Explainable Artificial Intelligence-based Industrial Internet of Things

Technologies and Applications
1. Auflage 2025
ISBN: 978-1-032-88367-0
Verlag: Taylor & Francis Ltd

Buch, Englisch, 400 Seiten, Format (B × H): 156 mm x 234 mm

The text explains how explainable artificial intelligence impacts problem-solving and aims to provide practical suggestions across various emerging industries. It further discusses important topics such as the strategic utilization of explainable artificial intelligence in supply chain enhancement, the integral role of explainable artificial intelligence in smart farming and smart cities with the industrial Internet of Things integration.

Features:

• Discusses local interpretable model-agnostic explanations, and Shapley additive explanations for transparent data analysis, modeling, and prediction.
• Highlights the importance of using artificial intelligence (AI) in optimizing processes by studying decision-making interpretability in supply chain optimization.
• Explains the use of explainable artificial intelligence to optimize supply chains by predicting demand, identifying bottlenecks, and making informed decisions about inventory management.
• Illustrates the benefit of employing explainable artificial intelligence in optimizing resource utilization, improving decision-making, and creating more efficient and sustainable ecosystems.
• Explores the integration of explainable artificial intelligence into smart appliances to provide insights into their operations and improve user experience.

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electrical and communications engineering, computer science and engineering, and information technology.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Khamparia, Aditya

Vinta, Surendra Reddy

Pande, Sagar Dhanraj

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Mathematik | Informatik EDV | Informatik Technische Informatik

Inhaltsverzeichnis

Chapter 1. Enhancing substation maintenance and anomaly detection through Image processing, Explainable AI and Industrial Internet of Things (IIOT). Chapter 2. Real-time cardiovascular health monitoring using ECG, ML models, and Interpretation using Explainable AI methods (LIME & SHAP) within IIoT systems. Chapter 3. Enhancing Brain Tumor Detection and Classification with Vision Transformers, Ensemble Models, and Explainable AI in an Industrial IoT Framework. Chapter 4. Advancements and Applications of Explainable Artificial Intelligence (XAI) in Smart Cities: Enhancing User Perception, Decision-Making, and Behavioral Insights. Chapter 5. Amplifying Song Clustering with Explainable AI and Hybrid Deep Learning for Industrial IoT Applications in Music Recommendation Systems. Chapter 6. Predicting e-mental health using machine learning models and explainable AI (XAI) methods with IoT devices involves. Chapter 7. Automatic detection of heart disease with IIOT and enhanced explainable (EXAI)approach. Chapter 8. Explainable AI & its Contributions to Smart Cities, Smart Homes, and eHealth. Chapter 9. EFL Lecturers' and Students' Academic Writing Experience and Challenges in Using Explainable AI and Industrial Internet of Things (IIoT) Writing Tools: A Qualitative Study. Chapter 10. Designing Trustworthy IIIOT Intrusion Prevention Systems Using Explainable AI Techniques. Chapter 11. Leveraging Explainable AI for Threat Detection in Industrial IIOT-Based Intrusion Prevention Systems. Chapter 12. Hybrid Ensemble Optimisation and Explainable AI for Robust IIoT Decision-Making. Chapter 13. Transparent and Reliable AI for Real-Time Facial Expression Recognition in IIoT and Healthcare. Chapter 14. Brain Tumor Classification Integrating Deep Learning, Explainable AI and Industrial IOT (IIOT) With GRAD-CAM For Bio-Medical Images.Chapter 15. DepressNet-HO: An Explainable AI Framework for Depression Detection in IIoT Applications

Über Autor(innen)

Surendra Reddy Vinta is currently working as the Associate Professor of the School of Computer Science and Engineering, VIT-AP University, Amaravathi (India). His area of interests includes Image Processing, Machine Learning, Deep Learning,NLP, Computer Vision, Features extraction, and Programming, such as Digital Image Processing, Feature Extraction, Machine Learning, Deep Learning, NLP, Computer Vision, C, Python, Data Structure, C++, C# and Java.

Sagar Dhanraj Pande is working as an Assistant Professor Senior Grade at VIT-AP University, Amaravati, Andhra Pradesh, India. His research interest is Deep Learning, Machine Learning, Network Attacks, Cyber Security, and the Internet of Medical Things (IoMT).

Aditya Khamparia is currently working as an Assistant Professor and Coordinator of Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. His research interest includes machine learning, deep learning, educational technologies, computer vision.