Results 181 to 190 of about 23,460 (215)
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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), 2019Although 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 +2 more
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PROSIDING SEMINAR NASIONAL SAINS DATA, 2023
The 2022 G20 Bali summit in Indonesia provides an opportunity to revitalize the tourism industry after the Covid-19 pandemic. The perspectives of foreign tourists visiting Indonesia, including the advantages and disadvantages of their tourism experience are an important aspect to improve tourism in Indonesia.
Arya Duta Gordra Sumitro Putra +5 more
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The 2022 G20 Bali summit in Indonesia provides an opportunity to revitalize the tourism industry after the Covid-19 pandemic. The perspectives of foreign tourists visiting Indonesia, including the advantages and disadvantages of their tourism experience are an important aspect to improve tourism in Indonesia.
Arya Duta Gordra Sumitro Putra +5 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 +3 more
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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 +2 more
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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 +2 more
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A Case-Study on Topic Modeling Approach with Latent Dirichlet Allocation (LDA) Model
2021In 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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Analisis Pemodelan Topik Saran Pengguna Jalan Tol dengan Latent Dirichlet Allocation (LDA)
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)Service and safety for toll road users are two crucial aspects in the operation of toll roads. This research aims to evaluate and analyze toll road users feedback after receiving assistance by officers and determine the dimensions of Service Quality that have been fulfilled from the distribution of topics obtained.
Ashrul Khair, A. N. Hidayanto
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Normalized Approach to Find Optimal Number of Topics in Latent Dirichlet Allocation (LDA)
2020Feature 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 +4 more
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Sentiment Analysis of AI Generated Music Using Latent Dirichlet Allocation (LDA)
https://aimc2024.pubpub.org/pub/ofarmc55/release ...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), 2023Ontiretse Ishmael +3 more
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NEWPORT INTERNATIONAL JOURNAL OF ENGINEERING AND PHYSICAL SCIENCES
In an era dominated by an unprecedented deluge of textual information, the need for effective methods to make sense of large datasets is more pressing than ever. This article takes a pragmatic approach to unraveling the intricacies of topic modeling, with a specific focus on the widely used Latent Dirichlet Allocation (LDA) algorithm.
Ugorji C. Calistus +3 more
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In an era dominated by an unprecedented deluge of textual information, the need for effective methods to make sense of large datasets is more pressing than ever. This article takes a pragmatic approach to unraveling the intricacies of topic modeling, with a specific focus on the widely used Latent Dirichlet Allocation (LDA) algorithm.
Ugorji C. Calistus +3 more
openaire +1 more source