Shallow Learning vs. Deep Learning

Preface
Contents
Machine Learning Methods from Shallow Learning to Deep Learning
	1 Introduction
	2 Relationships Between Artificial Intelligence and Its Concepts
		2.1 The Difference Between Artificial Intelligence and Machine Learning
		2.2 The Difference Between Deep Learning and Machine Learning
		2.3 The Difference Between Deep Learning and Neural Networks
		2.4 The Difference Between AI and Neural Networks
	3 Definition and Basic Concept of Machine Learning
	4 Shallow Learning
		4.1 Linear Regression
		4.2 Logistic Regression
		4.3 Decision Trees
		4.4 Support Vector Machines
		4.5 K-Nearest Neighbors
		4.6 Naïve Bayes
	5 Deep Learning
		5.1 Brief Overview of How Deep Learning Networks Function
		5.2 Convolutional Neural Networks
		5.3 Recurrent Neural Networks
		5.4 Memory-Based Enhanced Networks
		5.5 Transformer-Based Neural Networks
		5.6 Generative Adversarial Networks
		5.7 Large Language Model Networks
		5.8 Deep Learning and Its Future
	6 Comparison of Machine Learning, Shallow Learning, and Deep Learning
	7 Relationships and Differences
	8 Challenges and Future Directions and Ethical Considerations
	9 Conclusion
	References
Shallow Learning vs. Deep Learning in Engineering Applications
	1 Introduction
	2 Application of Machine Learning in Engineering Applications
		2.1 Application of Shallow Learning in Engineering
		2.2 Application of Deep Learning in Engineering
	3 Machine Learning in Mechanical Engineering
		3.1 Mechatronics
		3.2 Microelectromechanical
		3.3 Biomechanical
		3.4 Propulsion
	4 Machine Learning in Chemical Engineering
	5 Machine Learning in Biomedical Engineering
		5.1 Biomechanics
		5.2 Drug Delivery
		5.3 Imaging
		5.4 Nanotechnology
	6 Machine Learning in Materials Engineering
	7 Machine Learning in Civil Engineering
	8 Machine Learning in Computer Engineering
	9 Machine Learning in Aerospace Engineering
	10 Machine Learning in Automotive Engineering
	11 Machine Learning in Marine Engineering
	12 Machine Learning in Manufacturing Engineering
	13 Machine Learning in System Engineering
	14 Machine Learning in Architectural Engineering
		14.1 Transformative Shift in Architectural Practices
		14.2 ML in Architectural Fabrication
	15 Machine Learning in Petroleum Engineering
		15.1 Challenges and Opportunities
	16 Machine Learning in Nuclear Engineering
	17 Machine Learning in Robotics
		17.1 Deep Learning in Advanced Robotics
		17.2 Advantages and Challenges of AI in Robotics Applications
	18 Machine Learning in Agricultural Engineering
	19 Machine Learning in Electrical Engineering
	20 Shallow Learning vs. Deep Learning in Modeling of PV Panels: An Example
		20.1 PV Modeling with Polynomial Regression
		20.2 PV Modeling with NNs
	21 Conclusion
	References
Shallow Learning vs. Deep Learning in Finance, Marketing, and e-Commerce
	1 Introduction: Shallow Learning and Deep Learning
	2 Method: Shallow Learning vs Deep Learning in Finance, Marketing, and e-Commerce
		2.1 Finance
		2.2 Marketing
		2.3 E-Commerce
	3 Results
	4 Concluding Remarks and Future Research Directions
	References
Shallow Learning vs. Deep Learning in Social Applications
	1 Introduction
		1.1 Shallow Learning (SL) and Deep Learning (DL)
	2 Methods: Social Applications
		2.1 Sentiment Analysis
		2.2 Opinion Mining
		2.3 Social Network Analysis
	3 Results: Applications of Shallow and Deep Learning Techniques in the Social Domain
		3.1 Shallow Learning vs. Deep Learning in Sentiment Analysis
		3.2 Shallow Learning vs. Deep Learning in Opinion Mining
		3.3 Shallow Learning vs. Deep Learning in Social Network Analysis
	4 Discussion
		4.1 Some Challenging Open Problems
	5 Concluding Remarks and Future Research Directions
	References
Shallow Learning vs. Deep Learning in Image Processing
	1 Introduction
	2 Materials and Method
		2.1 Dataset
	3 Methodology
	4 Experiment Results
	5 Discussion
	6 Conclusion
	References
Shallow Learning Versus Deep Learning in Biomedical Applications
	1 Introduction
	2 Material and Methods
		2.1 Methods
			2.1.1 Overview
			2.1.2 Pre-processing
			2.1.3 Segmentation and Feature Extraction of EEG Signals
			2.1.4 Shallow Learning Classification
				2.1.4.1 AdaBoost
				2.1.4.2 CatBoost
				2.1.4.3 XGBoost
				2.1.4.4 Decision Tree
				2.1.4.5 Gaussian Naïve Bayes
