Mobile Ad-hoc Networks Topic Modelling and Dataset Querying
Jedidiah Aqui, Michael Hosein · 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC) · 2022
Topic Modelling is an unsupervised machine learning technique that’s capable of scanning a set of documents, detecting word and phrase patterns within them, and automatically clustering word groups and similar expressions that best characterize a set of documents. It is considered an unsupervised machine learning technique as it does not require a predefined list of tags or training data that’s been previously classified by humans. The research conducted in this paper can be viewed as supplemental to the qualitative analysis conducted in the sphere of MANETS and the current applications of machine learning, intrusion detection and risk calculation. This is accomplished via the use of Topic Modelling. The dissection, review and discussion of the empirical data is some of the key processes which stem from the outputs of utilizing topic modelling. The produced empirical data of this paper is intrinsic to the further understanding of the scope of studies done particularly in the realm of Risk Calculation and contemporary Machine Learning and Intrusion Detection research related to MANETS. This paper seeks to provide guiding indicators that direct the literary review of existing bodies of work.