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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

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AISD 2024 - Proceedings of the 1st international workshop of Young Scientists on Artificial Intelligence for Sustainable Development

AISD 2024 - Proceedings of the 1st International Workshop of...

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1st international workshop of Young Scientists on Artificial Intelligence for Sustainable Development, AISD 2024

The proceedings contain 19 papers. The topics discussed include: artificial intelligence application in renewable energy sources;intelligent system for visual testing of software products;verifying the economic potential of low-carbon energy using artificial intelligence in transport;enhancing the efficiency of decision support systems in the warehousing sector;data transformation review in deep learning;comparative analysis of CNN architecture for emotion classification on human faces;content analysis of court decisions: a GPT-4 based sentence-by-sentence data generation and association rules mining;exploring the future of UAE judiciary: ai integration, bias mitigation, and systemic enhancements;comparative analysis of stress factors of humanities and technical specialities students;integrated approach to the international aspects of online dispute resolution formation;fuzzy audit system of enterprise activity;and does expectations affect inflation forecasting abilities of machine learning techniques: case of Ukraine.

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CI4PM-PAI 2022 - Joint Proceedings of the 1st international workshop on Computational Intelligence for Process mining and the 1st international workshop on Pervasive Artificial Intelligence, co-located with the IEEE World Congress on Computational Intelligence, WCCI 2022

CI4PM-PAI 2022 - Joint Proceedings of the 1st International ...

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Joint 1st international workshop on Computational Intelligence for Process mining and the 1st international workshop on Pervasive Artificial Intelligence, CI4PM-PAI 2022

The proceedings contain 7 papers. The topics discussed include: interactive trace clustering to enhance incident completion time prediction in process mining;pattern-based reconstruction of anomalous traces in business process event logs;impact of non-fitting cases for remaining time prediction in a multi-attribute process-aware method;continual-learning-as-a-service (CLaaS): on-demand efficient adaptation of predictive models;efficient anomaly detection on temporal data via echo state networks and dynamic thresholding;making FreeRTOS pervasive systems learn to select energy saving technique for mixed taskset;and a cloud-based continual learning system for road sign classification in autonomous driving.

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Intelligent Planning of Power Transmission and Transformation Projects Based on AI and Multi-source Heterogeneous data Integration 24

Intelligent Planning of Power Transmission and Transformatio...

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1st international Conference on Image Processing machine learning and pattern recognition

作者： Qu, Zhanfei Dong, Gang Sun, Qigang He, Chunhui Lu, Ling State Grid Shandong Elect Power Co Econ & Technol Res Inst Beijing Peoples R China State Grid Shandong Elect Power Co Beijing Peoples R China

ISBN: (纸本)9798400707032

The acceleration of smart grid construction imposes higher demands on the intelligent planning of power transmission and transformation projects. To address the inefficiencies and high costs in existing planning methods, this paper proposes an intelligent planning approach based on multi-source heterogeneous data integration and artificial intelligence. By integrating multi-source heterogeneous data to analyze on-site information, and utilizing Bayesian networks and Convolutional Neural Networks with Attention Mechanisms (CNNAM), the planning and construction processes of power transmission and transformation projects are optimized. This approach significantly improves the accuracy and reliability of cost estimation, providing strong data support and technical assurance for the intelligent planning of power transmission and transformation projects, thereby making the planning process more effective and practical.

关键词： Smart Grid Intelligent Planning Multi-Source Heterogeneous data Bayesian Networks Convolutional Neural Networks

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Predicting Song Popularity Through machine learning and Sentiment Analysis on Social Networks 13th

Predicting Song Popularity Through Machine Learning and Sen...

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13th mining Humanistic data workshop, MHDW 2024, 9th workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Rompolas, Gerasimos Smpoukis, Athanasios Kafeza, Eleanna Makris, Christos Computer Engineering and Informatics Department University of Patras Patras26504 Greece College of Interdisciplinary Studies Zayed University Dubai144534 United Arab Emirates

ISBN: (纸本)9783031632266

In this paper, we delve into the intricate relationship between technology, music, and success. Our research focuses on leveraging the capabilities of machine learning algorithms to forecast the success and popularity of songs. By combining audio features, social media data, and emotion analysis, our machine learning-based model demonstrates its capability as a reliable tool for artists, amateurs, and music industry stakeholders to make informed and targeted decisions. Our analysis encompassed not only the inherent audio characteristics of each track but also the extent of audience engagement and emotional response of the fans. Through the integration of textual emotion analysis, we quantified the reactions of the audience and performed an emotion labelling, leveraging our social information to uncover the emotions surrounding each track’s release. The experimental results show that the feature data from which the classification algorithms were trained, unfolded to be qualitative and precise, while they also show that the use of the social media features improves the classifiers’ performance in predicting the popularity of the tracks. © IFIP international Federation for Information Processing 2024.

