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检索条件"任意字段=2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021"

共 1034 条记录，以下是1-10 订阅

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icmlca 2021 - 2nd international conference on machine learning and computer application

ICMLCA 2021 - 2nd International Conference on Machine Learni...

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2021 2nd international conference on machine learning and computer application, icmlca 2021

ISBN: (纸本)9783800757398

The proceedings contain 235 papers. The topics discussed include: real-time age estimation system based on convolutional neural network;Siamese network based target-aware pixel-wise cross-correlation for aerial tracking;computer simulation study of kinetic energy projectiles penetrating concrete targets;research on automatic sentiment analysis of text movie reviews with machine learning methods;a pipeline dialogue system scheme;experimental research on the influence of multi-channel interaction mode on information reporting task;generative adversarial networks used in color modulation;customer loan risk prediction based on decision tree;end-to-end text recognition model based on handwritten Chinese characters;design and development a 3D adventure puzzle game using machine learning concept;sentiment analysis of Tibetan commentary based on hybrid neural network model;how to use machine learning to find alpha in stock market;and an improved multi-target tracking method integrating visual tracking mechanism.

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5th Asia-Pacific Web and Web-Age Information Management international Joint conference on Web and Big Data, APWeb-WAIM 2021, 4th international Workshop on Knowledge Graph Management and applications, KGMA 2021, 3rd international Workshop on Semi-structured Big Data Management and application, SemiBDMA 2021, and 2nd international Workshop on Deep learning in Large-scale Unstructured Data Analytics, DeepLUDA 2021

5th Asia-Pacific Web and Web-Age Information Management Inte...

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5th Asia-Pacific Web and Web-Age Information Management international Joint conference on Web and Big Data, APWeb-WAIM 2021, 4th international Workshop on Knowledge Graph Management and applications, KGMA 2021, 3rd international Workshop on Semi-structured Big Data Management and application, SemiBDMA 2021, and 2nd international Workshop on Deep learning in Large-scale Unstructured Data Analytics, DeepLUDA 2021

ISBN: (纸本)9789811681424

The proceedings contain 11 papers. The special focus in this conference is on Web and Big Data. The topics include: Survival Effect of Internet Macroscopic Topology Evolution;the Method for Image Noise Detection Based on the Amount of Knowledge Associated with Intuitionistic Fuzzy Sets;link Prediction of Heterogeneous Information Networks Based on Frequent Subgraph Evolution;memory Attentive Cognitive Diagnosis for Student Performance Prediction;A Study on the Privacy Threat Analysis of PHI-Code;preface;should I Stay or Should I Go: Predicting Changes in Cluster Membership;logistics Policy Evaluation Model Based on Text Mining;MRRVOS: Modular Refinement Referring Video Object Segmentation.

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Using Data Analytics, machine learning, and KPIs for Urban Planning and Design 2nd

Using Data Analytics, Machine Learning, and KPIs for Urban...

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2nd Decision Science Alliance international Summer conference, DSA ISC 2024

作者： Tsertsvadze, Veronika Ammouriova, Majsa Fernandez, Trinidad Kapetas, Leon Universitat Politècnica de València Ferrandiz-Carbonell Alcoy03801 Spain German Jordanian University Amman11180 Jordan Fraunhofer Institute for Industrial Engineering IAO Nobelstrasse 12 Stuttgart70569 Germany Resilient Cities Network Korte Hoogstraat 31 Rotterdam3011 GK Netherlands

ISBN: (纸本)9783031782374

Worldwide initiatives aim to facilitate urban transitions toward climate neutrality objectives through diverse urban planning and design methodologies. This paper focuses on approaches that integrate empirically-grounded tools to guide stakeholders and local authorities in achieving climate neutrality targets. Within this context, the paper explores the utilization of data analytics, machine learning, and key performance indicators for informed decision-making in urban planning and design. The paper explores some key performance indicators, in the context of sustainable development goals, recorded for European countries and analyses them utilizing machine learning techniques. Accordingly, the main indicators are identified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sustainable city

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Estimating Solid Waste Generation in Tenerife by machine learning Models 2nd

Estimating Solid Waste Generation in Tenerife by Machine L...

