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检索条件"任意字段=21st International Conference on Intelligent Data Engineering and Automated Learning"

共 3148 条记录，以下是1-10 订阅

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21th international conference on intelligent data engineering and automated learning, IDEAL 2020

21th International Conference on Intelligent Data Engineerin...

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21th international conference on intelligent data engineering and automated learning, IDEAL 2020

ISBN: (纸本)9783030623647

The proceedings contain 34 papers. The special focus in this conference is on intelligent data engineering and automated learning. The topics include: Network Analysis for Fraud Detection in Portuguese Public Procurement;biased Language Detection in Court Decisions;a One-by-One Method for Community Detection in Attributed Networks;data Pre-processing and data Generation in the student Flow Case study;a Hybrid Approach to the Analysis of a Collection of Research Papers;Sequential Self-tuning Clustering for Automatic Delimitation of Coastal Upwelling on Sst Images;time Series Clustering for Knowledge Discovery on Metal Additive Manufacturing;quaternion Neural Networks: state-of-the-Art and Research Challenges;a Solar Thermal System Temperature Prediction of a Smart Building for data Recovery and Security Purposes;a Fault Detection System for Power Cells During Capacity Confirmation Test Through a Global One-Class Classifier;a Deep Metric Neural Network with Disentangled Representation for Detecting Smartphone Glass Defects;improving Performance of Recommendation System Architecture;automated learning of In-vehicle Noise Representation with Triplet-Loss Embedded Convolutional Beamforming Network;Sequence Mining for Automatic Generation of Software Tests from GUI Event Traces;enhanced Credit Prediction Using Artificial data;deep learning Based Algorithms for Welding Edge Points Detection;detecting Performance Anomalies in the Multi-component Software a Collaborative Robot;prediction of Small-Wind Turbine Performance from Time Series Modelling Using intelligent Techniques;review of Trends in Automatic Human Activity Recognition Using Synthetic Audio-Visual data;atmospheric Tomography Using Convolutional Neural Networks;workshop on Machine learning in Smart Mobility;Driver Monitoring System Based on CNN Models: An Approach for Attention Level Detection;preface.

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21th international conference on intelligent data engineering and automated learning, IDEAL 2020

21th International Conference on Intelligent Data Engineerin...

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21th international conference on intelligent data engineering and automated learning, IDEAL 2020

ISBN: (纸本)9783030623616

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Quantitative Estimation of Reputation Risk 25th

Quantitative Estimation of Reputation Risk

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25th international conference on intelligent data engineering and automated learning

作者： Mitic, Peter UCL Dept Comp Sci Gower St London WC1E 6BT England

ISBN: (纸本)9783031777301;9783031777318

We present a quantitative definition of reputation risk, formulated in terms of a reputation time series comprising daily sentiment measurements. Self Supported learning is used to quantify reputation risk by progressively refining an initial proposal for a Minimum Acceptable Sentiment, calculated from descriptive statistics of the reputation data. The derived values are validated using a "sense test" based on a Loess quantile. The results show that the Minimum Acceptable Sentiment value is given approximately by a two standard deviation lower tail of the observed data.

关键词： Reputation Sentiment Time Series Descriptive statistics Value-at-Risk Normal Mixture Distribution CNN Loess Self Supervised learning SSL NLP

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FuzzTheREst: An intelligent automated Black-Box REstful API Fuzzer 21st

FuzzTheREST: An Intelligent Automated Black-Box RESTful API ...

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21st international Symposium on Distributed Computing and Artificial Intelligence

作者： Dias, Tiago Maia, Eva Praca, Isabel Porto Sch Engn ISEP Res Grp Intelligent Engn & Comp Adv Innovat & Dev P-4200072 Porto Portugal

ISBN: (纸本)9783031820724;9783031820731

Software's pervasive impact and increasing reliance in the era of digital transformation raise concerns about vulnerabilities, emphasizing the need for software security. Fuzzy testing is a dynamic analysis software testing technique that consists of feeding faulty input data to a System Under Test (SUT) and observing its behavior. Specifically regarding black-box REstful API testing, recent literature has attempted to automate this technique using heuristics to perform the input search and using the HTTP response status codes for classification. However, most approaches do not keep track of code coverage, which is important to validate the solution. This work introduces a black-box REstful API fuzzy testing tool that employs Reinforcement learning (RL) for vulnerability detection. The fuzzer operates via the OpenAPI Specification (OAS) file and a scenarios file, which includes information to communicate with the SUT and the sequences of functionalities to test, respectively. To evaluate its effectiveness, the tool was tested on the Petstore API. The tool found a total of six unique vulnerabilities and achieved 55% code coverage.

