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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3244 条记录，以下是331-340 订阅

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Process mining of Interactions During Computer-Based Testing for Detecting and Modelling Guessing Behavior 3rd

Process Mining of Interactions During Computer-Based Testing...

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3rd international conference on learning and Collaboration Technologies (LCT) Held as Part of 18th international conference on Human-Computer Interaction (HCI international)

作者： Papamitsiou, Zacharoula Economides, Anastasios A. Univ Macedonia IPPS Informat Syst Thessaloniki Greece

ISBN: (纸本)9783319394831;9783319394824

Detecting, recognizing and modelling patterns of observed examinee behaviors during assessment is a topic of great interest for the educational research community. In this paper we investigate the perspectives of process-centric inference of guessing behavior patterns. The underlying idea is to extract knowledge from real processes (i.e., not assumed nor truncated), logged automatically by the assessment environment. We applied a three-step process mining methodology on logged interaction traces from a case study with 259 undergraduate university students. The analysis revealed sequences of interactions in which low goal-orientation students answered quickly and correctly on difficult items, without reviewing them, while they submitted wrong answers on easier items. We assumed that this implies guessing behavior. From the conformance checking and performance analysis we found that the fitness of our process model is almost 85 %. Hence, initial results are encouraging towards modelling guessing behavior. Potential implications and future work plans are also discussed.

关键词： Assessment analytics Educational data mining Guessing behavior pattern recognition Process mining Student interaction analysis

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data mining application for predictive learning from visiting nurse association data

Data mining application for predictive learning from visitin...

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3rd international conference on machine learning and Applications

作者： Vyas, P Elmaghraby, A Topp, R Univ Louisville Louisville KY 40292 USA

ISBN: (纸本)0780388232

In the following paper the process of knowledge generation from the Visiting Nurse Association database for elderly care is explored This inquiry is concerned with predicting falls. Predicting which patients are likely to fall can assist the clinician in the identification of high-risk patients and suggest the need for early falls prevention programs. The entire data mining process is described beginning with data gathering followed by cleaning, aggregation, and integration. Issues faced while conducting the research are discussed. Results of decision trees and decision rules, and artificial neural networks used to predict falls are presented.

关键词： falls community gerontology fall prevention predictive data mining and health informatics

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Fast pattern recognition and Deep learning Using Multi-Rooted Binary Decision Diagrams

Fast Pattern Recognition and Deep Learning Using Multi-Roote...

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10th international conference on machine learning and data mining (MLDM)

作者： Bugaychenko, Dmitry Zubarevich, Dmitry St Petersburg State Univ St Petersburg 199034 Russia

ISBN: (纸本)9783319089799;9783319089782

Binary decision diagrams (BDD) is a compact and efficient representation of Boolean functions with extensions available for sets and finite-valued functions. The key feature of the BDD is an ability to employ internal structure (not necessary known upfront) of an object being modelled in order to provide a compact in-memory representation. In this paper we propose application of the BDD for machine learning as a tool for fast general pattern recognition. Multiple BDDs are used to capture a sets of training samples (patterns) and to estimate the similarity of a given test sample with the memorized training sets. Then, having multiple similarity estimates further analysis is done using additional layer of BDDs or common machine learning techniques. We describe training algorithms for BDDs (supervised, unsupervised and combined), an approach for constructing multi-layered networks combining BDDs with traditional artificial neurons and present experimental results for handwritten digits recognition on the MNIST dataset.

关键词： pattern recognition binary decision diagrams

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Developing Automated Optical Character recognition System Using machine learning Algorithm to Solve Payment Verification Issues 3

Developing Automated Optical Character Recognition System Us...

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3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Siek, Michael Soeharto, Rafi Bina Nusantara Univ Business Informat Syst Program Informat Syst Dept Fac Comp & Media Jakarta 11480 Indonesia

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 machine learning frameworks of pyTorch utilizing Tensors and CUDA-GPU parallel computing, the machine learning 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.

关键词： Convolutional neural network deep learning algorithm electronic payment technology using text recognition

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Research on Automatic Workpiece Sorting System Based on machine Vision 3

Research on Automatic Workpiece Sorting System Based on Mach...

