Artificial Intelligence Technologies, Applications, and Challenges

In this review of Artificial Intelligence: Technologies, Applications, and Challenges, the authors Lavanya Sharma and Pradeep Kumar Garg present a practical, application-focused guide for readers who want to understand how modern AI tools are used across real-world domains. The single biggest reason to buy this book is its breadth of case studies: from robotic surgery to intelligent traffic monitoring, the book consistently connects foundational concepts to current and emerging uses. This review looks at who benefits most from the book and what its main strengths and limitations are.

Key Features

• Comprehensive application coverage: The book outlines many real-world applications such as object detection, augmented reality, and medical imaging so readers can see how AI is deployed across domains.
• Technology-context focus: Chapters link core technologies to their applied uses, helping practitioners understand the tools behind systems like robotic surgery and character recognition.
• Practical examples: Present and future application scenarios, including indoor-outdoor security and intelligent traffic monitoring, offer practical contexts for experimentation and learning.
• Interdisciplinary scope: Topics range from graphic signal processing to IoT communications, which benefits readers seeking cross-disciplinary insight into AI systems.
• Problem-oriented discussion: The authors address challenges in deployment, enabling readers to appreciate limitations and realistic implementation considerations.

Who It's For

The book is best suited to graduate students, early-career engineers, and technical managers who need a broad, applied view of artificial intelligence across multiple domains; it helps readers move from theoretical concepts to implementation thinking. It also works well as a course supplement for computer science modules that emphasize applied AI and systems integration.

Readers seeking a hands-on programming tutorial or a deeply mathematical treatment of machine learning theory should look elsewhere, as the focus here is on applications, technologies, and practical challenges rather than step-by-step code or heavy theorem-proof exposition.

Pros & Cons

Pros

• Wide range of application case studies that make it easy to see practical uses of AI systems.
• Clear linkage between technologies and deployment contexts, useful for implementation planning.
• Coverage of both present and future applications helps readers anticipate where the field is headed.

Cons

• The book is not a coding workbook or reference for low-level algorithm derivations, so readers wanting detailed mathematical proofs or source code may need supplementary materials.

Specifications

Our Verdict

Artificial Intelligence: Technologies, Applications, and Challenges is a solid, applied overview that delivers strong value for readers who want practical case studies and technology context rather than implementation code. For its intended audience of students and practitioners seeking to connect AI concepts with real systems, it is a useful reference that balances breadth and application-focused insight.

Frequently Asked Questions

Does this book include programming examples?
Answer. The book emphasizes applications and technologies and does not serve as a hands-on programming manual with extensive code listings.

Which application domains are emphasized?
Answer. The authors focus on domains like robotic surgery, medical imaging, intelligent traffic monitoring, augmented reality, and security systems.

Is this suitable for beginners?
Answer. It is appropriate for readers with some technical background such as students or engineers; absolute beginners without prior exposure to computing concepts may find the scope broad.