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Combining Regular Expressions and Supervised Algorithms for Clinical Text Classification 24th

Combining Regular Expressions and Supervised Algorithms for...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Flores, Christopher A. Verschae, Rodrigo Institute of Engineering Sciences Universidad de O’Higgins O’Higgins Rancagua2841959 Chile

ISBN: (纸本)9783031482311

Clinical text classification allows assigning labels to content-based data using machine learning algorithms. However, unlike other study domains, clinical texts present complex linguistic diversity, including abbreviations, typos, and numerical patterns that are difficult to represent by the most-used classification algorithms. In this sense, sequences of character strings and symbols, known as Regular Expressions (RegExs), offer an alternative to represent complex patterns from the texts and could be used jointly with the most commonly used classification algorithms for accurate text classification. thus, a classification algorithm can label test texts when RegExs produce no matches. this work proposes a method that combines automatically-generated RegExs and supervised algorithms for classifying clinical texts. RegExs are automatically generated using alignment algorithms in a supervised manner, filtering out those that do not meet a minimum confidence threshold and do not contain specific keywords for the classification problem. At prediction time, our method assigns the class of the most confident RegEx that matches a test text. When no RegExs matches a test text, a supervised algorithm assigns a class. three clinical datasets with textual information on obesity and smoking habits were used to assess the performance of four classifiers based on Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and Bidirectional Encoder Representations from Transformers (BERT). Classification results indicate that our method, on average, improved the classifiers’ performance by up to 12% in all performance metrics. these results show the ability of our method to generate confident RegExs that capture representative patterns from the texts for use with supervised algorithms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Support vector machines

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Uniform Design of Experiments for Equality Constraints 24th

Uniform Design of Experiments for Equality Constraints

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Schneider, Fabian Hellmig, Ralph J. Nelles, Oliver Institute of Mechanics and Control Engineering – Mechatronics University of Siegen Siegen Germany Institute of Material Science University of Siegen Siegen Germany

ISBN: (纸本)9783031482311

Design of Experiments (DoE) is essential for data-driven models. It is applied to create uniformly distributed designs for the model’s input space. For good uniformity, the point density of the generated design shall be kept roughly constant, i.e., too close points and too big data holes shall be avoided. the generation of space-filling designs, even for the unconstrained design space, is challenging, especially with increasing dimensionality and design size. these difficulties increase in the presence of constraints that occurs in many engineering applications. Equality constraints are one group, i.e., mixture or volume constraints. Specialized approaches are required for such constraints. A point-distance-based optimization is not suitable. the computational effort for large designs is too high, and for higher-dimensional designs, too many points are placed close to the boundary of the design space. the proposed approach, instead, is based on the projection of uniformly distributed designs onto the constraint. thus, this work presents the idea of using maximin Latin Hypercubes (LH) with their good space-filling properties and projecting them onto the constraint. the resulting design might be non-uniformly distributed so that large data holes could occur. To fill these holes, this projection of an LH onto the constraint is repeated for the remaining inputs by going over all inputs. this method is analyzed in a first two-dimensional case study. In a second case study, it is applied to a nonlinear constraint. A design has to be placed on a spherical surface. the results are compared to an existing state-of-the-art method. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Design of experiments

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24th international conference on Web Information Systems engineering, WISE 2023

24th International Conference on Web Information Systems Eng...

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24th international conference on Web Information Systems engineering, WISE 2023

ISBN: (纸本)9789819972531

the proceedings contain 72 papers. the special focus in this conference is on Web Information Systems engineering. the topics include: Highly Applicable Linear Event Detection Algorithm on Social Media with Graph Stream;leveraging Social Networks for Mergers and Acquisitions Forecasting;enhancing Trust Prediction in Attributed Social Networks with Self-Supervised learning;bilateral Insider threat Detection: Harnessing Standalone and Sequential Activities with Recurrent Neural Networks;ATDG: An Automatic Cyber threat Intelligence Extraction Model of DPCNN and BIGRU Combined with Attention Mechanism;blockchain-Empowered Resource Allocation and data Security for Efficient Vehicular Edge Computing;priv-S: Privacy-Sensitive data Identification in Online Social Networks;TLEF: Two-Layer Evolutionary Framework for t-Closeness Anonymization;a Dual-Layer Privacy-Preserving Federated learning Framework;fuzzy Based Text Quality Assessment for Sentiment Analysis;a Privacy-Preserving Evolutionary Computation Framework for Feature Selection;local Difference-Based Federated learning Against Preference Profiling Attacks;empowering Vulnerability Prioritization: A Heterogeneous Graph-Driven Framework for Exploitability Prediction;ICAD: An intelligent Framework for Real-Time Criminal Analytics and Detection;Web Page Segmentation: A DOM-Structural Cohesion Analysis Approach;learning to Select the Relevant History Turns in Conversational Question Answering;a Methodological Approach for data-Intensive Web Application Design on Top of data Lakes;ESPRESSO: A Framework for Empowering Search on Decentralized Web;primary Building Blocks for Web Automation;prompt-learning for Semi-supervised Text Classification;a Web Service Oriented Integration Solution for Capital Facilities Information Handover;deep Neural Network-Based Approach for IoT Service QoS Prediction;Path-KGE: Preference-Aware Knowledge Graph Embedding with Path Semantics for Link Prediction;efficient Graph Embedding Method for Link