				2.1.4.6 Linear Discriminant Analysis
				2.1.4.7 K-Nearest Neighbors
			2.1.5 Deep Learning Classification
				2.1.5.1 Deep Neural Network
				2.1.5.2 2-Dimensional Convolutional Neural Network
	3 The Simulation Results and Discussion
		3.1 Performance Measures
		3.2 Results and Discussion
	4 Conclusions
	References
Shallow Learning vs. Deep Learning in Anomaly Detection Applications
	1 Background
		1.1 Anomaly Detection and Its Application Domains
		1.2 Foundational Background on Shallow Learning and Deep Learning
	2 Performance Comparison of Shallow and Deep Learning Algorithms in Anomaly Detection Applications
		2.1 Shallow Learning Algorithms for Anomaly Detection Applications
		2.2 Deep Learning Algorithms for Anomaly Detection Applications
		2.3 Comparative Evaluation of Anomaly Detection Algorithms
		2.4 Areas of Improvement for Anomaly Detection
	3 Some Challenging Open Problems
	4 Concluding Remarks and Future Research Directions
	References
Shallow Learning Versus Deep Learning in Natural Language Processing Applications
	1 Introduction
	2 Overview of Natural Language Processing
		2.1 Brief History of NLP
		2.2 NLP Levels
		2.3 NLP Tasks
	3 Classification of NLP Models
		3.1 Shallow NLP Models
		3.2 Deep NLP Models
	4 Shallow Methods for NLP
		4.1 ML-Based Shallow Learning
	5 Deep Learning Methods for NLP
	6 Comparison
	7 Discussion
	8 Challenges and Future Work
	9 Conclusion
	References
Shallow Learning Versus Deep Learning in Speech Recognition Applications
	1 Introduction
	2 Shallow Learning
		2.1 Techniques Used in Shallow Learning for Speech Recognition
		2.2 Advantages and Disadvantages of Shallow Learning for Speech Recognition
	3 Deep Learning
		3.1 Techniques Used in Deep Learning for Speech Recognition
		3.2 Advantages and Disadvantages of Deep Learning for Speech Recognition
	4 Challenges in Shallow and Deep Learning
		4.1 Limitations and Issues in Shallow Learning
		4.2 Limitations and Issues in Deep Learning
		4.3 Dependency on Hand-Crafted Features
		4.4 Inability to Handle Large and Complex Datasets
		4.5 Challenges in Deep Learning
		4.6 Need for a Large Amount of Training Data
		4.7 Difficulty in Interpreting and Explaining Results
	5 Conclusion
	References
Shallow Learning vs Deep Learning in Recommendation Systems
	1 Introduction
	2 Materials and Method
		2.1 The Experimental Area
		2.2 Sentinel-1 Satellite Data Collection
		2.3 Object Data Collection
		2.4 Feature Extraction
		2.5 Applied Methods
		2.6 Statistical Metrics
	3 Results and Discussion
		3.1 Statistical Results
			3.1.1 Statistical Analysis of Sentinel-1 VV Polarization Band Parameters
			3.1.2 Statistical Analysis of Sentinel-1 VH Polarization Band Parameters
		3.2 Deep Learning and Shallow Learning Results
		3.3 Comparison of Deep Learning and Shallow Learning in This Application
	4 Conclusion
	References
Advanced Techniques and Application Areas in Remote Sensing Images: Integration of Deep Learning and YOLOv5 Algorithms
	1 Introduction
	2 Analyzes Data Sets Enriched with TensorFlow Object Detection API
		2.1 Model Architectures
			2.1.1 YOLOv5
			2.1.2 YOLOR
			2.1.3 SAHI
			2.1.4 YOLOv7
	3 Implementation Steps of the Model
	4 Results of the Study and Evaluations
	5 Comparison of Deep and Shallow Learning in the Context of Advanced Techniques and Application Areas in Remote Sensing Images
	6 Conclusion
	References
Shallow Learning vs Deep Learning in Smart Grid Applications
	1 Introduction
	2 Smart Grids and Data Collection
	3 Shallow Learning
	4 Deep Learning
		4.1 Deep Neural Networks (DNNs) and Their Use in Smart Grids
		4.2 Deep Learning Models and Algorithms for Processing Large Datasets
	5 Applications of Deep and Shallow Learning in Smart Grids
		5.1 Shallow Learning and Smart Grid Application Areas
		5.2 Deep Learning and Smart Grid Application Areas
		5.3 Success Stories
	6 Performance Comparison and Results
		6.1 Performance Comparison
		6.2 Which Approach Is More Effective Under What Conditions
		6.3 Future Directions and Research Needs
			6.3.1 Future Developments in Smart Grid Technology
			6.3.2 Potential Research Topics and Trends in Deep and Shallow Learning
	7 Applications
		7.1 Shallow Learning Applications
		7.2 Deep Learning Applications
	8 Conclusion
	References
Index