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检索条件"任意字段=8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007"

共 204 条记录，以下是21-30 订阅

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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 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...

引用

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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CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care data Interoperability 25th

CSSDH: An Ontology for Social Determinants of Health to Oper...

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25th international conference on intelligent data engineering and automated learning

作者： Das, Subhashis Naskar, Debashis Rodriguez Gonzalez, Sara Univ Salamanca Dept Comp Sci & Automat BISITE Res Grp Salamanca 37007 Spain

ISBN: (纸本)9783031777301;9783031777318

the rise of digital platforms has led to an increasing reliance on technology-driven, home-based healthcare solutions, enabling individuals to monitor their health and share information with healthcare professionals as needed. However, creating an efficient care plan management system requires more than just analyzing hospital summaries and Electronic Health Records (EHRs). Factors such as individual user needs and social determinants of health, including living conditions and the flow of healthcare information between different settings, must also be considered. Challenges in this complex healthcare network involve schema diversity (in EHRs, personal health records, etc.) and terminology diversity (e.g., ICD, SNOMED-CT) across ancillary healthcare operations. Establishing interoperability among various systems and applications is crucial, with the European Interoperability Framework (EIF) emphasizing the need for patient-centric access and control of healthcare data. In this paper, we propose an integrated ontological model, the Common Semantic data Model for Social Determinants of Health (CSSDH), by combining ISO/DIS 13940:2024 ContSys with WHO Social Determinants of Health. CSSDH aims to achieve interoperability within the Continuity of Care Network.

关键词： Continuity of care EHR OWL Social Determinants of Health

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Providing Informative Feedback in a Low-Cost Rehabilitation System Using Machine learning 25th

Providing Informative Feedback in a Low-Cost Rehabilitation ...

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25th international conference on intelligent data engineering and automated learning

作者： Rodrigues, Paul Amorim, Ivone Cunha, Bruno Univ Porto FEUP Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal Polytech Porto IPP PORTIC Porto Res Technol & Innovat Ctr P-4200374 Porto Portugal Portucalense Univ Res Econ Management & Informat Technol REMIT Rua Dr Antonio Bernardino Almeida P-4200072 Porto Portugal

ISBN: (纸本)9783031777370;9783031777387

Rehabilitation is a core process in helping people recover from a wide range of health issues, including injuries and diseases. Although advancements in technology and the use of artificial intelligence have facilitated the development of tools to aid in rehabilitation processes, there is a lack of low-cost solutions that patients, without requiring advanced care, can use at home. In this work, we propose a low-cost intelligent system for lower limb rehabilitation that uses machine learning to provide informative feedback to users. Compared to existing solutions, our system offers the advantage of real-time feedback, informing patients whether they are performing exercises correctly. It also suggests posture corrections to prevent injuries and accelerate the recovery process. Moreover, our system can be used at home on a smartphone, tablet, or personal computer, and does not require patients to purchase additional devices, which is a significant benefit. the system includes four exercises: Squat, Romanian Deadlift, Glute Bridge, and Donkey Kick. Validation tests with end-users reinforced the usability of this system and confirmed the importance of real-time feedback. the results were also useful for identifying areas for improvement, particularly with the Squat exercise, which is among the more challenging exercises to perform correctly.

关键词： Rehabilitation Machine learning Real-time feedback Lower limb

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Analysis and prediction model of college sports learning Performance based on Big data Collaborative filtering 8

Analysis and prediction model of college sports Learning Per...

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8th international conference on Information Systems engineering, ICISE 2023

作者： Zhang, Qiqi Wuhan Donghu University Department of Physical Education Wuhan City430212 China

ISBN: (纸本)9798350307009

With the expansion of the scale of higher education and the deepening of reform, it is necessary to adopt intelligent technology to improve the efficiency of teaching quality management in colleges and universities. Aiming at the problems of difficulty in statistical analysis of physical education teaching results, difficulty in dynamically grasping students' learning status, and difficulty in accurately predicting grades to evaluate teaching quality, this paper, based on big data technology, uses behavioral data inside and outside physical education classroom in colleges and universities to make dynamic correction, and analyzes the distribution characteristics of grades and students' learning status. KM-RF algorithm model is used to build a set of numerical prediction model of grades, and sample data is used to train the model. the results of the comparison between the predicted results and the actual results show that the error of the predicted data of this model is in a relatively ideal range and has a high precision. © 2023 IEEE.

关键词： Collaborative filtering

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Exploring data Symbion EI Deep learning and Model Sharing Modules 25th

Exploring Data Symbion EI Deep Learning and Model Sharing Mo...

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25th international conference on intelligent data engineering and automated learning

作者： Huszcza, Rafael Mendes, Amanda Lopes, Jeferson Borges, Eduardo N. Lucca, Giancarlo B. Guilherme, Pablo D. B. Pereira, Leandro A. Univ Fed Rio Grande FURG Ctr Ciencias Computac C3 Rio Grande Brazil Univ Catolica Pelotas Ctr Ciencias Sociais & Tecnol Mestrado Engenharia Eletron & Computacao Pelotas RS Brazil Univ Estadual Parana UNESPAR Ctr Educ Humanidades Ciencias Biolog Paranagua Brazil Inst Fed Parana IFPR Eixo Tecnolog Meio Ambiente Paranagua Brazil

ISBN: (纸本)9783031777370;9783031777387

Considering the growing volume of available information, storing and managing data is an increasing challenge. Leveraging these raw datasets to extract meaningful knowledge and make informed decisions requires data mining methods, especially when combined with Machine learning algorithms to uncover hidden patterns in data. Considering this context, this work describes a project to contribute to datadriven initiatives by seamlessly integrating a data repository system with Machine learning (ML) tools. this integration allows users to implement pipelines within a single system-from storing and managing datasets to training and deploying ML models, making easier, more efficient, and intuitive data science workflow accessible to all, not just computer scientists. Furthermore, the system aims to enhance knowledge sharing among researchers and practitioners by enabling users to share datasets and trained machine learning models within the platform. this work highlights the system's key features, particularly the Machine learning module, which includes classic ML algorithms for tabular data and the newly introduced Deep learning algorithms for image classification. the new Model Sharing module also emphasizes promoting collaboration by allowing users to share and manage trained models effectively.

