The proceedings contain 244 papers. The topics discussed include: a proposed Siamese convolutional neural network for fingerprint recognition;research of early warning of corporate financial crisis based on machine le...
ISBN:
(纸本)9783800758760
The proceedings contain 244 papers. The topics discussed include: a proposed Siamese convolutional neural network for fingerprint recognition;research of early warning of corporate financial crisis based on machinelearning neural network;corporate influence analysis by integrating social network models;research named entity recognition based on transfer learning;classification of complex network based on improved hierarchical model;handwritten character recognition based on convolution neural network models;back propagation neural network based stroke prediction;design of a network storage system based on peer-to-peer network with supernode;hybrid Weibo tags and topic mining for user similarity model;stability analysis of power grid based on generative adversarial network;convolutional neural network for English character recognition;build a precision marketing prediction model for intelligent recommendation technology;application of text mining technology in power data prediction;research on used car transaction price and transaction cycle based on model fusion;and underwater wireless sensor networks trustworthiness evaluation using Monte Carlo simulation.
One primary aspect in customer services is to provide immediate solution towards payment verification issues, such as a time delay of payment confirmation by the payment service provider or supplier. This paper presen...
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ISBN:
(纸本)9781665425803
One primary aspect in customer services is to provide immediate solution towards payment verification issues, such as a time delay of payment confirmation by the payment service provider or supplier. This paper presents a development of an accurate optical character recognition (OCR) system using convolutional neural network with deep learning algorithm, which can skip some steps in the workflow of manual payment approval to fasten the process of payment verification and confirmation. By using some machinelearning frameworks of pyTorch utilizing Tensors and CUDA-GPU parallel computing, the machinelearning based OCR system was developed and tested with the actual data. The real data sets used here cover the non-uniformity of the receipt bill's papers with various conditions (crumple, water drops, and folds) with some nature of the customer's overall camera noise, angle, and lighting. Several experiments associated with data preparation, deep learning parameter settings, and model performance comparison, were properly conducted to obtain a high quality of OCR system to detect trace number, approval codes, and nominals on the widely-used payment receipts. The resulting OCR system performed very satisfactory with 100% accuracy on testing data set. This promising results permit for the integration between this accurate and automated OCR system and chat environment with chatbot technology in order to provide better user experience and immediate and reliable solution toward payment verification issues.
This paper expounds the automatic recognition method of parts based on computer vision. The feature database of the processed parts is constructed by using machinelearning method. Image preprocessing, threshold segme...
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This paper explores opinion mining using supervised learning algorithms to find the polarity of the student feedback based on pre-defined features of teaching and learning. The study conducted involves the application...
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ISBN:
(纸本)9781467395847
This paper explores opinion mining using supervised learning algorithms to find the polarity of the student feedback based on pre-defined features of teaching and learning. The study conducted involves the application of a combination of machinelearning and natural language processing techniques on student feedback data gathered from module evaluation survey results of Middle East College, Oman. In addition to providing a step by step explanation of the process of implementation of opinion mining from student comments using the open source data analytics tool Rapid Miner, this paper also presents a comparative performance study of the algorithms like SVM, Naive Bayes, K Nearest Neighbor and Neural Network classifier. The data set extracted from the survey is subjected to data preprocessing which is then used to train the algorithms for binomial classification. The trained models are also capable of predicting the polarity of the student comments based on extracted features like examination, teaching etc. The results are compared to find the better performance with respect to various evaluation criteria for the different algorithms.
In recent past there has been phenomenal growth in biomedical literature and health care records. Robust text mining techniques are essential in order to properly organize the documents as well as to extract relevant ...
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ISBN:
(纸本)9781509048472
In recent past there has been phenomenal growth in biomedical literature and health care records. Robust text mining techniques are essential in order to properly organize the documents as well as to extract relevant information. Traditional techniques for document classification focus on machinelearning algorithms where learning of classifier is decided on the basis of labeled data and the features that are prominent. In this paper we focus on developing an automated technique for classifying biomedical articles containing protein-protein interaction related information against the others. Our proposed approach is based on deep neural network framework. We investigate the role of convolution neural network (CNN) and propose two model variants. We evaluate the proposed approach on the benchmark datasets of BioCreative-IT Interaction Article Subtask (JAS) data sets. Effectiveness of our proposed model is evident with the significant performance gains, 2.8 % in terms of F-measure and 5 % in terms of accuracy over the traditional models.
