Reading Guide: AI, Machine Learning and Intelligent Systems (ICAMS-2025)

Artificial Intelligence, Machine Learning and Intelligent Systems collects the papers from ICAMS-2025, the Second International Conference on Artificial Intelligence, Machine Learning and Intelligent Systems, held at the National Institute of Technology Hamirpur in Himachal Pradesh, India, on 7 and 8 February 2025. Edited by Kusum Deep, Millie Pant, Mohammad Khalid Pandit, Naveen Chauhan and Ajay Kumar Mallick, it was published by Springer in April 2026 as volume 520 of Springer Proceedings in Mathematics & Statistics. Its 35 chapters share a common thread: using mathematics to make AI and machine learning work on real engineering and business problems. This guide helps you decide where to start.

Get the book: View the ICAMS-2025 proceedings on Amazon

What this book is (and who it's for)

This is a conference proceedings volume: a set of independent research papers by different author teams, each describing a method, an experiment or an application. It is not a textbook, so it does not build concepts step by step. Instead it gives you a snapshot of current applied research.

According to the publisher, the topics range from facial recognition security and cryptocurrency and blockchain to automated trading, sustainable inventory management, personalized recommendation systems and IoT-driven smart solutions, with both theoretical advances and practical implementations. The conference itself was organized by NIT Hamirpur's Department of Computer Science and Engineering with support from IIT Roorkee's Department of Applied Mathematics and Scientific Computing.

The book is best suited to:

• Graduate students in computer science, applied mathematics or engineering looking for project ideas and recent references.
• Researchers who want to see how optimization and mathematical modeling are being combined with machine learning.
• Practitioners in finance, operations or IoT who want to scan applied approaches in their area.

Before you start

Most papers assume you are comfortable with undergraduate mathematics: linear algebra, probability and statistics, and some calculus. Familiarity with core machine learning ideas (supervised learning, neural networks, evaluation metrics such as accuracy and F1 score) and with optimization will help a lot.

If you want to reproduce results, a working Python setup with common scientific and machine learning libraries is the practical choice, though individual papers may use other tools. Not every paper will publish code or data, so expect to fill gaps yourself.

How to read it

Proceedings reward a selective approach. The conference's call for papers named several tracks, and these make a good way to organize your reading even if the published chapters do not map onto them exactly:

• AI and machine learning for healthcare and agriculture
• Mathematical foundations of machine learning and optimization
• Natural language processing, speech and signal processing
• Generative AI and computer vision
• Edge AI and AI in hardware
• A general track for other related topics

Suggested starting points by reader

• Mathematics and optimization readers: start with papers on optimization methods and the mathematical foundations of learning, then look at the inventory management and trading applications to see those methods applied.
• Finance and fintech readers: begin with the automated trading and cryptocurrency or blockchain papers, then read recommendation-system work, which shares many modeling ideas.
• Security and vision readers: start with facial recognition security and related computer vision papers.
• IoT and systems readers: go to the IoT-driven smart solution papers first, then read anything on edge AI.

How to read a single paper

For each paper, read the abstract, introduction and conclusion first. If it is relevant, look at the problem setup, the datasets and the evaluation. Only then dive into the full method. On a first pass, skim long derivations and focus on what problem is solved, how results are measured and what baselines are used.

Key ideas to take away

• Mathematics is the engine of applied AI. Optimization, statistics and modeling sit underneath most of the systems described.
• Applications are broad. The same core techniques appear in security, finance, supply chains, recommendation and IoT.
• Evaluation matters. Comparing papers means looking closely at datasets, metrics and baselines, not just headline accuracy.
• Theory and practice inform each other. The volume deliberately mixes theoretical advances with practical implementations.
• Proceedings are a starting point. Use the reference lists to trace each idea back to its foundations and forward to newer work.

Practice and discussion questions

1. Choose one application paper. What exactly is being optimized or predicted, and how is success measured?
2. For a paper of your choice, list its baselines. Are they strong enough to support the paper's claims?
3. What are the main risks of deploying facial recognition for security, and how would you test a system for fairness and robustness?
4. How might the methods used for sustainable inventory management transfer to another resource-planning problem?
5. What extra data or experiments would you want before trusting an automated trading model with real money?
6. Pick one paper and sketch how you would reproduce its main result with the information given.

Pair it with

• Optimization and numerical methods. A refresher on optimization will make the mathematical papers far easier to follow.
• Hands-on machine learning in Python. Implementing a small version of one paper's method is the best way to understand it.
• Other recent proceedings. The BooksPlusAi shelf also carries Advances and Applications of Artificial Intelligence & Machine Learning (ICAAAIML 2022) and Artificial Intelligence and Applications (ICAIA 2025), which make good companions for comparing research trends.

Keep going

You do not need to read all 35 chapters to get real value from this volume. Pick a track, read two or three papers carefully, and try to reproduce one idea. That small project will teach you more than skimming everything. Get the ICAMS-2025 proceedings on Amazon, or browse more titles on the BooksPlusAi shelf.

As an Amazon Associate, BooksPlusAi may earn from qualifying purchases.

Sources

• Springer: Artificial Intelligence, Machine Learning and Intelligent Systems (ICAMS-2025)
• EasyChair: ICAMS 2025 call for papers
• Schweitzer Fachinformationen listing