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.
Author(s): Neha Sharma, Mandar Bhatavdekar
Series: Studies in Autonomic, Data-driven and Industrial Computing
Publisher: Springer
Year: 2022
Language: English
Pages: 210
City: Singapore
Preface
Acknowledgements
Contents
About the Editors
1 Dynamic Demand Planning for Distorted Historical Data Due to Pandemic
1 Introduction
2 Literature Review
3 Methodology
4 Results
5 Conclusion
References
2 Cognitive Models to Predict Pipeline Leaks and Ruptures
1 Introduction
2 Literature Review
3 Material and Methodology
3.1 Defining the Solution Using Data and Analytics
4 Results
5 Conclusion
References
3 Network Optimization of the Electricity Grid to Manage Distributed Energy Resources Using Data and Analytics
1 Introduction
2 Literature Review
3 Methodology
3.1 Defining a Network Optimization Solution to Build an Agile Grid
3.2 Defining the Problem
3.3 Defining a Solution for the Problem
4 Results
5 Conclusion
References
4 Enhancing Market Agility Through Accurate Price Indicators Using Contextualized Data Analytics
1 Introduction
2 Literature Review
3 Data-Flow in Utility Value Chain
4 Handaling Market data Volatility and Coherency
5 Leveraging Data Analytics in Improving Accuracy of Price-Prediction Models
6 Data-Reliant Congestion Management
7 Unlocking Techno Commercial Benefits to Utility
8 Conclusion
References
5 Infrastructure for Automated Surface Damage Classification and Detection in Production Industries Using ResUNet-based Deep Learning Architecture
1 Introduction
2 Literature Review
3 Dataset Description
4 Methodology
4.1 Two-Phase Learning Approach
5 Results
6 Conclusion
References
6 Cardiac Arrhythmias Classification and Detection for Medical Industry Using Wavelet Transformation and Probabilistic Neural Network Architecture
1 Introduction
2 Literature Review
3 The Solution
3.1 Discrete Wavelet Transformation
3.2 Probabilistic Neural Network
4 Experimental Outcome
5 Results and Discussion
6 Conclusion
References
7 Investor Behavior Towards Mutual Fund
1 Introduction
2 Literature Review
3 Materials and Methods
4 Experimental Result and Evaluation
5 Results and Discussions
6 Conclusion and Future Scope
References
8 iMask—An Artificial Intelligence Based Redaction Engine
1 Introduction
2 Literature Review
3 Methodology
4 Results
5 Conclusion
References
9 Intrusion Detection System Using Signature-Based Detection and Data Mining Technique
1 Introduction
2 Literature Review
3 Materials and Methods
4 Experimental Result and Evaluation
5 Conclusion
References
10 Cloud Cost Intelligence Using Machine Learning
1 Introduction
2 Literature Review
3 Materials and Methods
4 Results and Recommendations
5 Conclusion
References
11 Mining Deeper Insights from Texts Using Unsupervised NLP
1 Introduction
2 Literature Review
3 Materials and Methods
4 Result
5 Conclusion
References
12 Explainable AI for ML Ops
1 Introduction
1.1 ML and The “Last Mile” Problem
1.2 Keeping Tabs on the Model
1.3 Explainable AI for Model Monitoring
2 Literature Review
2.1 AI/ML Maturity
2.2 Rise of ML Ops
2.3 ML Ops in Postproduction
3 Materials and Methods
3.1 Datasets
3.2 Explainable AI 101
3.3 Explainability and ML Monitoring
4 Exploratory Data Analysis
5 Experimental Analysis
6 Results
6.1 SOLUTION 1: Local Explanation with One Particular Observation
6.2 SOLUTION 2: Global Monitoring: Iterating the Model 100 Times, Introduce the Manipulation from the 30th Iteration
7 Conclusion
References
