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.
The text explains how explainable artificial intelligence impacts problem-solving and aims to provide practical suggestions across various emerging industries.
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
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.
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