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2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Ji, Linfeng Zhenjiang Vocational Technical College Zhenjiang China

ISBN: (纸本)9798350341546

This paper expounds the automatic recognition method of parts based on computer vision. The feature database of the processed parts is constructed by using machine learning method. Image preprocessing, threshold segmentation, edge and contour extraction are used to analyze the features of the image of the inspected part to judge whether the inspected part is qualified or not. Aiming at the attitude error generated when the workpiece is placed, a method for positioning the center of gravity and rotation angle of the workpiece based on boundary detection and contour tracking is proposed. Use the similarity measure to determine the characteristics of the object to be processed. Simulation results show that the method proposed in this paper is effective. © 2023 IEEE.

关键词： image processing machine vision pattern recognition workpiece

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international JOINT conference ON pattern recognition, 3rd, PROCEEDINGS, 1976.

INTERNATIONAL JOINT CONFERENCE ON PATTERN RECOGNITION, 3RD, ...

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Int Jt Conf on pattern recognition, 3rd, Proc

作者： Anon

One hundred and fifty-six papers were presented at the Third international Joint conference on pattern recognition. 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 pattern recognition Theory;Perceptual Modeling;pattern recognition 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 pattern recognition Technique;Image Analysis and Texture;data Base Computer Systems.

关键词： pattern recognition systems

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Research and application of a hierarchical fault diagnosis system based on support vector machine

Research and application of a hierarchical fault diagnosis s...

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3rd international conference on Natural Computation (ICNC 2007)

作者： Liu, Ailun Yuan, Xiaoyan Yu, Jinshou E China Univ Sci & Technol Res Inst Automat Shanghai 200237 Peoples R China

ISBN: (纸本)0769528759

Support Vector machine (SVM) is a kind of machine learning method based on the statistical learning theory, it has been applied in the fault diagnosis field. After analyzing SVM pattern classification theory, a hierarchical structure Fault Detection and Identification (FDI) system is presented in this paper, and simulation results show that this method can effectively handle the complex process characteristic and improve FDI model performance.

关键词： support Vector machine (SVM) fault diagnosis pattern recognition

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Performing an assortment of tasks on machine learning and Benchmarking based Clinical Time Series data 3

Performing an assortment of tasks on Machine Learning and Be...

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3rd international conference on Computing Methodologies and Communication (ICCMC)

作者： Ramya, P. Geetha, G. Sindhura, V VR Siddhartha Engn Coll Dept IT Vijayawada Kanuru India

ISBN: (纸本)9781538678084

Conceptual Health care is one of the most exciting borders in data mining and machine learning. Appropriation of electronic health records (EHRs) made a blast in advanced clinical information which is accessible for examination, but progress in machine learning 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.

关键词： Mortality Prediction LOS machine learning Deep learning classification

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Congestion Detection in Wireless Sensor Networks Using MLP and Classification by Regression 3

Congestion Detection in Wireless Sensor Networks Using MLP a...

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3rd international conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

作者： Madalgi, Jayashri B. Kumar, S. Anupama VTU Research Scholar Gogte Institute of technology RV College of Engineering Bangalore Karnataka IN

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 machine learning 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 machine learning 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.

关键词： machine learning MLP regression classification Congestion control in WSN data mining

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Design and Implementation of Inspection Model for knowledge patterns Classification in Diabetic Retinal Images 3

Design and Implementation of Inspection Model for knowledge ...

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3rd international conference on Computing Methodologies and Communication (ICCMC)

作者： Kothare, Kajal Sanjay Malpe, Kalpana Guru Nanak Inst Engn & Technol Dept Comp Sci & Engn Nagpur Maharashtra India

ISBN: (纸本)9781538678084

Diabetes is one of the major health issues. In diabetes patient one serious problem experience is the Diabetic Retinopathy (DR) and visual deficiency and is vascular disease of retina. Hence prediction of DR from patient eye retina becomes wry crucial at early stage to cure. We focuses on presenting an empirical method in this research to collect required data and then developing several models to predict the chance of diabetic retinopathy. Here we use diabetic eye retina image dataset as input for prediction and evaluation. There are many techniques and algorithms that help to diagnose DR in retinal fundus images. We utilized some data mining techniques such as Support vector machine (SVM), naive bayes and Local binary pattern (LBP) to extract image features and analyze image dataset.

关键词： Diabetic Retinopathy Local Binary pattern Support Vector machine Naive Bayes

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