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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining, MLDM 2003"

共 1147 条记录，以下是1-10 订阅

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

3rd International Conference on Machine Learning and Big Dat...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

The proceedings contain 150 papers. The topics discussed include: analysis of communication strategy of Guzheng art based on artificial intelligence;research on the health detection and seismic performance evaluation of high-rise buildings;application of ecological energy saving design in building reconstruction project;computer network communication security encryption system based on ant colony optimization algorithm;an analysis of data mining techniques in software engineering database design;intelligent analysis algorithm for hidden danger identification of intelligent network monitoring system from the perspective of big data;human behavior recognition of video surveillance system based on neural network;application of AI intelligent learning system in multimedia demonstration;and application of artificial intelligence technology in power system stability assessment.

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3rd Workshop on mining data for Financial Applications, MIDAS 2018 and 2nd international Workshop on Personal Analytics and Privacy, PAP 2018 held at 18th European conference on machine learning and Knowledge Discovery in databases, ECML PKDD 2018

3rd Workshop on Mining Data for Financial Applications, MIDA...

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3rd Workshop on mining data for Financial Applications, MIDAS 2018 and 2nd international Workshop on Personal Analytics and Privacy, PAP 2018 held at 18th European conference on machine learning and Knowledge Discovery in databases, ECML PKDD 2018

ISBN: (纸本)9783030134624

The proceedings contain 12 papers. The special focus in this conference is on mining data for Financial Applications. The topics include: Privacy risk for individual basket patterns;exploring students eating habits through individual profiling and clustering analysis;calibrating the mean-reversion parameter in the hull-white model using neural networks;deep factor model: Explaining deep learning decisions for forecasting stock returns with layer-wise relevance propagation;a comparison of neural network methods for accurate sentiment analysis of stock market tweets;a progressive resampling algorithm for finding very sparse investment portfolios;ICIE 1.0: A novel tool for interactive contextual interaction explanations;testing for self-excitation in financial events: A bayesian approach;a web crawling environment to support financial strategies and trend correlation: – extended abstract –;a differential privacy workflow for inference of parameters in the rasch model.

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12th international Workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on database and Expert Systems Applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

引用

12th international Workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on database and Expert Systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired data-Sampling Prototype for Time-Series Analysis;membership-Mappings for data Representation learning: Measure Theoretic Conceptualization;membership-Mappings for data Representation learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep learning Processes;approximate Fault Tolerance for Edge Stream Processing;deep learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series Pattern Discovery by Deep learning and Graph mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical Systems;integrating Gene Ontology Based Grouping and Ranking into the machine learning Algorithm for Gene Expression data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative Study of Deep learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case Study;Privacy Preserving machine learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction Strategies for Node Embeddings with rdF2Vec in Knowledge Graphs;bridging Semantic Web and machine learning: First Results of a Systematic Mapping Study.

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Research on the outlier value analysis algorithm of water conservancy engineering quality detection based on machine learning 24

Research on the outlier value analysis algorithm of water co...

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3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Chu, Dongdong Wang, Xiaoyong Zou, Dan Jiangsu Hydraulic Research Institute Jiangsu Nanjing China Yangzhou City Urban River Management Office Jiangsu Nanjing China

ISBN: (纸本)9798400718120

This study aims to improve the accuracy of the quality detection of water conservancy projects, and to identify and analyze the outliers in the detection data by introducing machine learning algorithms. A variety of technical techniques, including data preprocessing, feature extraction, supervised learning and unsupervised learning, were used to construct and optimize the outlier detection model. The experimental results show that the machine learning-based detection method significantly improves the accuracy and efficiency of outlier identification, especially when using deep learning models. The research conclusion points out that the machine learning algorithm has obvious advantages in processing complex and massive monitoring data, and can be widely used in the quality monitoring and management of water conservancy projects, providing a strong guarantee for the engineering safety. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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SmartBlend: Smart Video Recommendations on YouTube 3

SmartBlend: Smart Video Recommendations on YouTube

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Singh, Kulvinder Maurya, Aditya Kumar Solanki, Drishti Chandigarh University Dept of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350375190

In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. A transformative approach in online education by integrating Multimodal data mining and data Synthesis to develop a Smart System for learning Video Recommendations. With a focus on Personalized learning and Adaptability, the methodology employs advanced techniques like Multimodal learning Analytics and machine learning for a comprehensive analysis of learning materials. SmartBlend introduces a novel approach to personalized learning video recommendations on YouTube, leveraging Multimodal data mining and data Synthesis. The SmartBlendEdu framework employs advanced techniques like Multimodal learning Analytics and machine learning for a comprehensive analysis of learning materials. © 2024 IEEE.

关键词： data mining

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From data to Decisions: An Ontology-Driven Method for Opinion mining 3rd

From Data to Decisions: An Ontology-Driven Method for Opinio...

