The effect of hysteresis induced non-linear and memory effects on transmission line behavior using perturbation theory for non-linear differential equation is analyzed. Generation of higher harmonics is observed. The ...
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For serving traffic in the inter data centers which provide services such as, duplication of data and migration of the virtual machines, it is requisited to transfer voluminous data for which, under guarantee of a fin...
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The centrality measure of nodes gives the influential power of the entities in a given network. Closeness centrality measure of a node is the amount to which a node is close to all other nodes. More recently, centrali...
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The centrality measure of nodes gives the influential power of the entities in a given network. Closeness centrality measure of a node is the amount to which a node is close to all other nodes. More recently, centrality measures in the multiplex social networks have develops a great interest among researchers. The multiplex social network means the system, which includes multiple networks in the form of layers, and each layer belongs to different types of the association between nodes. Here, we present a new formulation to compute the closeness centrality of nodes for the multiplex social networks or multi-layer networks. There are numerals of approaches defined to find the closeness centrality of the node in a single layer network, the problem for computing the closeness centrality measure for the multiplex networks node is still open. With this, we define a new metric called cross-layer closeness centrality (CCC) for the multiplex social networks. The CCC is the measure, which computes closeness degree of a node to every other node of the multiplex network.
This paper describes the de-identification of personally identifiable information (PIIs) in electronic health records (EHRs) using two models of conditional random fields (CRFs) and bidirectional long short term memor...
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ISBN:
(纸本)9781538646939
This paper describes the de-identification of personally identifiable information (PIIs) in electronic health records (EHRs) using two models of conditional random fields (CRFs) and bidirectional long short term memory networks (LSTMs). Most medical records store private information such as PATIENT NAME, HOSPITAL NAME, LOCATION, etc. that needs to be de-identified or redacted before being passed on for further medical research. The process of removing such information using machine learning techniques is started with pre-processing of raw data by tokenization and detection of sentences. On comparing the techniques, it is noted that CRFs require manual feature engineering to train the model whereas LSTM is capable of handling long term dependencies without much insight about the dataset. Bi-directional LSTM network was used to generate context information from suitable word representations. Finally, a predictive layer was applied to predict the protected health information (PHI) terms having maximum probability. Evaluated with the i2b2 gold data set of clinical narratives of patients of 2014 De-identification challenge, we propose an efficient solution for redaction using two models, both of which achieve good F-scores for PHIs of all types. The LSTM-based model achieved a micro-F1 measure of 0.9592, which performs better than the CRF-based model.
This paper aims to identify the trends in machine learning research using text mining. The researcharticles contain significant knowledge and research results. However, they are long and have many noisy results such t...
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This paper proposes an approach for Sentiment Analysis on online textual reviews that leverages polarity switches and domain ontologies to first perform Aspect Based Sentiment Analysis and uses it to then refine the o...
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The main objective of virtual distributed computing is optimization of load in an efficient manner. This is achieved using load scheduling in cloud computing. The cloud is a virtual distributed environment having a hu...
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In this research, effect of varying spatial orientations on the build time requirements for fused deposition modelling process is studied. Constructive solid geometry cylindrical primitive is taken as work piece and m...
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In this research, effect of varying spatial orientations on the build time requirements for fused deposition modelling process is studied. Constructive solid geometry cylindrical primitive is taken as work piece and modeling is accomplished for it. Response surface methodology is used to design the experiments and obtain statistical models for build time requirements corresponding to different orientations of the given primitive in modeller build volume. Contour width, air gap, slice height, raster width, raster angle and angle of orientation are treated as process parameters. Percentage contribution of individual process parameter is found to change for build time corresponding to different spatial orientations. Also, the average of build time requirement changes with spatial orientation. This paper attempts to clearly discuss and describe the observations with an aim to develop a clear understanding of effect of spatial variations on the build time for Fused Deposition Modelling process. This work is an integral part of process layout optimization and these results can effectively aid designers specially while tackling nesting issues.
The aim of this work is to design a fractional delay second order Volterra filter that takes a discrete time sequence as input and its output is as close as possible to the output of a given nonlinear unknown system w...
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The aim of this work is to design a fractional delay second order Volterra filter that takes a discrete time sequence as input and its output is as close as possible to the output of a given nonlinear unknown system which may have higher degree nonlinearities in the least square sense. The basic reason for such a design is that rather than including higher than second degree nonlinearities in the designed system, we use the fractional delay degrees of freedom to approximate the given system. The advantage is in terms of obtaining a better approximation of the given nonlinear system than is possible by using only integer delays ( since we are giving more degrees of freedom via the fractional delays ) and simultaneously it does not require to incorporate higher degree nonlinearities than two. This work hinges around the fact that if the input signal is a decimated version of another signal by a factor of M, then fractional delays can be regarded as delays by integers less than M. Using the well known formula for calculating the discrete time Fourier transform ( TFT ) of a decimated signal, we then arrive at an expression for the DTFT of the output of a fractional delay system in terms of the unknown first and second order Volterra system coefficients and the fractional delays. The final energy function to be minimized is the norm square of the difference between the DTFT of the given output and the DTFT of the output of the fractional delay system. Minimization over the filter coefficients is a linear problem and thus the final problem is to minimize a highly nonlinear function of the fractional delays which is accomplished using search techniques like the gradient-search and nature inspired optimization algorithms. The effectiveness of the proposed method is demonstrated using two nonlinear benchmark systems tested with five different input signals. The accuracy of the stated models using the globally convergent metaheuristic, cuckoo-search al
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