supplement and improve urban studies to understand the profound relationship between Urban Environmental Psychology and Artificial Intelligence. It can serve the urban designers and planners by using the knowledge discovery to enhance urban practice by providing a more comprehensive range of solutions.