Neuro-Symbolic Artificial Intelligence: Concepts and Applications

Format:

Book

Contributor:

Nidhya, R.

Kumar, A. Dinesh

Peng, Sheng-Lung

Karthik, S.

Balamurugan, S.

Language:

English

Subjects (All):

Artificial intelligence.

Pattern recognition systems.

Deep learning (Machine learning).

Physical Description:

1 online resource (1 p.)

Place of Publication:

John Wiley and Sons, Inc., 2026.

Summary:

Master the next frontier of artificial intelligence with this essential guide to uniting the pattern recognition of deep learning with the transparent, logical reasoning of symbolic AI. The field of artificial intelligence has witnessed rapid advancements in recent years, driven primarily by deep learning and data-centric approaches. Despite their impressive performance, purely neural methods often lack interpretability, logical reasoning capabilities, and the ability to generalize beyond training data. In contrast, symbolic AI, rooted in formal logic and structured representations, offers transparency and reasoning strength, but struggles with adaptability and learning from raw data. In response to these challenges, neuro-symbolic AI has emerged as a compelling paradigm that unifies the strengths of both approaches. This book is a comprehensive exploration of one of the most transformative frontiers in artificial intelligence. By combining the pattern recognition power of neural networks with the logical reasoning capabilities of symbolic systems, neuro-symbolic AI promises to deliver systems that are not only accurate but also interpretable, adaptable, and aligned with human cognitive processes. This book brings together a diverse range of research contributions that showcase both foundational theory and practical applications across domains like natural language processing, healthcare, intelligent transport, cybersecurity, and ethical AI. Spanning topics such as hybrid architectures, logic-enhanced deep learning, graph neural networks, transfer learning, and explainable AI, the volume addresses the technical and conceptual challenges of building trustworthy intelligent systems. Each chapter provides technical depth, experimental insights, and future directions, making this guide a vital resource for researchers, graduate students, and professionals in AI and machine learning. Readers will find the volume introduces the fundamental concepts of neuro-symbolic AI, explores real-world applications in healthcare, natural language processing, and ethical AI, and presents a forward-looking perspective on the next generation of robust, transparent, and trustworthy AI technologies. Audience Engineering research scholars and students, IT professionals, network administrators, artificial intelligence and deep learning experts, and government research agencies.

Contents:

