“Fusing Words and Pixels” : Building a CNN-Driven Chatbot for Rich Multimodal Experiences

“Fusing Words and Pixels” : Building a CNN-Driven Chatbot for Rich Multimodal Experiences

Registration ID: IJNRD_210316

Published ID: IJNRD2312035

DOI: Click Here to Get

DEEPAK P , Vaishnavi BS , Ramya BN

— Convolutional Neural Networks (CNNs), Chatbot, Natural Language Understanding, Conversational Agents, Neural Network Architecture, NLP (Natural Language Processing), Hierarchical Feature Extraction.

Improving natural language understanding in chatbots is a critical topic in the quickly changing field of conversational agents, which is addressed in this research. While effective, current models sometimes have trouble capturing complex linguistic subtleties and context. We provide a unique paradigm for chatbots that makes use of convolutional neural networks (CNNs) in order to get around these restrictions. Our method makes use of the spatial hierarchies that CNNs have learnt, which have shown impressive performance in tasks involving images. We modify this design for use in natural language processing, where filters are used to identify linguistic links and patterns in textual input. The CNN-based design of the chatbot uses many layers to extract hierarchical characteristics, allowing for a more sophisticated understanding of user inputs to enable efficient training, the technique entails the methodical gathering and preparation of a varied dataset. To enable efficient training, the technique entails systematic gathering and preparation of a varied dataset. In order to attain peak performance, the CNN-based chatbot is put through a rigorous training process that includes optimization methods and hyperparameter fine-tuning. The method we used is effective, as demonstrated by the results of experiments, which show significant gains in accuracy, precision, and contextual comprehension over traditional chatbot designs. The results are analyzed and discussed, with a focus on how important the suggested CNN-based chatbot is to the advancement of conversational agent technology. Comparative studies with current models highlight our approach's unique features and establish it as a potential development in the area. Beyond theoretical renders, this study investigates the real-world uses of CNN-based chatbots in customer service, medical, and educational settings. We recognize the inherent constraints of our work and provide directions for future research with the goal of resolving these issues and improving CNN-based chatbot capabilities.

Published Paper   E-Certificate

"“Fusing Words and Pixels” : Building a CNN-Driven Chatbot for Rich Multimodal Experiences", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 12, page no.a267-a271, December-2023, Available :https://ijnrd.org/papers/IJNRD2312035.pdf

Volume 8 Issue 12, December-2023

Pages : a267-a271

Paper Reg. ID: IJNRD_210316

Published Paper Id: IJNRD2312035

Downloads: 000121161

Research Area: Computer Science & Technology 

Country: Bangalore, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2312035.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312035

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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