2 results listed
Traditional databases support data in one dimension
only and, consequently, can’t keep a complete history of changes
made to the state of the data over time. In contrast, the bitemporal
database automatically handles both dimensions such as
transaction time and valid time simultaneously. This advanced
approach offers greater integrity and traceability of data. This is
important in decision-making, for instance, in areas such as fraud
detection, compliance with law, digital forensics, and regulation
compliance. This research assesses articles obtainable from IEEE,
Scopus, ProQuest, PubMed, and Web of Science up to 2025 with a
systematic literature review methodology. Through the PRISMA
framework, 74 primary studies were acquired from 102 relevant
articles, providing a comprehensive and transparent evidential
basis. Next, we performed a scientometric analysis using
bibliometric tools like VOSviewer to examine citation trends,
keywords, leading authors, and prominent journals. This research
analyzes the temporal aspect of existing models of bitemporal
systems. Also, it summarizes the strengths and weaknesses of
bitemporal databases from existing studies. It examines each use
case that adopted bitemporality in its research and identifies the
research gaps. Finally, it reveals the new research directions for
upcoming technologies like Artificial Intelligence, cloud
architecture, blockchain, and improvement of bitemporal
databases to detect their probable use in various domains like
supply chain, healthcare, and financial. The originality of this
research contributes to the existing knowledge by offering a
scientometric analysis alongside a systematic literature review
while also identifying critical research gaps that need to be
addressed in future studies.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Gokul Neelamegam
Jagdev Bhogal
Parnia Samimi
Omer Ozturkoglu
The refugee problem is one of the most important
issues worldwide which has gained tremendous media attention
that has influenced public opinion in relation to displaced
populations. This study investigates the application of natural
language processing (NLP) to predict and analyze news articles
on the refugee crisis. The refugee crisis remains an urgent
humanitarian issue with over 117 million forcibly displaced
people by the end of 2024. This study aims to develop an
automated system to predict and analyze news articles on the
refugee crisis using NLP techniques to gain better insights into
migration patterns and highlight key areas for consideration.
Mass migration and asylum-seeking pose major challenges for
host country authorities, non-governmental organizations
(NGOs) and international humanitarian organizations trying to
solve the problems associated with the refugee crisis. Using news
datasets from sources such as The Guardian and Kaggle, the
study refined over 55,000 general category news articles to
extract 6344 refugee-related articles by fine-tuning a Large
Language Model (LLM), “Mistral 7b v0.3”. This study addresses
existing gaps in AI applications by employing LLM to predict key
themes, detect bias, and analyze media narratives on the refugee
crisis. The methodology follows the CRISP-DM framework and
uses pre-processing, prediction and visualization techniques. The
results of this study include highlighting key refugee issues such
as health, shelter, nutrition, security and women’s issues. In
addition, it identifies potential gaps in the treatment of
disadvantaged groups such as LGBTQ+ and disabled people. The
study shows that, LLM outperforms traditional keyword
searches, as three times more relevant articles were extracted
through LLM. The findings of this study give significant details to
strategy makers and NGOs to make more informed decisions on
these important issues. Limitations, such as resource
compatibility and dataset availability, affect the overall results
yet the study highlights the potential of Artificial Intelligence (AI)
in addressing the complexity of global crises and provides a
foundation for future work in multilingual and multimedia
analysis.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Ahmed Asif
Jagdev Bhogal