Jerry Chun-Wei Lin
Biographic Data
| ID | 7565010 |
|---|---|
| NAME | Jerry Chun-Wei Lin |
| GIVEN NAMES | Jerry Chun-Wei |
| FAMILY NAME | Lin |
| SIGNATURE | LIN J C |
| AFFILIATIONS | Western Norway University of Applied Sciences |
| ORCID | 0000-0001-8768-9709 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Deep Explainable Hate Speech Active Learning on Social-Media Data
Hate speech is demonstrably aimed at social tension and violence. Detection becomes increasingly difficult as overlapping emotional feelings occur. However, there are still several unresolved issues with informal and indirect targeting of negative communication, including sarcasm, misrepresentation, and praise for the target's or society's immoral behavior. In this study, we proposed a method for instance selection based on attention network visu…
Semisupervised Federated Learning for Temporal News Hyperpatism Detection
The proliferation of false and erroneous information on the Internet has posed a challenge to the accurate exchange of information. To address this issue, a semisupervised system based on self-embedding has been proposed. This system verifies information before it is shared, allowing only reliable and accurate content to be disseminated and protecting individuals from the negative effects of false information. In this article, we present a news a…
Advanced Pattern-Mining System for Fake News Analysis
Decomposition MapReduce mining for fake news analysis (DMRM-FNA), a novel generic parallel pattern-mining framework, is developed in this article to solve difficulties in social network analysis using big data exploration. The first difficulty faced by existing techniques is the inability to retrieve actionable insights into the structure of fake news data. This can be solved by extracting patterns from fake news and matching them with real news …
Social Media Multiaspect Detection by Using Unsupervised Deep Active Attention
Depression is a severe medical condition that substantially impacts people’s daily lives. Recently, researchers have examined user-generated data from social media platforms to detect and diagnose this mental illness. As a result, in this paper we have focused on phrases used in personal remarks to solve recognizing grief on social media. This research aims to develop generalized attention networks (GATs) that employ masked self-attention layers …
Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis
This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitte…
Medicine Drug Name Detection Based Object Recognition Using Augmented Reality
Augmented Reality (AR) is an innovation that empowers us in coordinating computerized data into the client's real-world space. It offers an advanced and progressive methodology for medicines, providing medication training. AR aids in surgery planning, and patient therapy discloses complex medical circumstances to patients and their family members. With accelerated upgrades in innovation, the ever-increasing number of medical records get accessibl…
Knowledge graph based trajectory outlier detection in sustainable smart cities
Revealing top-k dominant individuals in incomplete data based on spark environment
No prominent works on this page.
Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis
This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitte…
Medicine Drug Name Detection Based Object Recognition Using Augmented Reality
Augmented Reality (AR) is an innovation that empowers us in coordinating computerized data into the client's real-world space. It offers an advanced and progressive methodology for medicines, providing medication training. AR aids in surgery planning, and patient therapy discloses complex medical circumstances to patients and their family members. With accelerated upgrades in innovation, the ever-increasing number of medical records get accessibl…
Knowledge graph based trajectory outlier detection in sustainable smart cities
Revealing top-k dominant individuals in incomplete data based on spark environment
Semisupervised Federated Learning for Temporal News Hyperpatism Detection
The proliferation of false and erroneous information on the Internet has posed a challenge to the accurate exchange of information. To address this issue, a semisupervised system based on self-embedding has been proposed. This system verifies information before it is shared, allowing only reliable and accurate content to be disseminated and protecting individuals from the negative effects of false information. In this article, we present a news a…
Advanced Pattern-Mining System for Fake News Analysis
Decomposition MapReduce mining for fake news analysis (DMRM-FNA), a novel generic parallel pattern-mining framework, is developed in this article to solve difficulties in social network analysis using big data exploration. The first difficulty faced by existing techniques is the inability to retrieve actionable insights into the structure of fake news data. This can be solved by extracting patterns from fake news and matching them with real news …
Social Media Multiaspect Detection by Using Unsupervised Deep Active Attention
Depression is a severe medical condition that substantially impacts people’s daily lives. Recently, researchers have examined user-generated data from social media platforms to detect and diagnose this mental illness. As a result, in this paper we have focused on phrases used in personal remarks to solve recognizing grief on social media. This research aims to develop generalized attention networks (GATs) that employ masked self-attention layers …
Deep Explainable Hate Speech Active Learning on Social-Media Data
Hate speech is demonstrably aimed at social tension and violence. Detection becomes increasingly difficult as overlapping emotional feelings occur. However, there are still several unresolved issues with informal and indirect targeting of negative communication, including sarcasm, misrepresentation, and praise for the target's or society's immoral behavior. In this study, we proposed a method for instance selection based on attention network visu…
Artificial Intelligence (8 works) · Computer Science (8 works) · Machine learning (5 works) · Data mining (4 works) · Information retrieval (3 works) · Lexicon (3 works) · Natural language processing (3 works) · Spam and Phishing Detection (3 works) · Big data (2 works) · Categorization (2 works)