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Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study

Unsupervised Event Abstraction in a Process Mining Context: ...

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2nd international conference on Process Mining (ICPM)

作者： Van Houdt, Greg Depaire, Benoit Martin, Niels UHasselt Hasselt Univ Res Grp Business Informat Hasselt Belgium Res Fdn Flanders FWO Brussels Belgium Vrije Univ Brussel Data Analyt Lab Brussels Belgium

ISBN: (纸本)9783030726928;9783030726935

Due to the rise of IoT, event data becomes increasingly fine-grained. Faced with such data, process discovery often produces incomprehensible spaghetti-models expressed at a granularity level that doesn't match the mental model of a business user. One approach is to use event abstraction patterns to transform the event log towards a more coarse-grained level and to discover process models from this transformed log. Recent literature has produced various (partial) implementations of this approach, but insights how these techniques compare against each other is still limited. This paper focuses on the use of Local Process models and Combination based Behavioural Pattern Mining to discover event abstraction patterns in combination with the approach of Mannhardt et al. [15] to transform the event log. Experiments are conducted to gain insights into the performance of these techniques. Results are very limited with a general decrease in fitness and precision and only a minimal improvement of complexity. Results also show that the combination of the process discovery algorithm and the event abstraction pattern miner matters. In particular, the combination of Local Process models with Split Miner seems to improve precision.

关键词： Process mining Unsupervised learning Event log Abstraction

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Enhancing SVM Classification of Meningitis with Feature-Adaptive Adagrad in CSF Analysis

Enhancing SVM Classification of Meningitis with Feature-Adap...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： S Sathiya Priya M Amanullah Manivannan D Department of Computer Science & Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Meningitis, a condition characterized by a serious inflammation of the meninges, necessitates timely diagnosis to get the best possible results for the patient. An examination of cerebrospinal fluid (CSF) continues to be an essential method for detecting meningitis. The study examines the capacity of Support Vector Machines (SVM) enhanced with the Adagrad algorithm to enhance the precision of meningitis classification by using CSF data. This study investigates the effectiveness of Adagrad in improving the performance of Support Vector Machines (SVM) for predicting meningitis. Two Support Vector Machine (SVM) models were created in this study to examine the effects of optimization strategies. The objective of this comparison was to assess if the use of Adagrad might enhance the precision of the Support Vector Machine (SVM) in a research study. Both models are trained using a dataset that includes labeled CSF analysis findings, which are classed based on whether meningitis is present or absent. The assessment of the testing dataset and training dataset was conducted using measures such as accuracy, sensitivity (the ability to properly detect meningitis), and specificity (the ability to accurately identify non-meningitis). Though Support Vector Machine (SVMs) is a well-established tool for performing classification tasks like meningitis prediction, this research explores how the Adagrad algorithm can optimize SVM performance. By dynamically adjusting learning rates, Adagrad can potentially help the SVM converge faster to a better solution, leading to more accurate meningitis diagnosis from CSF analysis. The effectiveness of this approach depends on the quality and relevance of the features extracted from the CSF analysis. Choosing informative features is crucial for both SVM and Adagrad to perform well. The influence of Adagrad optimization on the model's capacity to reliably identify meningitis based on CSF variables may be examined by comparing the results. Healthcar

关键词： Support vector machines Training Accuracy Training data Prediction algorithms Feature extraction Vectors Classification algorithms Cerebrospinal fluid Testing

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A time series data augmentation method based on dynamic time warping

A time series data augmentation method based on dynamic time...

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2021 international conference on Computer Communication and Artificial Intelligence, CCAI 2021

作者： Yang, Xinyu Zhang, Zhenguo Cui, Xu Cui, Rongyi Department of Computer Science and Technology Yanbian University 977 Gongyuan Road Yanji133002 China

ISBN: (纸本)9781728194011

Deep learning has become a hot research topic in the field of time series analysis and data mining. Training models often requires balanced and large data sets, but on the one hand, the number of different types of data in time series data sets is often extremely imbalanced, on the other hand, some time series data are often difficult to collect. Therefore, it is necessary to augment the training data before training the deep learning model. SMOTE is a data augmentation method widely used in preprocessing imbalanced data sets, but the classical SMOTE method does not satisfy the characteristics of time series when processing time series, so it is not effective when applied to time series data sets. To address this point, we propose an oversampling data augmentation method based on dynamic time warping - DTW-SMOTE. For the possible phase shifts of time series with the same characteristics, this method uses dynamic time warping to obtain more reasonable similarity between different time series, which improves the classical SMOTE method and achieves data augmentation for time series data. We conducted comparison experiments using two models with different architectures: ResNet and LSTM. The experimental results show that the performance of the above models is significantly improved after training with the DTW-SMOTE method pre-processed dataset. © 2021 IEEE.

