2 research outputs found
ECG Analysis Using DWT and Wavelet Coefficient to Reduce the Feature and SVM-ICP for Classification and Matching
The Electrocardiogram (ECG) considered as one of the important issue in the medical field (hospitals and clinics), which is used to represent the health of a heart. Increasing patients of heart has supposed to design an automatic computerization technique to classify various abnormalities of the heart activities; to reduce the analysis time and detection mistakes. This research focusing on achieve high performance of classifying abnormal ECG by applying different methods. The first method is Discrete Wavelet Transform (DWT) with 4-level to transform the ECG signal and extract the feature extraction and Wavelet Energy (WE) during feature extraction as feature vector. In classification phase has used Support Vector Machine (SVM) to train datasets and classify the test samples, in matching phase, find closest vector of test to the training datasets method has used by applying Iterative Closest Point (ICP)
Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection
In this paper, a new learning algorithm for adaptive network intrusion
detection using naive Bayesian classifier and decision tree is presented, which
performs balance detections and keeps false positives at acceptable level for
different types of network attacks, and eliminates redundant attributes as well
as contradictory examples from training data that make the detection model
complex. The proposed algorithm also addresses some difficulties of data mining
such as handling continuous attribute, dealing with missing attribute values,
and reducing noise in training data. Due to the large volumes of security audit
data as well as the complex and dynamic properties of intrusion behaviours,
several data miningbased intrusion detection techniques have been applied to
network-based traffic data and host-based data in the last decades. However,
there remain various issues needed to be examined towards current intrusion
detection systems (IDS). We tested the performance of our proposed algorithm
with existing learning algorithms by employing on the KDD99 benchmark intrusion
detection dataset. The experimental results prove that the proposed algorithm
achieved high detection rates (DR) and significant reduce false positives (FP)
for different types of network intrusions using limited computational
resources.Comment: 14 Pages, IJNS