513 research outputs found
Advances in Character Recognition
This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject
Feature Selection and Non-Euclidean Dimensionality Reduction: Application to Electrocardiology.
Heart disease has been the leading cause of human death for decades.
To improve treatment of heart disease, algorithms to perform reliable computer diagnosis using electrocardiogram (ECG) data have become an area of active research. This thesis utilizes well-established methods from cluster analysis, classification, and localization to cluster and classify ECG data, and aims to help clinicians diagnose and treat heart diseases. The power of these methods is enhanced by state-of-the-art feature selection and dimensionality reduction.
The specific contributions of this thesis are as follows. First, a unique combination of ECG feature selection and mixture model clustering is introduced to classify the sites of origin of ventricular tachycardias. Second, we apply a restricted Boltzmann machine (RBM) to learn sparse representations of ECG signals and to build an enriched classifier from patient data. Third, a novel manifold learning algorithm is introduced, called Quaternion Laplacian Information Maps (QLIM), and is applied to visualize high-dimensional ECG signals. These methods are applied to design of an automated supervised classification algorithm to help a physician identify the origin of ventricular arrhythmias (VA) directed from a patient's ECG data. The algorithm is trained on a large database of ECGs and catheter positions collected during the electrophysiology (EP) pace-mapping procedures. The proposed algorithm is demonstrated to have a correct classification rate of over 80% for the difficult task of classifying VAs having epicardial or endocardial origins.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113303/1/dyjung_1.pd
Proceedings of the Conference on Natural Language Processing 2010
This book contains state-of-the-art contributions to the 10th
conference on Natural Language Processing, KONVENS 2010
(Konferenz zur Verarbeitung natĂĽrlicher Sprache), with a focus
on semantic processing.
The KONVENS in general aims at offering a broad perspective
on current research and developments within the interdisciplinary
field of natural language processing. The central theme
draws specific attention towards addressing linguistic aspects
ofmeaning, covering deep as well as shallow approaches to semantic
processing. The contributions address both knowledgebased
and data-driven methods for modelling and acquiring
semantic information, and discuss the role of semantic information
in applications of language technology.
The articles demonstrate the importance of semantic processing,
and present novel and creative approaches to natural
language processing in general. Some contributions put their
focus on developing and improving NLP systems for tasks like
Named Entity Recognition or Word Sense Disambiguation, or
focus on semantic knowledge acquisition and exploitation with
respect to collaboratively built ressources, or harvesting semantic
information in virtual games. Others are set within the
context of real-world applications, such as Authoring Aids, Text
Summarisation and Information Retrieval. The collection highlights
the importance of semantic processing for different areas
and applications in Natural Language Processing, and provides
the reader with an overview of current research in this field
Integrated characterisation of mud-rich overburden sediment sequences using limited log and seismic data: Application to seal risk
Muds and mudstones are the most abundant sediments in sedimentary basins and can
control fluid migration and pressure. In petroleum systems, they can also act as source,
reservoir or seal rocks. More recently, the sealing properties of mudstones have been
used for nuclear waste storage and geological CO2 sequestration. Despite the growing
importance of mudstones, their geological modelling is poorly understood and clear
quantitative studies are needed to address 3D lithology and flow properties distribution
within these sediments. The key issues in this respect are the high degree of
heterogeneity in mudstones and the alteration of lithology and flow properties with time
and depth. In addition, there are often very limited field data (log and seismic), with
lower quality within these sediments, which makes the common geostatistical modelling
practices ineffective.
In this study we assess/capture quantitatively the flow-important characteristics of
heterogeneous mud-rich sequences based on limited conventional log and post-stack
seismic data in a deep offshore West African case study. Additionally, we develop a
practical technique of log-seismic integration at the cross-well scale to translate 3D
seismic attributes into lithology probabilities. The final products are probabilistic
multiattribute transforms at different resolutions which allow prediction of lithologies
away from wells while keeping the important sub-seismic stratigraphic and structural
flow features. As a key result, we introduced a seismically-driven risk attribute (so-called
Seal Risk Factor "SRF") which showed robust correspondence to the lithologies
within the seismic volume. High seismic SRFs were often a good approximation for
volumes containing a higher percentage of coarser-grained and distorted sediments, and
vice versa.
We believe that this is the first attempt at quantitative, integrated characterisation of
mud-rich overburden sediment sequences using log and seismic data. Its application on
modern seismic surveys can save days of processing/mapping time and can reduce
exploration risk by basing decisions on seal texture and lithology probabilities
Modelling the interpretation of digital mammography using high order statistics and deep machine learning
Visual search is an inhomogeneous, yet efficient sampling process accomplished by the saccades and the central (foveal) vision. Areas that attract the central vision have been studied for errors in interpretation of medical images. In this study, we extend existing visual search studies to understand features of areas that receive direct visual attention and elicit a mark by the radiologist (True and False Positive decisions) from those that elicit a mark but were captured by the peripheral vision. We also investigate if there are any differences between these areas and those that are never fixated by radiologists. Extending these investigations, we further explore the possibility of modelling radiologists’ search behavior and their interpretation of mammograms using deep machine learning techniques. We demonstrated that energy profiles of foveated (FC), peripherally fixated (PC), and never fixated (NFC) areas are distinct. It was shown that FCs are selected on the basis of being most informative. Never fixated regions were found to be least informative. Evidences that energy profiles and dwell time of these areas influence radiologists’ decisions (and confidence in such decisions) were also shown. High-order features provided additional information to the radiologists, however their effect on decision (and confidence in such decision) was not significant. We also showed that deep-convolution neural network can successfully be used to model radiologists’ attentional level, decisions and confidence in their decisions. High accuracy and high agreement (between true and predicted values) in such predictions can be achieved in modelling attentional level (accuracy: 0.90, kappa: 0.82) and decisions (accuracy: 0.92, kappa: 0.86) of radiologists. Our results indicated that an ensembled model for radiologist’s search behavior and decision can successfully be built. Convolution networks failed to model missed cancers however
International Conference of Territorial Intelligence, Alba Iulia 2006. Vol.1, Papers on region, identity and sustainable development (deliverable 12 of caENTI, project funded under FP6 research program of the European Union), Aeternitas, Alba Iulia, 2007
GIRARDOT J.-J., PASCARU M., ILEANA I., 2007A.deliverable 12 of caENTIThese acts gather the communications of the International Conference of Territorial Intelligence that took place in ALBA IULIA in Romania, from September, the 20th to September, the 22nd 2006. This conference was the fourth conference of territorial intelligence, but the conference of ALBA IULIA is the first one that took place in the CAENTI, Coordination Action of the European Network of Territorial Intelligence, framework. Consequently, it has a particular organization. A part is devoted to the presentation of the CAENTI research activities and of their prospects. The CAENTI specific communications are published in another volume
Proceedings / 6th International Symposium of Industrial Engineering - SIE 2015, 24th-25th September, 2015, Belgrade
editors Vesna Spasojević-Brkić, Mirjana Misita, Dragan D. Milanovi
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