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2016 international conference on data mining and advanced computing, sapience 2016

ISBN: (纸本)9781467385947

The proceedings contain 67 papers. The topics discussed include: VRITTHI - a theoretical framework for IT recruitment based on machine learning techniques applied over Twitter, LinkedIn, SPOJ and GitHub profiles;an implementation of efficient techniques for tree based mining in human social dynamics;performance evaluation of classifiers for spam detection with benchmark datasets;a survey of diversification techniques in recommendation systems;book recommendation system through content based and collaborative filtering method;modified k-means algorithm and genetic approach for cluster optimization;feature selection for mining SNP from leukaemia cancer using genetic algorithm with BCQ;prediction of election result by enhanced sentiment analysis on twitter data using classifier ensemble approach;attribute selection and customer churn prediction in telecom industry;prediction of students performance using educational data mining;implementation of Rete algorithm using course finder system;co-clustering with side information for text mining;a binomial heap extractor for automatic keyword;a review on data mining techniques and factors used in educational data mining to predict student amelioration;and a new efficient watershed based color image segmentation approach.

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A Comprehensive Investigation into the Dimensions of Educational data mining Using Artificial Intelligence

A Comprehensive Investigation into the Dimensions of Educati...

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2023 international conference on advanced computing and Communication Technologies, ICACCTech 2023

作者： Mahawar, Kajal Rattan, Punam School of Computer Applications Lovely Professional University Phagwara India

ISBN: (纸本)9798350380880

Artificial intelligence in education (AIEd) is one rapidly developing area. The significant challenge for researchers is determining the best machine learning approach to use when submitting a study to AIEd. The purpose of this research is to provide a comprehensive and scientific assessment of important AIEd studies across five domains. This study looked into surveys and analyses of 25 papers, the majority of which sought to identify the most effective approaches to using machine learning tools in education. These analyses were based on identified research gaps and suggested future directions. Researchers want to know which AI techniques are more beneficial and which educational data mining (EDM) dimensions are limited and prominent in AIEd research. From 2016 to 2023, this article includes significant research studies based on five critical EDM with AIEd dimensions: decision support system, performance evaluation, text mining, outlier detection, and opinion mining. © 2023 IEEE.

关键词： Decision support systems

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data and Analysis Preservation in the PHENIX Experiment at RHIC 20

Data and Analysis Preservation in the PHENIX Experiment at R...

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20th international Workshop on advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Potekhin, M. Brookhaven Natl Lab Phys Dept Upton NY 11973 USA

The PHENIX experiment (the Pioneering High Energy Nuclear Interaction eXperiment) is the largest of the four experiments that have operated at the Relativistic Heavy Ion Collider (RHIC), taking data in 2000-2016. PHENIX has made fundamental contributions to the discovery and study of the Quark-Gluon Plasma, advancement of spin physics and other areas. Currently, the PHENIX Collaboration is analyzing large data samples previously collected, while facing challenges in the area of data and Analysis Preservation. We describe the strategy and practices employed by the PHENIX Collaboration to meet these challenges by leveraging state-of-the-art platforms and tools created and maintained by the High Energy and Nuclear Physics communities, including Zenodo, HEPdata, Opendata, REANA and others.

关键词： Heavy ions

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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals data

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data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601387

The proceedings contain 49 papers. The special focus in this conference is on Frontiers of Intelligent computing: Theory and Applications. The topics include: Nepali Word Spelling Correction Using Ensemble Learning Technique;the Impact of Information Security Policies and Innovations in Digital Technology on the Transformation of Healthcare in Developing Nations: A Literature Review;integrating Multi-omics and Clinical Narratives for Predictive Modeling: Genomics, Transcriptomics, Proteomics, and Medical Texts in Disease Analysis;a Comprehensive Survey of Fake Review Detection Technology;a Hybrid Intelligent Decision Support System for Automating Financial Credit Evaluation;Advancing Taxation in the New Era: Enhancing Tax Ratios with the Core Tax Administration System (CTAS);Enabling Grid Stability: Harnessing μPMU data for data-Driven Analysis of Grid Frequency Events;machine Learning Techniques to Detect Fake News on Social Media: A Systematic Review;cutting-Edge Technology-Based Social Enterprises in India for Sustainable, Inclusive Healthcare;enhancing Telugu Sarcasm Classification Models with Word Embeddings in Imbalanced datasets;capital Punishment: Analyzing Trends in the United States (1976–2016);exploring the Technostress Issue Among Indonesian Young Entrepreneurs;Teaching Computer Science Using Cloud-Based IDE with Perspectives in Inclusivity;Comparison of data Encryption Standard (DES) and advanced Encryption Standard (AES) in Security Issues of Cloud computing: A Literature Review;enhancing Corporate and Factory Training Through Game Development: A Comparative Review;usability of Intelligent System in Implementing Tutoring Lesson in Education: A Literature Review;performance Efficiency of Cloud computing—A Literature Review;decentralized Finance (DeFi) Wallets: A Review of its Efficiency, Usability, and Effectiveness;Social Networks on WEB 3.0.

