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

共 585 条记录，以下是1-10 订阅

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

2nd International Workshop on Recent Advances in Digital Sec...

引用

2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

ISBN: (纸本)9783030307530

The proceedings contain 39 papers. The special focus in this conference is on Recent Advances in Digital Security. The topics include: Modern vs Diplomatic Transcripts for Historical Handwritten Text recognition;improving Ancient Cham Glyph recognition from Cham Inscription Images Using data Augmentation and Transfer learning;Oracle Bone Inscription Detector Based on SSD;shot Boundary Detection for Automatic Video Analysis of Historical Films;the Epistle to Cangrande Through the Lens of Computational Authorship Verification;a Cockpit of Measures for Image Quality Assessment in Digital Film Restoration;augmented Reality for the Valorization and Communication of Ruined Architecture;classification of Arabic Poems: from the 5th to the 15th Century;a Page-Based Reject Option for Writer Identification in Medieval Books;on the Cross-Finger Similarity of Vein patterns;minimizing Training data for Reliable Writer Identification in Medieval Manuscripts;fusion of Visual and Anamnestic data for the Classification of Skin Lesions with Deep learning;slide Screening of Metastases in Lymph Nodes via Conditional, Fully Convolutional Segmentation;A learning Approach for Informative-Frame Selection in US Rheumatology Images;a Serious Game to Support Decision Making in Medical Education;nerve Contour Tracking for Ultrasound-Guided Regional Anesthesia;Skin Lesions Classification: A Radiomics Approach with Deep CNN;semantic 3D Object Maps for Everyday Robotic Retail Inspection;collecting Retail data Using a Deep learning Identification Experience;a Large Scale Trajectory dataset for Shopper Behaviour Understanding;Improving Multi-scale Face recognition Using VGGFace2;An IOT Edge-Fog-Cloud Architecture for Vision Based Pallet Integrity;the Vending Shopper Science Lab: Deep learning for Consumer Research.

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11th mining Humanistic data workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st workshop/Special Session on machine learning and Big data in Health Care, ML@HC 2022 and 2nd workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international Conference on Artificial Intelligence Applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

引用

11th mining Humanistic data workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st workshop/Special Session on machine learning and Big data in Health Care, ML@HC 2022 and 2nd workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international Conference on Artificial Intelligence Applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

The proceedings contain 35 papers. The special focus in this conference is on mining Humanistic data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for machine learning-Based Resource Management;efficient data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep learning Based Facial Expressions An

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workshop on Smart and Precise Agriculture, WSPA 2021, PAKDD 2021 workshop on machine learning for MEasurement Informatics, MLMEIN 2021, 1st workshop and Shared Task on Scope Detection of the Peer Review Articles, SDPRA 2021, 1st international workshop on data Assessment and Readiness for AI, DARAI 2021 and 1st international workshop on Artificial Intelligence for Enterprise Process Transformation, AI4EPT 2021 held in conjunction with 25th Pacific-Asia Conference on Knowledge Discovery and data mining, PAKDD 2021

Workshop on Smart and Precise Agriculture, WSPA 2021, PAKDD ...

引用

workshop on Smart and Precise Agriculture, WSPA 2021, PAKDD 2021 workshop on machine learning for MEasurement Informatics, MLMEIN 2021, 1st workshop and Shared Task on Scope Detection of the Peer Review Articles, SDPRA 2021, 1st international workshop on data Assessment and Readiness for AI, DARAI 2021 and 1st international workshop on Artificial Intelligence for Enterprise Process Transformation, AI4EPT 2021 held in conjunction with 25th Pacific-Asia Conference on Knowledge Discovery and data mining, PAKDD 2021

ISBN: (纸本)9783030750145

The proceedings contain 17 papers. The special focus in this conference is on Smart and Precise Agriculture. The topics include: Using Transformer Based Ensemble learning to Classify Scientific Articles;1st international workshop on data Assessment and Readiness for AI;cooperative Monitoring of Malicious Activity in stock Exchanges;data-Debugging Through Interactive Visual Explanations;data Augmentation for Fairness in Personal Knowledge Base Population;ROC Bot: Towards Designing Virtual Command Centre for Energy Management;Digitize-PID: Automatic Digitization of Piping and Instrumentation Diagrams;Plant Leaf Disease Segmentation Using Compressed UNet Architecture;hierarchical Topic Model for Tensor data and Extraction of Weekly and Daily patterns from Activity Monitor Records;convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images;unsupervised Noise Reduction for Nanochannel Measurement Using Noise2Noise Deep learning;classification Bandits: Classification Using Expected Rewards as Imperfect Discriminators;overview and Insights from Scope Detection of the Peer Review Articles Shared Tasks 2021;Scholarly Text Classification with Sentence BERT and Entity Embeddings;domain Identification of Scientific Articles Using Transfer learning and Ensembles.

