Biomedical subjects

Sung-Bae Cho

Publications and source records attributed to Sung-Bae Cho.

4 recordsLinked to original sources

PubMed2006

Evolutionary neural networks for anomaly detection based on the behavior of a program.

The process of learning the behavior of a given program by using machine-learning techniques (based on system-call audit data) is effective to detect intrusions. Rule learning, neural networks, statistics, and hidden Markov models (HMMs) are some of the kinds of representative methods for intrusion detection. Among them, neural networks are known for good performance in learning system-call sequences. In order to apply this knowledge to real-world problems successfully, it is important to determine the structures and weights of these call sequences. However, finding the appropriate structures requires very long time periods because there are no suitable analytical solutions. In this paper, a novel intrusion-detection technique based on evolutionary neural networks (ENNs) is proposed. One advantage of using ENNs is that it takes less time to obtain superior neural networks than when using conventional approaches. This is because they discover the structures and weights of the neural networks simultaneously. Experimental results with the 1999 Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation (IDEVAL) data confirm that ENNs are promising tools for intrusion detection.

Algorithms↗

PubMed2006

A comprehensive overview of the applications of artificial life.

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

Artificial Intelligence↗

PubMed2005

The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming.

OBJECT: The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a new ensemble approach that can enhance the successful classification of these datasets. MATERIALS AND METHODS: An effective ensemble approach that does use diversity in genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating. RESULTS: Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of the proposed method in relation to the conventional approaches. CONCLUSION: Diversity measured by comparing the structure of the classification rules obtained by genetic programming is useful to improve the performance of the ensemble classifier.

Artificial Intelligence↗

PubMed2002

Estimating the efficiency of recognizing gender and affect from biological motion.

It is often claimed that point-light displays provide sufficient information to easily recognize properties of the actor and action being performed. We examined this claim by obtaining estimates of human efficiency in the categorization of movement. We began by recording a database of three-dimensional human arm movements from 13 males and 13 females that contained multiple repetitions of knocking, waving and lifting movements done both in an angry and a neutral style. Point-light displays of each individual for all of the six different combinations were presented to participants who were asked to judge the gender of the model in Experiment 1 and the affect in Experiment 2. To obtain estimates of efficiency, results of human performance were compared to the output of automatic pattern classifiers based on artificial neural networks designed and trained to perform the same classification task on the same movements. Efficiency was expressed as the squared ratio of human sensitivity (d') to neural network sensitivity (d'). Average results for gender recognition showed a proportion correct of 0.51 and an efficiency of 0.27%. Results for affect recognition showed a proportion correct of 0.71 and an efficiency of 32.5%. These results are discussed in the context of how different cues inform the recognition of movement style.

Affect↗