book

Trends in Deep Learning Methodologies

by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava

November 2020

Intermediate to advanced

306 pages

8h 36m

English

Academic Press

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1. Introduction2. Methods3. Comparative analysis among different modalities4. Further advancement5. Conclusion
1. Introduction2. Overview of big data analysis3. Introduction4. Applications of deep learning in data mining5. Conclusion
1. Introduction2. Discussion3. Conclusions4. Future trends
1. Introduction2. Related work and proposed work3. Data collection and preparation4. Research set-up and experimentation5. Results6. Discussion7. Conclusion

1. Introduction to the Internet of Things2. Role of IoT in the healthcare sector3. IoT architecture4. Role of deep learning in IoT5. Design of IoT for a hospital6. Security features considered while designing and implementing IoT for healthcare7. Advantages and limitations of IoT for healthcare technology8. Discussions, conclusions, and future scope of IoT
1. Introduction2. Hematology background3. Deep learning concepts4. Convolutional neural network5. Scientific review6. Methodology proposal7. Results and discussion8. Conclusions9. Future research directions
1. Introduction2. Related work3. Basics of a convolution neural network4. Materials and methods5. Experimental results6. Conclusion
1. Introduction2. State of the art3. Materials and methods4. Results and discussion5. Conclusions and future work
1. Introduction2. Birth and history of deep learning3. Frameworks of deep learning4. Statistics behind deep learning algorithms and neural networks5. Deep learning algorithms for securing networks6. Performance measures for intrusion detection systems7. Security aspects changing with deep learning8. Conclusion and future work
1. Introduction2. Related study3. E-CART: proposed approach4. Experiment5. Conclusion
1. Introduction2. Basics of a convolution neural network3. Literature review4. Proposed system architecture and methodology5. Experimentation6. Conclusion
1. Introduction2. Literature review3. Hand symbol classification mechanism4. Proposed work5. Results and discussion6. Conclusion

Content preview from Trends in Deep Learning Methodologies

Chapter 10: E-CART

An improved data stream mining approach

Pardeep Kumar Department of Computer Science and Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

Abstract

Data stream mining has become important because of the emergence of huge, autonomous, complex, and evolving data in different real-world applications like e-commerce, telecommunication providers, credit-card fraud detection, network traffic, natural calamities forecasting, etc. Decision trees are the most popular technique for the mining the data streams. Concept-adapting Very Fast Decision Tree (CVFDT) and Efficient-Concept-adapting Very Fast Decision Tree (E-CVFDT) are the most popular decision tree-based approaches to handle the concept ...