Results 161 to 170 of about 5,406 (194)

Latent Dirichlet Allocation (LDA) Based on Automated Bug Severity Prediction Model

Lecture Notes on Data Engineering and Communications Technologies, 2022
Ritu Bibyan, Sameer Anand, Ajay Jaiswal
exaly   +2 more sources

Augmented Latent Dirichlet Allocation (Lda) Topic Model with Gaussian Mixture Topics

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Latent Dirichlet allocation (LDA) is a statistical model that is often used to discover topics or themes in a large collection of documents. In the LDA model, topics are modeled as discrete distributions over a finite vocabulary of words. The LDA is also a popular choice to model other datasets spanning a discrete domain, such as population genetics ...
Boyla Mainsah, Leslie Collins
exaly   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

An analysis of Disaster Risk Suggestions using Latent Dirichlet Allocation and Hierarchical Dirichlet Process (Nonparametric LDA)

2021 The 9th International Conference on Information Technology: IoT and Smart City, 2021
Ken Dalino Gorro, Glicerio A. Baguia, Moustafa F. Ali   +2 more
openaire   +1 more source

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
openaire   +1 more source

Topic Modeling Applied to Business Research: A Latent Dirichlet Allocation (LDA)-Based Classification for Organization Studies

2019
More than 1.5 million academic documents are published each year, and this trend shows an incremental tendency for the following years. One of the main challenges for the academic community is how to organize this huge volume of documentation to have a sense of the knowledge frontier.
Carlos Vílchez Román, Farita Huamán-Delgado, Sol Sanguinetti-Cordero   +2 more
openaire   +2 more sources

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
openaire   +1 more source