Artificial Intelligence in Industrial Applications: Approaches to Solve the Intrinsic Industrial Optimization Problems
- Length: 206 pages
- Edition: 1
- Language: English
- Publisher: Springer
- Publication Date: 2021-12-08
- ISBN-10: 3030853829
- ISBN-13: 9783030853822
- Sales Rank: #0 (See Top 100 Books)
This book highlights the analytics and optimization issues in industry, to propose new approaches, and to present applications of innovative approaches in real facilities. In the past few decades there has been an exponential rise in the application of artificial intelligence for solving complex and intricate problems arising in industrial domain. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The book is edited to serve a broad readership, including computer scientists, medical professionals, and mathematicians interested in studying computational intelligence and their applications. It will also be helpful for researchers, graduate and undergraduate students with an interest in the fields of Artificial Intelligence and Industrial problems. This book will be a useful resource for researchers, academicians as well as professionals interested in the highly interdisciplinary field of Artificial Intelligence.
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
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