Neuro-Symbolic Artificial Intelligence

Concepts and Applications
1. Auflage 2026
ISBN: 978-1-394-35557-0
Verlag: Wiley

Buch, Englisch, 400 Seiten

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.

Autoren/Hrsg.

Nidhya, R.

Dinesh Kumar, A.

Peng, Sheng-Lung

Karthik, S.

Balamurugan, S.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Naturwissenschaften Biowissenschaften Biowissenschaften Neurobiologie, Verhaltensbiologie
• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft

Inhaltsverzeichnis

Series Preface xv
Preface xvii
Acknowledgements xxi

Part 1: Neuro-Symbolic AI: Concepts 1

1 Cataract Detection Systems Using Deep Learning Technique: A Survey 3
Arveti Mallikharjuna Rao and Suma Kamalesh Gandhimathi

1.1 Introduction 4
1.2 Deep Learning Models 6
1.3 Conclusion and Future Work 14

2 Agentic AI Workflows for Financial Large Language Models Using LlaMA and LangChain Framework 19
Mantri Udaya Jyothi, John Deva Prasanna D. S., Shanthini A. and Balasubramani S.

2.1 Introduction 20
2.2 Predict Stock Using Financial Analysis 21
2.3 Stock Market Prediction Using Agentic AI Using LLM 23
2.4 Llama Framework 24
2.5 Llama Framework Reduces AI Trading Risks 27
2.6 Working Principle of Agents 28
2.7 Results 33
2.8 Conclusion 36

3 Brain-Inspired Artificial Neural Network for Energy-Efficient and Adaptive Learning 39
R. Dhanalakshmi, Sahaya Beni Prathiba, Kavisankar L., Balasubramani S. and Pandiyanathan M.

3.1 Introduction 40
3.2 Literature Review 40
3.3 Methodology 42
3.4 Proposed System 46
3.5 Simulation Results 50
3.6 Conclusion 54

4 Neuro-Symbolic AI with a CNN-Based Framework for Detecting Tomato Leaf Diseases 59
Khaleelullah Shaik and Mohammed Ali Shaik

4.1 Introduction 60
4.2 Related Work 62
4.3 Methodology 66
4.4 Performance Analysis 72
4.5 Conclusion 75

5 Early Detection of Breast Cancer Using Multi-Modal Deep Learning Framework 79
Salma Mohammad and Mohammed Ali Shaik

5.1 Introduction 80
5.2 Related Work 81
5.3 Methodology 83
5.4 Results and Discussion 90
5.5 Conclusion 97

6 Neuro-Symbolic Transfer Learning Model with Logic-Based Intrusion Detection System in IoT 101
Deepak V., S. John Justin Thangaraj, Yogaraja C. A. and Iswariya S.

6.1 Introduction 102
6.2 Literature Review 104
6.3 Neuro-Symbolic Transfer Learning + BiLSTM Model 107
6.4 Results and Discussion 114
6.5 Conclusion 118

7 Integrating Artificial Intelligence in Neuro-Symbolic: Challenges, Applications, and Future Directions 121
J. D. Dorathi Jayaseeli, D. Malathi, R. S. Ponmagal, G. Abirami, S. Nagadevi and M. Senthil Raja

7.1 Introduction 122
7.2 Neuro-Symbolic AI: An Overview 123
7.3 Evolution of Neuro-Symbolic AI 125
7.4 Neural-Symbolic Integration 126
7.5 Applications of Neuro-Symbolic AI 129
7.6 Challenges and Future Directions in Neuro-Symbolic AI 135
7.7 Conclusion 137

Part 2: Neuro-Symbolic AI: Applications 143

8 A Rule-Based Decision Framework for Accident Prevention in Intelligent Transport Systems 145
D. Pavithra, T. Deepa, Shaik Naseema, R. Nidhya, G. Smilarubavathy and C. Kumar

8.1 Introduction 146
8.2 Related Work 147
8.3 Rule-Based Accident Prevention System 151
8.4 Simulation Results 160
8.5 Conclusion 163

9 A Hybrid Logic-Driven and Neural Parsing Framework for Enhanced Emotion Recognition in Natural Language Processing 167
R. Nidhya, V. Arun, T. Maragatham, D. J. Ashpin Pabi, Ajaypradeep N. and Manish Kumar

9.1 Introduction 168
9.2 Literature Review 170
9.3 Methodology 173
9.4 Results and Discussion 179
9.5 Conclusion 185

10 A Neuro-Symbolic AI Approach for Lumbar Spinal Stenosis Detection Using Graph Convolutional Networks and Fuzzy Logic 189
Gurusamy Murugesan, Sabenabanu Abdulkadhar, Selvamuthukumar T., Velkumar K. and Rajkumar K.

