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AI Practical eBooks Collection | Applied AI, Industry 5.0 & Research 5.0
Books published on this page are independent scholarly works. Book sales are separate from the journal’s editorial and peer-review processes.
Mastering AI Models in the Insurance Domain
This book presents a practical and structured guide to applying Artificial Intelligence in the insurance sector, designed for students, engineers, researchers, and insurance professionals. It is built around two end-to-end case studies: precision and personalization in predicting insurance premium costs (based on a synthetic dataset of 21,001 records) and insurance claim fraud detection (using a synthetic dataset of 35,000 records). The datasets used throughout the book are synthetic and intentionally designed to partially simulate real-world insurance environments. They are intended strictly for educational and research-training purposes and do not represent actual policyholder data. Their structure reflects selected industry-level complexities, providing a controlled and ethical setting for hands-on learning and experimentation. Readers are guided through the complete machine learning workflow, including data preprocessing, feature engineering, model training, evaluation, and deployment. The book includes fully executable Python code, downloadable datasets, and implementation examples that can be used in Google Colab, Jupyter Notebook, or standalone desktop environments. Through practical exercises, readers develop applied skills in building AI-driven insurance models while exploring approaches related to fairness, personalization, and fraud risk mitigation.
PDF format
AI in Healthcare: A Practical Journey Through Machine Learning and Deep learning
This book provides a structured, hands-on journey into applying Artificial Intelligence in healthcare, designed for students, engineers, researchers, and healthcare professionals.
It focuses on two end-to-end case studies:
- Predicting breast cancer recurrence using machine learning (24,206 records)
- Forecasting patient mortality risk using deep learning (17,635 records)
The datasets used throughout the book are synthetic and carefully designed to partially simulate real-world healthcare complexity. They are intended strictly for educational and research-training purposes.
Readers will learn how to build, train, evaluate, and deploy AI models using fully executable source code, downloadable datasets, and deployment-ready examples compatible with Google Colab, Jupyter Notebook, and standalone Python environments.
Disclaimer: This book is intended to support learning and research development. It does not provide medical advice or clinical decision-making guidance.
PDF format
$120.25 $65.00
To purchase this book, please follow the steps below:
- Contact us by email and mention the Book Reference: AIHC-MLDL-B02
- Complete the payment using the provided bank information.
- After payment confirmation, the PDF copy of the book, including access to both datasets available for download from our platform, will be sent to your email address within 24–48 hours.
For inquiries and payment instructions, please contact:
billing@jngr5.com
Smart Supply Chain Solutions with AI: From Forecasting to Delivery
This book offers a hands-on and structured guide to applying Artificial Intelligence (AI) in supply chain management, designed for students, researchers, and industry professionals.
It is built around two detailed case studies focusing on demand forecasting and logistics optimization.
- AI-based demand forecasting and inventory management, using a synthetic dataset of 41,001 records that partially simulates historical sales patterns, inventory levels, and seasonal effects to illustrate how machine learning models can support forecasting accuracy and operational planning.
- AI-enhanced logistics and route optimization, based on 29,653 synthetic records reflecting selected factors such as traffic conditions, weather variability, and delivery urgency, demonstrating how AI techniques can be applied to optimize routing decisions and improve logistics performance.
Both datasets are synthetic and intentionally designed to partially simulate real-world supply chain environments. They are intended strictly for educational and research-training purposes.
Readers are guided through complete and reproducible machine learning workflows, including data preprocessing, model development, and evaluation, using executable Python code, compatible with Google Colab, Jupyter Notebook, and standalone Python environments.
Disclaimer: This book is intended to support learning and research development. It does not provide operational, financial, or commercial decision-making advice.
Format: PDF (Digital Book)
Price: $130 $55
Book Reference: SSC-AI-B03
Preview Sample : Preview Sample
How to Purchase
To purchase this book, please follow the steps below:
- Contact us by email and mention the Book Reference: SSC-AI-B03.
- Complete the payment using the provided bank information.
- After payment confirmation, the PDF copy of the book, including access to both datasets available for download from our platform, will be sent to your email address within 24–48 hours.
For inquiries and payment instructions, please contact:
billing@jngr5.com
Machine Learning in Banking: Building Predictive Models for Risk and Fraud
This book provides a comprehensive and hands-on exploration of Artificial Intelligence (AI) applications in the banking sector, with a focus on fraud detection and credit risk assessment.
It is structured around two detailed case studies developed using high-quality synthetic datasets.
- AI-based fraud detection system, built on a synthetic dataset of 32,101 records that partially simulates financial transaction patterns. This case study guides readers through the complete machine learning pipeline, including data cleaning, feature engineering, model development, evaluation, and deployment.
- AI-driven risk assessment and credit scoring, using 33,510 synthetic records designed to partially reflect borrower behavior and financial characteristics, demonstrating approaches for building robust and interpretable credit scoring models.
The synthetic datasets used throughout the book are intentionally designed to support safe experimentation while preserving educational value and practical relevance.
Intended for researchers, students, and professionals, the book integrates theoretical foundations with executable Python code and established best practices, offering an end-to-end learning resource for applying AI methods in modern financial environments.
Disclaimer: This book is intended to support learning and research development. It does not provide financial, regulatory, or commercial decision-making advice.
Format: PDF (Digital Book)
Price: $125 $63
Book Reference: ML-BANK-AI-B04
Preview Sample: Preview Sample
How to Purchase
To purchase this book, please follow the steps below:
- Contact us by email and mention the Book Reference: ML-BANK-AI-B04.
- Complete the payment using the provided bank information.
- After payment confirmation, the PDF copy of the book, including access to both datasets available for download from our platform, will be sent to your email address within 24–48 hours.
For inquiries and payment instructions, please contact:
billing@jngr5.com
AI-Powered Energy Management: Forecasting Consumption and Detecting Fraud with Machine Learning
This book provides a practical and comprehensive exploration of Artificial Intelligence (AI) applications in the energy sector, focusing on optimizing energy consumption and detecting anomalous patterns in smart meter data.
Designed for students, researchers, data scientists, and industry professionals, the book offers applied insights into AI-driven energy systems through reproducible and educational case studies.
It is structured around two in-depth case studies developed using high-quality synthetic datasets that partially simulate real-world energy environments.
- AI-driven building energy consumption forecasting, using a synthetic dataset of 51,001 records. This case study demonstrates how predictive modeling techniques, including linear regression and ensemble methods, can support energy efficiency analysis in building contexts.
- AI-driven anomaly detection in smart meter data, based on 44,316 synthetic records. This case study illustrates how machine learning models such as decision trees, logistic regression, and random forest algorithms can be applied to identify irregular and potentially fraudulent energy consumption patterns.
Each case study guides readers through the complete machine learning workflow, including data preprocessing, model selection, evaluation, and performance interpretation.
By emphasizing practical implementation and reproducible methodologies, the book bridges theoretical concepts with applied use cases relevant to modern energy systems, while supporting responsible and ethical experimentation.
Disclaimer: This book is intended to support learning and research development. It does not provide operational, regulatory, or commercial decision-making advice.
Format: PDF (Digital Book)
Price: $110 $52
Book Reference: AI-ENERGY-ML-B05
Preview Sample (Free): Preview Sample
How to Purchase
To purchase this book, please follow the steps below:
- Contact us by email and mention the Book Reference: AI-ENERGY-ML-B05.
- Complete the payment using the provided bank information.
- After payment confirmation, the PDF copy of the book, including access to both datasets available for download from our platform, will be sent to your email address within 24–48 hours.
For inquiries and payment instructions, please contact:
billing@jngr5.com
Support
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