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601424

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601462

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Multiagent-based Youtube Content Discovery Bot 15

Multiagent-based Youtube Content Discovery Bot

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15th IEEE/ACM Annual international conference on Advances in Social Networks Analysis and mining (ASONAM)

作者： Solaiman, Ishmam Ahmed Agarwal, Nitin Univ Arkansas COSMOS Res Ctr Little Rock AR 72204 USA

ISBN: (纸本)9798400704093

YouTube Content Discovery Bot (YTCDB) is a cutting-edge multi-agent system designed to revolutionize the video discovery process. Traditionally, researchers have faced the arduous task of manually sorting through YouTube videos to find relevant content. YTCDB leverages an analytics-driven approach to autonomously locate videos given a seed video. Each task or process within YTCDB, such as comment scraping, gathering statistics, and collecting channel data, can be efficiently handled by one or multiple agents working in tandem. This distributed approach allows for seamless coordination and delegation of tasks, ensuring optimal performance and scalability. Insights gathered from video barcoding and content analysis of the video metadata and transcripts from the initial round of video discovery are used to propagate further exploration and data collection. This feedback loop refines the search criteria to provide a focused search through the endless content on YouTube. This multi-agent system represents a significant advancement in analytics-driven video discovery and in facilitating efficient data collection, analysis, and knowledge extraction in a time-efficient manner.

关键词： YouTube data collection video discovery multi-agent system recommendation API analytics-driven insights video content analysis

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ProtoMix: Learnable data Augmentation on Few-Shot Features with Vector Quantization in CTR Prediction 9th

ProtoMix: Learnable Data Augmentation on Few-Shot Features ...

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19th international conference on advanced data mining and Applications, ADMA 2023

作者： Zhao, Haijun Xu, Ronghai Wang, Chang-Dong Jiang, Ying School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783031466601

Click-Through Rate (CTR) prediction is a critical problem in recommendation systems since it involves enormous business interest. Most deep CTR model follows an Embedding & Feature Interaction paradigm. However, the feature interaction module cannot work well without a good embedding representation of features. Due to the long-tail phenomenon in real scenes, few samples are provided in the dataset for a large proportion of features. In this paper, we present ProtoMix, a model-agnostic framework for learnable data augmentation on few-shot features in CTR prediction. ProtoMix automatically extracts information from co-occurred features within the same instance to assign prototype embedding with vector quantization for few-shot features and further synthesize the embedding representation of the augmented virtual instance for training. Original embedding, feature interaction module, and the embedding generator are jointly trained on a well-designed objective in an end-to-end manner in ProtoMix. We experimentally validate the effectiveness and compatibility of ProtoMix by comparing it with baseline and other data augmentation methods on different deep CTR models and multiple real-world CTR benchmark datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Embeddings

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Knowledge Graph Embedding for Topical and Entity Classification in Multi-Source Social Network data 15

Knowledge Graph Embedding for Topical and Entity Classificat...

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15th IEEE/ACM Annual international conference on Advances in Social Networks Analysis and mining (ASONAM)

作者： Akinnubi, Abiola Agarwal, Nitin Alassad, Mustafa Ajiboye, Jeremiah Univ Arkansas COSMOS Res Ctr Little Rock AR 72204 USA Univ East London London England

ISBN: (纸本)9798400704093

Historically, online data has provided meaningful insights for information mining, leading to the adoption of knowledge graphs for application to online data. Knowledge embedding has become an important aspect of encoding and decoding links, relationships, and predicting the ties of an entity to an existing knowledge graph. This study applied topic modeling to extract topics, entities, and themes from heterogeneous web data from different sources around the Indo-Pacific region and modeled a knowledge graph. The knowledge graph was subjected to knowledge embedding by applying four scoring mechanisms: ComplEx, TransE, DistMult, and HolE, on a domain knowledge graph of Indo-Pacific Belt and Road initiatives to determine whether it was capable of revealing missing insights. This work significantly uses knowledge graphs and embedding to understand socioeconomic-related discussions online. Valuable insights were gained from the data in this research's clustering results of knowledge embedding. Important themes such as NASAKOM and BRI were identified in Cluster 0. Cluster 1 contained themes that discussed Marxist movements synonymous with Indonesia, and Cluster 2 showed themes on China's road policies, such as Asia-Pacific Economic Cooperation and Export-Import Bank China. Cluster 3 focused mainly on China's economic policies and the Philippines. Overall, this study demonstrates the usefulness of topic modeling and knowledge embedding in uncovering insights from online data and has implications for understanding socioeconomic trends in the Indo-Pacific region.

关键词： Multi-Source Knowledge Graph Knowledge Embedding Belt and Road Initiative Knowledge Graph Clustering Classification Blogosphere

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