24468427
Induction of Probabilistic Rules Based on Rough Set Theory
Automated knowledge acquisition is an important research issue in machine learning. There have been proposed several methods of inductive learning, such as ID3 family and AQ family. These methods are applied to discover meaningful knowledge from large database, and their usefulness is in some aspects ensured. However, in most of the cases, their methods are of deterministic nature, and the reliability of acquired knowledge is not evaluated statistically, which makes these methods ineffective when applied to the domain of essentially probabilistic nature, such as medical one. Extending concepts of rough set theory to probabilistic domain, we introduce a new approach to knowledge acquistion, which induces probabilistic rules based on rough set theory(PRIMEROSE) and develop an program that extracts rules for an expert system from clinical database, based on this method. The results show that the derived rules almost correspond to those of the medical experts.
42031107
Algebraic Specification of Empirical Inductive Learning Methods based on Rough Sets and Matroid Theory
In order to acquire knowledge from databases, there have been proposed several methods of inductive learning, such as ID3 family and AQ family. These methods are applied to discover meaningful knowledge from large databases, and their usefulness is ensured. However, since there has been no formal approach proposed to treat these methods, efficiency of each method is only compared empirically. In this paper, we introduce matroid theory and rough sets to construct a common framework for empirical machine learning methods which induce the combination of attribute-value pairs from databases. Combination of the concepts of rough sets and matroid theory gives us an excellent framework and enables us to understand the differences and the similarities between these methods clearly. In this paper, we compare three classical methods, AQ, Pawlak's Consistent Rules and ID3. The results show that there exists the differences in algebraic structure between the former two and the latter and that this causes the differences between AQ and ID3.
6820086
BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network
SUMMARY
BioCichlid is a 3D visualization system of time-course microarray data on molecular networks, aiming at interpretation of gene expression data by transcriptional relationships based on the central dogma with physical and genetic interactions. BioCichlid visualizes both physical (protein) and genetic (regulatory) network layers, and provides animation of time-course gene expression data on the genetic network layer. Transcriptional regulations are represented to bridge the physical network (transcription factors) and genetic network (regulated genes) layers, thus integrating promoter analysis into the pathway mapping. BioCichlid enhances the interpretation of microarray data and allows for revealing the underlying mechanisms causing differential gene expressions.


AVAILABILITY
BioCichlid is freely available and can be accessed at http://newton.tmd.ac.jp/. Source codes for both biocichlid server and client are also available.
206639604
COBRA: Integration of Knowledge-Bases with Case-Databases in the Domain of Congenital Malformation
There have been developed many medical decision support systems, such as MYCIN since the end of 1970's [2]. One of the most important problems of these decision support systems is that their input interface is based on verbal information and that they cannot handle multi-data, which causes the following two problems. First, users have to know correctly the meaning of medical technical terms on the input screen. However, medical experts who majors in respiratory diseases do not know technical terms in neurology usually. Therefore it is hard even for medical experts to use a medical expert system, which makes them difficult to apply to real-world situations. Second, we often need non-verbal information, such as visual images in order to understand medical technical terms, since those technical terms are closely related to those kinds of data. For example, a neurological symptom "ptosis" can be explained in words as shown in [1]. However, even medical experts cannot easily understand several technical terms in the explanation. Thus, if we can use visual images then it may be easier to understand those terms. Interestingly, those problems are also closely related not only to practical use, but also medical education. Medical education also needs non-verbal information, and the way how to describe non-verbal information by using technical terms. For example, medical students do not exactly know about ptosis. In order to understand this symptom, we need a picture or a photograph which shows a typical case of ptosis. In other words, medical education can be viewed as a process which learns the relations between verbal information and non-verbal information. Therefore, these problems suggest that medical intelligent systems should deal with many different kinds of data for practical use. Actually, medical da ta
5781949
Automated Discovery of Medical Expert System Rules from Clinical Databases Based on Rough Sets
Automated knowledge acquisition is an important research issue to solve the bottleneck problem in developing expert systems. Although many inductive learning methods have been proposed for this purpose, most of the approaches focus only on inducing classification rules. However, medical experts also learn other information important for diagnosis from clinical cases. In this paper, a rule induction method is introduced, which extracts not only classification rules but also other medical knowledge needed for diagnosis. This system is evaluated on a clinical database of headache, whose experimental results show that our proposed method correctly induces diagnostic rules and estimates the statistical measures of rules.