关键词： Emotion recognition

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3rd international workshop on Explainable Artificial Intelligence in Healthcare, XAI-Healthcare 2023 and 1st international workshop on Process mining Applications for Healthcare, PM4H 2023

3rd International Workshop on Explainable Artificial Intelli...

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3rd international workshop on Explainable Artificial Intelligence in Healthcare, XAI-Healthcare 2023 and 1st international workshop on Process mining Applications for Healthcare, PM4H 2023

ISBN: (纸本)9783031543029

The proceedings contain 12 papers. The special focus in this conference is on Explainable Artificial Intelligence in Healthcare. The topics include: Interpreting machine learning Models for Survival Analysis: A study of Cutaneous Melanoma Using the SEER database;probExplainer: A Library for Unified Explainability of Probabilistic Models and an Application in Interneuron Classification;An Explainable AI Framework for Treatment Failure Model for Oncology Patients;explanations of Symbolic Reasoning to Effect Patient Persuasion and Education;explainable Artificial Intelligence in Response to the Failures of Musculoskeletal Disorder Rehabilitation;phenotypes vs Processes: Understanding the Progression of Complications in Type 2 Diabetes. A Case study;A data-Driven Framework for Improving Clinical Managements of Severe Paralytic Ileus in ICU: From Path Discovery, Model Generation to Validation;preface;understanding Prostate Cancer Care Process Using Process mining: A Case study;From Script to Application. A bupaR Integration into PMApp for Interactive Process mining Research.

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SMARTINDUstRY 2024 - Proceedings of the 1st international Conference on Smart Automation and Robotics for Future Industry

SMARTINDUSTRY 2024 - Proceedings of the 1st International Co...

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1st international Conference on Smart Automation and Robotics for Future Industry, SMARTINDUstRY 2024

The proceedings contain 16 papers. The topics discussed include: artificial intelligence for the future of construction;cobots and industrial robots;predictive maintenance for wind turbine bearings: an MLOps approach with the DIAFS machine learning model;development of an artificial intelligence tool and sensing in informatization systems of mobile robots;PCA-NuSVR framework for predicting local and global indicators of tunneling-induced building damage;design and deployment of data development toolkit in cloud manufacturing environments;research and development of image processing algorithms for effective recognition of various gestures in real time;machine learning models for the recognition of commands in smart home technologies;responsive dehydration: sensor-driven optimisation of production cycles in a solar dehydrator;and formation of the method of description and control of the relative position of the links of the upper limbs of the grip of an anthropomorphic robot.

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Surface waste identification and detection based on improved YOLOV8 24

Surface waste identification and detection based on improved...

引用

1st international Conference on Image Processing machine learning and pattern recognition

作者： Wang, Junxi Zhan, Xin Jiang, Yizhen Deng, Kairong Yang, Zicong Zeng, Huixin Wang, Zhengpeng Zhuhai Univ Sci & Technol Inst Intelligent Mfg & Aviat Zhuhai Peoples R China Zhuhai Univ Sci & Technol Elect Informat Sci & Technol Zhuhai Peoples R China Zhuhai Univ Sci & Technol Data Sci & Big Data Technol Zhuhai Peoples R China

ISBN: (纸本)9798400707032

In response to the challenge of surface waste detection, an advanced surface waste recognition algorithm based on YOLOV8 has been carefully developed. The RepVGG module is used to replace the traditional Conv module, so the detection speed and accuracy of the model are significantly improved in the inference stage. At the same time, in order to further enhance the training effect of the model, Wasserstein Distance Loss was introduced as a loss function to optimize the training process of the model and further enhance its training accuracy. After rigorous experimental verification, the experimental data have excellent performance, and the improved network model has an average precision mean (mAP) of 82.6%, which proves its high-precision performance in target detection, effectively reduces the case of missing detection, and meets the strict requirements for surface garbage detection.

关键词： Object detection Surface waste identification YOLOV8 improved RepVGG Wasserstein Distance Loss

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An Adaptive Knowledge Graph Construction Method for Semi-structured data 24

An Adaptive Knowledge Graph Construction Method for Semi-Str...

