In this paper, we present an effective puncturing scheme for rate-compatible low-density parity-check (RC-LDPC) codes. Our scheme is based on the criterion of maximum average girth which is an important performance pa...
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ISBN:
(纸本)9781457701009
In this paper, we present an effective puncturing scheme for rate-compatible low-density parity-check (RC-LDPC) codes. Our scheme is based on the criterion of maximum average girth which is an important performance parameter for the construction of RC-LDPC codes. We select a pattern wherein the punctured bits are far apart from each other in the Tanner graph of the codes by sorting algorithm, and then puncture the bits with the maximum average girth. In the process, there is no need to do any optimization. By simulations, the proposed codes show good performance under puncturing over a wide range of rates. The introduced puncturing criterion results in lower bit error rate and error floor when compared with random puncturing. Moreover, the performance of proposed method is similar to the direct structured LDPC codes by progressive edge growth (PEG). But the scheme is very simple to implement and lowers the complexity of encoding and decoding.
Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4....
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ISBN:
(纸本)9781424492695
Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.
We present a novel classification algorithm for learning with test time budgets. In this setting, the goal is to reduce feature acquisition cost while maintaining classification accuracy. For every decision, our appro...
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ISBN:
(纸本)9781479928941
We present a novel classification algorithm for learning with test time budgets. In this setting, the goal is to reduce feature acquisition cost while maintaining classification accuracy. For every decision, our approach dynamically selects features based on previously observed information. Once a desired confidence of a decision is achieved, the acquisition stops and the test instance is classified. Our approach can be used in conjunction with many popular margin based classification algorithms. We use margin information from training data in the partial feature neighborhood of a test point to compute a probability of correct classification. This estimate is used to either select the next feature or to stop. We compare our algorithm to other cost-sensitive methods on real world datasets. The experiments demonstrate that our algorithm provides an accurate estimate of classification confidence and outperforms other approaches while being significantly more efficient in computation.
An analysis of speckle filtering influence on B-mode ultrasound image texture-based determination of the liver fibrosis stage has been performed. We developed a comprehensive method for liver texture analysis based on...
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ISBN:
(纸本)9781479986385
An analysis of speckle filtering influence on B-mode ultrasound image texture-based determination of the liver fibrosis stage has been performed. We developed a comprehensive method for liver texture analysis based on 10-20 textural characteristics. These characteristics were found as most informative from 1390 textural features calculated using Laws' masks, co-occurrence matrix, gray level run-length matrix, wavelets and statistical characteristics of the images. We used Siemens ACUSON S2000 ultrasound images of liver cuts along the right midclavicular line for more than 50 patients for fibrosis classification using the METAVIR score. The classification was performed using Multi-layer Perceptron, Random Forests and KNN classifiers with data balancing using SMOTE algorithm. The ultrasound despeckling was performed using SRAD algorithm with an entropy-based stopping criterion. It was found that speckle filtering procedure enhances the classification and increases AUROC value by 5%.
Microwave-induced thermoacoustic (TA) imaging combines the dielectric/conductivity contrast in the microwave range with the high resolution of ultrasound imaging. Lack of ionizing radiation exposure in TA imaging make...
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ISBN:
(纸本)9781424492695
Microwave-induced thermoacoustic (TA) imaging combines the dielectric/conductivity contrast in the microwave range with the high resolution of ultrasound imaging. Lack of ionizing radiation exposure in TA imaging makes this technique suitable for frequent screening applications, as with breast cancer screening. In this paper we demonstrate breast tumor classification based on TA imaging. The sensitivity of the signal-based classification algorithm to errors in the estimation of tumor locations is investigated. To reduce this sensitivity, we propose to use the interferogram of received pressure waves as the feature basis used for classification, and demonstrate the robustness based on a finite-difference time-domain (FDTD) simulation framework.
One of the most challenging projects in information systems is extracting information from unstructured texts, including medical document classification. I am developing a classification algorithm that classifies a me...
