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From spoken narratives to domain knowledge: Mining linguistic data for medical image understanding

ARTIFICIAL INTELLIGENCE IN MEDICINE 2014年第2期62卷 79-90页

作者： Guo, Xuan Yu, Qi Alm, Cecilia Ovesdotter Calvelli, Cara Pelz, Jeff B. Shi, Pengcheng Haake, Anne R. Rochester Inst Technol Coll Comp & Informat Sci Rochester NY 14623 USA Rochester Inst Technol Coll Liberal Arts Rochester NY 14623 USA Rochester Inst Technol Coll Hlth Sci & Technol Rochester NY 14623 USA Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA

Objectives: Extracting useful visual clues from medical images allowing accurate diagnoses requires physicians' domain knowledge acquired through years of systematic study and clinical training. This is especially true in the dermatology domain, a medical specialty that requires physicians to have image inspection experience. Automating or at least aiding such efforts requires understanding physicians' reasoning processes and their use of domain knowledge. Mining physicians' references to medical concepts in narratives during image-based diagnosis of a disease is an interesting research topic that can help reveal experts' reasoning processes. It can also be a useful resource to assist with design of information technologies for image use and for image case-based medical education systems. Methods and materials: We collected data for analyzing physicians' diagnostic reasoning processes by conducting an experiment that recorded their spoken descriptions during inspection of dermatology images. In this paper we focus on the benefit of physicians' spoken descriptions and provide a general workflow for mining medical domain knowledge based on linguistic data from these narratives. The challenge of a medical image case can influence the accuracy of the diagnosis as well as how physicians pursue the diagnostic process. Accordingly, we define two lexical metrics for physicians' narratives lexical consensus score and top N relatedness score and evaluate their usefulness by assessing the diagnostic challenge levels of corresponding medical images. We also report on clustering medical images based on anchor concepts obtained from physicians' medical term usage. These analyses are based on physicians' spoken narratives that have been preprocessed by incorporating the Unified Medical Language System for detecting medical concepts. Results: The image rankings based on lexical consensus score and on top 1 relatedness score are well correlated with those based on challenge levels (

关键词： Unified Medical Language System Lexical consensus Semantic relatedness clustering algorithm Image-based diagnostic reasoning Medical data analysis

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A module generation algorithm for product architecture based on component interactions and strategic drivers

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RESEARCH IN ENGINEERING DESIGN 2014年第1期25卷 31-51页

作者： Borjesson, Fredrik Hoelttae-Otto, Katja Modular Management USA Inc Bloomington MN USA Singapore Univ Technol & Design Singapore 138682 Singapore

Benefits of modularity are often achieved from module independence that allows for independent development to reduce overall lead time and economies of scale due to sharing similar modules across products in a product family. Current modularity methods tend to describe only one of these views, either the module-module independence or the product-product shared module similarity. This paper proposes a new hybrid module generation algorithm that balances both module independence and product similarity, allowing product similarity strategy to influence the coupling-driven architecture considerations. The proposed method builds on two popular matrix-based methods: the design structure matrix approach and modular function deployment that each has been developed to support these two different aspects of the module generation. This paper presents a novel algorithm that integrates both views and allows a balanced clustering that takes both interactions and company portfolio strategy into account. Usefulness of the algorithm is presented using a cordless handheld vacuum cleaner as a case study and by comparing it to alternative approaches.

关键词： Product architecture clustering algorithm Modular function deployment (MFD) Design structure matrix (DSM) Module driver Reangularity

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Improving the clustering algorithm K-means using a new distance function and its application to the population databases of breast cancer

Improving the clustering algorithm K-means using a new dista...

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2010 Third Pacific-Asia Conference on Web Mining and Web-based Application (WMWA 2010)

作者： Ing.Nieto B. Wilson. Nio Ruiz Elías David MSc. Ing. Bolívar Muoz Juan Alberto MSc(c) Departamento de Ing. De Sistemas Universidad del Norte Barranquilla

In this paper we propose an improve to the clustering heuristic algorithm *** improvement has been tested with databases of breast ***,clustering problems are everywhere;we can see its application in data mining,learning machines,knowledge discovery,data compression,pattern recognition,among *** of the most popular and used clustering methods is the K-means,on this algorithm has been worked hard,basically have made several improvements,many of these based on the definition of the initial *** contrast,this paper proposes a new function to calculate the distance;this improvement comes from the experimental analysis of the classical ***,the improved algorithm showed a better quality solution being applied to population databases of breast ***,we believe that this improvement may be useful in many types of applications,so this application can serve as a support tool for research on breast cancer and as a decision making in the allocation of resources for prevention and treatment.