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1st international Conference on Image Processing machine learning and pattern recognition

作者： Yang, Qi Li, Jing Yu, Xinping Wu, Yanzhou Zeng, Yuanwei Naval Univ Engn Wuhan Peoples R China Navy Unit 91646 Jiujiang Jiangxi Peoples R China Navy Unit 92467 Fuzhou Fujian Peoples R China Navy Unit 91515 Sanya Hainan Peoples R China

ISBN: (纸本)9798400707032

In the current transition of warehousing and logistics companies from traditional databases to knowledge bases, numerous challenges arise, including low efficiency due to massive data volumes and extended investment recovery periods due to high implicit costs. To address these issues and enable the rapid construction of a Knowledge Graph (KG) utilizing existing semi-structured data, this paper introduces an automated method for reading Excel content, identifying KG ontologies and entities, and importing data into Neo4j software. Firstly, the method is designed to automatically extract internal data from Excel, achieving the automatic recognition of KG ontologies and entity information. Secondly, this method is tailored to the KG triplet structure, enabling the automated transcription of these triplets into Neo4j software. Thirdly, to enhance the universal adaptability of the method, the functionality has been extended to enable arbitrary additions, reductions, and modifications to the content and data length of Excel spreadsheets. Finally, experiments have validated the method's effectiveness, universal applicability, and convenience. Moreover, this method is broadly applicable across various industries, offering an efficient approach for the automatic construction of KGs by business personnel.

关键词： Adaptive Semi-structured data Knowledge Graph Automatic Construction Excel Neo4j

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international Conference on Web Information Systems Engineering, WISE 2024

PhD Symposium, Posters, Demos, and A Web for more inclusive,...

引用

PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international Conference on Web Information Systems Engineering, WISE 2024

ISBN: (纸本)9789819614820

The proceedings contain 36 papers. The special focus in this conference is on Web Information Systems Engineering. The topics include: Effective Transparent Monitoring of Personal data;iCNN-LstM: An Incremental CNN-LstM Based Ransomware Detection System;a Mini Review on Purchaser Security in the Metaverse: Challenges and Solutions;low-Resource dataset Synthetic Generation for Hate Speech Detection;Web Open data to SDG Indicators: Towards an LLM-Augmented Knowledge Graph Solution;leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval;time Distance Aware for Multi-component Graph Collaborative Filtering;scientific Documents Recommendation Based on Graph Convolutional Network;semantic Communication of Images Using Image Generation and Image Captioning Models;leveraging Optimization Techniques for Effective Arabic Query Expansion;DURLLCON: Deep Reinforcement learning for URLLC Optimization in Multi-edge Networks;FMM-RNS: A Fast HMM Map Matching Method Based on Road Network Simplification;Context-Aware Selection of machine learning as a Service (MLaaS) in IoT Environments;GraphTFD: A Fraud Detection System Based on Graph Transformer;MLGE-AC-UFD: Multi-level Graph Embedding and Approximate Computation for Unsupervised Fraud Detection;SeCORE: Quantitative Security Assurance and Evaluation Platform;developing Geospatial Web Applications Using Question Answering Engines, Knowledge Graphs and Linked data Tools;A Visual Query Builder for DBpedia;FL-PPELA: Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning;ORCPM: An Online Regional Core pattern mining System;MTRM: A Web-Miner Multi-Threshold mining Co-location patterns to Mitigate Redundancy;constrained Path Optimization on Time-Dependent Road Networks;prompt strategies for Sarcastic Meme Detection: A Comparative Analysis;Deepfake Detection in Cancer Medical Imaging Using CNN Architectures;strengthening Cybersecurity: The Influence o

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The NGT200 dataset Geometric Multi-View Isolated Sign recognition 1

The NGT200 Dataset Geometric Multi-View Isolated Sign Recogn...

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1st Geometry-Grounded Representation learning and Generative Modeling workshop, GRaM 2024 at the 41st international Conference on machine learning, ICML 2024

作者： Ranum, Oline Wessels, David Otterspeer, Gomèr Bekkers, Erik J. Roelofsen, Floris Andersen, Jari I. SignLab Amsterdam UvA Netherlands AMLab UvA Netherlands

Sign Language Processing (SLP) provides a foundation for a more inclusive future in language technology;however, the field faces several significant challenges that must be addressed to achieve practical, real-world applications. This work addresses multi-view isolated sign recognition (MV-ISR), and highlights the essential role of 3D awareness and geometry in SLP systems. We introduce the NGT200 dataset, a novel spatio-temporal multi-view benchmark, establishing MV-ISR as distinct from single-view ISR (SV-ISR). We demonstrate the benefits of synthetic data and propose conditioning sign representations on spatial symmetries inherent in sign language. Leveraging an SE(2) equivariant model improves MV-ISR performance by 8%-22% over the baseline. Copyright 2024 by the author(s).

关键词： Spatio-temporal data

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