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2nd Decision Science Alliance international Summer conference, DSA ISC 2024

作者： Corbea-Pérez, Alejandro Brito, Julio Moreno-Pérez, José Andrés Instituto Universitario de Desarrollo Regional Universidad de La Laguna Santa Cruz de Tenerife38206 Spain

ISBN: (纸本)9783031782374

Solid waste management is a major challenge in municipalities and cities due to population growth and consumption habits. Therefore, the need for efficient use of resources, based on a reliable estimate of the waste generated, is the reason why artificial intelligence systems are being increasingly used. This will result in appropriate planning, which is particularly important in island territories. In response to this problem, the purpose of this work is to implement and test models to predict the volumes of mixed waste and biodegradable waste in Tenerife, Spain, using linear regressions and different artificial intelligence methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Investigating the Transferability of Evasion Attacks in Network Intrusion Detection Systems Considering Domain-Specific Constraints 25th

Investigating the Transferability of Evasion Attacks in N...

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25th international conference on Information Security applications, WISA 2024

作者： Mbow, Mariama Roman, Rodrigo Seal, Ayan Wang, Kevin I-Kai Mohanty, Sraban Kumar Sakurai, Kouichi Department of Information Science and Technology Kyushu University Fukuoka Japan Lab University of Malaga Málaga Spain PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (纸本)9789819616237

Recently, adversarial attacks against machine learning-based network intrusion detection systems (NIDS) have gained significant attention in cybersecurity. This study investigates the transferability of these adversarial attacks in the context of NIDS. In addition, most existing studies in adversarial learning against NIDS adopted attack strategies designed originally for image classification without considering network traffic characteristics. However, this is impractical and will fail in the real world as network traffic features are constrained, and ignoring the functional behavior of the network traffic features leads to invalid network traffic flow or produces adversarial samples that do not retain their original functionality (malicious or benign). To address these issues, we propose a constrained momentum iterative fast gradient sign method (C-MIFGSM) to generate adversarial network flows that can successfully evade an ML-based IDS through transfer-based attacks while preserving the functional behavior of the network traffic. Our approach was validated using several target NIDS models built with the NSLKDD benchmark dataset. Experimental results demonstrate that even without knowledge of the target model and under feature constraints, it is possible to generate adversarial network traffic flows that achieve a high evasion attack success rate against NIDS built with deep learning and classical ML models. For example, the attack degraded the detection rate of the DoS traffic drops from 92.35% to 20.27% for the MLP model with an evasion increase rate of 78.04%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Value learning for Value-Aligned Route Choice Modeling via Inverse Reinforcement learning 2nd

Value Learning for Value-Aligned Route Choice Modeling via...

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2nd international Workshop on Value Engineering in Artificial Intelligence, VALE 2024

作者： Holgado-Sánchez, Andrés Bajo, Javier Billhardt, Holger Ossowski, Sascha Arias, Joaquín CETINIA Universidad Rey Juan Carlos Madrid Spain Departamento de Inteligencia Artificial ETSI Informáticos Universidad Politécnica de Madrid Campus de Montegancedo Madrid28660 Spain

ISBN: (纸本)9783031854620

Acquiring computational specifications of human values is key for building value-aligned and value-aware AI systems. We surveyed methods for learning ethical principles and value-aligned behaviour from human demonstrations or specifications of values. However, to our knowledge, no attempt has been proposed for learning these value specifications from demonstrations, while satisfying the possibly diverse value preferences of different agents. In this work we propose a novel value learning framework (agnostic of specific learning techniques) for (i) learning value groundings (specifications of given values) and (ii) identifying value systems (the particular value preferences of agents) from observed behavior in an application domain. We illustrate our framework in a route choice modeling scenario, using tailored inverse reinforcement learning algorithms. The results show that we can successfully learn value systems that are coherent with observed route choices. Our findings open up intriguing concerns and challenges for future research in the area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Overlapping Community Detection Using Dynamic Residual Deep GCN 2nd

Overlapping Community Detection Using Dynamic Residual Deep ...