Series Preface xvPreface xviiAcknowledgements xxi Part 1: Neuro-Symbolic AI: Concepts 1 1 Cataract Detection Systems Using Deep Learning Technique: A Survey 3Arveti Mallikharjuna Rao and Suma Kamalesh Gandhimathi 1.1 Introduction 41.2 Deep Learning Models 61.3 Conclusion and Future Work 14 2 Agentic AI Workflows for Financial Large Language Models Using LlaMA and LangChain Framework 19Mantri Udaya Jyothi, John Deva Prasanna D. S., Shanthini A. and Balasubramani S. 2.1 Introduction 202.2 Predict Stock Using Financial Analysis 212.3 Stock Market Prediction Using Agentic AI Using LLM 232.4 Llama Framework 242.5 Llama Framework Reduces AI Trading Risks 272.6 Working Principle of Agents 282.7 Results 332.8 Conclusion 36 3 Brain-Inspired Artificial Neural Network for Energy-Efficient and Adaptive Learning 39R. Dhanalakshmi, Sahaya Beni Prathiba, Kavisankar L., Balasubramani S. and Pandiyanathan M. 3.1 Introduction 403.2 Literature Review 403.3 Methodology 423.4 Proposed System 463.5 Simulation Results 503.6 Conclusion 54 4 Neuro-Symbolic AI with a CNN-Based Framework for Detecting Tomato Leaf Diseases 59Khaleelullah Shaik and Mohammed Ali Shaik 4.1 Introduction 604.2 Related Work 624.3 Methodology 664.4 Performance Analysis 724.5 Conclusion 75 5 Early Detection of Breast Cancer Using Multi-Modal Deep Learning Framework 79Salma Mohammad and Mohammed Ali Shaik 5.1 Introduction 805.2 Related Work 815.3 Methodology 835.4 Results and Discussion 905.5 Conclusion 97 6 Neuro-Symbolic Transfer Learning Model with Logic-Based Intrusion Detection System in IoT 101Deepak V., S. John Justin Thangaraj, Yogaraja C. A. and Iswariya S. 6.1 Introduction 1026.2 Literature Review 1046.3 Neuro-Symbolic Transfer Learning + BiLSTM Model 1076.4 Results and Discussion 1146.5 Conclusion 118 7 Integrating Artificial Intelligence in Neuro-Symbolic: Challenges, Applications, and Future Directions 121J. D. Dorathi Jayaseeli, D. Malathi, R. S. Ponmagal, G. Abirami, S. Nagadevi and M. Senthil Raja 7.1 Introduction 1227.2 Neuro-Symbolic AI: An Overview 1237.3 Evolution of Neuro-Symbolic AI 1257.4 Neural-Symbolic Integration 1267.5 Applications of Neuro-Symbolic AI 1297.6 Challenges and Future Directions in Neuro-Symbolic AI 1357.7 Conclusion 137 Part 2: Neuro-Symbolic AI: Applications 143 8 A Rule-Based Decision Framework for Accident Prevention in Intelligent Transport Systems 145D. Pavithra, T. Deepa, Shaik Naseema, R. Nidhya, G. Smilarubavathy and C. Kumar 8.1 Introduction 1468.2 Related Work 1478.3 Rule-Based Accident Prevention System 1518.4 Simulation Results 1608.5 Conclusion 163 9 A Hybrid Logic-Driven and Neural Parsing Framework for Enhanced Emotion Recognition in Natural Language Processing 167R. Nidhya, V. Arun, T. Maragatham, D. J. Ashpin Pabi, Ajaypradeep N. and Manish Kumar 9.1 Introduction 1689.2 Literature Review 1709.3 Methodology 1739.4 Results and Discussion 1799.5 Conclusion 185 10 A Neuro-Symbolic AI Approach for Lumbar Spinal Stenosis Detection Using Graph Convolutional Networks and Fuzzy Logic 189Gurusamy Murugesan, Sabenabanu Abdulkadhar, Selvamuthukumar T., Velkumar K. and Rajkumar K. 10.1 Introduction 19010.2 Materials and Methods 19110.3 Results and Discussion 19810.4 Conclusion and Future Work 202 11 Adaptive Filtering Framework for Medical Image Denoising across Spatial and Wavelet Filters 207Naveen Kumar Penjarla, Tejaswi Vallabhapurapu, Syamala Rao P., Nissankara Lakshmi Prasanna, Kamesh Sonti and P. Vishnu Priya 11.1 Introduction 20811.2 Literature Review 21111.3 Methodology 21211.4 Results and Discussion 21511.5 Conclusion 222 12 Heritage Monument Classification Using Hybrid Deep Attention-Based Architecture for Cultural Preservation 225A. Satya Phani Kumari, Kalai Vani Y.S., Savitha S., Sujata Kulkarni and Jyothi N. M. 12.1 Introduction 22612.2 Literature Survey 22712.3 Methodology 22912.4 Experimentation 23312.5 Results 23512.6 Discussion 23912.7 Conclusion 241 13 Comparative Analysis and Classification of Age-Related Medical Conditions Applying Neural Network and Transformer-Based Deep Models 245Levina Tukaram, Umme Najma, D. Chandravathi, Bh. Padma and Jyothi N. M. 13.1 Introduction 24613.2 Literature Survey 24713.3 Methodology 25013.4 Experimentation 25313.5 Results 25513.6 Discussion 25913.7 Conclusion and Future Enhancements 265 14 Integrating Locality Sensitive Hashing and Embeddings into Collaborative Filtering for the Visual-Image-Based View 269Balaji Maram, Rekha Sundari, Anupama Angadi, Satya Keerthi Gorripati and Venubabu Rachapudi 14.1 Introduction 27014.2 Related Works 27114.3 Methodology 27314.4 Experimental Results and Analysis 28314.5 Conclusion 287 15 Adversarial Architectures and BERT for Mitigating Gender Bias in Word Embeddings towards Ethical AI Systems 291Saraswathi Rangaraju, P. Lakshmilavanya, Karunsagar Kanda, Bh. Padma, Kothapalli Ramesh Chandra and Jyothi N. M. 15.1 Introduction 29215.2 Literature Survey 29315.3 Methodology 29515.4 Results 30315.5 Discussion 31015.6 Conclusion 312 16 Exploring Machine Learning in Voice-Based Parkinson's Disease Diagnosis: A Comprehensive Survey 317G. Smilarubavathy and K. Vijayakumar 16.1 Introduction 31816.2 Background 31816.3 Flowchart 31916.4 Performance Metrics 32316.5 Comparative Analysis 32416.6 Challenges and Limitations 32716.7 Future Directions 329 17 Neuro-AI-Driven Image Augmentation and Data Leak Prevention via Automated Classification Agents 337S. M. Keerthana and K. Vijayakumar 17.1 Introduction 33817.2 Background 33917.3 Methods 34617.3.1 Preprocessing and Automatic Annotating 34617.4 Result 349 Conclusion 352References 352Index 355

ISBN:

9781394355594

9781394355600

OCLC:

1610959267

Publisher Number:

CIPO000428842