29142536
Algebraic Formulation of Empirical Learning Methods Based on Rough Sets and Matroid Theory
17400401
On-line Optimization of Avoidance Ability for Redundant Manipulator
This study is intended to build a controller of redundant manipulator with the simultaneous ability of trajectory tracking and obstacle avoidance without any preparation of path planning to achieve full automation even for one production of one kind, while keeping the avoidance ability high and keeping its shape away from obstacle to reduce the possibility that the manipulator crashes to the obstacle. To evaluate the avoidance ability of the intermediate links, a scalar value of avoidance manipulability shape index (AMSI) was proposed, which is independent of the obstacle's shape. On the other hand, the danger to crash to the obstacle is depending on the shape of the obstacle, which could be evaluated by the potential field set around the obstacle. This paper just proposes a control method of the manipulator's shape based on avoidance manipulability shape index with potential (AMSIP) to simultaneously avoid obstacle and keep the avoidance ability high while tracking the hand trajectory
46587618
Architecture of TMN-based integrated management system for SDH/PDH mixed large-scale transport network
For efficient and integrated network management of SDH/PDH mixed large-scale transport networks, KDD has developed a TMN-based integrated management system. This paper describes an architecture of the system and its implementation. The features of the architecture are: (1) to employ a distributed system configuration based on TMN multi-layered functional architecture, (2) to newly define an integrated management information model which unifies different management information models for many kinds of NEs (Network Elements) and which enables us to manage a network with mixed SDH and PDH hierarchies of, and (3) to introduce a new alarm correlation mechanism for high system performance.
39591082
Prototyping of Digital Ink E-Mail System Based on a Common Ink Format
This paper describes the prototyping of a digital ink e-mail system based on a common ink format. This e-mail system can deal with handwritten figures and text strings on a same electronic paper. Digital ink is useful to express one's message on electronic paper (digital paper). Because digital ink is similar to real ink used on real paper, it is easy to use even for the end user. But on existing e-mail systems, most messages are written in text only. We think that the e-mail system must be made more user-friendly and useful if digital-ink is to be easily used on it. To deal with digital ink on an e-mail system, we have designed a new document format HandsDraw which can be used for text, simple figures and digital ink. This format is so simple that it is easy to implement software which can deal with it on any computer system. We also have made a prototype digital ink e-mail system. This system uses HandsDraw to exchange handwritten messages by e-mail. We think the digital ink e-mail system allows e-mail users to express their messages more quickly, directly and intuitively.
206775678
Hybrid pen-input character recognition system based on integration of online-offline recognition
Describes a handwritten character recognition system that integrates offline recognition requiring a bitmap image and online recognition involving an input pattern as a sequence of x-y coordinates. Offline recognition performs well for painted or overwritten patterns (for which online recognition would not be suited), whereas online recognition is suitable for very deformed patterns (for which offline recognition is not suited). Because each method has different recognition capabilities, the methods complement each other when integrated together. We have implemented a hybrid handwritten character recognition system in which the recognition results of the offline and online recognizer are integrated to create an improved product. After testing several integration methods for a handwritten character database, we found that the best method increased the recognition rate from 73.8% (offline) and 84.8% (online) to 87.6% (integrated).
31262490
Equipment Operation by Motion Recognition with Wearable Wireless Acceleration Sensor
This paper proposes new system architecture for equipment operation based on the motion recognition using a 3-axis acceleration sensor. The small sensor is attached to the wrist with a band and is worn as a wearable device so as not to disturb body motion. The motion of the arm is detected by the acceleration sensor, and data are transmitted to the PC by Bluetooth signals. It has a wide detection range compared to video methods because the Bluetooth radio system has omni-directional characteristics. The data are analyzed using a DP matching method in the PC and the templates stored in advance are compared. Each template data correspond to the equipment operation. The IR signal is transmitted based on the results of matching and this signal operates the equipment. A television and a room light are selected for the system demonstration, and it is confirmed that turn on/off of each piece of equipment, changing channels and volume alternation can be performed through body motion.