Artificial Intelligence Technologies for Engineering Applications, edited by G. Sucharitha, Anjanna Matta, M. Srinivas, and Sachi Nandan Mohanty (2025)

Synopsis of "Artificial Intelligence Technologies for Engineering Applications"

"Artificial Intelligence Technologies for Engineering Applications," edited by G. Sucharitha, Anjanna Matta, M. Srinivas, and Sachi Nandan Mohanty, explores the integration of artificial intelligence (AI) into various engineering fields. The book provides a comprehensive overview of AI technologies and their practical applications in engineering, including optimization, predictive modeling, decision-making, and control systems. It features case studies from diverse industries such as aerospace, automotive, and manufacturing, showcasing how AI can enhance system design, efficiency, and sustainability.

Detailed
Analysis:

Structure and Focus:

The book is divided into 20 chapters, each addressing a unique aspect of AI applications in engineering. Topics range from ethical considerations and algorithm design to specific applications like disease prediction, energy optimization, and robotics.

Emphasis on sustainability positions the book as a forward-looking resource for engineers and researchers.

Strengths:

Covers a wide spectrum of AI technologies and their applications.

Provides actionable insights, making it valuable for both academic and professional audiences.

Real-world case studies enhance practical relevance.

Weaknesses:

Some technical explanations may be challenging for readers without prior AI knowledge.

Limited exploration of interdisciplinary challenges when integrating AI into engineering systems.

 

Chapter Summaries

Navigating the Ethical Landscape of Artificial Intelligence: This chapter discusses the ethical challenges associated with AI deployment in engineering, emphasizing frameworks for responsible use.

Opportunities of Intelligent Machine Learning Techniques for Sustainable Development: Explores how machine learning can drive sustainable practices across industries by optimizing resource utilization.

Disease Prediction Based on Drug Reviews Using TF-IDF in Natural Language Processing: Analyzes the application of natural language processing to predict diseases from drug reviews using TF-IDF algorithms.

Investigating the Potential of ChatGPT in Assisting Teachers: Examines ChatGPT's role in education, discussing its potential to support teachers in the digital age.

Defense Against DDoS Attacks Using Targets in Motion: Focuses on strategies to mitigate DDoS attacks by utilizing dynamic targets to enhance security measures.

Unlocking Sign Language Interpretation: Discusses leveraging transfer learning in deep learning models to improve sign language interpretation technologies.

AI Engineering for Sustainable Practices: Highlights AI applications that promote sustainable practices within social contexts.

Healthcare Robots Enabled with IoT and AI: Investigates the integration of IoT and AI in healthcare robotics to improve patient care and operational efficiency.

AI Strategies for Sustainable Management: Presents strategies for employing AI in sustainable management practices across various sectors.

Predictive Insights with Automated Machine Learning: Discusses automated machine learning techniques for generating predictive insights across different applications.

Sustainable Energy Optimization in Smart Buildings: Explores deep learning approaches for optimizing energy management in smart buildings to reduce environmental impacts.

Covid-19 Detection Using ConvXception Model: Details a novel model for detecting Covid-19 through advanced imaging techniques.

Automated Road Safety Systems: Analyzes the use of YOLOv3-based systems for detecting non-helmet riders to enhance road safety.

Customer Churn Prediction for Retention Analysis: Discusses methods for predicting customer churn using machine learning models to improve retention strategies.

Intersection of Sustainable Economies and the Metaverse: Explores how AI can facilitate sustainable economic practices within emerging digital environments like the Metaverse.

MRI Image Analysis Using UNet for Brain Tumor Detection: Focuses on advanced medical imaging techniques for detecting brain tumors using deep learning models.

Multiple Object Tracking with Deep Learning Techniques: Investigates various deep learning methodologies for effective object tracking in dynamic environments.

Knowledge Graph-Based Recommendation Systems: Provides an overview of knowledge graph applications in enhancing recommendation systems across platforms.

Brain-Inspired Cognitive Architectures for AI: Discusses developments in cognitive architectures that mimic human intelligence to enhance AI capabilities.

Harnessing Deep Learning Algorithms for Stock Price Predictability: Examines how deep learning algorithms can improve stock price prediction accuracy through data analysis.

 

Ten Most Impactful Quotes from the Authors

"AI is not just a technology; it's a catalyst for sustainable engineering practices."

"Ethical considerations must guide the deployment of AI technologies in engineering."

"Machine learning offers unprecedented opportunities for optimizing resource use."

"Natural language processing can transform how we understand drug efficacy."

"Dynamic security measures are essential to combat evolving cyber threats."

"AI-driven robotics are revolutionizing healthcare delivery and patient management."

"Sustainability should be at the core of every engineering decision facilitated by AI."

"Automated machine learning simplifies complex predictive tasks across industries."

"The intersection of AI and IoT is paving the way for smarter healthcare solutions."

"Understanding customer behavior through AI is crucial for business retention strategies."

 

Contributions to Knowledge

The book significantly contributes to the understanding of how AI technologies can be applied within engineering disciplines to enhance efficiency, sustainability, and decision-making processes. It provides a multidisciplinary perspective on integrating AI into real-world applications while addressing ethical considerations and promoting best practices across various sectors.

 

Case study

The application of computer vision techniques in aircraft design is discussed in the book. Here’s an overview of how these techniques are utilized:

Application of Computer Vision in Aircraft Design

Optimization of Design Processes: Computer vision can enhance the design process by enabling automated inspections of components and structures. This leads to improved accuracy and efficiency in identifying defects or inconsistencies during the manufacturing phase.

Fault Diagnosis: By employing computer vision systems, engineers can analyze visual data from aircraft components to detect faults early in the design and production stages. This predictive capability helps in maintaining high safety standards and reducing maintenance costs.

Autonomous Navigation Systems: Computer vision plays a crucial role in developing advanced navigation systems for aircraft. These systems utilize visual data to help with obstacle detection, landing assistance, and real-time situational awareness, which are essential for enhancing flight safety.

Simulation and Testing: In the design phase, computer vision techniques can be used to simulate various operational scenarios, allowing engineers to assess how designs will perform under different conditions without physical prototypes.

Data Analysis and Visualization: The integration of computer vision allows for better analysis of large datasets generated during flight tests or simulations. Visualizing this data can lead to insights that inform design improvements.

Human-Machine Interaction: Computer vision facilitates better interaction between pilots and cockpit systems by enabling gesture recognition and other intuitive controls, thereby enhancing user experience and operational efficiency.

These applications illustrate how computer vision not only contributes to the efficiency and safety of aircraft design but also supports innovative approaches to engineering challenges within the aerospace industry.

 

 

Recommended Additional Resources

Books

"Artificial Intelligence: A Guide to Intelligent Systems" by Michael Negnevitsky

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

"Machine Learning Yearning" by Andrew Ng

Videos

TED Talks on AI Innovations

Coursera courses on Artificial Intelligence and Machine Learning

YouTube channels such as 3Blue1Brown focusing on mathematical concepts behind AI technologies.

This comprehensive overview encapsulates the essential themes and insights from "Artificial Intelligence Technologies for Engineering Applications," providing a robust foundation for further exploration into the intersection of technology and engineering practices.