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3rd international conference on Language Processing and Knowledge Management-LPKM

作者： Belguith, Mehdi Khabou, Wafa Aloulou, Chafik Gargouri, Bilel Univ Sfax ANLP RG FSEGS MIRACL Lab Sfax Tunisia Univ Sfax MIRACL Lab FSEGS Sfax Tunisia

ISBN: (纸本)9783031850660;9783031850677

In this paper, we propose an ontology-driven method for opinion mining in social media. We first construct our Tunisian datasets from YouTube and social media content, mainly Twitter and Facebook. Then, based on natural language techniques and machine learning, we built four ontologies related to the Mobile phone, Election, Restaurant, and TV channel domains. We also propose an opinion mining method based on our domain ontologies.

关键词： Domain ontologies Opinion mining Aspects Tunisian dialect machine learning NLP

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Research on User Demand mining and Repurchase Behavior Prediction Algorithm Selection Based on Big data 3

Research on User Demand Mining and Repurchase Behavior Predi...

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3rd IEEE international conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Zhou, Tingwei Shanghai University Shanghai China

ISBN: (纸本)9798350380989

Based on the text data of authoritative skin care community, this study analyzes the user's needs and summarizes six topics on the premise that it has the option of repurchase intention. We need to calculate the emotional scores of six topics of each text data and contact the repurchase intention, thus constructing a feature data set, and then exploring which machine learning method is used to predict the repurchase behavior of online community review texts, which is relatively more accurate and reliable. © 2024 IEEE.

关键词： big data machine learning sentiment analysis text mining topic clustering

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English Teaching Evaluation Algorithm and Modeling based on machine learning and data mining Technology 3

English Teaching Evaluation Algorithm and Modeling based on ...

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3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Sun, Wei Heilongjiang International University Heilongjiang Harbin150025 China

ISBN: (纸本)9798331519032

Traditional English teaching evaluation has problems such as subjectivity, limitations, quantitative bias, and lacks feedback and support. The evaluation algorithm based on data analysis technology provides an accurate, comprehensive, and personalized evaluation method for teachers and students, which can understand their learning needs and ability levels by collecting and analyzing students' learning data. This study adopts a method based on data analysis technology to develop English teaching evaluation algorithms. Firstly, this paper collected students' learning data, including information on language expression, learning behavior, and academic performance. Then, machine learning and data mining techniques are used to analyze and model these data to predict students' English proficiency level and learning needs. The research results indicate that the F1 value range of the algorithm in this paper is 0.85-0.94, and the English teaching evaluation algorithm based on data analysis technology has achieved significant results in evaluating students' English proficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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Police data mining and Classification Based on PDCSC Algorithm in international Police Cooperation 24

Police Data Mining and Classification Based on PDCSC Algorit...

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3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Du, Qian Sun, Hongjian Shandong Police Coll Foreign Language Teaching & Researching Dept Jinan Shandong Peoples R China

ISBN: (纸本)9798400709777

Aiming at the problems of low data collaboration rate and weak storage capacity of police surveillance data in national border police cooperation, the research is based on Parallel Distributed Clustering with Semantic Constraints (PDCSC) algorithm. Police surveillance data processing model is constructed. The research firstly utilizes local density clustering algorithm to mine and classify the surveillance data, and then introduces the concept of parallelism and constructs the processing model using PDCSC algorithm. The results show that the clustering purity of police surveillance data based on PDCSC method is 92.37% and the data clustering accuracy is 93.67%. Meanwhile, the surveillance data summary of PDCSC method is 7108 which is 1085 and 2241 higher than that of DBSCAN and K-means. This indicates that the PDCSC algorithm can effectively process and classify police surveillance data, providing accurate police incident identification and analysis. The research aims to provide strong support for international police cooperation and improve the efficiency and accuracy of police work.

关键词： international police cooperation PDCSC algorithm Surveillance data data mining data classification

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machine learning methods for identifying destructive texts in the Kazakh language 24

Machine learning methods for identifying destructive texts i...

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3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Bolatbek, Milana Mussiraliyeva, Shynar Sagynay, Moldir Zhastay, Yeltay Al-Farabi Kazakh National University Kazakhstan

ISBN: (纸本)9798400718120

In the digital age, society is faced with an increasing number of destructive messages on the Internet, including insults, racism, violent extremism and national extremism. This paper presents a machine learning-based approach to classify destructive texts in the Kazakh language, collected from social networks. During the study a text corpora has been accumulated on social networks: YouTube, Vkontakte, Telegram. The study uses advanced text vectorization methods, including Bag of Words, TF-IDF, and Word2Vec, to preprocess text data. Our findings demonstrate that Logistic Regression outperforms other models, achieving an accuracy of 86%. These results contribute to the development of automated systems for identifying destructive content, promoting safer online communication". © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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