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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024"

共 168 条记录，以下是31-40 订阅

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Cooperative-Competitive Decision-Making in Resource Management: A Reinforcement learning Perspective 25th

Cooperative-Competitive Decision-Making in Resource Manageme...

引用

25th international conference on intelligent data engineering and automated learning

作者： Isakov, Artem Peregorodiev, Danil Brunko, Pavel Tomilov, Ivan Gusarova, Natalia Vatian, Alexandra ITMO Univ St Petersburg 197101 Russia Almazov Natl Med Res Ctr World Class Res Ctr Personalized Med St Petersburg 194156 Russia

ISBN: (纸本)9783031777301;9783031777318

this study presents a multi-agent approach to modeling deci-sion-making processes for cooperative-competitive game scenarios generated by long-tailed distributions. We introduce a novel simulation environment that aims to replicate real-world resource allocation challenges, particularly in healthcare settings. the environment incorporates dynamic changes in agents' perceptions and utilizes heavy-tailed distributions to model unpredictable events. We adapt the Multi-Agent Deep Deterministic Policy Gradient algorithm to this environment, demonstrating its effectiveness in learning and strategy development. Our approach includes a role-based system and a two-component reward function that guides agents through the learning process. the results, evaluated using metrics such as cumulative reward, average episodic reward, and approximate Kullback-Leibler divergence, indicate successful learning and sufficent agent strategies. this research contributes to the field of multi-agent systems and resource management by providing a framework that can generalize learned policies to varying numbers of agents and resources, making it applicable to real-world scenarios with changing conditions.

关键词： Multi-agent systems Human-in-the-loop Reinforcement learning Resource allocation Cooperative-competitive games Heavy-tailed distribution

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Towards Sustainable Precision: Machine learning for Laser Micromachining Optimization 25th

Towards Sustainable Precision: Machine Learning for Laser Mi...

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25th international conference on intelligent data engineering and automated learning

作者： Correas-Naranjo, Luis Camacho-Sanchez, Miguel Launet, Laetitia Zuric, Milena Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Ctr Ser Humano HUMAN Tech Valencia Spain Fraunhofer Inst Laser Technol ILT Steinbachstr 15 D-52074 Aachen Germany

ISBN: (纸本)9783031777301;9783031777318

In the pursuit of sustainable manufacturing, ultra-short pulse laser micromachining stands out as a promising solution while also offering high-precision and qualitative laser processing. However, unlocking the full potential of ultra-short pulse lasers requires an optimized monitoring system capable of early detection of defective workpieces, regardless of the preprocessing technique employed. While advances in machine learning can help predict process quality features, the complexity of monitoring data necessitates reducing both model size and data dimensionality to enable real-time analysis. To address these challenges, this paper introduces a machine learning framework designed to enhance surface quality assessment across diverse preprocessing techniques. To facilitate real-time laser processing monitoring, our solution aims to optimize the computational requirements of the machine learning model. Experimental results show that the proposed model not only outperforms the generalizability achieved by previous works across diverse preprocessing techniques but also significantly reduces the computational requirements for training. through these advancements, we aim to establish the baseline for a more sustainable manufacturing process.

关键词： Laser Processing Laser Structuring Process Monitoring Machine learning Sustainable Manufacturing

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Prediction Of Power Demand And Factor Identification Using Sparse Modeling AI With Ensemble learning For Improvement Of Accuracy 13

Prediction Of Power Demand And Factor Identification Using S...

引用

Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Hachiya, Tatsuma Kamei, Keiji Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Nishinippon Institute of Technology Faculty of Engineering Fukuoka Japan

ISBN: (纸本)9798350373332

We have been using sparse modeling AI to identify factors and predict in power demand. In this research, we applied ensemble learning to further improve the accuracy by integrating the results of multiple models. Experimental results showed that sparse modeling AI identified the demand factors, and ensemble learning achieved an average absolute prediction error of 7.5%. © 2024 IEEE.

关键词： Demand response

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Dissecting data Practices in Android Apps: A Comparative Study of data Collection and Sharing Behaviors 25th

Dissecting Data Practices in Android Apps: A Comparative Stu...

