Deep Learning Concepts in Operations Research (Advances in Computational Collective Intelligence)

✍ Scribed by Biswadip Basu Mallik (editor), Gunjan Mukherjee (editor), Rahul Kar (editor), Aryan Chaudhary (editor)

• Publisher: Auerbach Publications
• Year: 2024
• Tongue: English
• Leaves: 277
• Edition: 1
• Category: Library

✦ Synopsis

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:

• An overview of applications and computing devices
• Deep learning impacts in the field of AI
• Deep learning as state-of-the-art approach to AI
• Exploring deep learning architecture for cutting-edge AI solutions

Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

✦ Table of Contents

Cover
Half Title
Series Information
Title Page
Copyright Page
Table of Contents
Preface
List of Contributors
1 Deep Learning: Overview, Applications and Computing Devices
1.1 Introduction
1.2 Deep Learning: Overview
1.3 Applications of Deep Learning
1.3.1 Localization
1.3.2 Detection
1.3.3 Segmentation
1.3.4 Registration
1.4 Computational Methods
1.4.1 Central Processing Unit-based Approach
1.4.2 GPU-based Approach
1.4.3 FPGA-based Approach
1.5 Summary and Conclusion
References
2 Deep Learning Impacts in the Field of Artificial Intelligence
2.1 Introduction
2.1.1 The Contribution of this Chapter
2.1.2 The Organisation of this Chapter
2.2 Background, Key Concepts, and Components of Deep Learning
2.2.1 Key Concepts
2.2.1.1 Artificial Neural Networks
2.2.1.2 Activation Functions
2.2.1.3 Backpropagation
2.2.1.4 Convolutional Neural Networks
2.2.1.5 Recurrent Neural Networks
2.2.1.6 Pre-Training and Transfer Learning
2.2.2 Components of Deep Learning
2.2.2.1 Neurons
2.2.2.2 Layers
2.2.2.3 Weights and Biases
2.2.2.4 Activation Functions
2.2.2.5 Loss Functions
2.2.2.6 Optimization Algorithms
2.2.2.7 Backpropagation
2.2.2.8 Regularization Techniques
2.2.2.9 Batch Normalization
2.2.2.10 Architectures
2.3 Applications of Deep Learning in Artificial Intelligence
2.3.1 Computer Vision
2.3.2 Natural Language Processing
2.3.3 Speech Recognition
2.3.4 Autonomous Systems
2.3.5 Image and Video Analysis
2.3.6 Fraud Detection
2.3.7 Predictive Analytics
2.3.8 Healthcare
2.3.9 Gaming
2.3.10 Robotics
2.3.11 Manufacturing
2.3.12 Agriculture
2.3.13 Transportation
2.3.14 Energy
2.3.15 Astronomy
2.4 Deep Learning Impacts in the AI Field
2.4.1 Improved Accuracy
2.4.2 Reduced Cost
2.4.3 Faster Processing
2.4.4 Improved Language Processing
2.4.5 Improved Computer Vision
2.4.6 Increased Personalization
2.4.7 Enhanced Security
2.4.8 Automation
2.4.9 Increased Efficiency
2.4.10 Improved Decision-Making
2.5 Future Directions of Deep Learning’s Impact On AI
2.6 Conclusion
References
3 Deep Learning Is a State-Of-The-Art Approach to Artificial Intelligence
3.1 Introduction
3.2 Fundamentals of Deep Learning
3.2.1 Neural Networks: Foundations of Deep Learning
3.2.2 Training Algorithms: Unlocking Learning Potential
3.2.3 Architectural Components: Layers, Nodes, Activation Functions, and Weights
3.2.4 Training and Optimization: Backpropagation and Beyond
3.3 Real-World Applications
3.3.1 Deep Learning in Image Recognition
3.3.2 Natural Language Processing and Deep Learning
3.3.3 Speech Recognition and Deep Learning
3.3.4 Recommendation Systems Empowered By Deep Learning
3.3.5 Breakthroughs in Autonomous Vehicles With Deep Learning
3.3.6 Deep Learning’s Impact On Medical Diagnosis
3.3.7 Intelligent Personal Assistants: a Deep Learning Frontier