关键词： heuristic algorithms applications in health optimization artificial intelligence clustering algorithm

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Mapping Levees Using LiDAR Data and Multispectral Orthoimages in the Nakdong River Basins, South Korea

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REMOTE SENSING 2014年第9期6卷 8696-8717页

作者： Choung, Yunjae Ohio State Univ Dept Civil Environm & Geodet Engn Columbus OH 43210 USA

Mapping levees is important for analyzing levee surfaces, assessing levee stability, etc. Historically, mapping levees has been carried out using ground surveying methods or only one type of remote sensing dataset. This research aims to map levees using airborne topographic LiDAR data and multispectral orthoimages taken in the Nakdong River Basins. Levee surfaces consist of multiple objects with different geometric and spectral patterns. This research investigates different methods for identifying multiple levee components, such as major objects and eroded areas. Multiple geometric analysis approaches such as the slope classification method, and elevation and area analysis are used to identify the levee crown, berm, slope surfaces, and the eroded area, with different geometric patterns using the LiDAR data. Next, a spectral analysis approach, such as the clustering algorithm, is used to identify the major objects with different spectral patterns on the identified components using multispectral orthoimages. Finally, multiple levee components, including major objects and eroded areas, are identified. The accuracy of the results shows that the various components on the levee surfaces are well identified using the proposed methodology. The obtained results are applied for evaluating the physical condition of the levees in the study area.

关键词： levee surfaces LiDAR multispectral orthoimages geometric analysis clustering algorithm

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Data Analysis of Roadway Attributes' Influences upon Speed of Small Car on Mountain Highway through clustering algorithm

Data Analysis of Roadway Attributes' Influences upon Speed o...

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2010 2nd IEEE International Conference on Information and Financial Engineering(ICIFE2010)

作者： Kun Qian 1,2,Xiaoping Du 1,2,Weiguo Li 2,Huamei Sun 2,Cuiying Li 2,Dezao Hou 1 1 Key Laboratory of Road Safety Technologies,Ministry of Communications,P.R.China 2 Beihang University,Beijing,P.R.China

On mountain highways,excessive speeding appears to be the major factor that causes fatal traffic *** order to find out the most significant attributes that affects the vehicle speed so that countermeasures could be ***,based on the actual traffic data collected from black spots in four provinces of southwestern China;this paper investigated the significant roadway attributes that influence the speed of small cars on mountain highways through Two step clustering algorithm,which is capable of clustering both categorical and continuous *** experiments,it shows that "material of roadway" and "presence of protective facilities at roadside" are two major attributes that affect the vehicle speed;furthermore,"material of roadway" contributes relatively more on affecting vehicle speed.

关键词： clustering algorithm traffic data mining vehicle speed roadway attributes

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A survey of dynamic equivalent modeling for wind farm

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2014年 40卷 956-963页

作者： Zou, Jianxiao Peng, Chao Yan, Yan Zheng, Hong Li, Yan Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

With the increasing of grid connected wind power capacity, dynamic equivalent modeling for large wind farm have become more and more important as the tool to analyze the influence of power system stability with large-scale grid-connected wind farms. Recently doubly-fed induction wind generator (DFIG) has become the mainstream wind turbine used in wind farm for its virtue in wind power conversion efficiency and active power regulation activity. Thus, DFIG based wind farm has attracted more and more attention. This paper provided an overview of dynamic equivalent modeling for wind farm. At first, the structure of DFIG dynamic model, the principle and characteristic of each part are introduced. Secondly, various dynamic equivalent modeling methods for wind farm, including single-machine representation method and multi-machine representation method, are discussed in details. Meanwhile, the calculation methods for equivalent unit parameters for multi-machine representation based modeling are discussed. Finally the current researches and existing problems of the dynamic equivalent modeling for wind farm are summarized. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Wind farm Dynamic equivalent modeling Multi-machine representation method Support Vector Machine clustering algorithm

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A Signal Characteristic Based Cluster Scheme for Aeronautical Ad Hoc Networks

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2014年第10期8卷 3439-3457页

作者： Tian, Yu Ma, Linhua Ru, Le Tang, Hong Song, Bo Air Force Engn Univ Sch Aeronaut & Astronaut Engn Xian 710038 Peoples R China

clustering is an effective method for improving the performance of large scale mobile ad hoc networks. However, when the moving speed is very fast, the topology changes quickly, which leads to frequent cluster topology updates. The drastically increasing control overheads severely threaten the throughput of the network. SCBCS (Signal Characteristic Based Cluster Scheme) is proposed as a method to potentially reduce the control overheads caused by cluster formation and maintenance in aeronautical ad hoc networks. Each node periodically broadcasts Hello packets. The Hello packets can be replaced by data packets, which preserve bandwidth. The characteristics of the received packets, such as the Doppler shift and the power of two successive Hello packets, help to calculate the relative speed and direction of motion. Then, the link connection lifetime is estimated by the relative speed and direction of motion. In the clustering formation procedure, the node with the longest estimated link connection time to its one-hop neighbors is chosen as the cluster head. In the cluster maintenance procedure, re-affiliation and re-clustering schemes are designed to keep the clusters more stable. The re-clustering phenomenon is reduced by limiting the ripple effect. Simulations have shown that SCBCS prolongs the link connection lifetime and the cluster lifetime, which can reduce the topology update overheads in highly dynamic aeronautical ad hoc networks.