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25th international conference on intelligent data engineering and automated learning

作者： Nguyen, Triet M. Pham, Nghiem T. Doan, Hieu M. Tran, Khoa D. Tran, Bao Q. Huynh, Khiem G. Tran, Nam B. Vo, Khanh H. FPT Univ Can Tho Vietnam

ISBN: (纸本)9783031777301;9783031777318

the exponential growth of mobile apps within the Android ecosystem has underscored the critical need for robust user privacy and data protection measures. Central to these concerns are the privacy policies that serve as the primary channel of communication between organizations and users, detailing data collection, utilization, and sharing practices. However, the efficacy of these policies is often undermined by their inaccessibility and the legalese that obfuscates their intent, presenting a barrier to informed user consent. this study addresses these challenges by harnessing the capabilities of Generative AI (GenAI) to perform a detailed analysis of data practices in Android apps. Our methodology extends beyond the traditional scope of AI-assisted analysis by not only identifying third-party entities but also by elucidating their data handling purposes. We introduce a classification system that distinguishes between 'Regular' and 'Irregular' app behaviors, offering a benchmark for app evaluation and compliance assessment. Our comparative analysis across various apps reveals patterns and anomalies in data management, providing actionable insights for developers, regulators, and users.

关键词： Android Apps data Collection data Sharing Privacy Policy data Usage Behavior Mobile Privacy

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Drowsiness Detection Using Vital Sign Sensors and Deep learning on Smartwatches 25th

Drowsiness Detection Using Vital Sign Sensors and Deep Learn...

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25th international conference on intelligent data engineering and automated learning

作者： da Rosa Pereira, Vitor Augusto Berri, Rafael Alceste Osorio, Fernando Santos Univ Sao Paulo BR-13566590 Sao Carlos Brazil Fed Univ Rio Grande BR-96203900 Rio Grande RS Brazil

ISBN: (纸本)9783031777301;9783031777318

Drowsy drivers contribute to high rates of road accidents, leading to numerous fatalities and injuries. the dangers of drowsiness extend beyond the roads, affecting workplaces that require continuous attention and focus. this work aims to develop a non-intrusive, real-time system capable of assessing the user's drowsiness level by processing physiological signals collected by smartwatch sensors. the study utilized photoplethysmography (PPG) and heart rate data from a smartwatch, labeled with an adapted Karolinska Sleepiness Scale (KSS). Deep learning techniques were used to fine-tune hyperparameters and train a 1-dimensional Convolutional Neural Network to reach better prediction performance on drowsiness data. Besides, a 5-fold cross-validation technique was used to evaluate performance and create an ensemble, enhancing prediction robustness through election method. the results show significant prediction accuracy using the ensemble on test data (91.41%). the final pre-trained model was integrated into the smartwatch, creating a real-time drowsiness detection system. this system alerts the user through sound and vibration feedback, preventing them from falling asleep and reducing the risk of accidents.

关键词： Drowsiness Smartwatch Photoplethysmography (PPG) Artificial neural networks Deep learning Ensemble model

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data Mining in Credit Card Approval: Feature Importance Testing Comparison 25th

Data Mining in Credit Card Approval: Feature Importance Test...

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25th international conference on intelligent data engineering and automated learning

作者： Ye, Qingyu Fong, Simon Yu, Jiahui Tallon-Ballesteros, Antonio J. Univ Macau Dept Comp & Informat Sci Zhuhai Macau Peoples R China Univ Huelva Dept Elect Engn Comp Syst & Automat Huelva Spain

ISBN: (纸本)9783031777370;9783031777387

Understanding the significance of features in data mining is crucial for accurately analyzing customer behavior, constructing reliable credit scoring models, and detecting fraud within the credit card approval process. this paper explores the application of data mining techniques in the credit industry, with a specific focus on credit card approval classification. We investigate seven feature importance testing techniques and three classification methods, assessing their performance through various metrics. the research demonstrates that FLOFO with linear regression and ShapFlex with agnostic causal relations substantially improve the performance of all classifiers, with SVM emerging as the most effective classifier across all feature selection techniques. Feature importance testing is pivotal as it not only enhances model accuracy but also provides deeper insights into the factors driving credit card approval decisions. the findings underscore the essential role of data mining in financial risk analysis and credit approval processes, offering valuable perspectives for advancing research and practices in financial technology. the results emphasize the potential of specific feature importance testing techniques and classificationmethods in refining credit card approval classification tasks.

关键词： Feature Importance data Mining Credit Card Approval Classification Methods Financial Risk Analysis

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Deep learning Inference on Edge: A Preliminary Device Comparison 25th

Deep Learning Inference on Edge: A Preliminary Device Compar...

