Intelligent techniques derived from knowledge-based engineering and related computing paradigms have provided useful concepts and tools to undertake a variety of real-world problems. These systems mimic the analytical and learning capabilities of the human brain. They harness the benefits of knowledge and intelligence to form an integrated framework for problem solving. In this special issue, a...

The Short Message Service (SMS) have an important economic impact for end users and service providers. Spam is a serious universal problem that causes problems for almost all users. Several studies have been presented, including implementations of spam filters that prevent spam from reaching their destination. Naïve Bayesian algorithm is one of the most effective approaches used in filtering te...

In this paper, we introduce new learning algorithms for reducing false positives in intrusion detection. It is based on decision tree-based attribute weighting with adaptive naïve Bayesian tree, which not only reduce the false positives (FP) at acceptable level, but also scale up the detection rates (DR) for different types of network intrusions. Due to the tremendous growth of network-based se...

One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These "false alarms" delay the detection of fraudulent transactions and can cause unnecessary concerns for customers. In this study, over 1 million unique credit card transactions from 11 months of data from a large Canadian bank w...

Mel-frequency cepstrum coefficients (MFCCs) extracted from speech recordings has been proven to be the most effective feature set to capture the frequency spectra produced by a recording device. This paper claims that audio evidence such as a recorded call contains intrinsic artifacts at both transmitting and receiving ends. These artifacts allow recognition of the source mobile device on the o...

Brain tumors vary widely in size and form, making detection diagnosis difficult. This study's main aim is to identify abnormal brain images., classify them from normal images, then segment the tumor areas categorised images. In this study, we offer a technique based on Nave Bayesian classification approach that can efficiently tumors. Noises are identified filtered out during preprocessing phas...