关键词： Time series analysis

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Perceptual-Aware and Restorable Real-Time Image Downscaling

Perceptual-Aware and Restorable Real-Time Image Downscaling

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2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Niu, Yuzhen Zheng, Luwei Lin, Jiaqi Wu, Jianbin Liu, Wenxi College of Computer and Data Science Fuzhou University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Image downscaling has been a classical problem and has recently been linked to super-resolution (SR). In this paper, we aim to propose a learning-based image downscaling model, FastDownscaler, which can efficiently produce low-resolution (LR) images that not only preserve the rich details of the original high-resolution images but also be highly restorable for existing SR models. We first present two separate lightweight networks with different upsampling losses, the bilinear loss and the bicubic loss, which are better for SR restoration and LR downscaling, respectively. To produce versatile LR images, we then propose to distill bilinear loss guided network with bicubic loss guided one. To our best knowledge, we establish the first image downscaling quality assessment dataset to evaluate the downscaling performance. Experimental results demonstrate the superior performance of the proposed model on image downscaling and SR. Furthermore, our model can achieve over 600 FPS for downscaling a 1920\times 1280 image. © 2022 IEEE.

关键词： Optical resolving power

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Tropospheric Delay Modeling Based on Multi-source data Fusion and Machine Learning algorithms 12th

Tropospheric Delay Modeling Based on Multi-source Data Fusio...

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12th China Satellite Navigation conference, CSNC 2021

作者： Li, Song Xu, Tianhe Jiang, Nan School of Geological and Surveying Engineering Chang’an University Xi’an710054 China Key Laboratory of Marine Environmental Exploration Technology and Application Ministry of Natural Resources Beijing China Institute of Space Sciences Shandong University Weihai264209 China

ISBN: (纸本)9789811631375

Machine learning algorithms have been widely applied in various fields, including the research of the tropospheric delay of Global Navigation Satellite System (GNSS) signal. In this paper, back-propagation neural network (BPNN), radial basis function (RBF) neural network, and least square support vector machine (LSSVM) algorithm are applied to develop the regional zenith troposphere delay (ZTD) models by merging international GNSS Service ZTD products (GNSS-ZTD) and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over North America throughout 2020. Among them, two data-fusion strategies are designed with different input parameters of modeling, including the fusion of ERA5 meteorological parameters with GNSS-ZTD and the fusion of ZTD estimations from ERA5 data (ERA5-ZTD) and GNSS-ZTD. With ZTD derived at 77 IGS stations, the accuracy of regional ZTD models is verified by month, as well as the stability and efficiency. The results show that the effect of RBF is best when modeling ZTD with small-size training samples. Among them, the average RMSE of the ZTD models with RBF is 20.8 mm and 20.1 mm for two data-fusion strategies, respectively. The accuracy of RBF is improved by 40.4% and 38.5% over the BPNN in modeling ZTD. The result of the model using the LSSVM is close to the RBF. Moreover, the BPNN has an obvious advantage in modeling ZTD with large-size training samples. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data fusion

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Prediction of Alzheimer's Disease by Analyzing Handwriting Dynamics Using Machine Learning algorithms

Prediction of Alzheimer's Disease by Analyzing Handwriting D...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： M. Vimaladevi R. Thangamani Priyadharshini S Varshini B Tarun A Department of AI Kongu Engineering College Perundurai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Alzheimer's disease is a degenerative neurological condition that impairs everyday functioning and quality of life. Handwritten data scripts are related in the determination of Alzheimer's disease with the use of handwriting patterns, which can reveal early signs of cognitive decline and motor impairments. Changes in handwriting, such as tremors, irregularities, and slower writing speeds, can be indicative of the onset and progression of Alzheimer's, aiding in early diagnosis and monitoring of the disease. Traditionally, statistical tests or conventional classification models have been used in handwriting analysis studies. Machine learning techniques have only lately been investigated, and they were mostly used to identify Parkinson's disease rather than Alzheimer's. This research work aims to close this gap and investigate this by developing a machine learning model that uses handwriting data to categorize an individual as an Alzheimer patient. This research aims to advance the science of Alzheimer's disease prediction by examining the use of handwritten data as a unique medium for early diagnosis. PCA is one of the data preprocessing parameters that are employed, while the linear support vector classifier, random forest classifier, and XGBoost, together with their accuracy, are the model training parameters. Each participant's handwriting sample yielded 451 handwriting features, such as air_time1, disp_index1, gmrt_in_air1, gmrt_on_paper1, mean_gmrt1, etc., which are considered as the input parameters. A binary class label indicating whether a person is healthy or may have Alzheimer's disease is considered as the expected output.

关键词： Training Parkinson's disease Writing Motors data models Vectors Alzheimer's disease Random forests Diseases Principal component analysis

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SOUP: A Fleet Management System for Passenger Demand Prediction and Competitive Taxi Supply 37

SOUP: A Fleet Management System for Passenger Demand Predict...