关键词： Fuzzy Testing REstful APIs Vulnerability Detection Code Security Artificial Intelligence

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Enhancing Power Forecasting Through Contextual Awareness with C-Means and K-Means Approaches 21st

Enhancing Power Forecasting Through Contextual Awareness wit...

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21st international Symposium on Distributed Computing and Artificial Intelligence

作者： Teixeira, Brigida Pinto, Tiago Vale, Zita Polytech Porto GECAD Res Grp Intelligent Engn & Comp Adv Innova LASI Intelligent Syst Assoc Lab R Dr Antonio Bernardino Almeida 431 P-4249015 Porto Portugal Univ Tras Os Montes & Alto Douro P-5000801 Vila Real Portugal INESC TEC P-5000801 Vila Real Portugal

ISBN: (纸本)9783031820724;9783031820731

This study delves into enhancing energy consumption forecasting in smart buildings by analyzing the implications of different contextual detection strategies, employing both K-Means and Fuzzy C-Means clustering techniques. Utilizing a comprehensive dataset from various sensors, the research segments power usage data to uncover distinct consumption patterns, extracted from user's behavior. A suite of seven machine learning forecasting algorithms undergoes evaluation, guided by two distinct methodologies: crafting specialized models for individual clusters and embedding contextual data as features within an integrated model. To perform the selection of the most suitable algorithm for each approach and cluster, an automated machine learning process is utilized, streamlining the optimization and comparison process. Moreover, the study explores the influence of soft clustering in providing nuanced, probabilistic cluster memberships and their impact on forecasting accuracy. A baseline scenario, devoid of contextual considerations, serves as a benchmark for comparison, allowing for a clear evaluation of the benefits introduced by integrating context into predictive models. The research aims to identify the optimal contextual detection strategy, considering the unique advantages of soft and hard clustering, to significantly improve energy consumption forecasts.

关键词： Contextual Awareness Fuzzy C-Means (FCM) K-Means Power Consumption Forecasting

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Adaptive Traffic Light Control Through Reinforcement learning Based on Sensor Integration 21st

Adaptive Traffic Light Control Through Reinforcement Learnin...

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21st international Symposium on Distributed Computing and Artificial Intelligence

作者： Uribe-Chavert, Pedro Lopez-Cuerva, Luis Posadas-Yague, Juan-Luis Poza-Lujan, Jose-Luis Univ Politecn Valencia Doctoral Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Informat Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Res Inst Ind Comp & Automat Camino VeraSn Valencia 46022 Spain

ISBN: (纸本)9783031820724;9783031820731

This paper explores intelligent traffic light management advancements, focusing on controlling intersection traffic opening times. The decision-making process is influenced by factors such as traffic density. The information for these decisions is gathered from sensors placed on the streets, whose accuracy can vary. data collected are processed to aid control agents in decision-making. The paper proposes an intersection control algorithm that operates under the assumption of lacking sensorisation. To balance raw sensor data, control nodes implement a reinforced learning algorithm to select the most suitable combination of sensors to improve traffic parameters. The paper also introduces a method for calculating traffic density by combining sensors with imprecise data. This research contributes to intelligent traffic management by providing a novel approach to intersection control and traffic density calculation.

关键词： intelligent Traffic Light Management Reinforced learning Algorithm Traffic Density Calculation

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Towards Reliable Drift Detection and Explanation in Text data 25th

Towards Reliable Drift Detection and Explanation in Text Dat...

