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检索条件"任意字段=16th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2015"

共 86 条记录，以下是1-10 订阅

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16th international conference on intelligent data engineering and automated learning, ideal 2015

16th International Conference on Intelligent Data Engineerin...

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16th international conference on intelligent data engineering and automated learning, ideal 2015

the proceedings contain 64 papers. the special focus in this conference is on intelligent data engineering and automated learning. the topics include: data streams fusion by frequent correlations mining;web genre classification via hierarchical multi-label classification;multi-agent reinforcement learning for control systems: challenges and proposals;optimal filtering for time series classification;managing monotonicity in classification by a pruned random forest;ensemble selection based on discriminant functions in binary classification task;an extension of multi-label binary relevance models based on randomized reference classifier and local fuzzy confusion matrix;fusion of self-organizing maps with different sizes;a particle swarm clustering algorithm with fuzzy weighted step sizes;a bacterial colony algorithm for association rule mining;information retrieval and data forecasting via probabilistic nodes combination;knowledge discovery in enterprise databases for forecasting new product success;deterministic extraction of compact sets of rules for subgroup discovery;variable transformation for granularity change in hierarchical databases in actual data mining solutions;an empirical evaluation of robust Gaussian process models for system identification;throughput analysis of automatic production lines based on simulation methods;study of collective robotic tasks based on the behavioral model of the agent;minimalist artificial eye for autonomous robots and path planning;intelligent automated design of machine components using antipatterns;application of fuzzy logic controller for machine load balancing in discrete manufacturing system;local search based on a local utopia point for the multiobjective travelling salesman problem;a hybrid programming framework for resource-constrained scheduling problems and building an efficient evolutionary algorithm for forex market predictions.

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Pipeline for Semantic Segmentation of Large Railway Point Clouds 25th

Pipeline for Semantic Segmentation of Large Railway Point Cl...

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25th international conference on intelligent data engineering and automated learning

作者： Gabrielidis, Hugo Gatti, Filippo Vialle, Stephane Univ Paris Saclay LMPS Lab Mecan Paris Saclay UMR 9026 CNRS Cent SupelecENS Paris Saclay F-91190 Gif Sur Yvette France Univ Paris Saclay LISN Lab Interdisciplinaire Sci Numer CNRS F-91405 Orsay France Cent Supelec F-91192 Gif Sur Yvette France

ISBN: (纸本)9783031777301;9783031777318

this paper presents a novel deep-learning pipeline to segment large railway datasets with minimal manual annotation, notoriously time consuming. the pipeline adapts DINOv2 [11] for labeling point clouds, with tailored self-distillation pre-training and fine-tuning. the adopted transformer architecture successfully generalizes to multiple railway datasets, with a lightweight pipeline that outperforms manual labeling speed by a factor of 6, despite requiring a final segmentation check and correction. this groundbreaking achievement bridges the gap between the need for annotated point clouds in railway industry and the lack of publicly available annotated datasets.

关键词： 3D Point Cloud Deep learning Semantic Segmentation Large Scale data

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Automatic Classification of Signal and Noise in Functional Magnetic Resonance Imaging Scans Using Convolutional Neural Networks 25th

Automatic Classification of Signal and Noise in Functional M...

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25th international conference on intelligent data engineering and automated learning

作者： Arighelescu, Georgian Chira, Camelia Mansson, Kristoffer N. T. Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania Babes Bolyai Univ Dept Clin Psychol & Psychotherapy Cluj Napoca Romania Karolinska Inst Ctr Psychiat Res Dept Clin Neurosci Stockholm Sweden

ISBN: (纸本)9783031777301;9783031777318

the integration of Artificial Intelligence (AI), particularly deep learning models like VGG16 and ResNet50, in the analysis of functional magnetic resonance imaging (fMRI) data has significantly advanced our understanding of brain functionality and the diagnosis of neurological disorders. this paper explores the application of Convolutional Neural Networks (CNNs) to enhance the accuracy and efficiency of fMRI data analysis, addressing challenges such as high dimensionality, noise, and the need for complex data preprocessing. Our study evaluates the performance of VGG16 and ResNet50 models and one 3D CNN on 2D and 3D fMRI datasets, highlighting the limitations of VGG16 in handling 2D data and demonstrating the superior performance of ResNet50 on balanced and unbalanced datasets. Additionally, we investigate the impact of using 3D data from the Human Connectome Project (HCP), achieving up to 98% accuracy on the validation set. the results indicate that CNNs can effectively replace traditional Independent Component Analysis (ICA) methods by leveraging their capability for automatic feature extraction and end-to-end learning.

关键词： fMRI Artificial Intelligence Deep learning CNN VGG16 ResNet50

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Optimizing Vehicle Coordination at Multi-lane Intersections Using Traffic Control Algorithms 25th

Optimizing Vehicle Coordination at Multi-lane Intersections ...

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25th international conference on intelligent data engineering and automated learning

作者： Gonzalez, Cesar L. Delgado, Santiago L. Alberola, Juan Miguel Nino, Luis F. Julian, Vicente Univ Politecn Valencia Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain Univ Nacl Colombia Lab Invest Sistemas Inteligentes LISI Ingn Sistemas Ind Carrera 45 26-85 Bogota 111321 Colombia Univ ECCI Grp Invest SIGCIENTY Carrera 10 19-64 Bogota 110311 Colombia

ISBN: (纸本)9783031777370;9783031777387

intelligent Transportation Systems (ITS) encompass several strategies aimed at ensuring safe and efficient vehicle-to-vehicle interactions on roads. these strategies involve the implementation of advanced technologies, including intelligent traffic lights, wireless (V2X) communication systems, visual recognition systems, and algorithms. In this research, we introduce the implementation of a distributed algorithm tailored for managing highly complex road infrastructure, particularly intersections in urban areas with multiple lanes. Our evaluation of the system's performance involves applying a type of density distribution called "Biased" and comparing it against a traditional traffic control system (traffic lights - "Green Wave"). the inclusion of this type of density distribution aims to align our approach with real-world traffic conditions, specifically the variation in densities across different lanes. this comprehensive assessment allows for a more thorough evaluation of system performance.