关键词： data Repository Machine learning Deep learning Model sharing data Symbion EI

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8th international conference on intelligent Transport Systems, INTSYS 2024

8th International Conference on Intelligent Transport System...

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8th international conference on intelligent Transport Systems, INTSYS 2024

ISBN: (纸本)9783031863691

the proceedings contain 25 papers. the special focus in this conference is on intelligent Transport Systems. the topics include: Interoperable Traceability in Supply Chains: A Use Case in Agritech;treemob: Expressive Mobility data Representation through Tree-Based Structures;From GPS Traces to Individual Emission Exposure: A data-Driven Four-Step Process;capacity Vehicle Routing Problem with Time Windows: Simulation Tool for Footprint Network Design;chain of Portable Health Folders: A Systematic Literature Review;integrating Metro Infrastructure in Circular Food Supply Chains: A Model for Decentralized Quito’s Food Bank Network Redesign;An AutoML Approach for Bike Demand Forecasting and Redistribution;adaptive Stop-Skipping Scheduling Approach Using Reinforcement learning;machine learning Approach for Labeling Undetected Planned Trips in Public Transport Operators;reinforcement learning Algorithms with Graph Convolution Networks for Traffic Signal Control;optimizing intelligent Transportation Systems with Multi-agent Reinforcement learning: A Socio-economic Impact Assessment;a Mobile Application to Secure Pedestrians Interacting with automated Vehicles;A Simulation-Based Security Benchmarking Approach for Assessing Cooperative Driving Automation (CDA) Applications;wireless Interference and Regulatory Frameworks for Frequency Allocation in V2X Communication Systems;runtime Norms Regulation Framework for Drones’ Smart Cities Applications;a data-Driven Integrated Framework for Virtual Testing of Autonomous Vehicles in Mixed Traffic Scenarios;federated learning for Lane-Change Prediction;Evaluating Traffic Control Strategies for Autonomous Shuttle in Different AV Penetration, Using SUMO Traffic Simulation;Adaptive Video Bitrate Allocation for Remotely Operated Vehicles (ROV);experimental Evaluation of Road-Crossing Decisions by Autonomous Wheelchairs Against Environmental Factors;impact of Network Delays on Edge-Assisted Platooning Systems in 5G Networks: Addressing Lat

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Cluster Head Selection and Cluster Construction for Federated learning in Mobile Ad Hoc Networks 8

Cluster Head Selection and Cluster Construction for Federate...

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8th international conference on intelligent Computing and Signal Processing, ICSP 2023

作者： Mei, Linsheng Meng, Xiaowei Yi, Yibo Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

ISBN: (纸本)9798350302455

Federated learning (FL), as a distributed learning framework, which can effectively protect user privacy and address data imbalance issues. In this paper, we investigate cluster head (CH) selection and client clustering issues based on hierarchical FL framework in mobile ad hoc networks (MANETs), wherein all the clients are randomly distributed without infrastructure support. Specifically, we formulate the joint client association and CH selection problem, with the target of minimizing the training latency while maximizing the system lifetime. Furthermore, we propose a heuristic algorithm for client association and an entropy weight Technique for Order Preference by Similarity to an ideal Solution (TOPSIS) method for CH selection. Simulation results demonstrate that our proposed algorithms can effectively shorten training latency and extend lifetime compared with other schemes. © 2023 IEEE.

关键词： Heuristic algorithms

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Refining Multiple Instance learning with Attention Regularization for Whole Slide Image Classification 25th

Refining Multiple Instance Learning with Attention Regulariz...

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25th international conference on intelligent data engineering and automated learning

作者： Carretero, Ilan Meseguer, Pablo del Amor, Rock Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Centrada Ser Humano HUMAN Valencia 46022 Spain Valencian Grad Sch & Res Network Artificial Intel Valencia 46022 Spain

ISBN: (纸本)9783031777301;9783031777318

Histopathological analysis of biopsy sections is crucial for the detection of cancer and the distinction between different tumor subtypes. To this end, pathologists identify certain key regions of the biopsy from which a diagnosis is derived. the classification of whole slide images (WSIs) can be addressed as a multiple instance learning (MIL) problem where only slide-level labels are available. In order to model the relevance scores of the different WSI patches, attention mechanisms are implemented within the MIL framework. However, excessive flexibility in the attention mechanisms for computing attention scores to patches may lead to nearly uniform attention distributions, potentially deteriorating the model's performance. In this paper, we introduce an unsupervised auxiliary loss function to recalibrate the attention mechanism enhancing emphasis on crucial patches and downscaling the influence of less relevant ones. the proposed MIL framework has been evaluated on invasive breast carcinoma (TCGA-BRCA) and renal cell carcinoma (TCGA-RCC) subtyping. the results obtained show that attention regularization not only improves the predictive capacity of the model but also significantly increases its interpretability by identifying regions and patterns of high diagnostic value.

关键词： Hispathology imaging Multiple Instance learning Attention mechanisms Attention regularization Explainable Deep learning

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