3.4 Challenges and Future Directions
3.4.1 Data Challenges in Deep Learning
3.4.2 Computational Requirements
3.4.3 Distributed Deep Learning
3.4.4 Explainability and Interpretability
3.4.5 Integration With Other AI Domains
3.4.6 Future Directions and Emerging Trends
3.5 Advancements in Deep Learning Architectures
3.5.1 Convolutional Neural Networks (CNNs) for Computer Vision
3.5.2 Recurrent Neural Networks (RNNs) for Sequential Data
3.5.3 Deep Learning in Generative Models
3.5.4 Reinforcement Learning (RL) and Deep Q-Networks (DQNs)
3.5.5 Transformer Models: Revolutionizing Natural Language Processing
3.6 Deep Learning’s Power in Complex Tasks
3.6.1 Deep Learning’s Advantage in Handling Complexity
3.6.2 Deep Learning’s Impact On Image Recognition
3.6.3 Natural Language Processing Revolutionized By Deep Learning
3.6.4 Speech Recognition: a Deep Learning Success Story
3.6.5 Recommendation Systems Enhanced By Deep Learning
3.7 The Future of Deep Learning in AI
3.7.1 The Path to Advancements: Evolving Architectures and Techniques
3.7.2 Overcoming Challenges in Deep Learning
3.7.3 Ethical Considerations: Bias, Fairness, and Accountability
3.7.4 Combining Deep Learning With Other AI Domains
3.7.5 The Role of Deep Learning in Shaping the Future of AI
3.8 Conclusion
References
4 Unleashing the Power: Exploring Deep Learning Architecture for Cutting-Edge AI Solutions
4.1 Introduction
4.2 Understanding Deep Learning
4.2.1 Deep Learning and Its Subfields
4.2.2 Evolution of Deep Learning
4.3 Key Components of Deep Learning Architecture
4.3.1 Neural Networks
4.3.1.1 Artificial Neurons
4.3.1.2 Activation Functions
4.3.1.3 Overview of Different Types of Neural Networks
4.3.2 Layers and Connections
4.3.2.1 Input, Hidden, and Output Layers
4.3.2.2 Concept of Layer-To-Layer Connections
4.3.2.3 Importance of Depth in Deep Learning Architecture
4.3.3 Training Algorithms
4.3.3.1 Overview of Backpropagation and Gradient Descent
4.3.3.2 Optimization Techniques
4.3.3.3 Regularization and Dropout Techniques
4.4 Deep Learning Architectures and Applications
4.4.1 Convolutional Neural Networks (CNNs)
4.4.1.1 CNN Architecture and Its Applications in Image and Video Processing
4.4.1.2 Convolutional Layers, Pooling Layers, and Fully Connected Layers
4.4.1.3 CNNs in Real-World Applications
4.4.2 Recurrent Neural Networks (RNNs)
4.4.2.1 RNN Architecture and Its Applications in Sequential Data Analysis
4.4.2.2 Recurrent Connections and Memory Cells (LSTM, GRU)
4.4.2.3 RNNs in Natural Language Processing, Speech Recognition, and Time Series Analysis
4.4.3 Generative Adversarial Networks (GANs)
4.4.3.1 Overview of GAN Architecture and Its Applications in Generating Synthetic Data
4.4.3.2 Generator and Discriminator Components
4.4.3.3 GANs in Image Synthesis, Text Generation, and Data Augmentation
4.5 Advances and Challenges in Deep Learning Architecture
4.5.1 Recent Advancements
4.5.2 Overview of Challenges in Deep Learning Architecture
4.5.3 Ongoing Research and Future Directions
4.6 Conclusion
References
5 Deep Learning for ECG Classification: Techniques, Applications, and Challenges
5.1 Introduction
5.2 Pre-Processing Techniques for ECG Signals: Enhancing Accuracy and Reliability
5.2.1 Noise Removal
5.2.2 Baseline Wander Correction
5.2.3 Challenges and Considerations
5.3 Deep Learning Architectures for ECG Classification
5.4 Data Augmentation Strategies for ECG
5.5 ECG Classification Tasks
5.6 Evaluation Metrics and Performance Analysis