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Collaborative scheduling algorithm for full quantity materials based on process and machine learning 24

Collaborative scheduling algorithm for full quantity materia...

引用

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

作者： Gu, Sanlin State Grid Gansu Power Supply Co Nanjing Peoples R China

ISBN: (纸本)9798400707032

Participants in the supply chain may have different information, leading to incomplete or inaccurate information when making decisions. To this end, a process and machine learning based collaborative scheduling algorithm for all materials is proposed. Design a health monitoring process for material supply chain based on R-tree dynamic indexing algorithm. Based on this, artificial neural networks in machine learning are applied to mine the data of the entire material supply chain. Through data mining, various data in the supply chain can be integrated and analyzed to improve information transparency and accuracy, and reduce information asymmetry. Adopting a dual layer scheduling model to achieve dual layer collaborative scheduling of materials. The experimental results show that the research method effectively improves the accuracy of data mining in the entire material supply chain, and the utilization rate of materials under this method is always higher than 95%.

关键词： Process design Artificial neural network Full quantity of materials Scheduling algorithm

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Application of data Sharing Platform Based on data Directory System in Power Systems 24

Application of Data Sharing Platform Based on Data Directory...

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

作者： Zhang, Feifei Dang, Fangfang Guo, Yongqiang Jia, Haifeng Wang, Hao Wang, Zhiying State Grid Henan Informat & Telecommun Co Data Ct Zhengzhou Henan Peoples R China

ISBN: (纸本)9798400707032

The problem of corporate data management in power systems is increasingly prominent, such as unclear data responsibility division, insufficient value mining and lack of effective data sharing and open. In this paper, a data resource sharing platform base on directory system is proposed. On the one hand, based on the innovation of data management model of the company, following the general working idea of "inventory, regulation, management, use", the paper establishes a set of business system as the directory framework, to achieve the goal of data sharing company-level data management system, which is improving the readability and usability of the database table and field information. On the other hand, develop the data resource sharing platform to solidify achivement base on the company-level data directory system. By opening it to all departments and grass-roots units, system achievess the quick query and location, identifys the data maintenance department, shares data and realizes the data value.

关键词： data management Power systems data sharing platform data directory system data value

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Galaxy classification based on deep learning 24

Galaxy classification based on deep learning

引用

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

作者： Huang, Ruijie Wu, Haoran Huang, Jiayi Beijing Normal Univ Hong Kong Baptist Univ United AI FST Beijing Peoples R China

ISBN: (纸本)9798400707032

In this study, a deep learning-based galaxy classification method is discussed. The classification of galaxies is important for understanding the formation and evolution of the universe, and traditional classification methods rely on artificial visual analysis, but in the face of large amounts of data, this method is time-consuming and prone to error. In recent years, automated classification methods, especially using deep learning techniques, have gradually come into focus. Deep learning models, particularly convolutional neural networks (CNNS), are capable of automatically extracting and learning complex features in galactic images, enabling efficient and accurate classification. The research plan is to integrate multimodal data, train models with large-scale datasets, and introduce interpretative analysis into the classification process to improve model transparency. Ultimately, the goal is to develop an efficient galactic classification system to support data processing and analysis in the field of astronomy.

关键词： Deep learning Image classification Galaxy Probability distribution

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Research on intelligent line selection and optimization scheme of transmission lines based on big data analysis and artificial intelligence 24

Research on intelligent line selection and optimization sche...

