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World of Business with Data and Analytics
Title
World of Business with Data and Analytics [electronic resource] / edited by Neha Sharma, Mandar Bhatavdekar.
ISBN
9789811956898
Edition
1st ed. 2022.
Publication
Singapore : Springer Nature Singapore : Imprint: Springer, 2022.
Physical Description
1 online resource (XIV, 201 p.) 141 illus., 114 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
October 06, 2022
Series
Studies in Autonomic, Data-driven and Industrial Computing,
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. .
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