关键词： Autonomous driving Distributed intersection management Multiple lanes intelligent transportation system Density distribution

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A Deep Multi-Task learning Network for Activity Recognition and User Identification Using Smartphone Sensors 16

A Deep Multi-Task Learning Network for Activity Recognition ...

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16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-than-ideal performance and restricted applicability. this research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. the CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. these features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. the approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-the-Art methods. this demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 the Author(s).

关键词： Convolutional neural networks

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data-Driven Simulation Model Generation for ERP and DES Systems Integration 16th

Data-Driven Simulation Model Generation for ERP and DES Syst...

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16th international conference on intelligent data engineering and automated learning (ideal)

作者： Krenczyk, Damian Bocewicz, Grzegorz Silesian Tech Univ Fac Mech Engn Gliwice Poland Koszalin Univ Technol Fac Elect & Comp Sci Koszalin Poland

ISBN: (纸本)9783319248349;9783319248332

In the paper the concept of data driven automatic simulation model generation method based on hybrid parametric-approach and data mapping and transformation methods in combination with concept of neutral data model is presented. As a key element of the proposed approach, author's own method of data transformation into internal programming languages script code, based on the transformation template is described. Developed simulation model generator is also an effective tool for the integration of ERP and DES systems. A practical implementation of the presented methodology - original software RapidSim is presented as well.

关键词： ERP data-driven Simulation Visualization data mapping data transformation Automatic model generation

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16th international conference on intelligent data engineering and automated learning (ideal)

作者： Ferchichi, Abdelwaheb Gouider, Mohamed Salah Ben Said, Lamjed Univ Tunis SOIE Ave Liberte Le Bardo 2000 Tunisia

ISBN: (纸本)9783319248349;9783319248332

Large amount of data generated by sensors, and increased use of privacy-preserving techniques have led to an increasing interest in mining uncertain data streams. Traditional distance measures such as the Euclidean distance do not always work well for uncertain data streams. In this paper, we present EVIDIST, a new distance measure for uncertain data streams, where uncertainty is modeled as sample observations at each time slot. We conduct an extensive experimental evaluation of EVIDIST (Evidential Distance) on the 1-NN classification task with 15 real datasets. the results show that, compared with Euclidean distance, EVIDIST increases the classification accuracy by about 13 % and is also far more resilient to error.

关键词： data mining Distance measure Similarity Uncertain data streams

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Information Retrieval and data Forecasting via Probabilistic Nodes Combination 16th

Information Retrieval and Data Forecasting via Probabilistic...

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16th international conference on intelligent data engineering and automated learning (ideal)

作者： Jakobczak, Dariusz Jacek Tech Univ Koszalin Dept Elect & Comp Sci Koszalin Poland

ISBN: (纸本)9783319248349;9783319248332

Proposed method, called Probabilistic Nodes Combination (PNC), is the method of 2D data interpolation and extrapolation. Nodes are treated as characteristic points of information retrieval and data forecasting. PNC modeling via nodes combination and parameter. as probability distribution function enables 2D point extrapolation and interpolation. Two-dimensional information is modeled via nodes combination and some functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function. Extrapolated values are used as the support in data forecasting.

关键词： Information retrieval data extrapolation Curve interpolation PNC method Probabilistic modeling Forecasting

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Knowledge Discovery in Enterprise databases for Forecasting New Product Success 16th

Knowledge Discovery in Enterprise Databases for Forecasting ...

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16th international conference on intelligent data engineering and automated learning (ideal)

作者： Relich, Marcin Bzdyra, Krzysztof Univ Zielona Gora Fac Econ & Management Zielona Gora Poland Koszalin Univ Technol Fac Elect & Comp Engn Koszalin Poland

ISBN: (纸本)9783319248349;9783319248332

this paper presents the knowledge discovery process that aims to improve the forecast quality of the success of new product development projects. the critical success factors for new product development are identified on the basis of information acquired from an enterprise system, including the fields of sales and marketing, research and development, production, and project management. the proposed knowledge discovery process consists of stages such as data selection from enterprise databases, data preprocessing, data mining, and the use of the discovered patterns for forecasting new product success. the illustrative example presents the use of fuzzy neural networks for forecasting net profit from new products.

关键词： Knowledge retrieval data mining Fuzzy neural systems New product development Rule-based systems

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the Belief theory for Emotion Recognition 16th

The Belief Theory for Emotion Recognition

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16th international conference on intelligent data engineering and automated learning (ideal)

作者： Mhamdi, Halima Jarray, Hnia Bouhlel, Med Salim Sfax Univ UR SETIT Rd Soukra Km 4 Sfax 3038 Tunisia Gabes Univ Higher Inst Comp & Multimedia Gabes Gabes 6033 Tunisia

ISBN: (纸本)9783319248349;9783319248332

this paper presents a facial expression classification system based on a data fusion process using the theory of belief. Such expressions correspond to the six universal emotions (happiness, surprise, disgust, sadness, anger, and fear) as well as the neutral expression. the suggested algorithm rests on the decision fusion of both approaches: the global analysis and the local analysis of facial components. the classification result, throughout these two approaches, will be enhanced by fusion. the performance and the limitations of the recognition system and its ability to deal with different databases are identified through the analysis of a large number of results on the FEEDTUM database.

关键词： data-fusion the belief theory Emotions classification Local analysis Global analysis

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