2 research outputs found
A Novel Multidimensional Reference Model For Heterogeneous Textual Datasets Using Context, Semantic And Syntactic Clues
With the advent of technology and use of latest devices, they produces
voluminous data. Out of it, 80% of the data are unstructured and remaining 20%
are structured and semi-structured. The produced data are in heterogeneous
format and without following any standards. Among heterogeneous (structured,
semi-structured and unstructured) data, textual data are nowadays used by
industries for prediction and visualization of future challenges. Extracting
useful information from it is really challenging for stakeholders due to
lexical and semantic matching. Few studies have been solving this issue by
using ontologies and semantic tools, but the main limitations of proposed work
were the less coverage of multidimensional terms. To solve this problem, this
study aims to produce a novel multidimensional reference model using
linguistics categories for heterogeneous textual datasets. The categories such
context, semantic and syntactic clues are focused along with their score. The
main contribution of MRM is that it checks each tokens with each term based on
indexing of linguistic categories such as synonym, antonym, formal, lexical
word order and co-occurrence. The experiments show that the percentage of MRM
is better than the state-of-the-art single dimension reference model in terms
of more coverage, linguistics categories and heterogeneous datasets.Comment: International Journal of Advanced Science and Applications, Volume
14, Issue 10, pp. 754-763, 202
Search-Based Fairness Testing: An Overview
Artificial Intelligence (AI) has demonstrated remarkable capabilities in
domains such as recruitment, finance, healthcare, and the judiciary. However,
biases in AI systems raise ethical and societal concerns, emphasizing the need
for effective fairness testing methods. This paper reviews current research on
fairness testing, particularly its application through search-based testing.
Our analysis highlights progress and identifies areas of improvement in
addressing AI systems biases. Future research should focus on leveraging
established search-based testing methodologies for fairness testing.Comment: IEEE International Conference on Computing (ICOCO 2023), Langkawi
Island, Malaysia, pp. 89-94, October 202