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

作者： He, Chunhui Liu, Haitao Wang, Long Chen, Xiangjia Sun, Yongxin State Grid Shandong Elect Power Co Econ & Technol Res Inst Beijing Peoples R China

ISBN: (纸本)9798400707032

As the wave of data-driven informatization sweeps the world, society is moving from the information age (IT) to the data age (DT). GIS technology has become an important tool for complex regional development and prediction. However, the existing power grid design methods cannot meet the needs of improving production efficiency and controlling engineering costs. This paper proposes an intelligent line selection and optimization scheme for transmission lines based on big data analysis and artificial intelligence. The map model is designed using GIS multi-band raster maps, and data mining technology is used to maximize the application value of multi-factor geographic information data. The reverse dynamic programming algorithm is used for intelligent line selection, and the particle swarm optimization-convolutional neural network (PSO-CNN) model is used for engineering quantity prediction to improve the prediction accuracy and obtain more accurate optimization scheme ranking results. The scheme aims to reduce the design workload, reduce the industry design cost, improve the intelligence and standardization level of transmission and transformation engineering design, and provide strong support for the construction of smart grids.

关键词： artificial intelligence power transmission line intelligent line selection GIS grid map data mining sequential dynamic programming PSO-CNN

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machine learning models and methods aspects of processing unstructured data 1

Machine learning models and methods aspects of processing un...

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1st international workshop on Bioinformatics and Applied Information Technologies, BAIT 2024

作者： Bryk, Oleksandr Mudryk, Ivan Holubovskyi, Mykhailo stoianov, Yurii Ternopil Ivan Puluj National Technical University 56 Ruska str. Ternopil46001 Ukraine

The ever-increasing amount of unstructured data, including text, images, audio, and video, poses a serious challenge to traditional data mining techniques. machine learning (ML) offers powerful tools and techniques to unlock the valuable insights hidden in this vast amount of information. This article explores the role of machine learning models and methods in processing unstructured data. We delve into key aspects of unstructured data processing, including data cleaning, feature development, and model selection. We explore specific ML techniques developed for different types of data, such as natural language processing (NLP) for text analysis and computer vision for image recognition. The paper also discusses the challenges and considerations involved in building and deploying ML models to handle unstructured data[1]. By understanding the capabilities of ML on unstructured data, organizations can gain a competitive advantage by deriving valuable insights for various applications. This information can range from understanding customer sentiment in social media posts to detecting anomalies in sensor data for predictive maintenance. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Predictive maintenance

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Defect Detection of Forming Mesh Based on YOLOV8N Model 24

Defect Detection of Forming Mesh Based on YOLOV8N Model

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

作者： Li, Jing Wang, Lijuan Zhang, Peng Guangdong Mech & Elect Polytech Guangzhou Guangdong Peoples R China Macao Polytech Univ Macau Peoples R China

ISBN: (纸本)9798400707032

A method for detecting defects in forming mesh based on the YOLOV8N model is proposed. Firstly, an illumination system is designed, and then image data with defects is collected to construct an image dataset. The constructed image dataset is annotated and augmented to build a corresponding image sample set. The YOLOV8N model is trained using the sample set to obtain a defect recognition and localization model. The original image to be recognized is input into the defect recognition and localization model, which then outputs the corresponding defect recognition and localization results. Experimental results show that this method provides accurate localization and fast speed.

关键词： Forming mesh 1 YOLOV8N model 2 Illumination system 3 Sample set 4

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Optimization of Intelligent Substation Location Scheme Based on data Fusion Technology and Artificial Intelligence 24

Optimization of Intelligent Substation Location Scheme Based...

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

作者： Wang, Zhipeng Xie, Dan Feng, Cai He, Chunhui Lan, Feng State Grid Shandong Elect Power Co Econ &Technol Res Inst Beijing Peoples R China

ISBN: (纸本)9798400707032

At present, the world is experiencing the second information wave with data as the core and the Internet as the means, and the society is shifting from IT (information) era to DT (data) era. The planning of substation location is mainly based on the existing satellite vector data through the software simulation platform, and the optimal scheme and path planning are often based on the experience of designers in actual design and erection. However, with the continuous expansion of the project scale and increasing complexity, it has brought great challenges to the design work. Therefore, substation location needs to be combined with current data fusion technology and artificial intelligence technology to effectively improve the intelligent level of substation location.

关键词： Substation location data fusion technology Artificial intelligence technology Weighted Voronoi diagram

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