5.7 Applications and Future Directions
5.8 Conclusion
References
6 Social Distancing Detection System Using Single Shot Detection (SSD) and Neural Networks
6.1 Introduction
6.1.1 Motivation
6.2 Literature Survey
6.3 Proposed Methodology
6.3.1 Object Detection
6.3.2 Measuring Distance Between Objects
6.3.3 Calculate the Standard Circular Area for a Safe Distance
6.3.4 Comparing the Calculated Area With the Standard Area
6.4 Experimental Results and Performance Analysis
6.5 Conclusion and Future Scope
References
7 Recognition of Voice and Speech Using NLP Techniques
7.1 Introduction
7.1.1 Motivation
7.2 Literature Survey
7.3 Proposed Methodology
7.3.1 Speech Recognition Model
7.3.2 Voice Recognition Model
7.4 Experimental Results
7.4.1 Speech Recognition Model
7.4.2 Voice Recognition Model
7.5 Conclusion and Future Scope
References
8 Transfer Learning With Joint Fine-Tuning for Multimodal Sentiment Analysis
8.1 Introduction
8.2 Background and Literature Review
8.2.1 Symbolic Reasoning
8.2.2 Deep Learning
8.3 Proposed Approach
8.4 Results
8.4.1 SNLI Dataset
8.4.2 Multi NLI Dataset
8.4.3 WSC Dataset
8.5 Discussion
8.5.1 Implications of the Results
8.6 Future Work
8.6.1 Improving the Performance of the Model
8.6.2 Applying the Model to Downstream NLP Tasks
8.7 Conclusion
References
9 Machine Learning for Traffic Flow Prediction Addressing Congestion Challenges
9.1 Introduction
9.2 Machine Learning Approach
9.3 Machine Learning Techniques Are Used
9.4 Simulation Results
9.5 Conclusions
References
10 Enhancing Autistic Spectrum Disorder Diagnosis Using ML Techniques: A Study On Deep Neural Network and Drop-Out Deep Neural Network
10.1 Introduction
10.2 Review of Literature
10.3 Proposed Methodology
10.3.1 Dataset
10.3.2 Deep Neural Network
10.3.3 Dropout Deep Neural Network
10.4 Result and Discussion
10.5 Conclusion
References
11 Deep Learning: A State-Of-The-Art Approach To Artificial Intelligence “AI”
11.1 Introduction: AI – Transforming Industries and Improving People’s Lives
11.1.1 The Rise of Deep Learning: a State-Of-The-Art Approach
11.2 Deep Learning Fundamentals
11.2.1 Understanding Principles and Concepts: Deep Learning
11.2.2 AI Neural Networks: Mimicking the Human Brain
11.2.3 Architecture and Structure of Artificial Neural Networks
11.2.4 Working Mechanisms of Artificial Neural Networks
11.2.5 Convolutional Neural Networks (CNNs)
11.2.6 Recurrent Neural Networks (RNNs)
11.3 Training Deep Neural Networks
11.3.1 Key Aspects
11.3.1.1 Architecture Design
11.3.1.2 Data Preprocessing
11.3.1.3 Optimization Algorithms
11.3.1.4 Regularization and Avoiding Overfitting
11.3.1.5 Hardware Considerations
11.3.2 The Training Process
11.3.3 Challenges in Training: Deep Networks
11.3.4 Solutions to Training Challenges
11.4 Applications of Deep Learning
11.5 Recent Advances In Deep Learning
11.6 Challenges and Future Directions in Deep Learning
11.6.1 Challenges
11.6.2 Future Directions
11.7 Summary Of Deep Learning As A State-Of-The-Art Approach
11.7.1 Implications for AI and Society
11.7.2 Inspiring Future Research and Innovation
11.8 Conclusion
References
12 An Approach Through Different Mathematical Models to Enhance the Utility in Different Areas of Machine Learning
12.1 Introduction
12.2 Classification of Machine Learning
12.2.1 Supervised Learning
12.2.2 Unsupervised Learning
12.2.3 Reinforcement Learning
12.2.4 Semi-Supervised Learning
12.3 Learning Models
12.3.1 Logical Models
12.3.2 Geometric Models
12.3.3 Linear Models
