The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity
Abstract
Sri Lanka is one of the global biodiversity hotspots that contain a large variety of fauna and flora. But nowadays Sri Lankan wildlife has faced many issues because of poor management and policies to protect wildlife. The lack of technical and research support leads many researchers to retreat to select wildlife as their domain of study. This study demonstrates a novel approach to data mining to find hidden keywords and automated labeling for past research work in this area. Then use those results to predict the trending topics of researches in the field of biodiversity. To model topics and extract the main keywords, the authors used the latent dirichlet allocation (LDA) algorithms. Using the topic modeling performance, an ontology model was also developed to describe the relationships between each keyword. They classified the research papers using the artificial neural network (ANN) using ontology instances to predict the future gaps for wildlife research papers. The automatic classification and labeling will lead many researchers to find their desired research papers accurately and quickly.
Related Content
Elisha Mupaikwa, Kelvin Joseph Bwalya.
© 2024.
28 pages.
|
Nkholedzeni Sidney Netshakhuma.
© 2024.
21 pages.
|
Amrita Sarkar, Satyaki Sarkar.
© 2024.
27 pages.
|
Ahmad Said, Yulita Hanum P. Iskandar.
© 2024.
17 pages.
|
Manish Kumar.
© 2024.
19 pages.
|
Stansilas Bigirimana, Ganyanhewe Masanga.
© 2024.
22 pages.
|
Mampilo M. Phahlane.
© 2024.
20 pages.
|
|
|