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2nd international conference on Applied Algorithms, ICAA 2025

作者： Muttakin, Md. Nurul Hossain, Md. Iqbal Rahman, Md. Saidur Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh University of Arizona Tucson United States

ISBN: (纸本)9783031845420

Detecting overlapping communities is a significant challenge in unsupervised machine learning for network-structured data. While some researchers have applied graph convolutional networks (GCNs) to this problem, most existing GCNs are shallow, limiting their ability to detect communities with larger diameters. Furthermore, the integration of dynamic dilated aggregation into deep GCNs for irregular graphs remains an unresolved issue. To bridge these gaps, we introduce a dynamic residual deep GCN (DynaResGCN), built upon a random dynamic dilated aggregation algorithm within a unified encoder-decoder framework. The encoder utilizes the deep DynaResGCN model, and the decoder uses Bernoulli-Poisson (BP) model. Our framework is tested on three datasets: a topics dataset where ground truth is not available, Facebook graphs with human-labeled ground truth, and large co-authorship networks with less reliable ground truth. Extensive experiments validate that our method is significantly better than many existing methods in detecting overlapping communities in networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Unsupervised learning

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10th international Workshop on Clinical Image-Based Procedures, CLIP 2021, 2nd MICCAI Workshop on Distributed and Collaborative learning, DCL 2021, 1st MICCAI Workshop, LL-COVID19, 1st Secure and Privacy-Preserving machine learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with 24th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2021

10th International Workshop on Clinical Image-Based Procedur...

引用

10th international Workshop on Clinical Image-Based Procedures, CLIP 2021, 2nd MICCAI Workshop on Distributed and Collaborative learning, DCL 2021, 1st MICCAI Workshop, LL-COVID19, 1st Secure and Privacy-Preserving machine learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with 24th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2021

ISBN: (纸本)9783030908737

The proceedings contain 17 papers. The special focus in this conference is on Clinical Image-Based Procedures. The topics include: DCL preface;LL-COVID-19 preface;PPML preface;intestine Segmentation with Small Computational Cost for Diagnosis Assistance of Ileus and Intestinal Obstruction;multi-task Federated learning for Heterogeneous Pancreas Segmentation;federated learning in the Cloud for Analysis of Medical Images - Experience with Open Source Frameworks;on the Fairness of Swarm learning in Skin Lesion Classification;Lessons Learned from the Development and application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next;The Role of Pleura and Adipose in Lung Ultrasound AI;DuCN: Dual-Children Network for Medical Diagnosis and Similar Case Recommendation Towards COVID-19;data Imputation and Reconstruction of Distributed Parkinson’s Disease Clinical Assessments: A Comparative Evaluation of Two Aggregation Algorithms;defending Medical Image Diagnostics Against Privacy Attacks Using Generative Methods: application to Retinal Diagnostics;generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning;Bayesian Graph Neural Networks for EEG-Based Emotion Recognition;ViTBIS: Vision Transformer for Biomedical Image Segmentation;Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes;development of the Next Generation Hand-Held Doppler with Waveform Phasicity Predictive Capabilities Using Deep learning;learning from Mistakes: An Error-Driven Mechanism to Improve Segmentation Performance Based on Expert Feedback.

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machine learning Methods Comparison and Prediction for Heart Failure 2

Machine Learning Methods Comparison and Prediction for Heart...

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2021 2nd international conference on machine learning and computer application, icmlca 2021

作者： Cheng, Yiqing Emory University GA United States

ISBN: (纸本)9783800757398

In this paper, we use different machine learning algorithms to learn and predict the binary outcome of whether one would have heart failure. The results from this study show that the XGBoost model outperforms all other machine learning algorithms with accuracy near 0.852, recall of 0.918, and precision of 0.818. Of all the attributes, the slope of the ST curve in the electrocardiogram turns out to be the most important variable. Future work can develop on more industry-specific models and collect more data on electrocardiograms to feed in the existing model. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Electrocardiograms

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Research on applications Of machine learning In Mechanics 2

Research on Applications Of Machine Learning In Mechanics

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2021 2nd international conference on machine learning and computer application, icmlca 2021

作者： Tao, Yanru Sino-Canada Jiangsu Suzhou21500 China

ISBN: (纸本)9783800757398

machine learning is a technology that becomes more and more popular nowadays. Many physics researchers discovered its usages and advantages, then applied it to different models and obtained some achievements. This thesis introduces some papers' examples of using machine learning to solve physics problems, including double pendulum, conservation law, time series on chaotic systems, and it demonstrates the feasibility of machine learning on some mechanics projects by analyzing different application methods mentioned in those papers. The result of the experiments shows machine learning's good behaviors in this area, most of the models that used machine learning are successful. Overall, machine learning technology has great potential and features to do more contributions for solving mechanics and physics problems. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Chaotic systems

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