12.3.4 Distance-Based Models
12.3.5 Probabilistic Models
12.4 Perspectives and Issues in Machine Learning
12.5 Conclusion
References
13 Study of Different Regression Methods, Models and Application in Deep Learning Paradigm
13.1 Introduction
13.2 Literature Review of Different Regression Methods, Models and Applications
13.3 Essential Elements of Deep Learning Regression Methods
13.4 Types of Deep Learning Approaches
13.4.1 Supervised Learning
13.4.2 Unsupervised Learning
13.4.3 Hybrid Learning
13.5 Deep Learning in Supervised Learning Methods
13.5.1 Multi-Layer Perceptron (MLP)
13.5.2 Convolutional Neural Network
13.5.3 Recurrent Neural Networks
13.5.3.1 Long Short-Term Memory (LSTM)
13.5.3.2 Bi-LSTM
13.5.3.3 GRU (Gated Recurrent Unit)
13.6 Deep Learning in Unsupervised Learning Method
13.6.1 Generative Adversarial Network
13.6.2 Autoencoder
13.6.2.1 Sparse AutoEncoder (SAE)
13.6.2.2 Denoising AutoEncoder (DAE)
13.6.2.3 Contractive AutoEncoder (CAE)
13.6.2.4 Variational AutoEncoders (VAEs)
13.6.3 Self-Organizing Map
13.6.4 Restricted Boltzmann Machine
13.6.5 Deep Belief Network
13.7 Deep Learning: Hybrid Strategies
13.7.1 Hybrid Strategy No 1: CNN.+.LSTM
13.7.2 Hybrid Strategy No 2: GAN.+.AE
13.7.3 Deep Transfer Learning
13.7.4 Deep Reinforcement Learning
13.8 Feature Learning
13.9 Future Research Direction
13.9.1 Healthcare
13.9.2 Future-Oriented Smart Devices
13.9.3 Natural Language Processing (NLP)
13.9.4 Robotics
13.9.5 Internet of Things (IoT)
13.9.6 Scientific Research
13.10 Conclusion
References
14 Deep Learning Impacts in the Field of Artificial Intelligence
14.1 Introduction: Background and Driving Forces
14.2 Deep Learning in Computer Vision
14.2.1 Image Classification
14.2.2 Object Detection
14.2.2.1 Region-Based Convolutional Neural Networks (R-CNN)
14.2.2.2 Fast R-CNN
14.2.2.3 Faster R-CNN
14.2.3 Semantic Segmentation
14.2.4 Object Tracking
14.2.5 Generative Models
14.2.6 Visual Understanding
14.3 Deep Learning in Natural Language Processing
14.3.1 Sentiment Analysis and Text Classification
14.3.2 Named Entity Recognition and Information Extraction
14.3.2.1 Bidirectional LSTM
14.3.2.2 Conditional Random Fields (CRF)
14.3.3 Text Summarization
14.3.4 Language Translation
14.3.4.1 Neural Machine Translation (NMT)
14.3.4.2 End-To-End Translation
14.3.4.3 Improved Contextual Understanding
14.3.4.4 Handling Long Sentences
14.3.4.5 Multilingual Translation
14.3.5 Question Answering and Dialogue Systems
14.4 Automated Feature Extraction
14.4.1 End-To-End Learning
14.4.2 Learning Discriminative Features
14.4.3 Handling High-Dimensional Data
14.4.4 Transfer Learning and Generalization
14.4.5 Adaptability and Flexibility
14.5 Enhanced Data Analysis
14.6 Challenges and Future Direction of Deep Learning On Artificial Intelligence
14.6.1 Data Quality and Quantity
14.6.2 Computational Power and Efficiency
14.6.3 Domain Adaptation and Transfer Learning
14.6.4 Causal Reasoning and Explainable AI
14.6.5 Generalization and Robustness
14.6.6 Hardware and Scalability
References
15 Stock Prices Prediction of the FMCG Sector in NSE India: An Artificial Intelligence Approach
15.1 Introduction
15.2 Theoretical Approach
15.2.1 Panel Data
15.2.2 Neural Networks
15.3 Review of Literature
15.4 Methodology
15.4.1 Goal of the Study
15.4.2 Hypothesis of the Study
15.4.3 Population
15.4.4 Methods for Taking Samples
15.4.5 Collection of Data and Tools
15.4.6 Steps of TOPSIS Algorithm
15.4.7 Algorithm for Panel Data
