Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems (Hybrid Computational Intelligence for Pattern Analysis and Understanding)

TRENDS IN DEEP LEARNING METHODOLOGIES
Copyright
Contributors
Preface
1 . An introduction to deep learning applications in biometric recognition
	1. Introduction
		1.1 Biometric recognition
			1.1.1 Overview
			1.1.2 Identification versus verification
			1.1.3 Expectations from a biometric security system
	2. Methods
		2.1 Prominent deep learning algorithms
			2.1.1 Convolutional neural networks
			2.1.2 Recurrent neural networks
			2.1.3 Autoencoder network
			2.1.4 Siamese neural network
		2.2 Deep learning methodology for precise recognition
			2.2.1 Hard biometrics
				2.2.1.1 Face recognition
				2.2.1.2 Fingerprint recognition
				2.2.1.3 Palmprint recognition
				2.2.1.4 Iris recognition
				2.2.1.5 Vein recognition
		2.3 Voice recognition
		2.4 Gait recognition
			2.4.1 Signature recognition
			2.4.2 Soft biometrics
		2.5 Databases
		2.6 Deep learning methodology for spoof protection and template protection
			2.6.1 Spoof protection
			2.6.2 Template protection
				2.6.2.1 Cancelable biometrics
				2.6.2.2 Biometric cryptosystems
		2.7 Challenges in biometric recognition and security
	3. Comparative analysis among different modalities
	4. Further advancement
	5. Conclusion
	References
2 . Deep learning in big data and data mining
	1. Introduction
	2. Overview of big data analysis
		2.1 Variety of data
		2.2 Velocity of data
		2.3 Volume of data
		2.4 Veracity of data
		2.5 Variability of data
		2.6 Visualization of data
		2.7 Value of data
		2.8 Distributed computing
			2.8.1 MapReduce Framework
			2.8.2 Hive
			2.8.3 Apache Spark
				2.8.3.1 Visualizations using tableau
		2.9 Data warehouse versus data lake
	3. Introduction
		3.1 What is data mining?
		3.2 Why use deep learning in data mining?
	4. Applications of deep learning in data mining
		4.1 Multimedia data mining
		4.2 Aspect extraction
		4.3 Loss function
		4.4 Customer relationship management
	5. Conclusion
	References
	Further readings
3 . An overview of deep learning in big data, image, and signal processing in the modern digital age
	1. Introduction
		1.1 Deep learning concepts
		1.2 Machine learning
		1.3 Big data
		1.4 Scientific review
	2. Discussion
	3. Conclusions
	4. Future trends
	References
4 . Predicting retweet class using deep learning
	1. Introduction
	2. Related work and proposed work
		2.1 Handcrafted feature: basic machine learning classifiers
		2.2 Generative models
		2.3 Lexical semantics
		2.4 Proposed framework
	3. Data collection and preparation
		3.1 Tweet corpus creation
		3.2 Tweet data cleaning
		3.3 Tweet data preparation
		3.4 Word expletive, a proposed feature
	4. Research set-up and experimentation
		4.1 The long short-term memory neural network
	5. Results
	6. Discussion
	7. Conclusion
	References
5 . Role of the Internet of Things and deep learning for the growth of healthcare technology
	1. Introduction to the Internet of Things
	2. Role of IoT in the healthcare sector
	3. IoT architecture
	4. Role of deep learning in IoT
	5. Design of IoT for a hospital
	6. Security features considered while designing and implementing IoT for healthcare
	7. Advantages and limitations of IoT for healthcare technology
	8. Discussions, conclusions, and future scope of IoT
	References
6 . Deep learning methodology proposal for the classification of erythrocytes and leukocytes
	1. Introduction
	2. Hematology background
	3. Deep learning concepts
	4. Convolutional neural network
	5. Scientific review
	6. Methodology proposal
	7. Results and discussion
	8. Conclusions
	9. Future research directions
	References
7 . Dementia detection using the deep convolution neural network method
	1. Introduction
	2. Related work
	3. Basics of a convolution neural network
		3.1 Convolution layer
		3.2 Pooling layer
		3.3 Fully connected layer
	4. Materials and methods
		4.1 Dataset description
		4.2 Deep Learning methodology for dementia detection
	5. Experimental results
	6. Conclusion
	References
8 . Deep similarity learning for disease prediction
	1. Introduction
	2. State of the art
	3. Materials and methods
		3.1 Data gathering
		3.2 Data preprocessing
		3.3 Splitting the data
		3.4 Model training
			3.4.1 Representation learning
			3.4.2 Similarity learning
		3.5 Evaluation and prediction
	4. Results and discussion
	5. Conclusions and future work
	References
9 . Changing the outlook of security and privacy with approaches to deep learning
	1. Introduction
	2. Birth and history of deep learning
	3. Frameworks of deep learning
	4. Statistics behind deep learning algorithms and neural networks
	5. Deep learning algorithms for securing networks
	6. Performance measures for intrusion detection systems
	7. Security aspects changing with deep learning
	8. Conclusion and future work
	References
10 . E-CART: an improved data stream mining approach
	1. Introduction
	2. Related study
	3. E-CART: proposed approach
	4. Experiment
	5. Conclusion
	References
11 . Deep learning-based detection and classification of adenocarcinoma cell nuclei
	1. Introduction
	2. Basics of a convolution neural network
		2.1 Convolution layer
		2.2 Pooling layer
		2.3 Fully connected layer
	3. Literature review
	4. Proposed system architecture and methodology
		4.1 Cell detection using faster R-CNN
		4.2 Cell classification with ResNet-101
	5. Experimentation
		5.1 Dataset
		5.2 Discussion on results
	6. Conclusion
	References
12 . Segmentation and classification of hand symbol images using classifiers
	1. Introduction
	2. Literature review
	3. Hand symbol classification mechanism
		3.1 Image preprocessing stage
		3.2 Segmentation stage
			3.2.1 Thresholding methods
			3.2.2 Histogram-based image segmentation
			3.2.3 Feature extraction and selection for making a feature vector
			3.2.4 Types of features
		3.3 Classification stage
			3.3.1 Classification phases
	4. Proposed work
	5. Results and discussion
	6. Conclusion
	References
Index
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