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One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The…
Data mining methods have been widely applied in financial markets, with the purpose of providing suitable tools for prices forecasting and automatic trading. Particularly, learning methods aim to identify patterns in time series and, based…
Context-awareness in smart mobile applications is a growing area of study, because of it's intelligence in the applications. In order to build context-aware intelligent applications, mining contextual behavioral rules of individual…
We present a new approach to classification that combines data and knowledge. In this approach, data mining is used to derive association rules (possibly with negations) from data. Those rules are leveraged to increase the predictive…
Fault control and tolerance in wireless sensor network is a challenging problem because of limited energy, bandwidth, and computational complexity. While facing numerous threats these severely resource constrained nodes are responsible for…
The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contextual information and mobile phone usage records through the device logs. This paper formulates the problem of…
The aim of our research was to apply well-known data mining techniques (such as linear neural networks, multi-layered perceptrons, probabilistic neural networks, classification and regression trees, support vector machines and finally a…
Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. Educational data mining is used to study the data available in the…
Identifying mobile network problems in 4G cells is more challenging when the complexity of the network increases, and privacy concerns limit the information content of the data. This paper proposes a data driven model for identifying 4G…
Data mining is a new concept & an exploration and analysis of large data sets, in order to discover meaningful patterns and rules. Many organizations are now using the data mining techniques to find out meaningful patterns from the…
Network intrusion detection is the process of identifying malicious behaviors that target a network and its resources. Current systems implementing intrusion detection processes observe traffic at several data collecting points in the…
A wide range of Sensor Networks (SNs) are deployed in real world applications which generate large amount of raw sensory data. Data mining technique to extract useful knowledge from these applications is an emerging research area due to its…
The emerging wide area monitoring systems (WAMS) have brought significant improvements in electric grids' situational awareness. However, the newly introduced system can potentially increase the risk of cyber-attacks, which may be disguised…
This paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining…
Various studies on consumer purchasing behaviors have been presented and used in real problems. Data mining techniques are expected to be a more effective tool for analyzing consumer behaviors. However, the data mining method has…
The research identifies association rules that can inform marketing strategies and enhance operational efficiency. A structured methodology is applied to extract and interpret meaningful relationships within transactional data, emphasizing…
In this work, we aim to solve data-driven optimization problems, where the goal is to find an input that maximizes an unknown score function given access to a dataset of inputs with corresponding scores. When the inputs are high-dimensional…
In intrusion detection systems, classifiers still suffer from several drawbacks such as data dimensionality and dominance, different network feature types, and data impact on the classification. In this paper two significant enhancements…
Many organisations manage service quality and monitor a large set devices and servers where each entity is associated with telemetry or physical sensor data series. Recently, various methods have been proposed to detect behavioural…
Huge datasets in cyber security, such as network traffic logs, can be analyzed using machine learning and data mining methods. However, the amount of collected data is increasing, which makes analysis more difficult. Many machine learning…