Artificial Intelligence in Industrial Applications: Approaches to Solve the Intrinsic Industrial Optimization Problems

Preface
Contents
1 An Efficient Deep Learning Framework for People Detection in Overhead Images
1.1 Introduction
1.1.1 Literature Survey
1.1.2 Contribution Summary
1.2 Dataset
1.3 Methods
1.3.1 Object Detection Algorithms
1.3.2 The Detection Approach of Yolo-Based Algorithms
1.3.3 Proposed Multi-Scale Yolov4-Tiny Algorithm
1.4 Experimental Setup
1.4.1 Training Stage
1.4.2 Model Evaluation Metrics
1.5 Experimental Results and Discussion
1.5.1 Impact of the Different NMS Algorithms
1.5.2 Impact of Training Dataset Size
1.6 Conclusions
References
2 Machine Learning Techniques for Grocery Sales Forecasting by Analyzing Historical Data
2.1 Introduction
2.2 Literature Review
2.3 Exploratory Data Analysis
2.3.1 Data Source and Data Files
2.3.2 Data Analysis
2.4 Methodology
2.4.1 Linear Regression
2.4.2 Decision Trees
2.4.3 Random Forest
2.4.4 Artificial Neural Networks
2.5 Experimental Results and Analysis
2.6 Conclusions and Future Work
References
3 Medication Revelation Utilizing Neural Network
3.1 Introduction
3.2 Material and Methods
3.2.1 ML Algorithms used in Drug Discovery
3.2.2 Dataset
3.2.3 Structure Based Deep Convolutional Neural Network
3.3 Results and Analysis
3.4 Conclusion
References
4 Application of AI in SCM or Supply Chain 4.0
4.1 Introduction
4.2 Literature Survey
4.3 Advantages of AI iN SCM
4.3.1 Automated Decision-Making
4.3.2 Accurate Inventory Management
4.3.3 Warehouse Efficiency
4.3.4 Enhanced Safety
4.3.5 Reduced Operations Costs
4.3.6 On-Time Delivery
4.4 Reimbursement of AI-Based SCM
4.4.1 Bolstering Planning and Scheduling Activities
4.4.2 Intelligent Decision-Making
4.4.3 End-To-End Visibility
4.4.4 Actionable Analytical Insights
4.4.5 Inventory and Demand Management
4.4.6 Boosting Operational Efficiencies
4.4.7 Unlocking Fleet Management Efficiencies
4.4.8 Streamlining Enterprise Resource Planning (ERP)
4.5 Challenges of AI iN SCM
4.5.1 System Complexities
4.5.2 The Scalability Factor
4.5.3 The Cost of Training
4.5.4 The Operational Costs Involved
4.6 Conclusion
References
5 Upskilling and Curating the Potentials of IoT Enabled Smart Cities: Use Cases and Implementation Strategies
5.1 Introduction
5.1.1 IoT Devices
5.1.2 How Smart Cities Work
5.1.3 Features of Smart Cities
5.2 The Various Components of the Smart City and Their Impact in the IoT Era
5.2.1 Smart Infrastructure
5.2.2 Smart Industrial Environment
5.2.3 Smart City Services
5.2.4 Smart Energy Management
5.2.5 Smart Water System
5.3 Challenges and Opportunities of IoT Enabled Smart Cities
5.4 Future of IoT Enabled Smart Cities
5.5 Conclusion
References
6 Identifying Impact of Sanitation and Environmental Safety in Hospitality Sector to Identify the Spread of COVID-19 Using Polynomial Function Simulation
6.1 Introduction
6.2 Meaning of the Study: Possible Explanations and Implications for Clinicians and Policymakers
6.3 System Model and Algorithm
6.4 Machine Learning Approach as a Strength and Weakness of the Study
6.5 Conclusion
6.6 Limitations and Future Recommendations
References
7 The Internet of Things and Advanced Applications in Healthcare
7.1 Introduction
7.1.1 Evolution
7.2 Technologies Used in IoT
7.3 Literature Review
7.4 Architecture of IOT
7.4.1 Application Layer
7.4.2 Middleware Layer
7.4.3 Access Gateway Layer
7.4.4 Edge Layer / Hardware Layer
7.5 Applications of IOT
7.6 IOT Implementation Kits
7.7 IOT in Healthcare
7.8 Case Study: Electronically Controlled Nursing Bed
7.8.1 Related Study
7.8.2 Problem Definition
7.8.3 Objectives
7.8.4 Proposed Solution
7.8.5 Methodology
7.8.6 Results
7.9 Conclusion Future Scope
References
8 A Comprehensive Review of Recent Automatic Speech Summarization and Keyword Identification Techniques
8.1 Introduction
8.2 Brief History of Automatic Text Summarization and Keyword Extraction
8.3 Automatic Video Indexing using Deep Learning
8.4 Taxonomy of Automatic Speech Recognition Techniques
8.5 Speech summarization and Keyword Extraction based on Neural Network
8.6 Conclusion and Future Scope
References
9 Anomaly Detection in Industrial IoT Applications Using Deep Learning Approach
9.1 Introduction
9.2 Related Work
9.3 Proposed Model
9.4 Performance Evaluation
9.5 Results
9.6 Conclusion
References
10 Nurturing the Rudiments and Use Cases of Ongoing Natural Language Generation for a Future Profitable Business More Profitable
10.1 Introduction
10.2 Rudiments of Natural Language Generation
10.2.1 Understanding the Working of NLU and NLG
10.2.2 Language Modeling Toolkit for NLG
10.2.3 Preferred Programming Language for NLG
10.2.4 NLG Libraries to Detect Topics from a Given Text
10.2.5 List of Requirements for Effective Natural Language Generation
10.2.6 Different Variations of NLG
10.3 Use Cases of Natural Language Generation
10.4 The Best Natural Language Generation Platforms
10.5 Future of Natural Language Generation
10.6 The Major Challenges on Natural Language Generation (NLG)
10.7 Conclusion
References
11 A Study on Deep Learning Models for Medical Image Segmentation
11.1 Introduction
11.2 Medical Imaging
11.3 Segmentation of Image
11.4 Deep Learning Overview
11.4.1 Deep Learning Architecture
11.5 Review of DL Based Chapters Focusing on Medical Image Segmentation
11.6 Conclusion
References
12 Deep Learning-based Cyber Security Solutions for Smart-City: Application and Review
12.1 Introduction
12.2 Background Overview
12.2.1 Smart City
12.2.2 Security of Smart Cities
12.2.3 Cyber Security
12.2.4 Deep Learning
12.3 Applications of Deep Learning Algorithms for Safe and Secure Smart Cities
12.3.1 Convolutional Neural Networks (CNNs)
12.3.2 Fully Convolutional Neural Networks (FCNs)
12.3.3 Fully Convolutional Neural Networks (FCNs)
12.3.4 Boltzmann Machine (BM)
12.3.5 Deep Belief Networks (DBN)
12.4 Conclusions
References