In this paper, a large number of paper-based original records generated in the detection process, which are cumbersome in circulation and difficult in management, are proposed to realize the electronic management of o...
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Conceptual Health care is one of the most exciting borders in datamining and machinelearning. Appropriation of electronic health records (EHRs) made a blast in advanced clinical information which is accessible for e...
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ISBN:
(纸本)9781538678084
Conceptual Health care is one of the most exciting borders in datamining and machinelearning. Appropriation of electronic health records (EHRs) made a blast in advanced clinical information which is accessible for examination, but progress in machinelearning for healthcare research has been complicated to measure because of the absence of openly available benchmark data sets. In this paper we propose three clinical expectation benchmarks to overcome the issue of utilizing the information got from the freely accessible Medical Information Mart for Intensive Care (Emulate III) database. These assignments cover a scope of clinical issues counting demonstrating danger of mortality, anticipating length of remain and distinguishing physiologic decay. MIMIC-III (Medical Information Mart for Intensive Care III) is a considerable, openly accessible database containing de-identified wellbeing related information related with more than forty thousand patients who remained in basic consideration units of the Beth Israel Deaconess Medical Center somewhere in the range of 2001 and 2012. Our plan is to perform various tasks with an objective to mutually take in a variety of clinically important forecast assignments based on similar time arrangement information.
Wireless Sensor Network (WSN) is network of hundreds or thousands of sensors. Congestion occurs in wireless sensor networks when all the sensors nearby event start sending data to the base station. Congestion results ...
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ISBN:
(纸本)9781538611449
Wireless Sensor Network (WSN) is network of hundreds or thousands of sensors. Congestion occurs in wireless sensor networks when all the sensors nearby event start sending data to the base station. Congestion results in less throughput and non reliability of a system. The machinelearning algorithms can be applied for congestion detection in network and then congestion can be mitigated by lowering the transmission rate. In this paper we analyze the performance of multilayer level perception (MLP) a neural network technique and classification by regression algorithms. The machinelearning techniques are applied to detect the different levels of congestion in as low, medium or high. It is found that classification by regression is more efficient than MLP in detecting the congestion for the generated data set of WSN simulation using NS2.
Pairwise dissimilarity representations are frequently used as an alternative to feature vectors in patternrecognition. One of the problems encountered in the analysis of such data, is that the dissimilarities are rar...
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ISBN:
(纸本)9783319023090
Pairwise dissimilarity representations are frequently used as an alternative to feature vectors in patternrecognition. One of the problems encountered in the analysis of such data, is that the dissimilarities are rarely Euclidean, while statistical learning algorithms often rely on Euclidean distances. Such non-Euclidean dissimilarities are often corrected or imposed geometry via embedding. This talk reviews and and extends the field of analysing non-Euclidean dissimilarity data.
One hundred and fifty-six papers were presented at the Thirdinternational Joint conference on patternrecognition. The individual sessions covered the following topics: Industrial Applications;Feature Extraction and ...
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One hundred and fifty-six papers were presented at the Thirdinternational Joint conference on patternrecognition. The individual sessions covered the following topics: Industrial Applications;Feature Extraction and Primitive Selection;Syntactic Methods in pattern Analysis;Optical Character recognition;learning Algorithms and Sample Size;Line Drawing and Waveform Processing;Interactive pattern Analysis;Statistical patternrecognition Theory;Perceptual Modeling;patternrecognition Competition;General Applications;Clustering;Linguistic Applications and Natural Language Processing;Theoretical Problems;Segmentation and Shape Encoding;Medical Image Processing and pattern Analysis;Picture Description and Scene Analysis;Speech recognition and data Compression;Remote Sensing;Parallel Processing and Two-Dimensional Digital Filtering;Edge, Line and Object recognition;Applications of patternrecognition Technique;Image Analysis and Texture;data Base Computer Systems.
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