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检索条件"任意字段=5th Conference on Data Mining and Knowledge Discovery"

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5th International Workshop on Machine Learning and data mining for Sports Analytics, MLSA 2018, colocated with the European conference on Machine Learning and Principles and Practice of knowledge discovery in databases, ECML PKDD 2018

5th International Workshop on Machine Learning and Data Mini...

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5th International Workshop on Machine Learning and data mining for Sports Analytics, MLSA 2018, colocated with the European conference on Machine Learning and Principles and Practice of knowledge discovery in databases, ECML PKDD 2018

ISBN: (纸本)9783030172732

the proceedings contain 15 papers. the special focus in this conference is on Machine Learning and data mining for Sports Analytics. the topics include: Real-time power performance prediction in tour de france;automatic classification of strike techniques using limb trajectory data;predicting pass receiver in football using distance based features;football pass prediction using player locations;deep learning from spatial relations for soccer pass prediction;predicting the receivers of football passes;forecasting the fifa world cup – combining result- and goal-based team ability parameters;distinguishing between roles of football players in play-by-play match event data;player valuation in European football;ranking the teams in european football leagues with agony;Interpreting deep sports analytics: Valuing actions and players in the NHL;player pairs valuation in ice hockey;Model trees for identifying exceptional players in the NHL and NBA drafts.

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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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epiDAMIK 5.0: the 5th International Workshop on Epidemiology meets data mining and knowledge discovery 22

epiDAMIK 5.0: The 5th International Workshop on Epidemiology...

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28th ACM SIGKDD conference on knowledge discovery and data mining (KKD)

作者： Adhikari, Bijaya Yadav, Amulya Pei, Sen Srivastava, Ajitesh Kefayati, Sarah Rodriguez, Alexander Charpignon, Marie-Laure Vullikanti, Anil Prakash, B. Aditya Univ Iowa Iowa City IA 52240 USA Penn State Univ University Pk PA 16802 USA Columbia Univ New York NY USA Univ Southern Calif Los Angeles CA 90007 USA IBM Corp Boston MA USA Georgia Inst Technol Atlanta GA 30332 USA MIT Boston MA USA Univ Virginia Charlottesville VA USA

ISBN: (纸本)9781450393850

Similar to previous iterations, the epiDAMIK @ KDD workshop is a forum to promote data driven approaches in epidemiology and public health research. Even after the devastating impact of COVID-19 pandemic, data driven approaches are not as widely studied in epidemiology, as they are in other spaces. We aim to promote and raise the profile of the emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. the current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health. Homepage: https://***/.

关键词： epidemiology public health forecasting AI for good

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International Workshop on data Science and knowledge Graph, DSKG 2019, 5th International Workshop on knowledge discovery on the Web, KDWEB 2019 and 2nd International Workshop on Maturity of Web Engineering Practices, MATWEP 2019, co-located with the 19th International conference on Web Engineering, ICWE 2019

International Workshop on Data Science and Knowledge Graph, ...

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International Workshop on data Science and knowledge Graph, DSKG 2019, 5th International Workshop on knowledge discovery on the Web, KDWEB 2019 and 2nd International Workshop on Maturity of Web Engineering Practices, MATWEP 2019, co-located with the 19th International conference on Web Engineering, ICWE 2019

ISBN: (纸本)9783030512521

the proceedings contain 11 papers. the special focus in this conference is on data Science and knowledge Graph. the topics include: Environment-Aware and Human-Centric Software Testing Framework for Cyber-Physical Systems;Technical Invention Trend Analysis of Applicants Based on CPC Classification;a Study on Automatic Keyphrase Extraction and Its Refinement for Scientific Articles;a Study on the Characteristics of the Long-Term Web Forum Users Using Social Network Analysis;knowledge Exploration from Tables on the Web;context-Dependent Token-Wise Variational Autoencoder for Topic Modeling;extraction of Relations Between Entities from Human-Generated Content on Social Networks;social Networks as Communication Channels: A Logical Approach;dataset Anonyization on Cloud: Open Problems and Perspectives.

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KDH 2020 - Proceedings of the 5th International Workshop on knowledge discovery in Healthcare data, co-located with 24th European conference on Artificial Intelligence, ECAI 2020

KDH 2020 - Proceedings of the 5th International Workshop on ...

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5th International Workshop on knowledge discovery in Healthcare data, KDH 2020

the proceedings contain 27 papers. the topics discussed include: interactive explanations of internal representations of neural network layers: an exploratory study on outcome prediction of comatose patients;comparison of forecasting algorithms for type 1 diabetic glucose prediction on 30 and 60-minute prediction horizons;uncertainty quantification in chest x-ray image classification using Bayesian deep neural networks;prognosis prediction in covid-19 patients from lab tests and x-ray data through randomized decision trees;knowledge discovery and visualization in healthcare datasets using formal concept analysis and graph databases;a general neural architecture for carbohydrate and bolus recommendations in type 1 diabetes management;region proposal network for lung nodule detection and segmentation;and in silico comparison of continuous glucose monitor failure mode strategies for an artificial pancreas.

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the 5th International Workshop on Talent and Management Computing (TMC'2024) 24

The 5th International Workshop on Talent and Management Comp...

