Results 131 to 140 of about 1,075,906 (163)

LDA-AdaBoost.MH: Accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization

Journal of Information Science, 2014
AdaBoost.MH is a boosting algorithm that is considered to be one of the most accurate algorithms for multilabel classification. It works by iteratively building a committee of weak hypotheses of decision stumps. To build the weak hypotheses, in each iteration, AdaBoost.MH obtains the whole extracted features and examines them one by one to check their ...
Bassam Al-Salemi, Mohd Juzaiddin Ab Aziz, Shahrul Azman Noah   +2 more
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Extracting Promising Topics on Smart Manufacturing Based on Latent Dirichlet Allocation (LDA)

2019 International Conference on Information and Communication Technology Convergence (ICTC), 2019
Although smart manufacturing (SM) has attracted enormous attention, it is ambiguous how to realize it due to lack of practical evidence and academic knowledge on technological components. Accordingly, it is required to explore knowledge landscape to investigate promising technologies.
Young Seog Yoon, Junhee Lee, Kwangroh Park   +2 more
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Latent Dirichlet Allocation (LDA) for Anomaly Detection in Ground Vehicle Network Traffic

SAE Technical Paper Series, 2020
<title>ABSTRACT</title> <p>Latent Dirichlet Allocation (LDA) and Variational Inference are applied in near real-time to detect anomalies in ground vehicle network traffic for VICTORY enabled networks. The technical approach, that utilizes the Natural Language
Adam Thornton, Brandon Mieners, Donald Poole, Mark Russell   +3 more
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A Case-Study on Topic Modeling Approach with Latent Dirichlet Allocation (LDA) Model

2021
In natural language processing, subject displaying is a sort of factual information models for identifying the points from an enormous assortment of corpus of records. Subject demonstrating is a sort of text-digging device for revelation of stowed away semantic designs in a text body.
Abisheka Pon, C. Deisy, P. Sharmila
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Normalized Approach to Find Optimal Number of Topics in Latent Dirichlet Allocation (LDA)

2020
Feature extraction is one of the challenging works in the Machine Learning (ML) arena. The more features one able to extract correctly, the more accurate knowledge one can exploit from data. Latent Dirichlet Allocation (LDA) is a form of topic modeling used to extract features from text data.
Mahedi Hasan, Anichur Rahman, Md. Razaul Karim, Md. Saikat Islam Khan, Md. Jahidul Islam   +4 more
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MMDF-LDA: An improved Multi-Modal Latent Dirichlet Allocation model for social image annotation

Expert Systems with Applications, 2018
Abstract Social image annotation, which aims at inferring a set of semantic concepts for a social image, is an effective and straightforward way to facilitate social image search. Conventional approaches mainly demonstrated on adopting the visual features and tags, without considering other types of metadata.
Liu Zheng, Caiming Zhang 0001, Caixian Chen   +2 more
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An analysis of Disaster Risk Suggestions using Latent Dirichlet Allocation and Hierarchical Dirichlet Process (Nonparametric LDA)

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City, 2021
Ken Dalino Gorro, Glicerio A. Baguia, Moustafa F. Ali   +2 more
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Topic Modelling using Latent Dirichlet Allocation (LDA) and Analysis of Students Sentiments

2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2023
Ontiretse Ishmael, Etain Kiely, Cormac Quigley, Donal McGinty   +3 more
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Enhancing E-Learning Recommendation Systems using Latent Dirichlet Allocation (LDA)

Mesopotamian Journal of Big Data
Although the wide range of instructional materials, might overcome students and limit their capability to learn effectively, platform of education become important for providing personalized learning. Traditional recommendation systems (RS) try to resolve this issue; yet, they fail in understanding learning content semantically.
Zanbaq Hikmet Thanon, Javad Salimi Sartakhti   +1 more
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Dynamic Origin/Destination modeling with Gravity-Latent Dirichlet Allocation (G-LDA)

2016
The proposed approach follows and develops the research direction initiated almost 50 years ago, during which Origin/Destination (O/D) matrices were in a very simple way evaluated by means of Gravity Models (see Casey (1955)). In the classical framework, the population density of locations and the distance between two locations are often considered as ...
Randriamanamihaga, Andry, Come, Etienne, Oukhellou, Latifa   +2 more
openaire   +1 more source