PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning

Gupta, Aakansha; Katarya, Rahul

doi:10.1016/j.compbiomed.2021.104920

Cited by 27 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LDA model has two Dirichlet distributions: Alpha (ɑ) controls the document-topic distribution, and Beta (β) controls the topic-word distribution. Figure 1 represents the graphical model of LDA, where and as highlighted in (Gupta & Katarya, 2021 ):…”

Section: Context and Problem Statementmentioning

confidence: 99%

Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques

et al. 2022

View full text Add to dashboard Cite

Interests play an essential role in the process of learning, thereby enriching learners ‘interests will yield to an enhanced experience in MOOCs. Learners interact freely and spontaneously on social media through different forms of user-generated content which contain hidden information that reveals their real interests and preferences. In this paper, we aim to identify and extract the topical interest from the text content shared by learners on social media to enrich their course preferences in MOOCs. We apply NLP pipeline and topic modeling techniques to the textual feature using three well-known topic models: Latent Dirichlet Allocation, Latent Semantic Analysis, and BERTopic. The results of our experimentation have shown that BERTopic performed better on the scrapped dataset.

show abstract

Section: Context and Problem Statementmentioning

confidence: 99%

Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The COVID-19 pandemic has resulted in a variety of real-world challenges that have compelled the attention of the scientific and research communities. Exemplifications of direct research challenges include investigations of trends and analysis of pertinent information (Gupta and Katarya 2021a , b ; Katarya et al. 2021 ), as well as automated ways to identify COVID-19 patients (Kedia and Katarya 2021 ; Gupta et al.…”

Section: Background and Related Workmentioning

confidence: 99%

A deep feature-level fusion model for masked face identity recommendation system

Thaipisutikul

Tatiyamaneekul

Lin

et al. 2022

J Ambient Intell Human Comput

View full text Add to dashboard Cite

The widespread occurrences of airborne outbreaks (e.g., COVID-19) and pollution (e.g., PM2.5) have urged people in the affected regions to protect themselves by wearing face masks. In certain areas, wearing masks amidst such health-endangering times is even enforced by law. While most people wear masks to guard themselves against airborne substances, some exploit such excuses and use face masks to conceal their identity for criminal purposes such as shoplifting, robbery, drug transport, and assault. While automatic face recognition models have been proposed, most of these models aim to identify clear, unobstructed faces for authentication purposes and cannot effectively handle cases where masks cover most facial areas. To mitigate such a problem, this paper proposes a deep-learning-based feature-fusion framework, FIREC , that combines additional demographic-estimated features such as age, gender, and race into the underlying facial representation to compensate for the information lost due to mask obstruction. Given an image of a masked face, our system recommends a ranked list of potential identities of the person behind the mask. Empirical results show that the best configuration of our proposed framework can recognize bare faces and masked faces with the accuracy of 99.34% and 97.65% in terms of Hit@10, respectively. The proposed framework could greatly benefit high-recall facial identity recognition applications such as identifying potential suspects from CCTV or passers-by’s cameras, especially during crisis times when people commonly cover their faces with protective masks.

show abstract

“…Big Data plays a variety of important roles which critically support the world's manufacturing, legal, financial, cybersecurity, Furthermore, these error rates were based on computational model error rates based on actual infection, death, and hospitalization rates based on data mining using Big Data analytics (Gupta, et al [7,8]).…”

Section: Emerging Diseases and Deep Technologiesmentioning

confidence: 99%

"Data Warehouses, Decision Support Systems, and Deep Technologies During the Global COVID-19 Pandemic"

Alexander

2022

BJSTR

View full text Add to dashboard Cite

Data storage for businesses involves the storage of such information as stocks, raw materials, deposits, and other such information related to the daily operations of the business.The architecture of such a system needs to be aimed at data management. Data warehousing uses technologies that allow data from multiple sources to be compared and analyzed so that businesses can the consolidated data to make decisions (Simion, et al. [1]). The database is built to implement the volume and the requirements of the system and help project managers and organizational managers make decisions related to the development of the business structure or further daily operations (Simion, et al. [1]). Furthermore, database applications improve the reliability and efficiency of the user and the ability to make decisions, store, update, and get answers through reports (Simion, et al. [1]).Communication with the essential departments of the organization is facilitated by the efforts of the component dialog. The analysis highlights the structure between the data analysis and the simplest

show abstract

PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning

Cited by 27 publications

References 25 publications

Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques

Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques

A deep feature-level fusion model for masked face identity recommendation system

"Data Warehouses, Decision Support Systems, and Deep Technologies During the Global COVID-19 Pandemic"

Contact Info

Product

Resources

About