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30th ACM SIGKDD conference on knowledge discovery and data mining

作者： Zhu, Hengshu Ge, Yong Xiong, Hui Lim, Ee-Peng BOSS Zhipin Career Sci Lab Beijing Peoples R China Univ Arizona Tucson AZ USA HKUST GZ Thrust Artificial Intelligence Guangzhou Guangdong Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (纸本)9798400704901

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with talent and management-related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision-making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 5th International Workshop on Talent and Management Computing (TMC'2024), is to bring together researchers and practitioners to discuss both the critical problems faced by talent and management-related domains and potential data-driven solutions by leveraging state-of-the-art data mining technologies.

关键词： Talent behavior modeling professional social networks group based decision making strategic management

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A Random-function-based Observation-weighting Method for mining Actionable Behavioral Rules 5

A Random-function-based Observation-weighting Method for Min...

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5th Information Communication Technologies conference (ICTC)

作者： Liu, Yuan Qilu Normal Univ Sch Presch Educ Jinan Peoples R China

ISBN: (纸本)9798350373530;9798350373523

One of the critical challenges faced by the mainstream data mining community is to make the mined patterns or knowledge actionable. knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior in the users' best interest. the problem of mining such rules is a search problem in a framework of support and expected utility. the previous definition of a rule's support assumes that each instance which supports a rule has the uniform contribution to the support. However, this assumption is usually violated in practice to some extent, and thus will hinder the performance of algorithms for mining such rules. In this paper, to handle this problem, an observation-weighting model for support based on random function and corresponding mining algorithm are proposed. the experimental results strongly suggest the validity and the superiority of our approach.

关键词： actionable behavioral rules instance weighting actionable knowledge discovery

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Enhancing the Scalability of knowledge discovery Services for Massive data mining on Clouds 5

Enhancing the Scalability of Knowledge Discovery Services fo...

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5th International conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sajjan, Vijayakumar Kumar, Jayam Pradeep Kalvala, Vikram Domala, Narsaiah Kumar, B. Kiran Pavan Kumar Hota, C.P. Malla Reddy University Department of CSE Hyderabad India Chaitanya Deemed to Be University Department of ECE Hyderabad India MLR Institute of Technology Department of ECE Hyderabad India Shri Vishnu Engineering College for Women Department of Artificial Intelligence Bhimavaram India

ISBN: (纸本)9798331518097

the traditional data mining techniques do not work well when it comes to the big data and the tremendous growth rate of the digital data also becomes a major challenge to the discovery of knowledge. Cloud computing offers an ideal solution to computationally and storage intensive tasks with scalable and on-demand services that can process big data at short notice. this paper reviews the current models that employ cloud structure for distributed data mining as well as models like CosDic and the data mining Cloud Framework (DMCF). the way of processing large datasets is also represented well by the Map Reduce model where it is possible to implement the data processing in parallel on several nodes. Despite the fact that cloud-based systems are very useful when it comes to the management of large amounts of data, challenges such as privacy, data security and data coordination still persist. these innovative methodologies include Edge Computing and Federated Learning that are anticipated to solve the mentioned issues by enhancing the speed of data processing and its safety. thus, scalable cloud data mining to meet growing needs of knowledge discovery in big data contexts will have to develop new privacy protection mechanisms, integration of cloud models, and complex algorithms. © 2024 IEEE.

关键词： Cloud platforms

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An Introduction to KDB: knowledge discovery in Biodiversity 5th

An Introduction to KDB: Knowledge Discovery in Biodiversit...

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5th International conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Ghosh, Moumita Mondal, Sourav Roy, Anirban Mondal, Kartick Chandra Department of Information Technology Jadavpur University Kolkata India West Bengal Biodiversity Board Kolkata India

ISBN: (纸本)9783031488788

the most basic method of experimentation using data mining algorithms is the command prompt. A convenient approach of interactive graphical user interfaces can be supplied for data exploration to build up complex studies. A graphical user interface gives an upgraded status for experimental data mining. An innovative proposal for employing data mining methodology on biodiversity data is shown in this article through the KDB (knowledge discovery in Biodiversity). It provides a platform for domain researchers to apply their datasets to domain-specific data mining algorithms for further analysis. A convenient interactive graphical user interface is provided for data exploration for the biodiversity domain to build up complex studies. the proposed data mining methods are developed in Java, while the website is built in PHP. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biodiversity

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knowledge Extraction from Distributed Heterogeneous data Sources 5

Knowledge Extraction from Distributed Heterogeneous Data Sou...

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5th IEEE International conference for Emerging Technology, INCET 2024

作者： Ramachandran, Raji Abhijith, K. Karthik, J.J. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361155

the growing volume and complexity of unstructured and semi-structured data pose a significant challenge in extracting meaningful and relevant information. Information Extraction (IE) emerges as a powerful technique to transform unstructured data into structured format, facilitating further analysis and knowledge discovery. IE techniques can effectively extract information from unstructured data in the form of entities, relationships, objects, and events. Our objective is to develop a method for extracting or filtering out the most relevant information from data sources which are heterogeneous in nature as well as they are distributed over many systems and hence build a unified schema based on the collective knowledge extracted from the data sources. Several existing methods have been developed to extract knowledge from heterogeneous datasets. We focuses on datasets in the medical field that are distributed over multiple systems. Specifically, we aim to extract valuable information from patient records, including patient details, medical conditions and medications consumed. the unified schema can be utilized to study a new patient's condition and assist doctors in making a pre-examination of a patient by querying it. © 2024 IEEE.

关键词： data mining

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