10.1 Introduction 190
10.2 Materials and Methods 191
10.3 Results and Discussion 198
10.4 Conclusion and Future Work 202

11 Adaptive Filtering Framework for Medical Image Denoising across Spatial and Wavelet Filters 207
Naveen Kumar Penjarla, Tejaswi Vallabhapurapu, Syamala Rao P., Nissankara Lakshmi Prasanna, Kamesh Sonti and P. Vishnu Priya

11.1 Introduction 208
11.2 Literature Review 211
11.3 Methodology 212
11.4 Results and Discussion 215
11.5 Conclusion 222

12 Heritage Monument Classification Using Hybrid Deep Attention-Based Architecture for Cultural Preservation 225
A. Satya Phani Kumari, Kalai Vani Y.S., Savitha S., Sujata Kulkarni and Jyothi N. M.

12.1 Introduction 226
12.2 Literature Survey 227
12.3 Methodology 229
12.4 Experimentation 233
12.5 Results 235
12.6 Discussion 239
12.7 Conclusion 241

13 Comparative Analysis and Classification of Age-Related Medical Conditions Applying Neural Network and Transformer-Based Deep Models 245
Levina Tukaram, Umme Najma, D. Chandravathi, Bh. Padma and Jyothi N. M.

13.1 Introduction 246
13.2 Literature Survey 247
13.3 Methodology 250
13.4 Experimentation 253
13.5 Results 255
13.6 Discussion 259
13.7 Conclusion and Future Enhancements 265

14 Integrating Locality Sensitive Hashing and Embeddings into Collaborative Filtering for the Visual-Image-Based View 269
Balaji Maram, Rekha Sundari, Anupama Angadi, Satya Keerthi Gorripati and Venubabu Rachapudi

14.1 Introduction 270
14.2 Related Works 271
14.3 Methodology 273
14.4 Experimental Results and Analysis 283
14.5 Conclusion 287

15 Adversarial Architectures and BERT for Mitigating Gender Bias in Word Embeddings towards Ethical AI Systems 291
Saraswathi Rangaraju, P. Lakshmilavanya, Karunsagar Kanda, Bh. Padma, Kothapalli Ramesh Chandra and Jyothi N. M.

15.1 Introduction 292
15.2 Literature Survey 293
15.3 Methodology 295
15.4 Results 303
15.5 Discussion 310
15.6 Conclusion 312

16 Exploring Machine Learning in Voice-Based Parkinson's Disease Diagnosis: A Comprehensive Survey 317
G. Smilarubavathy and K. Vijayakumar

16.1 Introduction 318
16.2 Background 318
16.3 Flowchart 319
16.4 Performance Metrics 323
16.5 Comparative Analysis 324
16.6 Challenges and Limitations 327
16.7 Future Directions 329

17 Neuro-AI-Driven Image Augmentation and Data Leak Prevention via Automated Classification Agents 337
S. M. Keerthana and K. Vijayakumar

17.1 Introduction 338
17.2 Background 339
17.3 Methods 346
17.3.1 Preprocessing and Automatic Annotating 346
17.4 Result 349

Conclusion 352
References 352
Index 355

Über Autor(innen)

R. Nidhya, PhD is a Professor in the Department of Computer Science and Engineering, Manipal Institute of Technology and Science, Madanapalle, India, with more than 16 years of teaching experience. She has published many research papers in refereed international journals and conferences. Her research interests include machine learning, wireless body area networks, and network security.

A. Dineshkumar, PhD is an Associate Professor at Koneru Lakshmaiah Education Foundation, Vijayawada, Andhra Pradesh, India. He completed his PhD at Anna University in Chennai in 2018. His current research interests include wireless body area networks, wireless sensor networks, network security, and artificial intelligence.

Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan. He has edited several special issues of journals and published more than 100 research articles. His research interests are in designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and networks.

S. Karthik, PhD is a Professor and Dean in the Department of Computer Science and Engineering, SNS College of Technology, Anna University, Chennai, Tamil Nadu, India. He has published more than 150 papers in refereed international journals and 125 papers in international conferences. His research interests include network security, big data, cloud computing, web services, and wireless systems.

S. Balamurugan, PhD is the Director of Research, Intelligent Research Consultancy Services, Coimbatore, Tamil Nadu, India. He has published 100 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision-making for leading companies and startups.