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37th IEEE international conference on data Engineering (IEEE ICDE)

作者： Hu, Qi Ming, Lingfeng Xi, Ruijie Chen, Lu Jensen, Christian S. Zheng, Bolong Huazhong Univ Sci & Technol Wuhan Peoples R China Zhejiang Univ Hangzhou Peoples R China Aalborg Univ Aalborg Denmark

ISBN: (纸本)9781728191843

Online car-hailing services have gained substantial popularity. An effective taxi fleet management strategy should not only increase taxi utilization by reducing taxi idle time, but should also improve passenger satisfaction by minimizing passenger waiting time. We demonstrate a fleet management system called SOUP that aims at minimizing taxi idle time and that monitors the fleet movement status. SOUP includes a passenger request prediction model called ST-GCSL that predicts the number of requests in the near future, and it includes a demand-aware route planning algorithm called DROP that provides idle taxis with search routes to serve potential requests. In addition, SOUP supports visualizing and analyzing historical passenger requests, simulating fleet movement, and computing evaluation metrics. We demonstrate how SOUP accurately predicts passenger demand and significantly reduces taxi idle time.

关键词： Measurement conferences Computational modeling Predictive models Prediction algorithms data engineering Planning

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GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings onWorld Scale 21

GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings...

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30th ACM international conference on Information and Knowledge Management (CIKM)

作者： Tempelmeier, Nicolas Gottschalk, Simon Demidova, Elena Leibniz Univ Hannover L3S Res Ctr Hannover Germany Univ Bonn Data Sci & Intelligent Syst DSIS Bonn Germany

ISBN: (纸本)9781450384469

OpenStreetMap (OSM) is currently the richest publicly available information source on geographic entities (e.g., buildings and roads) worldwide. However, using OSM entities in machine learning models and other applications is challenging due to the large scale of OSM, the extreme heterogeneity of entity annotations, and a lack of a well-defined ontology to describe entity semantics and properties. This paper presents GeoVectors - a unique, comprehensive world-scale linked open corpus of OSM entity embeddings covering the entire OSM dataset and providing latent representations of over 980 million geographic entities in 180 countries. The GeoVectors corpus captures semantic and geographic dimensions of OSM entities and makes these entities directly accessible to machine learning algorithms and semantic applications. We create a semantic description of the GeoVectors corpus, including identity links to the Wikidata and DBpedia knowledge graphs to supply context information. Furthermore, we provide a SPARQL endpoint - a semantic interface that offers direct access to the semantic and latent representations of geographic entities in OSM.

关键词： OpenStreetMap OSM Embeddings Semantic Geographic data

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Inferring Functional Properties from Fluid Dynamics Features 25

Inferring Functional Properties from Fluid Dynamics Features

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25th international conference on Pattern Recognition (ICPR)

作者： Schillaci, Andrea Quadrio, Maurizio Pipolo, Carlotta Restelli, Marcello Boracchi, Giacomo Politecn Milan Dipartimento Sci & Tecnol Aerosp Milan Italy ASST Santi Paolo & Carlo Dip Sci Salute Unita Otorinolaringoiatria Milan Italy Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781728188089

In a wide range of applied problems involving fluid flows, Computational Fluid Dynamics (CFD) provides detailed quantitative information on the flow field, at variable level of fidelity and computational cost. However, CFD alone cannot predict high-level functional properties that are not easily obtained from the equations of fluid motion. In this work, we present a data-driven framework to extract these additional information, such as medical diagnostic output, from CFD solutions. This is a challenging task because of the huge data dimensionality of CFD, and the limited training data that can be typically gathered due to the large computational cost of CFD. By pursuing a traditional Machine Learning (ML) pipeline of pre-processing, feature extraction, and model training, we demonstrate that informative features can be extracted from CFI) data. Two experiments, pertaining to different application domains, support our claim that the convective properties implicit into a CFD solution can be leveraged to retrieve functional information that does not admit an analytical definition. Despite the preliminary nature of our study and the relative simplicity of both the geometrical and CFD models, for the first time we demonstrate that the combination of ML and CFD can diagnose a complex system in terms of high-level functional properties.

关键词： Computational Fluid Dynamics data-driven models Inference Nasal Breathing Difficulties

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A Method of Phase Unwrapping algorithms Efficiency Analysis for InSAR data processing 6

A Method of Phase Unwrapping Algorithms Efficiency Analysis ...

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6th international conference on Information Technology and Nanotechnology (IEEE ITNT)

作者： Sosnovsky, Andrey Kobernichenko, Victor Ural Fed Univ Inst Radio Elect & Informat Technol Ekaterinburg Russia

ISBN: (数字)9781728170411

ISBN: (纸本)9781728170411

The experimental study of algorithms for radar data interferometric processing effectiveness is complicated by the fact that a quantitative result can be obtained only after the completion of the entire processing procedure, so it is difficult to assess the effectiveness of its first stages. A method of experimental evaluation of the accuracy of the results of interferometric processing by various algorithms of phase unwrapping with such combination of image and reference data, which does not require the use of the ambiguity height parameter, is proposed. The results of the method implementation for ALOS PALSAR data in relation to areas with hilly and low-mountain terrain are presented. Estimates of the vertical accuracy of ALOS PALSAR interferometric data for Region Growing and Minimal Cost Flow phase unwrapping algorithms implemented in the SARscape software package are obtained.

关键词： synthesized aperture radars InSAR data processing phase unwrapping algorithms

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