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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1786 条记录，以下是601-610 订阅

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Research on Deep learning-based AI Information Extraction Methods of Substation Engineering Design 4

Research on Deep Learning-based AI Information Extraction Me...

引用

4th international conference on Energy, Electrical and Power Engineering, CEEPE 2021

作者： Chen, Ying Jiang, Wei Wang, Yali Hu, Junhui Guan, Lan Zhu, Zhanwei Power Grid Subsidiary Company Powerchina Sichuan Electric Power Engineering Co. LTD. Chengdu China State Grid Beijing Electric Power Company Construction Department Beijing China

ISBN: (纸本)9781665433631

Substation engineering construction has accumulated a large amount of documents and a wealth of explicit and implicit knowledge in the long-term work practice. Using artificial intelligence technology to extract knowledge from unstructured documents, drawings and photos into structured data, is a prerequisite for providing guidance for new engineering design. through the research on application of artificial intelligence in the design of substation engineering, two functions are realized in the structuring of data: 1) using deep learning-based image recognition technology (SSD_mobilenet), the elements intelligent identification and quantity statistic in engineering drawings is realized. It provides a possibility of automatic generation for the structuring of design data. 2) through computer vision and machine learning technology (CTPN and CRNN), an electrical information recognition system is established to errata automatically, which saves manpower and time and provides auxiliary functions for data collection operations of previous engineering projects. the implementation results verify the effectiveness of the deep learning methods using in substation engineering design. © 2021 IEEE.

关键词： Deep learning

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the Design of the Multimodal-Based Educational Support Platform for Music and Reading in Elementary Schools

The Design of the Multimodal-Based Educational Support Platf...

引用

Electronic Communications, Internet of things and Big data (ICEIB), international conference on

作者： Lin Mengzhe Hong Meiyu Liu Ye State Key Laboratory of Brain and Cognitive Sciences Institute of Psychology Chinese Academy of Sciences Beijing China Department of Psychology University of Chinese Academy of Sciences Beijing China Xiamen Huming Primary School Xiamen China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

this study aimes to design a multimodal educational support platform for music and reading in elementary schools to improve the reading ability of students. Numerous studies have shown that there is strong correlation between musical ability and reading ability of children in elementary school. this study further explored this correlation, and designed a multimodal educational support platform based on this finding. the platform integrateed the two disciplines of music and reading, and combined multimodal elements such as visual, auditory, and emotion feedback. this study has created an innovative educational tool. the platform is aimed for providing a fun, interactive, and personalized learning experience to stimulate the pupils' learning interest and to develop their linguistic and musical abilities comprehensively.

关键词： Emotion recognition Visualization Correlation Education Music machine learning Solids

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4th international conference on Optimization, learning Algorithms and Applications, OL2A 2024

4th International Conference on Optimization, Learning Algor...

引用

4th international conference on Optimization, learning Algorithms and Applications, OL2A 2024

ISBN: (纸本)9783031774256

the proceedings contain 41 papers. the special focus in this conference is on Optimization, learning Algorithms and Applications. the topics include: Facial Expression recognition in Virtual Reality Simulations;augmented Reality in Industrial Training: A Comparative Analysis of Teaching Methods;comparison Between Single and Multi-objective Clustering Algorithms: MathE Case Study;learning Algorithms for Breast Cancer Classification and Diagnosis;fuzzy c-Means as a Decision Support Tool for Liver Disease Diagnosis Based on data Analysis;optimization of machine learning Models Applied to Robot Localization in the RobotAtFactory 4.0 Competition;p-Fuzzy System Applied to Population Dynamics: A Case Study;enhancing K-Way Circuit Partitioning: A Deep Reinforcement learning Methodology;optimizing Olive Disease Classification through Hybrid machine learning and Deep learning Techniques;a Neural Network-Based Approach to Identifying Wrinkles and Recommending Cosmetic Products;colorectal Polyp Segmentation: Impact of Combining Different datasets on Deep learning Model Performance;A Comparative Analysis of MATLAB and Python Neural Networks for Diabetes Prediction;cochineal Colony Detection in Cactus Pear: A Deep learning Approach;enhancing Quadruped Robot Performance through Gait Optimization;assessment of Logistics Performance Indicators in Southern European Countries Optimizing the Decision Making;Predicting Retail Store Transaction patterns: A Comparison of ARIMA and machine learning Models;route Optimization for Urban Last-Mile Delivery: Truck vs. Drone Performance;speeding up Line Search for Composite Objective Function with a Linear Inside Part;the Infodemic Issue: Numerical Modelling;a Multi-objective Approach for Solving Distributed Job Shop Scheduling Problems.

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4th international conference on Optimization, learning Algorithms and Applications, OL2A 2024

4th International Conference on Optimization, Learning Algor...

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4th international conference on Optimization, learning Algorithms and Applications, OL2A 2024

ISBN: (纸本)9783031774317

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pattern recognition of Control Chart Based on Fuzzy c-Means Clustering Algorithm

Pattern Recognition of Control Chart Based on Fuzzy c-Means ...

引用

international conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Renping Gong School of Management Tianjin University of Technology Tianjin China

the pattern recognition of the control chart can distinguish the Normal factors and unnatural factors in the manufacturing process, improve the quality of products in the manufacturing process, reduce costs and improve benefits in the environment of industrial big data. Due to the fact that manufacturing process data are becoming more and more complicated under the environment of big data, the fundamental assumptions of traditional statistical process control methods are no longer invalid, which makes them unreliable to recognize the control chart patterns effectively. In this paper, the Monte Carlo method is used to generate samples. One-dimensional discrete wavelet transform is used to process the original data. the fuzzy c-means clustering algorithm is used to recognize the control chart pattern. the recognition accuracy was 99.43%, and its standard deviation was 0.0028. It shows that the method based on the control chart pattern recognition has high accuracy, good stability, and is simple and efficient compared with the existing control chart pattern recognition methods.