关键词： Mobile Ad Hoc Networks clustering algorithm Mobility Doppler shift Power

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A set of novel mining tools for efficient biological knowledge discovery

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ARTIFICIAL INTELLIGENCE REVIEW 2014年第3期42卷 461-478页

作者： Ioannou, Zafeiria-Marina Makris, Christos Patrinos, George P. Tzimas, Giannis Univ Patras Fac Engn Dept Comp Engn & Informat Patras Greece Univ Patras Dept Pharm Sch Hlth Sci Patras Greece Technol Educ Inst Messolonghi Fac Econ & Management Dept Appl Informat Management & Econ Mesolongion Greece

In last decades, Bioinformatics has become an emerging field of science with a wide variety of applications in many research areas. The primary goal of bioinformatics is to detect useful biological knowledge hidden under the large volumes of DNA/RNA sequences and structures, literature and other biological and biomedical data, to gain a greater insight into their relationships and, therefore, to enhance the discovery and the comprehension of biological processes. In order to fully exploit the new opportunities that emerge, novel data and text mining techniques have to be developed to effectively address the fundamental biological issue of managing and uncovering meaningful patterns and correlations from these large biological and biomedical data repositories. In this work, we propose an effective data mining technique for analysing biological and biomedical data. The proposed mining process is efficient enough to be applied to various types of biological and biomedical data. To prove the concept, we experiment with applying the data mining technique into two distinct areas, including biomedical text documents and data. In addition, based on the proposed approach, we develop two mining tools, namely the Bio Search Engine and the Genome-Based Population clustering.

关键词： Bioinformatics Data mining Text mining clustering algorithm Document clustering Visualization tools

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A mathematical approach to simulate spatio-temporal patterns of an insect-pest, the corn rootworm Diabrotica speciosa (Coleoptera: Chrysomelidae) in intercropping systems

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LANDSCAPE ECOLOGY 2014年第9期29卷 1531-1540页

作者： Garcia, Adriano Consoli, Fernando Luis Conde Godoy, Wesley Augusto Ferreira, Claudia Pio UNESP IBB Dept Bioestat BR-18618000 Botucatu SP Brazil Univ Sao Paulo ESALQ Dept Entomol & Acarol BR-13418900 Piracicaba SP Brazil

We report on the use of a spatially explicit model and clustering analysis in order to investigate habitat manipulation as a strategy to regulate natural population densities of the insect-pest Diabrotica speciosa. Habitat manipulation involved four major agricultural plants used as hosts by this herbivore to compose intercropping landscapes. Available biological parameters for D. speciosa on bean, soybean, potato and corn obtained under laboratory conditions were used to group the homogeneous landscapes, composed by each host plant, by a similarity measure of host suitability either for larval survival and development, and adult survival and fecundity. The results pointed corn as the most dissimilar culture. Therefore, intercropping corn with any other crop system tested could reduce insect spread through landscape. This was proved using a cellular automata model which simulate the physiological and behavioural traits of this insect, and also different spatial configurations of the intercropping. Spatio-temporal patterns obtained by simulations demonstrated that the availability of corn bordering the field edge, which are areas more likely to invasion, is key for insect population control.

关键词： Cellular automata clustering algorithm Nutritional ecology Agricultural landscapes

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An Adaptive Method to Determine the Number of Clusters in clustering Process

An Adaptive Method to Determine the Number of Clusters in Cl...

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International Conference on Computer and Information Sciences (ICCOINS)

作者： Huan Doan Dinh Thuan Nguyen Univ Informat Technol VNU HCM Ho Chi Minh City Vietnam

ISBN: (纸本)9781479943913

A difficult problem of most clustering algorithms is how to specify the appropriate number of clusters. This paper proposes an adaptive method of selecting of number of clusters in clustering process by making coefficients indicated the appropriate number of clusters. The intra-cluster coefficient reflects intra distortion of cluster through maximum distance and a mean distance of cluster's extremely marginal objects. The inter-cluster coefficient reflects distance among clusters. It is ratio between closest distance from this cluster's centre to an extremely marginal object of other cluster and mean distance from this cluster's centre to all of extremely marginal object of other cluster respectively. A new coefficient that indicates the appropriate number of clusters is build from the intra-cluster coefficient and inter-cluster coefficient. The looking for extremely marginal objects and the new coefficient are integrated in a weighted FCM algorithm and it is calculated adaptively while the weighted FCM is processing. The weighted FCM algorithm integrated new coefficient is called FCM++. We experiment with FCM++ on some data sets of UCI: Iris, Wine, Soybean-small and show encouraging results.

关键词： clustering algorithm determine the number of clusters fuzzy c-means

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