Welcome to CITE 2023

International Conference on Computing and Information Technology (CITE 2023)

March 11-12, 2023, Virtual Conference

Accepted Papers
Natural Language Processing

Graduate Student, Colorado Technical University, Colorado, USA


Hints of the outbreak are detected through the modified circumstances favoring the outbreaks, like the warm weather contributing to epidermal outbreaks or the loss of sanitation leading to cholera outbreaks typically relying on the routine reports from the healthcare facilities, secondary data like attendance monitoring at workplaces and schools, the web, and the media play a significant informational source with more than 60% of the initial outbreak reporting to the informal sources. (Abbood; Busche; Ghozzi. et al., Nov 2020) Through the application of natural language processing methods and machine learning technologies, a pipeline is developed which extracts the critical entities like country, confirmed case counts, disease, and case dates, which are mandatory entities from the epidemiological article and are saved in the database thereby facilitating the data entry easier. (Abbood; Busche; Ghozzi. et al., Nov 2020) The advantages are the facilitation of relevant score articles shown first, thereby providing the web service results termed EventEpi integrated into the Event Based Surveillance (EBS) workflows.


Event-based surveillance; Contribution; Methods; Information extraction; Entity filtering; Scoring; Evaluation.

Barracuda, an Open Source Framework for Parallelizing Divide and Conquer Algorithm

Abdourahmane Senghor, Department of Computer Science, Cheikh Anta Diop University, Dakar, Senegal


This paper presents a newly-created Barracuda open-source framework which aims to parallelize Java divide and conquer applications. This framework exploits implicit for-loop parallelism in dividing and merging operations. So, this makes it a mixture of parallel for-loop and task parallelism. It targets shared-memory multiprocessors and hybrid distributed shared-memory architectures. We highlight the effectiveness of the framework and focus on the performance gain and programming effort by using this framework. Barracuda aims at large public actors as well as various application domains. In terms of performance achievement, it is very close to Fork/Join framework while allowing end-users to only focus on refactoring code and experts to have the opportunity to improve it.


divide-and-conquer; task parallelism; parallel for-loop; Fork/Join framework; shared-memory multiprocessors