2018 ◽  
Vol 6 (4) ◽  
pp. 158-163
Author(s):  
Ari Fadli ◽  
Mulki Indana Zulfa ◽  
Yogi Ramadhani

Observation of growing academic data can be carried using data mining methods, for example, to obtain knowledge related to the determinants of timeliness of students graduation. This study conducted a performance comparison of the classification algorithms using decision tree (DT), support vector machine (SVM), and artificial neural network (ANN). This study used students academic data from Faculty of Engineering, Universitas Jenderal Soedirman in the 2014/2015 odd semester until the 2017/2018 odd semester and the attributes that conform to the academic regulations. The analytical method used is CRISP-DM. The results showed that SVM provided the best performance in an accuracy of 90.55% and AUC of 0.959, compared to other algorithms. A Model with SVM algorithm can be implemented in an early warning system for timeliness of student graduation.


PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  

Author(s):  
Ali Serhan Koyuncugil

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.


2013 ◽  
pp. 1349-1383
Author(s):  
Hakikur Rahman

This chapter is a conceptual contribution to this book on data mining applications upholding ethical issues related to two extremely important aspects of the Bangladeshi population: the early warning system and the disaster management system. The chapter tries to provide a few conceptual ideas to introduce ethical data mining application in these systems to support the agencies that are involved for an improved, efficient, and transparent support system in the country, especially across the Bay of Bengal. Resembling a triangular shape (deltaic), a major portion of the bay touches the southern portion of Bangladesh. Sediments from rivers have made the bay a shallow sea. Due to its shallowness and shape, monsoon rains and cyclone storms become destructive, causing great loss of life along the southern part of the country. Moreover, the three mighty rivers (Padma, Jamuna, and Meghna) form one of the largest river systems in the world. They have a large number of distributaries and tributaries, which cause a major portion of the country to be inundated by monsoon rain. In addition, being the lowest landing zone of the Himalayan water, Bangladesh becomes victim to floods almost every year. Loss of lives, destruction of properties, suffering of numerous people and hampering of economic development have become part and parcel of Bangladeshi communities. This chapter suggests that the newly emerged data mining techniques can be introduced to collect, synthesize, analyze, archive, disseminate, and even make future forecasts forming a reliable early warning system across the Bay of Bengal.


2017 ◽  
Vol 29 (4) ◽  
pp. 685-707 ◽  
Author(s):  
N. JOHNSON ◽  
A. HITCHMAN ◽  
D. PHAN ◽  
L. SMITH

In 2008, the Defense Advanced Research Project Agency commissioned a database known as the Integrated Crisis Early Warning System to serve as the foundation for models capable of detecting and predicting increases in political conflict worldwide. Such models, by signalling expected increases in political conflict, would help inform and prepare policymakers to react accordingly to conflict proliferation both domestically and internationally. Using data from the Integrated Crisis Early Warning System, we construct and test a self-exciting point process, or Hawkes process, model to describe and predict amounts of domestic, political conflict; we focus on Colombia and Venezuela as examples for this model. By comparing the accuracy of fitted models to the observed data, we find that we are able to closely describe occurrences of conflict in each country. Thus, using this model can allow policymakers to anticipate relative increases in the amount of domestic political conflict following major events.


Author(s):  
Hakikur Rahman

This chapter is a conceptual contribution to this book on data mining applications upholding ethical issues related to two extremely important aspects of the Bangladeshi population: the early warning system and the disaster management system. The chapter tries to provide a few conceptual ideas to introduce ethical data mining application in these systems to support the agencies that are involved for an improved, efficient, and transparent support system in the country, especially across the Bay of Bengal. Resembling a triangular shape (deltaic), a major portion of the bay touches the southern portion of Bangladesh. Sediments from rivers have made the bay a shallow sea. Due to its shallowness and shape, monsoon rains and cyclone storms become destructive, causing great loss of life along the southern part of the country. Moreover, the three mighty rivers (Padma, Jamuna, and Meghna) form one of the largest river systems in the world. They have a large number of distributaries and tributaries, which cause a major portion of the country to be inundated by monsoon rain. In addition, being the lowest landing zone of the Himalayan water, Bangladesh becomes victim to floods almost every year. Loss of lives, destruction of properties, suffering of numerous people and hampering of economic development have become part and parcel of Bangladeshi communities. This chapter suggests that the newly emerged data mining techniques can be introduced to collect, synthesize, analyze, archive, disseminate, and even make future forecasts forming a reliable early warning system across the Bay of Bengal.


Fuzzy Systems ◽  
2017 ◽  
pp. 202-234
Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mrsic

Early warning systems are made with purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible and with significant relevance. There are numerous ways to set up early warning systems within company. Those solutions are often based on single data mining methods, and they rarely provide the holistic and qualitative approach needed in modern market uncertainty conditions. This chapter gives a novel concept for early warning system design within company, applicable in different industries. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas in addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. As part of this framework, SNA methods are also explained and introduced as a very powerful and unique tool to be used in modern early warning systems.


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