In this paper, a variation of traditional Genetic Programming(GP) is used to model the MagnetoencephaloGram(MEG) of Epileptic Patients. This variation is Linear Genetic Programming(LGP). LGP is a particular subset of ...
In this paper, a variation of traditional Genetic Programming(GP) is used to model the MagnetoencephaloGram(MEG) of Epileptic Patients. This variation is Linear Genetic Programming(LGP). LGP is a particular subset of GP wherein computer programs in population are represented as a sequence of instructions from imperative programming language or machine language. The derived models from this method were simplified using genetic algorithms. The proposed method was used to model the MEG signal of epileptic patients using 6 different datasets. Each dataset uses different number of previous values of MEG to predict the next value. The models were tested in datasets different from the ones which were used to produce them and the results were very promising.
The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented ...
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The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard *** formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.
As the average life expectancy increases, particularly in developing countries, prevalence of neurodegenerative diseases has also increased. This trend is especially alarming for Alzheimer's disease (AD); as there...
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As the average life expectancy increases, particularly in developing countries, prevalence of neurodegenerative diseases has also increased. This trend is especially alarming for Alzheimer's disease (AD); as there is no cure to stop or reverse the effects of AD. However, recent pharmacological advances can slow the progression of AD, but only if AD is diagnosed at early stages. We have previously introduced an ensemble of classifiers based approach for combining event related potentials obtained from different electrode locations as an effective approach for early diagnosis of AD. We further expand this approach and analyze its robustness and stability in two ways: comparing the diagnostic accuracy on hand selected and cleaned data vs. standard automated preprocessing, but more importantly, comparing the diagnostic accuracy on two different cohorts, whose data are collected under different settings: a research university lab and a community clinic.
Distributed Digital Libraries (DLs) integration is significant for the enforcement of novel searching mechanisms in the internet. The great heterogeneity of systems storing and providing digital content requires the i...
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Scientific documents are unstructured data consisting of natural language and hard for scientists to read and manage. Keywords are very helpful for scientists to search the related documents and know about their conte...
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Scientific documents are unstructured data consisting of natural language and hard for scientists to read and manage. Keywords are very helpful for scientists to search the related documents and know about their contents in a prompt way. In this paper we investigate a kind of data preprocessing technique used in SVM-based keyword extraction from scientific documents. Four definitions of regular scientific documents are proposed, and the analysis on the experimental results is performed based on the proposed definitions. The experimental results confirm the intuition that abstract is important for keywords extraction.
This paper presents a new method for automatic palmprint recognition based on kernel PCA method by integrating the Gabor wavelet representation of palm images. Gabor wavelets are first applied to derive desirable palm...
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This paper presents a new method for automatic palmprint recognition based on kernel PCA method by integrating the Gabor wavelet representation of palm images. Gabor wavelets are first applied to derive desirable palmprint features. The Gabor transformed palm images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can produce salient features that are most suitable for palmprint recognition. The kernel PCA method then nonlinearly maps the Gabor-wavelet image into a high-dimensional feature space. The proposed algorithm has been successfully tested on two different public data sets from the PolyU palmprint databases for which the samples were collected in two different sessions.
In wireless sensor networks, to obtain a long network lifetime is a fundamental issue while without sacrificing crucial aspects of quality of service (area coverage, sensing reliability, and network connectivity). In ...
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In wireless sensor networks, to obtain a long network lifetime is a fundamental issue while without sacrificing crucial aspects of quality of service (area coverage, sensing reliability, and network connectivity). In this paper, we present a Voronoi-based sleeping configuration to deal with different sensing radii and location error. With our proposed sleeping candidate condition, redundant sensors are optionally identified and scheduled to sleep in order to extend the system lifetime while maintaining adequate sensor redundancy to tolerate sensor failures, energy depletions, and location error. Simulation results show that there is a tradeoff among energy conservation, area coverage, and fault tolerance, which varies between different sleeping candidate conditions.
The prevalence of Alzheimer's disease (AD) is rising alarmingly as the average age of our population increases. There is no treatment to halt or slow the pathology responsible for AD, however, new drugs are promis...
The prevalence of Alzheimer's disease (AD) is rising alarmingly as the average age of our population increases. There is no treatment to halt or slow the pathology responsible for AD, however, new drugs are promising to reduce the rate of progression. On the other hand, the efficacy of these new medications critically depends on our ability to diagnose AD at the earliest stage. Currently AD is diagnosed through longitudinal clinical evaluations, which are available only at specialized dementia clinics, hence beyond financial and geographic reach of most patients. Automated diagnosis tools that can be made available to community hospitals would therefore be very beneficial. To that end, we have previously shown that the event related potentials obtained from different scalp locations can be effectively used for early diagnosis of AD using an ensemble of classifiers based decision fusion approach. In this study, we expand our data fusion approach to include MRI based measures of regional brain atrophy. Our initial results indicate that ERPs and MRI carry complementary information, and the combination of these heterogeneous data sources using a decision fusion approach can significantly improve diagnostic accuracy.
The present work deals with the optimization of resource management in communications centers, with the aid of neural networks. Starting with a set of definitions, essential for the comprehension of environment, we at...
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ISBN:
(纸本)9780769530154;076953015X
The present work deals with the optimization of resource management in communications centers, with the aid of neural networks. Starting with a set of definitions, essential for the comprehension of environment, we attempt to provide a complete picture to the reader, describing at the same time an Information System of Communications Centre that serves a Collections application, focusing in issues relating with resources optimizations. We present with analytic way the application of a neural network, comparing it with currently used methods. In the following, a custom application is presented, implemented exclusively to support the present work, as well as acquisition and normalization of real-life data from a commercial CRM application, for feeding the input of our neural network. Finally, we present a number of results of our neural network. The paper concludes by identifying a number of subjects for future study and research.
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