This book has a wider scope as it not only covers deep learning problems in Engineering, Energy and Finance sectors but also it can be used as learning or teaching material for beginners in deep learning. It will contain ample illustrations to cater readers from interest domains ranging from pure deep learning to solar energy, different engineering branches to financial domain. The reason for choosing these specific domains is that these domains have a wide societal impact:

• The Solar power market value in 2021 was $167.83 billion in 2021.The global solar   power market is projected to grow from $234.86 billion in 2022 to $373.84 billion by 2029, at a CAGR of 6.9% in forecast period, 2022-2029.
•  As known, it plays a vital role in reducing greenhouse gas emissions and mitigates climate change. Solar energy plays important role in improving air quality and reducing use of water for energy production, there by helps in conserving the same.
• In order to study of solar energy one of the essential concepts to be known is solar radiation, which is a basic source of Earth’s energy. It also is significant as it helps in keeping surface radiation balance, balancing hydrological cycles, vegetation photosynthesis and extreme changes in the weather thereby affection almost every aspect of human existence.
• Even the domain of optimum integration of photovoltaic array with a battery and modeling it’s precise SoC (state of charge) can help in understanding the available battery capacity in real time and has wider societal impact that can be addressed using deep learning.
• Though being renewable source of energy with wide societal implications in terms of employment generation, cost-effective power consumption, environmental conservation, the solar domain is still not fully explored in terms of efficiency and effectiveness. Deep learning of the hard wares involved, distribution and management is still being explored and has a scope of innovation. A literature in the form of our book will give insight into this aspect of deep learning. It will not only address theoretical aspects, but also practical and hands on learning along with giving insights to the readers with regard to limitations as well as open avenues of exploration for the same.
• Smart cities and Digital twin: Deep learning can also help in multi-source data collection using big data analytics from IoT devices for creating smart cities and their digital twin.
• Use-cases covering optimizing renewable energy systems, reducing carbon footprints and improving efficiency of transport systems are also included.
• The Financial domain touches the life of every individual. Finances drive societies, states and in turn nations.  It will not be exaggeration if one says that the stock market, currency market and commodity markets of any country drive a Nation’s economy.
• The question that riddles every individual who observes stock markets is how to make sense of the indexes, the trends and analysis of stocks in stock market? How to have valuable insights from its data that is generated on day-to-day basis? What should be optimal strategy for secure investments and how stock market can help make my life more stable even with less income and weak finances? Thus, financial domain too has enormous impact on societal living of individuals.
• Deep learning challenges do exist in financial domain with regard to automation of business processes, assessment of assets, portfolios and risk, fraud detection, credit scoring and underwriting, cost control and risk reduction. Deep learning can help make more informed decisions by individuals as well as companies and financial institutions there by contributing to profit making and financial stability. Segregation of documents and resources by financial institutions such as banks which would make working smoother and documentation effective is also an active domain of deep learning applications.  A good literature in the form of a book will provide with some of the insights thereby making the reader more informed and knowledgeable regarding finance. Such a book can also be used as a learning resource for basics of deep learning in finance.
• Also use cases can help individuals be more vigilant and pragmatic when dealing with issues and deadlocks.
• Engineering branches have immense impact on standard of living of individuals, society and nations with continue to do so. The products and services made by engineers help to make life easier for everyone but more specifically for the most underprivileged section of the society. Engineering innovations are constantly happening in terms of startup, applications, API’s, learning platforms, streamlining operations to name a few. These innovations are constantly changing the ways in which the society perceives technology thereby enjoying benefits of the same. Societal impact of engineering is enormous. Deep learning is impacting practically every branch of engineering right from structural health modeling of a building, construction site safety, building occupancy modeling and cost management in civil engineering to logistics planning, boosting human decisions, intelligent automation and process innovation in production industry. This book can serve as a go to resource on learning applications of deep learning in different branches of engineering. Some use cases of deep learning and engineering in healthcare domain as also included.

Aim of the Book: The aim of the book is to provide a ready resource of reference to students, faculties and researchers working in the domain of deep learning. Deep learning and Computer Vision based research; Rapid prototyping and product development is extensively done in various automation industries with applications ranging from smart and intelligent manufacturing, human decision boosting and process innovation. This book is helpful for academicians and researchers alike. This also aims to providing a resource for engineering professionals, early career researchers a like working in the deep learning domain.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

All the abstracts must be submitted via EasyChair Only

At the time of ABSTRACT SUBMISSION, submit the following information:

1. Title---Make sure that it matches, the core theme of the book
2. Author details- Name, Department Name, Institute Name and Email Address
3. Abstract --- Min 200-250 words
4. 7 Keywords
5. Table of Contents- Tentative

Guidelines For Chapter:

All Chapters should be min 30-40 Pages, without references and Plagiarism should be less than 20%. And min 50-90 References, English Language should be good. And should comprehensively cover the content.

How to Submit Your Chapter:

Full chapters up to 8000-12000 words are expected to be submitted in single column format (Font 10) for initial submission and all authors must consult guidelines for manuscript submission at  https://www.routledge.com/our-customers/authors/publishing-guidelines

List of Topics

The topics, not limited to the following:

1. Fundamentals of Deep Learning: Linear Algebra and Differential Calculus
2. Model Evaluation and Hyper-parameter Tuning Techniques
3. Artificial Neural Networks: Learning Rules, Single and Multi-layer Perceptron
4. Convolution Neural Networks: concepts and applications in Solar Energy, Engineering and Finance
5. Recurrent Neural Networks in Solar Energy, Engineering and Finance
6. Long Short-Term Memory (LSTM) concepts and use cases in Solar Energy, Engineering and Finance
7. Self-Organizing Maps: Workflow, Reading and use cases in Solar Energy, Engineering and Finance
8. Auto-Encoders and Reinforcement learning: Concepts, illustrations and applications in Solar Energy, Engineering and Finance
9. Q-learning and Representation learning: Concepts, Mathematics and use cases in Solar Energy, Engineering and Finance
10. Deep Convolution Models: Basic definitions, types and applications in Solar Energy, Engineering and Finance
11. Boltzmann Machine: Types, Architectures, implementations and applications in Solar Energy, Engineering and Finance
12. Generative Adversarial Networks (GANs): Definitions, concepts and use cases in Solar Energy, Engineering and Finance
13. Graphical Neural Networks (GNNs): Definitions, concepts and use cases in Solar Energy, Engineering and Finance
14. Deployment of Machine Learning Models
15. Deployment of Deep Learning Models

Editors

• Dr. Vivek S. Sharma, NKC, Mumbai, India

• Dr. Shubham Mahajan, Ajeenkya D Y Patil University, India

• Dr. Anand Nayyar, Duy Tan University, Viet Nam

• Dr. Amit Kant Pandit, Shri Mata Vaishno Devi University, India

Publication

DLEEF 2023 proceedings will be published by CRC Press. The Publisher will submit the book for Possible Indexing in Scopus.

Contact

All questions about submissions should be emailed to:

Dr. Anand Nayyar, Duy Tan University, Da Nang 550000. Email: ; WhatsApp: +91-9878327635anandnayyar@duytan.edu.vn

Dr. Shubham Mahajan, Ajeenkya D Y Patil University, India. Email: ; WhatsApp: +91-9419610760shubhammahajan@ieee.org