Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches

Radiation pneumonitis (RP) is a potentially fatal side effect arising in lung cancer patients who receive radiotherapy as part of their treatment. For the modeling of RP outcomes data, several predictive models based on traditional statistical methods and machine learning techniques have been reported. However, no guidance to variation in performance has been provided to date. In this study, we explore several machine learning algorithms for classification of RP data. The performance of these classification algorithms is investigated in conjunction with several feature selection strategies and the impact of the feature selection strategy on performance is further evaluated. The extracted features include patients demographic, clinical and pathological variables, treatment techniques, and dose-volume metrics. In conjunction, we have been developing an in-house Matlab-based open source software tool, called DREES, customized for modeling and exploring dose response in radiation oncology. This software has been upgraded with a popular classification algorithm called support vector machine (SVM), which seems to provide improved performance in our exploration analysis and has strong potential to strengthen the ability of radiotherapy modelers in analyzing radiotherapy outcomes data

Unsupervised Optimal Discriminant Vector Based Feature Selection Method

An efficient unsupervised feature selection method based on unsupervised optimal discriminant vector is developed to find the important features without using class labels. Features are ranked according to the feature importance measurement based on unsupervised optimal discriminant vector in the following steps. First, fuzzy Fisher criterion is adopted as objective function to derive the optimal discriminant vector in unsupervised pattern. Second, the feature importance measurement based on elements of unsupervised optimal discriminant vector is defined to determine the importance of each feature. The features with little importance measurement are removed from the feature subset. Experiments on UCI dataset and fault diagnosis are carried out to show that the proposed method is very efficient and able to deliver reliable results

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Techniques for the realization of ultra- reliable spaceborne computer Final report

Bibliography and new techniques for use of error correction and redundancy to improve reliability of spaceborne computer

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)