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Extracting Knowledge from Incompletely Known Models 24th

Extracting Knowledge from Incompletely Known Models

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： D. Peribáñez, Alejandro Fernández-Isabel, Alberto Martín de Diego, Isaac Condado, Andrea M. Moguerza, Javier Data Science Laboratory Rey Juan Carlos University c/Tulipán s/n Móstoles28933 Spain

ISBN: (纸本)9783031482311

In the age of Artificial Intelligence (AI), the use of trained models is a common practice to solve a huge amount of different problems. However, it is highly complicated to understand the decision-making process of these models and how the used training data affect them during the production phase. For this reason, multiple techniques related to model extraction have appeared. these techniques consist of analyzing the behavior of a (sometimes partially) unknown model and generating a clone that reacts similarly. this process is relatively simple with basic models, but it becomes arduous when complex models must be analyzed and replicated. this paper tackles this issue by presenting the Neural NetwOrk Models (VENNOM) system. It is a general framework architecture to extract knowledge and provide explainability to unknown models. the proposed approach uses low-capacity, high-explainability neural networks to produce flexible and interpretable models. the proposed framework offers several advantages, particularly in terms of obtaining explanations through visual and textual content for models that are otherwise opaque. the framework has been tested on tabular data sets, demonstrating its performance and potential. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cloning

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Estimating UWB Positioning Integrity Based on Ranging Residuals 24

Estimating UWB Positioning Integrity Based on Ranging Residu...

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24th international conference on Applied Electromagnetics and Communications, ICECOM 2023

作者： Tommingas, Mihkel Ulp, Sander Alam, Muhammad Mahtab Muursepp, Ivo Laadung, Taavi Tallinn University of Technology Thomas Johann Seebeck Department of Electronics Tallinn Estonia Eliko Tehnoloogia Arenduskeskus OÜ Thomas Johann Seebeck Department of Electronics Tallinn Estonia

ISBN: (纸本)9798350313017

this paper investigates features based on Ultra-Wideband (UWB) ranging residuals used for coordinate integrity prediction. Usually, UWB-based positioning integrity is evaluated using channel statistics (CS). However, in practice this is not a feasible approach as gathering CS data takes too much time compared to the position update rate of an UWB system. In contrast to this approach, a set of features based on UWB ranging residuals are used in a machine learning (ML) algorithm to train a model for accurate integrity prediction. these features include those used in literature as well as proposed novel features. the trained model could predict measurements in the range of 0...0.2 m with the accuracy of 90% and having an overall accuracy of 84%. the results show that using the proposed residual features it is possible to predict UWB coordinate calculation integrity with high confidence. Lastly, the dataset used in this paper was collected during a measurement campaign in a complex industrial environment with constantly changing line-of-sight/non-line-ofsight (LOS/NLOS) conditions. © 2023 IEEE.

关键词： Machine learning

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Unsupervised Online Event Ranking for IT Operations 24th

Unsupervised Online Event Ranking for IT Operations

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Mendes, Tiago Costa Barata, André Azevedo Pereira, Miguel Mendes-Moreira, João Camacho, Rui Sousa, Ricardo Teixeira LIADD/INESC Tec R. Dr. Roberto Frias Porto4200-465 Portugal Faculty of Engineering of the University of Porto R. Dr. Roberto Frias Porto4200-465 Portugal IT Peers R. Eng. Frederico Ulrich 3210 1 andar s.101 Maia4470-605 Portugal

ISBN: (纸本)9783031482311

Keeping high service levels of a fast-growing number of servers is crucial and challenging for IT operations teams. Online monitoring systems trigger many occurrences that experts find hard to keep up with. In addition, most of the triggered warnings do not correspond to real, critical problems, making it difficult for technicians to know which to focus on and address in a timely manner. Outlier and concept drift detection techniques can be applied to multiple streams of readings related to server monitoring metrics, but they also generate many False Positives. Ranking algorithms can already prioritize relevant results in information retrieval and recommender systems. However, these approaches are supervised, making them inapplicable in event detection on data streams. We propose a framework that combines event aggregations and uses a customized clustering algorithm to score and rank alarms in the context of IT operations. To the best of our knowledge, this is the first unsupervised, online, high-dimensional approach to rank IT ops events and contributes to advancing knowledge about associated key concepts and challenges of this problem. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Big data

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Complexity-Driven Sampling for Bagging 24th

Complexity-Driven Sampling for Bagging

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Lancho, Carmen C. P. de Souto, Marcilio Lorena, Ana C. Martín de Diego, Isaac Data Science Laboratory Rey Juan Carlos University C/ Tulipán s/n Móstoles28933 Spain Fundamental Computer Science Laboratory University of Orléans Léonard de Vinci B.P. 6759 Orleans Cedex 2F-45067 France Aeronautics Institute of Technology Praça Marechal Eduardo Gomes 50 São José dos Campos São Paulo12228-900 Brazil

