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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Jamuna S. Murthy
Ramaiah Institute of Technology
Computer Science & IT Engineering

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A real-time quantum-conscious multimodal option mining framework using deep learning Jamuna S. Murthy; Siddesh Gaddadevara Matt; Sri Krishna H. Venkatesh; Kedarnath R. Gubbi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1019-1025

Abstract

Option mining is an arising yet testing artificial intelligence function. It aims at finding the emotional states and enthusiastic substitutes of expounders associated with a discussion based on their suppositions, which are conveyed by various techniques of data. But there exist an abundance of intra and inter expression collaboration data that influences the feelings ofexpounders in a perplexing and dynamic manner. Step by step instructions to precisely and completely model convoluted associations is the critical issue of the field. To pervade this break, an innovative and extensive system for multimodal option mining framework called a “quantum-conscious multimodal option mining framework (QMF)”, is introduced. This uses numerical ceremoniousness of quantum hypothesis and a long transientmemory organization. QMF system comprise of a multiple-modal choice combination method roused by quantum obstruction hypothesis to catch the co- operations inside every expression and a solid feeble impact model motivated by quantum multimodal (QM) hypothesis to demonstrate the communications between nearby expressions. Broad examinations are led on two generally utilized conversational assessment datasets: the multimodal emotional lines dataset (MELD) and interactive emotional dyadic motion capture (IEMOCAP) datasets. The exploratory outcomes manifest that our methodology fundamentally outflanks a broadscope of guidelines and best in class models.
Co-Authors Kedarnath R. Gubbi Siddesh Gaddadevara Matt Sri Krishna H. Venkatesh