Application knowledge base for diabetes such as expert systems has been developed, but generally using conventional methods that have limitations in representing knowledge. Ontology supports the search of data / infor...
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Application knowledge base for diabetes such as expert systems has been developed, but generally using conventional methods that have limitations in representing knowledge. Ontology supports the search of data / information by defining the concept of convergent intended by the user. This study using Diabetes Mellitus Classification based diabetes disease diagnosis from World Health Organization Geneva. This system receives input patient data from user. Then, system will build the patient ontology to represent patient knowledge. We are connecting Java applications to Protégé using OWL API. Then, system will calculate the weight of an ontology based on density. This system use JENA Inference Engine and working memory area for reasoning. The system would then do process similarity matching with Ontology Diabetes Mellitus using weighted tree similarity algorithm. Ontology has the highest similarity value will be the proposed diagnosis. Results of this study show that the representation in the form of OWL ontology using weighted ontology and weighted tree similarity algorithm can be used to represent knowledge about diabetes mellitus.
In this paper we address the task of hierarchical bird species identification from audio recordings. We evaluate three types of approaches to deal with hierarchical classification problems: the flat classification app...
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ISBN:
(纸本)9781479906505
In this paper we address the task of hierarchical bird species identification from audio recordings. We evaluate three types of approaches to deal with hierarchical classification problems: the flat classification approach, the local-model per parent node classifier approach and the global-model hierarchical-classification approach. For the flat and local-model classification approach we employ the classic Naive Bayes algorithm. For the global-model approach we use the Global Model Naive Bayes (GMNB) algorithm. As in the classical Naive Bayes, the algorithm computes prior probabilities and likelihoods, but these computations take into account the hierarchical classification scenario: it assumes that any example which belongs to a given class will also belong to all its ancestor classes. In the current application, the employed class hierarchy is the standard scientific taxonomy of birds used in Biology. In order to deal with the bird songs we obtain features by computing several acoustic quantities from intervals of the audio signal. We conduct three experiments in order to compare the three different approaches to the hierarchical bird species identification problem. Our experimental results show that the use of the GMNB hierarchical classification algorithm outperforms both the flat and local-model approaches (Using the Hierarchical F-measure metric);hence the use of a global-model approach (such as the GMNB) can be a feasible way to improve the classification performance for problems with a large number of classes.
The new trend in the process of data-intensive management indicates the importance of a distributed file system for both Internet large scale services and cloud computing environments. I/O latency and application buff...
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ISBN:
(纸本)9781479904051
The new trend in the process of data-intensive management indicates the importance of a distributed file system for both Internet large scale services and cloud computing environments. I/O latency and application buffering sizes are two of a number of issues that are essential to be analysed on different class of distributed file systems. In this paper, it is presented a research work comparing four different high performance distributed file systems. Those systems were employed to support a medical image server application in a private storage environment. Experimental results highlight the importance of an appropriate distributed file system to provide a differential level of performance considering application specific characteristics.
Next-generation sequencing technologies provide a powerful tool for studying genome evolution during progression of advanced diseases such as cancer. Although many recent studies have employed new sequencing technolog...
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The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy subm...
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ISBN:
(纸本)9781627480161
The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction. Text mining advances include a classification method to determine whether a published article contains pharmacological experiments relevant to drug-drug interactions, a fine-grained text mining approach for detecting the catalytic sites in proteins in the biomedical literature, and a method for automatically extending a taxonomy of health related terms to integrate consumer-friendly synonyms for medical terminologies.
Open Information Extraction (Open IE) is an unsupervised strategy to draw out relations from text without predefining these relations, regardless the domain. This paper describes a novel Open IE approach that performs...
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Open Information Extraction (Open IE) is an unsupervised strategy to draw out relations from text without predefining these relations, regardless the domain. This paper describes a novel Open IE approach that performs unsupervised extraction of triples by applying a few lexical-syntactic patterns to POS-tagged texts. In order to validate this strategy we developed a prototype and compared its performance with two Open IE systems. The proposed approach achieved promising results, overcoming those from the state-of-the-art systems. The paper concludes with an analysis of errors and directions for future work.
Crowd sourcing and Games with a Purpose (GWAP) have each received considerable attention in recent years. These two human computation mechanisms assist with tasks that cannot be solved by computers alone. Despite this...
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Crowd sourcing and Games with a Purpose (GWAP) have each received considerable attention in recent years. These two human computation mechanisms assist with tasks that cannot be solved by computers alone. Despite this increased attention, much of this transformation has been limited to a few aspects of Information Retrieval (IR). In this paper, we examine these two mechanisms' applicability to IR. Using an IR model, we apply criteria to determine the suitability of these crowd sourcing and GWAP mechanisms to each step of the model. Our analysis illustrates that these mechanisms can apply to several of these steps with good returns.
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