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ISBN:
(纸本)9781479925056
One of the most challenging projects in information systems is extracting information from unstructured texts, including medical document classification. I am developing a classification algorithm that classifies a medical document by analyzing its content and categorizing it under predefined topics from the Medical Subject Headings (MeSH). I collected a corpus of 50 full-text journal articles (N=50) from MEDLINE, which were already indexed by experts based on MeSH. Using natural language processing (NLP), my algorithm classifies the collected articles under MeSH subject headings. I evaluated the algorithm's outcome by measuring its precision and recall of resulting subject headings from the algorithm, comparing results to the actual documents' subject headings. The algorithm classified the articles correctly under 45% to 60% of the actual subject headings and got 40% to 53% of the total subject headings correct. This holds promising solutions for the global health arena to index and classify medical documents expeditiously.
We present an adaptive binary classification algorithm, based on transductive transfer learning. We illustrate the method in the context of electrocardiogram (ECG) analysis. Knowledge gained from a population of patie...
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ISBN:
(纸本)9781424441211
We present an adaptive binary classification algorithm, based on transductive transfer learning. We illustrate the method in the context of electrocardiogram (ECG) analysis. Knowledge gained from a population of patients is automatically adapted to patients' records to accurately detect ectopic beats. On patients from the MIT-BIH Arrhythmia Database, we achieve a median sensitivity of 94.59% and positive predictive value of 96.24%, for the binary classification task of separating premature ventricular contractions (PVCs), a type of ectopic beat, from non-PVCs.
The success of an image classification algorithm largely depends on how it incorporates local information in the global decision. Popular approaches such as average-pooling and max-pooling are suboptimal in many situa...
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ISBN:
(纸本)9781467388528
The success of an image classification algorithm largely depends on how it incorporates local information in the global decision. Popular approaches such as average-pooling and max-pooling are suboptimal in many situations. In this paper we propose Region Ranking SVM (RRSVM), a novel method for pooling local information from multiple regions. RRSVM exploits the correlation of local regions in an image, and it jointly learns a region evaluation function and a scheme for integrating multiple regions. Experiments on PASCAL VOC 2007, VOC 2012, and ILSVRC2014 datasets show that RRSVM outperforms the methods that use the same feature type and extract features from the same set of local regions. RRSVM achieves similar to or better than the state-of-the-art performance on all datasets.
The main task of medical image mining is to effectively analyze medical image data. Medical image classification algorithms have a high error rate near the threshold. To address the problem, the paper adopts a hybrid ...
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ISBN:
(纸本)9781728118680
The main task of medical image mining is to effectively analyze medical image data. Medical image classification algorithms have a high error rate near the threshold. To address the problem, the paper adopts a hybrid approach which combines computers algorithm and crowdsourcing system for image classification. A hybrid framework is proposed, which can achieve a higher accuracy significantly than only use classification algorithms. At the same time, it only processes the images that classification algorithms perform not well, so it has a lower monetary cost. In this framework, a range threshold is generated by using an efficient algorithm that assigns an image to a crowdsourcing or classification algorithm. To ensure the quality of crowdsourcing answers, this paper presents two worker models, Worker Quality Evaluation Model (WQEM) and Worker Performance Prediction Model (WPPM) respectively. Due to the lack of the crowdsourcing platform for processing medical information, medical image classification results are difficult to collect, so this paper proposed a crowdsourcing platform for medical image classification.
This paper develops a coordinated reactive power control system for grid integrated solar photovoltaic (PV) inverters. The proposed algorithm combines the reactive power control capabilities of PV inverter with flexib...
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ISBN:
(纸本)9781728139593
This paper develops a coordinated reactive power control system for grid integrated solar photovoltaic (PV) inverters. The proposed algorithm combines the reactive power control capabilities of PV inverter with flexible AC transmission devices for stabilizing the PV system during grid faults. The proposed control method works as per a predefined hierarchical structure by prioritizing the reactive power control with PV inverters. The complete methodology is realized by testing them with a two-stage single-phase grid-connected PV system simulated in MATLAB/Simulink software. The simulation results verify the accuracy of the classification algorithm and depict the effectiveness of the proposed controller in both the under and overvoltage situations.
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