15.4.8 Neural Network Algorithm
15.4.9 Methods of Data Analysis
15.5 Results
15.6 Conclusion
References
16 Multi-Attribute Decision Modelling
16.1 Introduction
16.2 Background of the Study
16.3 About the Chapter Topic
16.4 Methodology
16.4.1 Weighted Scoring Models
16.4.2 Analytic Hierarchy Process (AHP)
16.4.3 Outranking Methods
16.4.4 Fuzzy Logic in Multi-Attribute Decision Modelling
16.4.5 Multi-Objective Optimization
16.4.6 Data Collection and Analysis in Multi-Attribute Decision Modelling
16.4.7 Case Studies in Multi-Attribute Decision Modelling
16.4.8 Uncertainty and Sensitivity Analysis
16.4.9 Integration of Multi-Attribute Decision Modelling With Emerging Technologies
16.4.10 Applications of Multi-Attribute Decision Modelling in Business and Management
16.4.11 Multi-Attribute Decision Modelling in Healthcare and Public Policy
16.5 Conclusion
16.6 Future Enhancements
References
17 Regression Methods and Models
17.1 Introduction
17.2 Regression Models and Principles
17.2.1 Overview of Regression Models
17.2.2 Correlation and Forecasting in Regression Models
17.2.3 Assumptions and Limitations of Regression Models
17.3 Traditional Regression Techniques Used in Operation Research
17.3.1 Simple Linear Regression
17.3.2 Multiple Linear Regression
17.3.3 Polynomial Regression
17.3.4 Logistic Regression
17.3.5 Stepwise Regression
17.3.6 Time Series Regression
17.3.7 Need for More Complex and Adaptable Models
17.4 Regression Models
17.4.1 MLP
17.4.2 SVR
17.4.3 Gaussian Process Regression
17.4.4 XGBoost Regression
17.4.5 Generative Adversarial Network
17.5 Enhancing Regression Models With Deep Learning
17.5.1 Capturing Non-Linear Connections and Interactions
17.5.2 Improved Accuracy and Prediction Performance
17.5.3 Challenges and Considerations
17.6 Challenges of Deep Learning for Regression
17.6.1 Interpretability Issues
17.6.2 Overfitting and Generalization
17.6.3 Computational Resources and Training Time
17.6.4 Data Availability and Quality
17.6.5 Ethical Considerations and Bias
17.7 Conclusion
References
18 The Machine Learning Pipeline: Algorithms, Applications, and Managerial Implications
18.1 What Is Machine Learning?
18.1.1 History of Machine Learning
18.1.2 Principles of Machine Learning
18.2 Data Pre-Processing, Feature Engineering, and Model Selection in ML
18.3 Types of Machine Learning
18.3.1 Supervised Machine Learning
18.3.1.1 Classification
18.3.1.2 Regression
18.3.2 Unsupervised Machine Learning
18.3.2.1 Clustering
18.3.2.2 Dimensionality Reduction
18.3.3 Reinforcement Learning
18.3.4 Semi-Supervised Machine Learning
18.4 Popular Machine Learning Algorithms
18.4.1 Decision Trees
18.4.2 Support Vector Machine
18.4.3 Neural Network
18.4.4 Ensemble Methods (Random Forests and Gradient Boosting)
18.5 Performance Evaluation of Machine Learning Models
18.6 Real-World Applications of Machine Learning
18.7 Challenges of Machine Learning
References
19 Role of Fertamean Neutrosophic Sets for Decision Making Modelling in Machine Learning
19.1 Introduction
19.2 Operation of Fermatean Neutrosophic Sets (FNS)
19.3 Decision Making Algorithm
19.4 Application of Tangent Similarity Measure of Sets
19.5 Conclusion
References
20 Performance Evaluation of Machine Learning Algorithms in the Field of Security-Malware Detection
20.1 Introduction
20.2 Related Work
20.3 Malware Dataset and Pre-Processing
20.4 Machine Learning Algorithms for Malware Detection
20.5 Discussion
20.6 Conclusion
References
Index