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Data mining is an advanced technology, which is the process of discovering actionable information from large set of data, which is used to analyze large volumes of data and extracts patterns that can be converted to useful knowledge. Medical data mining has a great potential for exploring the hidden patterns in the data sets of medical domain. These patterns can be utilized to do clinical diagnosis...
Decision Tree induction is commonly used classification algorithm. One of the important problems is how to use records with unknown values from training as well as testing data. Many approaches have been proposed to address the impact of unknown values at training on accuracy of prediction. However, very few techniques are there to address the problem in testing data. In our earlier work, we discussed...
In this paper, we propose learning analytic tasks to understand the learning process in a smart classroom. Learning analytics can extract knowledge from a course to better understand students and their learning processes. The learning analytic tasks must evaluate different aspects in the course: the teaching and learning process, the student performance, and the pedagogical practices, among other...
Innovation in the public-sector refers to the development of important improvements in the public administration and their corresponding services. One of such public services is the social security, of which central process has been the information security of their offered services. The aim of the present study has been the analysis of the trends and the discovery of behavioural patterns in the attacks...
Decision Tree is one of the most popular supervised Machine Learning algorithms; it is also the easiest to understand. But finding an optimal decision tree for a given data is a harder task and the use of multiple performance metrics adds some complexity to the problem of selecting the most appropriate DT.
In many application areas, data that is being generated and processed goes beyond the petabyte scale. Analyzing such an increasing massive volume of data faces computational, as well as, statistical challenges. In order to solve these challenges, distributed and parallel processing frameworks have been used for implementing scalable data analysis algorithms. Nevertheless, processing the whole big...
With the increasing complexity of both data structures and computer architectures, the performance of applications needs fine tuning in order to achieve the expected runtime execution time. Performance tuning is traditionally based on the analysis of performance data. The analysis results may not be accurate, depending on the quality of the data and the applied analysis approaches. Therefore, application...
Mechanical and electrical equipments are widely used in industry. Existing electro-hydraulic mixing equipments mainly use expert systems for fault diagnasis. However, in order to increase the accuracy of diagnasis, the expert systems have to acquire more knowledge. And diagnosis system will bring great uncertainty due to limited knowledge. Furthermore, existing fault diagnosis system has the disadvantages...
The Wide-reaching usage of the standard called as IEEE 802.111 has been acting as a solution to support aggressive network coverage with high bandwidth raised various security threats. The wide use of the Wi-Fi (Wireless Fidelity) has enabled us to easily access the internet and it has also paved way for the origin of many hacking attacks. Anomaly detection as applied to detecting active data breaches...
Traffic monitoring at Signals are very important nowadays because the number of vehicles increased and also there is a growth in traffic jams. The video cameras which are placed at signals are used for this purpose. There is a possibility for the video cameras to get spoiled by weather. Traffic security cameras would be damaged or ruined by heat, wind, rain, snow and ice. Current transportation environment...
Distributed Data Mining (DDM) on Electronic Health Records (EHRs) has evolved in large space aiming for effective and efficient record retrieval with minimized communication cost and memory cost than Centralized Data Mining(CDM). In this paper, heterogeneous classifier techniques of DDM are compared and their performance is evaluated with respect to EHR as dataset. Finally an architectural model based...
In recent years, type II diabetes has become a serious disease that threaten the health and mind of human. Efficient predictive modeling is required for medical researchers and practitioners. This study proposes a type II diabetes prediction model based on random forest which aims at analyzing some readily available indicators (age, weight, waist, hip, etc.) effects on diabetes and discovering some...
Indoor systems cannot obtain a precise estimate of the location, due to unstable signals. In this paper, we use realistic wireless data from the IEEE International Conference on Data Mining (ICDM) dataset and Azure Machine Learning Studio to perform Bagging (also called bootstrap aggregating). By using the machine leaning technique in the Azure Machine Learning Studio, we can obtain more than 69 percent...
Hospital length of stay (LOS) of patients is an important factor for planning and managing the resource utilization of a hospital. There has been considerable interest in controlling hospital cost and increasing service efficiency, particularly in stroke and cardiac units where the resources are severely limited. This study introduces an approach for early prediction of LOS of stroke patients arriving...
This paper aims to predict the factors and build prediction models for the persuasive message changing student's attitude by applying classification techniques. We used a questionnaire to collect data such as gender, age and their satisfaction with persuasive messages, obtained from students at Khon Kaen University. The classification rule generation process is based on the decision tree as a classification...
E Learning courses are much in demand in recent times. The need to study student's performance and predicting their performance is increasing along with it. With the growing popularity of educational technology, various data mining algorithms suitable for predicting student performance have been reviewed. The best algorithm depends on the nature of prediction the faculty wants to make. As the amount...
Big data applications are developed and being explored by the computer science organization, which is classified and accepted by huge data sets collected from sensor networks, online networks, medical agencies, etc. To deal with the difficulty in analysis of data, we conduct research on the novel algorithms for data mining and knowledge discovery through network property. At first, we introduce necessary...
In insulin-dependent diabetes mellitus (IDDM) therapy, a suitable insulin dosage taken at the appropriate times is needed for each patient to sustain the necessary blood-glucose level for his or her body. In this article, a datastream mining approach is proposed that can computationally derive real-time decision rules for formulating IDDM therapy based on insulin prescription records and patients’...
One of the biggest challenges that higher learning institutions face today is to improve the placement performance of students. The placement prediction is more complex when the complexity of educational entities increase. Educational institutes look for more efficient technology that assist better management and support decision making procedures or assist them to set new strategies. One of the effective...
This paper focuses on use of Matlab for data mining. There is wide range of data mining software where free or cheaper solutions offer similar possibilities. We wanted to try Matlab for these purposes. Our data consists of parameters, which describes cloud usage at IT company that offers cloud services. We used phases from the CRISP-DM methodology in our work. We built clustering and classification...
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