The functions of automatically identify relationships between cancer diseases and external factors from medical records for supporting cancer diagnosis would be a valuable contribution in public health fields. Unfortu...
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ISBN:
(纸本)0769529941
The functions of automatically identify relationships between cancer diseases and external factors from medical records for supporting cancer diagnosis would be a valuable contribution in public health fields. Unfortunately, so far little attention has been paid on providing effective solutions to such a problem domain. In this work, we propose a framework to automating the extraction of relationships between cancer diseases and potential factors from clinical records and medical literature. We describe a platform framework integrating cancer microarray and developed datamining techniques of multimedia and multilingual, covering clustering of multimedia as well as multilingual classifiers to carry out the system development. In the implementation, we extracted the associated genes of cancers by clustering microarray data, and then exploited the resulting gene clusters to classify cancer related documents. The experimental results show that the platform is capable of extracting the potential patterns to enhancing more effective solutions for cancer diseases.
This paper presents a digital watermarking technology for guaranteeing the database integrity. The proposed scheme based on the fragile watermarking technique, exploits trained support vector regression (SVR) predicti...
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ISBN:
(纸本)9780769529943
This paper presents a digital watermarking technology for guaranteeing the database integrity. The proposed scheme based on the fragile watermarking technique, exploits trained support vector regression (SVR) predicting function to distribute the digital watermark over the particular numeric attributes to achieve embedding and detecting watermark by the same SVR predicting function. If the absolute value of the difference between predicted value and attribute value is more than the designed fixed value, like one, then the database content will be tampered with.
This paper introduces a multi-objective optimization approach to the problem of computing virtual reality spaces for the visual representation of relational structures (e.g. databases), symbolic knowledge and others, ...
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ISBN:
(纸本)9783540730064
This paper introduces a multi-objective optimization approach to the problem of computing virtual reality spaces for the visual representation of relational structures (e.g. databases), symbolic knowledge and others, in the context of visual datamining and knowledge discovery. Procedures based on evolutionary computation are discussed. In particular, the NSGA-II algorithm is used as a framework for an instance of this methodology;simultaneously minimizing Sammon's error for dissimilarity measures, and mean cross-validation error on a k-nn pattern classifier. The proposed approach is illustrated with an example from cancer genomics data (e.g. lung cancer) by constructing virtual reality spaces resulting from multi-objective optimization. Selected solutions along the Pareto front approximation are used as nonlinearly transformed features for new spaces that compromise similarity structure preservation (from an unsupervised perspective) and class separability (from a supervised patternrecognition perspective), simultaneously. The possibility of spanning a range of solutions between these two important goals, is a benefit for the knowledge discovery and data understanding process. The quality of the set of discovered solutions is superior to the ones obtained separately, from the point of view of visual datamining.
data clustering is a common technique for data analysis, which is used in many fields, including machinelearning, datamining, patternrecognition, image analysis and bioinformatics. Due to the continuous increase of...
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ISBN:
(纸本)9780889867048
data clustering is a common technique for data analysis, which is used in many fields, including machinelearning, datamining, patternrecognition, image analysis and bioinformatics. Due to the continuous increase of datasets size and the intensive computation of clustering algorithms when used for analyzing large datasets, developing of efficient clustering algorithms is needed for processing time reduction. This paper describes the design and implementation of a recently developed clustering algorithm RACAL [1], which is a RAdius based Clustering ALgorithm. The proposed parallel algorithm (PRACAL) has the ability to cluster large datasets of high dimensions in a reasonable time, which leads to a higher performance computing.
The search for frequent patterns in transactional databases is considered one of the most important datamining problems. Several parallel and sequential algorithms have been proposed in the literature to solve this p...
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In this paper, a new efficient fast terminal attractor based backpropagation learning algorithm for feedforward neural networks is proposed, which improves the convergence speed. The effectiveness of the proposed algo...
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In this paper, a new efficient fast terminal attractor based backpropagation learning algorithm for feedforward neural networks is proposed, which improves the convergence speed. The effectiveness of the proposed algorithm in improving learning speed is shown by the simulation results including a sensor network example.
In this paper, we present a novel active learning strategy, named dynamic active learning with SVM to improve the effectiveness of learning sample selection in active learning. The algorithm is divided into two steps....
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Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. Rough set theory is closely related to knowledge technology in a variety of ...
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Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. Rough set theory is closely related to knowledge technology in a variety of forms such as knowledge discovery, approximate reasoning, intelligent and multiagent system design, knowledge intensive computations .
It seems characteristic for humans to detect structural patterns in the world to anticipate future states. Therefore, scientific and common sense cognition could be described as information processing which infers rul...
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ISBN:
(纸本)9783540742616
It seems characteristic for humans to detect structural patterns in the world to anticipate future states. Therefore, scientific and common sense cognition could be described as information processing which infers rule-like laws from patterns in data-sets. Since information processing is the domain of computers, artificial cognitive systems are generally designed as pattern discoverers. This paper questions the validity of the information processing paradigm as an explanation for human cognition and a design principle for artificial cognitive systems. Firstly, it is known from the literature that people suffer from conditions such as information overload, superstition, and mental disorders. Secondly, cognitive limitations such as a small short-term memory, the set-effect, the illusion of explanatory depth, etc. raise doubts as to whether human information processing is able to cope with the enormous complexity of an infinitely rich (amorphous) world. It is suggested that, under normal conditions, humans construct information rather than process it. The constructed information contains anticipations which need to be met. This can be hardly called information processing, since patterns from the "outside" are not used to produce action but rather to either justify anticipations or restructure the cognitive apparatus. When it fails, cognition switches to pattern processing, which, given the amorphous nature of the experiential world, is a lost cause if these patterns and infer-red rules do not lead to a (partial) reorganisation of internal structures such that constructed anticipations can be met again. In this scenario, superstition and mental disorders are the result of a profound and/or random restructuring of already existing cognitive components (e.g., action sequences). This means that whenever a genuinely cognitive system is exposed to pattern processing it may start to behave superstitiously. The closer we get to autonomous self-motivated artificial cognitive sy
In this paper we describe a datamining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machine learnin...
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In this paper we describe a datamining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machinelearning techniques such as neural networks: (1) possibility to easily include expert knowledge into the model, and (2) possibility for humans to understand and interpret the learned model. Our approach is implemented and tested on synthetic data, where initial results show that it can be used for detection of single-object anomalies such as speeding.
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