Mining range associations for classification and characterization

In this paper, we propose a method that is able to derive rules involving range associations from numerical attributes, and to use such rules to build comprehensible classification and characterization (data summary) models. Our approach follows the classification association rule mining paradigm, where rules are generated in a way similar to association rule mining, but search is guided by rule consequents. This allows many credible rules, not just some dominant rules, to be mined from the data to build models. In so doing, we propose several sub-range analysis and rule formation heuristics to deal with numerical attributes. Our experiments show that our method is able to derive range-based rules that offer both accurate classification and comprehensible characterization for numerical data

進化的及び樹状突起のメカニズムを考慮したソフトコンピューティング技術の提案

富山大学・富理工博甲第117号・宋振宇・2017/03/23富山大学201

Multivariate Approaches to Classification in Extragalactic Astronomy

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono-or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.Comment: Open Access paper. http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>. \<10.3389/fspas.2015.00003 \&g

Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Meta-parametric design: Developing a computational approach for early stage collaborative practice

Computational design is the study of how programmable computers can be integrated into the process of design. It is not simply the use of pre-compiled computer aided design software that aims to replicate the drawing board, but rather the development of computer algorithms as an integral part of the design process. Programmable machines have begun to challenge traditional modes of thinking in architecture and engineering, placing further emphasis on process ahead of the final result. Just as Darwin and Wallace had to think beyond form and inquire into the development of biological organisms to understand evolution, so computational methods enable us to rethink how we approach the design process itself. The subject is broad and multidisciplinary, with influences from design, computer science, mathematics, biology and engineering. This thesis begins similarly wide in its scope, addressing both the technological aspects of computational design and its application on several case study projects in professional practice. By learning through participant observation in combination with secondary research, it is found that design teams can be most effective at the early stage of projects by engaging with the additional complexity this entails. At this concept stage, computational tools such as parametric models are found to have insufficient flexibility for wide design exploration. In response, an approach called Meta-Parametric Design is proposed, inspired by developments in genetic programming (GP). By moving to a higher level of abstraction as computational designers, a Meta-Parametric approach is able to adapt to changing constraints and requirements whilst maintaining an explicit record of process for collaborative working