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25th international conference on intelligent data engineering and automated learning

作者： Feldhans, Robert Hammer, Barbara Bielefeld Univ Bielefeld Germany

ISBN: (纸本)9783031777301;9783031777318

When delivered to the market, machine learning models face new data which are possibly subject to novel characteristics - a phenomenon known as concept drift. As this might lead to performance degradation, it is necessary to detect such drift and, if required, adapt the model accordingly. While a variety of drift detection and adaptation methods exists for standard vectorial data, a suitable treatment of text data is less researched. In this work we present a novel approach which detects and explains drift in text data based on their representation via transformer embeddings. In a nutshell, the method generates suitable statistical features from the original distribution and the possibly shifted variation. Based on these representations, drift scores can be assigned to individual data points, allowing a visualization and human-readable characterization of the type of drift. We demonstrate the approach's effectiveness in reliably detecting drift in several experiments.

关键词： Drift Explanation Text data Transformer Visualization

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structural and Semantic data Layers in Time Series Analyses 25th

Structural and Semantic Data Layers in Time Series Analyses

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25th international conference on intelligent data engineering and automated learning

作者： Grass, Alexander Beecks, Christian Decker, stefan Fraunhofer Inst Appl Informat Technol FIT St Augustin Germany Rhein Westfal TH Aachen Aachen Germany Univ Hagen Hagen Germany

ISBN: (纸本)9783031777301;9783031777318

Understanding the inherent complexity of temporal data is crucial for effective time series analytics. One dimension of complexity is the level of structural depth at which analysis methods operate. These levels range from entire time series collections down to individual sequences of reduced dimensionality and length. Complementary to this type of complexity, is the quantity and expressiveness of knowledge associated with time series data, including labels and other features that provide valuable information. Both, the structural as well as the semantic layer, define the suitability and effectiveness of different analysis methods. In this paper we introduce a conceptual framework to support the automated selection of analytical time series approaches. To this end, we specify a context-free grammar to describe hierarchies and compositions of time series data, while also defining different classes of semantic information, resulting in a data-specific classification of time series analysis methods. Along with a demonstration via concrete examples, we provide a discussion on challenges, opportunities and future work associated with the proposed approach.

关键词： Time Series data Analytics Knowledge Discovery

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Federated learning with Discriminative Naive Bayes Classifier 25th

Federated Learning with Discriminative Naive Bayes Classifie...

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25th international conference on intelligent data engineering and automated learning

作者： Torrijos, Pablo Alfaro, Juan C. Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha Inst Invest Informat Albacete Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informaticos Albacete 02071 Spain

ISBN: (纸本)9783031777370;9783031777387

Federated learning has emerged as a promising approach to train machine learning models on decentralized data sources while preserving data privacy. This paper proposes a new federated approach for Naive Bayes (NB) classification, assuming discrete variables. Our approach federates a discriminative variant of NB, sharing meaningless parameters instead of conditional probability tables. Therefore, this process is more reliable against possible attacks. We conduct extensive experiments on 12 datasets to validate the efficacy of our approach, comparing federated and non-federated settings. Additionally, we benchmark our method against the generative variant of NB, which serves as a baseline for comparison. Our experimental results demonstrate the effectiveness of our method in achieving accurate classification.

关键词： Federated learning Bayesian network classifiers Naive Bayes Discriminative learning

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data Analysis and Anomaly Detection in a Wind Farm with k-Nearest Neighbors 25th

Data Analysis and Anomaly Detection in a Wind Farm with k-Ne...

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25th international conference on intelligent data engineering and automated learning

作者： Weiss, Bassel Esteban, Segundo Santos, Matilde Univ Complutense Madrid Dpto Arquitectura Computadores Automat Madrid 28040 Spain UCM Inst Knowledge Technol Madrid 28040 Spain

ISBN: (纸本)9783031777370;9783031777387

This paper presents an in-depth analysis of data from the Alpha Ventus offshore wind farm, emphasizing the identification and detection of anomalies in wind turbine performance. Utilizing real-world data from the RAVE (Research at Alpha Ventus) project, we explore the complexities of offshore wind energy generation, including the effects of wind speed, nacelle position, and environmental factors on turbine behaviour. In this paper, among the various machine learning techniques, we have selected k-nearest neighbours (k-NN), to identify patterns and detect anomalies indicative of potential issues. Our findings demonstrate that some turbines of the wind farm, centrally located, are subject to significant wake effects and operational irregularities. By adjusting the parameters of the k-NN model, we achieved an anomaly detection framework, enhancing the reliability of turbine operation and maintenance.

关键词： Offshore wind energy Anomaly detection Wind turbine data analytics Machine learning K-nearest neighbours Predictive maintenance

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