| Description |
1 online resource (xiv, 201 pages) : illustrations (some color) |
| Series |
Studies in autonomic, data-driven and industrial computing, 2730-6445 |
|
Studies in autonomic, data-driven and industrial computing, 2730-6445
|
| Contents |
Chapter 1. Dynamic Demand Planning for Distorted Historical Data Due to Pandemic -- Chapter 2. Cognitive Models to Predict Pipeline Leaks and Ruptures -- Chapter 3. Network Optimization of the Electricity Grid to manage Distributed Energy Resources using Data & Analytics -- Chapter 4. Enhancing Market Agility Through Accurate Price Indicators using Contextualized Data Analytics -- Chapter 5. Infrastructure for Automated Surface Damage Classification and Detection in Production industries using ResUNet based Deep Learning Architecture -- Chapter 6. Cardiac Arrhythmias Classification & Detection for Medical Industry Using Wavelet Transformation & Probabilistic Neural Network Architecture -- Chapter 7. Investor Behavior towards Mutual Fund -- Chapter 8. iMask : An Artificial Intelligence Based Redaction Engine -- Chapter 9. Artificial Intelligence for Proactive Vulnerability Prediction and interpretability using Occlusion -- Chapter 10. Intrusion Detection System using Signature based Detection and Data Mining Technique. Chapter 11. Cloud Cost Intelligence using Machine Learning -- Chapter 12. Mining deeper Insights using Unsupervised NLP -- Chapter 13. Explainable AI for ML OPS |
| Summary |
This book covers research work spanning the breadth of ventures, a variety of challenges and the finest of techniques used to address data and analytics, by subject matter experts from the business world. The content of this book highlights the real-life business problems that are relevant to any industry and technology environment. This book helps us become a contributor to and accelerator of artificial intelligence, data science and analytics, deploy a structured life-cycle approach to data related issues, apply appropriate analytical tools & techniques to analyze data and deliver solutions with a difference. It also brings out the story-telling element in a compelling fashion using data and analytics. This prepares the readers to drive quantitative and qualitative outcomes and apply this mindset to various business actions in different domains such as energy, manufacturing, health care, BFSI, security, etc |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed October 7, 2022) |
| Subject |
Data mining.
|
|
Business -- Data processing.
|
|
Business -- Data processing
|
|
Data mining
|
| Form |
Electronic book
|
| Author |
Sharma, Neha, editor
|
|
Bhatavdekar, Mandar, editor
|
| ISBN |
9789811956898 |
|
9811956898 |
|
9788981195687 |
|
8981195684 |
|
