Multimodal Artificial Intelligence and Large Language Models

A Comprehensive Guide from Theory to Practice

L Ashok Kumar, D Karthika Renuka, Tanvi Banerjee, C. Deisy

• Publisher: CRC Press
• Year: 2026
• Print ISBN:
  9781041152132
• Edition: 1
• eBook ISBN:
  9781040986844
• Topic: Technology & Engineering
• Subtopic: Computer Engineering
• Index: Technology & Engineering

The book provides a comprehensive technical analysis of multimodal artificial intelligence systems and implementation frameworks. It offers thorough coverage of cross-modal processing methods for use, including speech recognition and automatic image captioning.

• It presents a detailed discussion of architecture for integrating text, image, audio, and video modalities, cross-modal processing pipelines, and data fusion techniques.
• Showcases real-time synchronization mechanisms across different modalities and scalable design patterns for multimodal systems.
• Discusses multimodal emotion recognition using deep Learning techniques, focusing on recent advancements, challenges, and ethical considerations.
• Investigates deployment optimization strategies to address issues with latency, resource usage, and scalability of multimodal systems.
• Focuses on techniques for performance optimization, memory management, and distributed processing for multimodal workloads using frameworks like PyTorch and TensorFlow.

The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

Table of contents

• Cover
• Half-Title Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Acknowledgments
• About the Editors
• Contributors
• 1 Empowering Agentic AI with Multimodal Large Language Models: Toward Autonomous, Goal-Driven Intelligence
• 2 Integrating Large Language Models in Conversational AI: Enhancing Multimodal Interaction
• 3 Architectural Advances in Multimodal Conversational AI Using Large Language Models
• 4 Integration of LLMs for Conversation AI Multimodal Conversational AI: Leveraging Ensemble Open-Source Generative Models for Enhanced Human-Like Interactions
• 5 Privacy and Data Security Concerns in Multimodal AI
• 6 The Role of Generative AI in Shaping Multimodal Experiences: Trends, Techniques, and Upcoming Paths
• 7 Enhancing Privacy and Data Security in Multimodal LLMs through Cryptography and Blockchain Technology
• 8 Next-Generation Multimodal Conversational Agents: Affective Reasoning, Cross-Modal Dialogue, and Embodied Interaction
• 9 Multimodal Chatbot for Monitoring Emotional Well-being in Children: Key Design Challenges and Approaches
• 10 Multimodal Emotion Recognition with Deep Learning: Challenges and Ethical Considerations
• 11 Multimodal Emotion Recognition Using Spatial and Temporal Attention-Based Convolution Network
• 12 Real-Time Sign Language Recognition and Grammatically Correct, Coherent Sentence Formation Using Deep Learning
• 13 Emotion Detection across Modalities: A Deep Dive into Multimodal Systems
• 14 Multimodal Disentangled Representation Learning for Enhanced User Behavior Analysis in Recommendation Systems
• 15 Generative AI in Multimodal Biological Data: Transformations, Techniques, and Future Directions
• 16 Multimodal Deepfakes: A Comprehensive Understanding of the Role of Multimodal AI in Recent Deepfake Research and Development
• Index