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The detection of outliers in spatio-temporal traffic data is an important research problem in the data mining and knowledge discovery community. However to the best of our knowledge, the discovery of relationships, especially causal interactions, among detected traffic outliers has not been investigated before. In this paper we propose algorithms which(More)
We propose a new decision tree algorithm, Class Confidence Proportion Decision Tree (CCPDT), which is robust and insensitive to class distribution and generates rules which are statistically significant. In order to make decision trees robust, we begin by expressing Information Gain, the metric used in C4.5, in terms of confidence of a rule. This allows us(More)
The detection of outliers from spatio-temporal data is an important task due to the increasing amount of spatio-temporal data available, and the need to understand and interpret it. Due to the limitations of previous data mining techniques, new techniques to detect spatio-temporal outliers need to be developed. In this paper, we propose a spatio-temporal(More)
The increasing availability of large-scale trajectory data provides us great opportunity to explore them for knowledge discovery in transportation systems using advanced data mining techniques. Nowadays, large number of taxicabs in major metropolitan cities are equipped with a GPS device. Since taxis are on the road nearly twenty four hours a day (with(More)
The main objective of this research was to construct accurate near-infrared reflectance (NIR) models of wood chemistry. Wet chemistry procedures and high-performance liquid chromatography methods were employed to analyze the chemical composition of southern pine. The NIR spectra were collected from 21 wood samples, which were milled down to different(More)
Adversarial learning is the study of machine learning techniques deployed in non-benign environments. Example applications include classifications for detecting spam email, network intrusion detection and credit card scoring. In fact as the gamut of application domains of machine learning grows, the possibility and opportunity for adversarial behavior will(More)
Amongst the wealth of available machine learning algorithms for forecasting time series, linear regression has remained one of the most important and widely used methods, due to its simplicity and inter-pretability. A disadvantage, however, is that a linear regression model may often have higher error than models that are produced by more sophisticated(More)
Excessive manganese (Mn) exposure can lead to oxidative injury. Nuclear factor erythroid 2-related factor 2 (Nrf2) exerts an antioxidant response toward various environmental toxicants in the brain. However, the role of Nrf2 against Mn-induced oxidative injury remains largely unexplored. This study investigated the role of melatonin (MLT), an agent that was(More)
Methylmercury (MeHg) is an extremely dangerous environmental contaminant, accumulating preferentially in CNS and causing a series of cytotoxic effects. However, the precise mechanisms are still incompletely understood. The current study explored the mechanisms that contribute to MeHg-induced cell injury focusing on the oxidative stress and Glu(More)