ISBN: (纸本)9783031482311

Ensemble learning consists of combining the prediction of different learners to obtain a final output. One key step for their success is the diversity among the learners. In this paper, we propose to reach the diversity in terms of the classification complexity by guiding the sampling of instances in the Bagging algorithm with complexity measures. the proposed Complexity-driven Bagging algorithm complements the classic Bagging algorithm by considering training samples of different complexity to cover the complexity space. Besides, the algorithm admits any complexity measure to guide the sampling. the proposal is tested in 28 real datasets and for a total of 9 complexity measures, providing satisfactory and promising results and revealing that training with samples of different complexity, ranging from easy to hard samples, is the best strategy when sampling based on complexity. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Computational complexity

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Sustainable On-Street Parking Mapping with Deep learning and Airborne Imagery 24th

Sustainable On-Street Parking Mapping with Deep Learning an...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Mahaarachchi, Bashini K. Cohen, Sarel Bookhagen, Bodo Doskoč, Vanja Friedrich, Tobias Institute of Computer Science University of Potsdam Potsdam Germany The Academic College of Tel Aviv-Yaffo Tel Aviv Israel Institute of Geosciences University of Potsdam Potsdam Germany Hasso Plattner Institute University of Potsdam Potsdam Germany

ISBN: (纸本)9783031482311

In large cities, a considerable amount of traffic congestion is caused by drivers actively looking for unoccupied parking spots, leading to reduced average velocities on the streets, reduced air quality, and an increase in sound pollution. A parking space map helps drivers to find vacant parking spaces more easily and helps minimising search time, fuel and energy consumption. We introduce a machine learning framework to detect occupied and empty parking spaces using airborne images. Our approach is based on three steps. First, we present novel ways to include observations from outside the visible spectrum (RGB) to delineate vegetation in urban environments. We remove roads on the images to minimise interference from moving vehicles and use a mask-RCNN to detect vacant parking spaces on the processed images. third, we propose to use a logistic regression model (LRM) as a post-processing step in order to better filter true positives. From our experiments we conclude that Red-Green-NIR (RG-NIR) spectrum contains the most important information for training Faster-Region-based Convolutional Neural Network with Feature Pyramid Network (RCNN FPN) for vehicle detection. Our empirical studies on aerial images of Berlin, Germany, show that our framework can be employed successfully to create trustworthy parking space maps. Our best model obtain an accuracy of 97.7% and an F1-score of 0.645 for detecting both occupied and vacant on-street packing. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Object detection

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Generating Wildfire Heat Maps with Twitter and BERT 24th

Generating Wildfire Heat Maps with Twitter and BERT

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Pinto, João Cabral Gonçalo Oliveira, Hugo Cardoso, Alberto Silva, Catarina Department of Informatics Engineering CISUC University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031482311

Recent developments in Natural Language Processing (NLP) have opened up a plethora of opportunities to extract pertinent information in settings where data overload is a critical issue. In this work, we address the highly relevant scenario of fire detection and deployment of firefighting resources. Social media posts commonly contain textual information during a fire event, but their vast volume and the necessity for swift actionable information often precludes their effective utilization. this paper proposes an information extraction pipeline capable of generating a wildfire heat map of Portugal from Twitter posts written in Portuguese. It uses a fine-tuned version of a BERT language model to extract fire reports from large batches of recent fire-related tweets as well as the spaCy NLP library to query the location of each recently reported fire. Wildfire locations are plotted to a colored map indicating the most probable fire locations, which could prove useful in the process of allocating firefighting resources for those regions. the system is easily adaptable to work with any other country or language, provided compatible BERT and spaCy models exist. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Fires

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Integration of Partially Prior Physics and data for Longitudinal Vehicle Dynamics Restoration 24

Integration of Partially Prior Physics and Data for Longitud...

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24th COTA international conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Yongsheng Xu, Bingrong Chu, Duanfeng Ding, Naikan Yin, Jianhua School of Information Engineering Wuhan Univ. of Technology Wuhan China School of Automation Wuhan Univ. of Technology Wuhan China Intelligent Transportation Systems Research Center Wuhan Univ. of Technology Wuhan China Chongqing Research Institute Wuhan Univ. of Technology Chongqing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Vehicle dynamics model is critical for autonomous vehicles since it provides certain rules of motion in response to the surrounding rapidly changing environment. However, current pure physics-based models and data-driven approaches are set against each other in constructing the dynamics model. In this work, we propose a vehicle dynamics restoration approach by integrating physics principles and data based on physics-informed machine learning. the vehicle dynamics model parameters are estimated by combining data and limited prior physical knowledge. the results of a case study demonstrate that (1) the proposed methods can accurately identify the dynamics model parameters and restore the vehicle dynamics model and (2) the neural network with physical knowledge can accurately predict the state of vehicle. the proposed method has the potential to be applied in quantifying the dynamics model uncertainty due to parameter uncertainty, and stochastic motion planning and control due to uncertainty, which will be further investigated in the future. © ASCE.

关键词： Stochastic programming

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