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25th international conference on intelligent data engineering and automated learning

作者： Gonzalez, Manuel L. Ruiz, Jorge Andres, Lidia Lozada, Randy Skibinsky, Erik S. Fernandez, Jorge Sedano, Javier Garcia-Vico, Angel M. Inst Tecnol Castilla & Leon Burgos Spain Univ Jaen Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain

ISBN: (纸本)9783031777301;9783031777318

the deployment and inference of Deep learning models are investigated in this article on seven different Edge Computing (EC) devices. this study addresses the features, performance and limitations of the NVIDIA Jetson Orin NX and Nano, Google Coral DevBoard and USB, Intel Neural Compute Stick 2, NXP ***8 Plus and Xilinx Zynq UltraScale+ MPSoC ZCU104 on Deep learning inference. Fully Connected, Convolutional and Long Short-Term Memory (LSTM) neural networks are implemented to test these EC devices. the benchmarking focuses on the performance metrics: inference latency, increase in error metric, and power consumption. the results show considerable variability among devices, with the ZCU104 and Jetson Orin achieving the lowest latencies across most models without any increase in the error metric. At the same time, Coral devices exhibit increased latency and error for complex convolutional models. NVIDIA Jetson devices, ZCU104 and Neural Compute Stick 2 are the only devices that support LSTM inference. the study also highlights differences in power consumption, with USB accelerators being the most energy-efficient.

关键词： Edge Computing Deep learning Real-Time Inference

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A Divide-and-Conquer Approach for Container License Plate Detection Using Multi-frame Analysis 25th

A Divide-and-Conquer Approach for Container License Plate De...

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25th international conference on intelligent data engineering and automated learning

作者： Geirinhas, Joao Pedro Estima, Jacinto Silva, Catarina Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9783031777370;9783031777387

the surge in global trade necessitates enhanced efficiency in logistics operations, particularly in container management. Current manual identification methods for container license plates and labels are prone to errors, causing significant delays and increased emissions. this paper presents a divide-and-conquer approach for detecting container license plates, distinguishing between the identification of numbers and letters, and leveraging multiple video frames to improve detection performance. We designed a set of experiments to define which models are optimized for each task, namely we assessed the performance of models to identify the whole license plate and models specifically for numbers and letters. Results on a real dataset of shipping containers entering dry ports show that the divide-and-conquer approach surpasses the holistic approach. Additionally, experiments show that for different contrasting colors analyzing multiple frames can improve the identification performance.

关键词： Container logistics License plate recognition Deep learning Logistics supply chain Dry ports

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A Methodology for automated Conversion of Axis-Aligned to Polygonal and Oriented Bounding Box Annotations in Aerial Imagery Object Detection 25th

A Methodology for Automated Conversion of Axis-Aligned to Po...

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25th international conference on intelligent data engineering and automated learning

作者： Freire, Daniela L. de Carvalho, Andre C. P. L. F. Peterlevitz, Augusto Jose Chinelatto, Mateus Antonio da Silva, Ricardo Dutra Rojas Perea, Juan Fernando Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil Inst Pesquisas Eldorado Campinas Brazil Transmissora Alianca Energia Elet SA Belo Horizonte MG Brazil

ISBN: (纸本)9783031777370;9783031777387

Object detection in aerial imagery presents significant challenges in computer vision due to the varied orientations and complex backgrounds of objects such as buildings and vehicles. Current annotation tools often fail to accurately delineate these objects, relying on manual bounding box methods that are both time-consuming and inconsistent. Our novel methodology automates the conversion of axis-aligned annotations into polygonal and rotated annotations, prioritising systematic and scalable enhancements to data quality rather than modifying the model itself. Precise annotations, crucial for determining object locations and boundaries, are fundamental to this approach. We evaluated this methodology through a case study involving electrical transmission towers in aerial images, using advanced object detectors based on variations of the YOLOv8 algorithm. Preliminary results indicate that our automated method not only improves annotation accuracy but also significantly reduces the manual effort required, thereby lowering overall costs and time for data preparation in object detection training. the success of this methodology underscores its potential for broader applications and further advancements in automated annotation technologies.

关键词： Object Detection Image Segmentation Machine learning YOLO Model Segment Anything Model Oriented Bounding Box Polygonal Annotation Annotation Conversion

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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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