关键词： Training Manufacturing processes machine learning algorithms Clustering algorithms Process control Control charts Production

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Chronic Disease Symptom Checker with NLP

Chronic Disease Symptom Checker with NLP

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Vinoth Kumar P Arul Selvam N Soundharajan P Artificial Intelligence and Data Science St.Joseph's Institue Of Technology Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Chronic disease remains a critical global health challenge due to lifestyle and environmental changes, leading to increased morbidity and mortality. this paper presents an intelligent symptom checker utilizing Natural Language Processing (NLP) to assess probable chronic diseases based on user-reported symptoms. By analyzing free-text symptom descriptions, the system maps them to potential medical conditions using Named Entity recognition (NER)and a rule-based approach. the methodology involves extensive medical data collection, pre-processing, and training of machine learning models to recognize symptom-disease patterns. the checker's backend integrates an NLP model with a user-friendly interface, providing real-time assessments of probable diseases with confidence levels. the system's accuracy is validated through preclinical tests and user studies, comparing outputs with professional diagnoses. Ethical considerations, including patient privacy and diagnostic reliability, are also addressed. With potential applications in early detection and healthcare support, this system aims to enhance disease management and reduce healthcare burdens. the key challenges include NLP accuracy, data privacy, and integration with existing healthcare systems, which are addressed through robust preprocessing, security measures, and model optimization techniques

关键词： Training data privacy Accuracy Terminology Named entity recognition Natural language processing Real-time systems data models Medical diagnostic imaging Diseases

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LoCoML: A Framework for Real-World ML Inference Pipelines

LoCoML: A Framework for Real-World ML Inference Pipelines

引用

AI Engineering - Software Engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Kritin Maddireddy Santhosh Kotekal Methukula Chandrasekar Sridhar Karthik Vaidhyanathan Software Engineering Research Center IIIT Hyderabad India

ISBN: (数字)9798331502195

ISBN: (纸本)9798331502201

the widespread adoption of machine learning (ML) has brought forth diverse models with varying architectures, data requirements, introducing new challenges in integrating these systems into real-world applications. Traditional solutions often struggle to manage the complexities of connecting heterogeneous models, especially when dealing with varied technical specifications. these limitations are amplified in large-scale, collaborative projects where stakeholders contribute models with different technical specifications. To address these challenges, we developed LoCoML, a low-code framework designed to simplify the integration of diverse ML models within the context of the Bhashini Project - a large-scale initiative aimed at integrating AI-driven language technologies such as automatic speech recognition, machine translation, text-to-speech, and optical character recognition to support seamless communication across more than 20 languages. Initial evaluations show that LoCoML adds only a small amount of computational load, making it efficient and effective for large-scale ML integration. Our practical insights show that a low-code approach can be a practical solution for connecting multiple ML models in a collaborative environment.

关键词： Codes Computational modeling Pipelines Optical character recognition Collaboration machine learning Text to speech Stakeholders machine translation Software engineering

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DDoS Attack Detection Method Based on Improved Bagging

DDoS Attack Detection Method Based on Improved Bagging

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Communication Technology and Information Technology (ICCTIT), international conference on

作者： Haiyou Zhi Jingxian Li Mengyue Yu Jin Wang Ziyan Hu Yihan Li School of Computer Science Wuhan Donghu University Wuhan China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

In view of the complexity and concealment of distributed denial of service attacks, a DDoS attack detection method based on improved Bagging ensemble learning is proposed. In order to effectively deal with the changing attack modes and improve the stability and accuracy of the detection model, this method enhances the recognition ability of DDoS attacks by introducing a weighted voting mechanism and combining multiple machine learning algorithms. Experiments on the CIC-DDoS2019 dataset show that after preprocessing steps such as feature selection, data normalization and feature correlation analysis, the improved Bagging model using RF, DT and KNN as base learners has an accuracy of $\mathbf{9 9. 9 8 \%}$ on the test set, which is significantly better than the traditional Bagging method and other detection models. this study shows that the improved Bagging method not only improves the accuracy and robustness of DDoS attack detection, but also provides new ideas for solving challenges such as high-dimensional data processing and dynamic network traffic analysis.

关键词： Radio frequency Accuracy machine learning algorithms Telecommunication traffic Denial-of-service attack Robustness Stability analysis Ensemble learning Computer crime Bagging

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Analysis of Big data Processing Based on Mapreduce and Convolution Neural Network 4th

Analysis of Big Data Processing Based on Mapreduce and Convo...

引用

4th international conference on Innovative Computing, IC 2021

作者： Xu, Hongsheng Wang, Lan Zhang, Yi Luoyang Normal University Luoyang China Henan Key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang China

ISBN: (纸本)9789811642579

this paper first describes the principle of convolution neural network. Convolution neural network (CNN) is proposed to deal with pattern recognition problem on the basis of neural cognitive machine. Convolution NN is a multi-layer neural network, which each layer is composed of multiple two-dimensional planes, and each plane is composed of multiple independent neurons. In this paper, the principle and design idea of MapReduce based on large-scale data are described in detail. the paper presents analysis of big data processing by MapReduce and Convolution Neural Network. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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Online Q&A for machine learning Courses Based on Formula recognition

Online Q&A for Machine Learning Courses Based on Formula Rec...

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Information Science and Education (ICISE-IE), international conference on

作者： Xiaoke Zhu Xiaopan Chen Minghao Li Tianxiang Zhang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Mathematical formulas are ubiquitous in courses such as machine learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. this paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection.

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