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1st CALL FOR CHAPTER PROPOSALS
Proposal Submission Deadline: August 18, 2019
Applications of Artificial Neural Networks for Nonlinear Data
A book edited by
Hiral R. Patel, Assistant Professor, Department of Computer Science, Ganpat University, India
A.V.Senthil Kumar, Professor, Hindusthan College of Arts and Science, Coimbatore, Tamilnadu, India
Introduction
Neural systems are propelled design acknowledgment calculations fit for removing complex, nonlinear connections among factors. This examination looks at those abilities by displaying nonlinearities in the activity fulfillment work execution association with multilayer perceptron what's more, spiral premise work neural systems. A system for examining nonlinear connections
With neural systems is utilized. It is actualized utilizing the activity fulfillment work execution association with results characteristic of unavoidable examples of nonlinearity
The essential distinction between neural systems and regular factual techniques is that ANNs are versatile. That is, information are gone through the organize ordinarily to such an extent that each go of information results in an anticipated worth that is contrasted with a known result. Changes are made to lessen blunder, and information is gone through the system until a worthy decrease in mistake is achieved. This procedure is alluded to as learning on the grounds that as information go through the system, mistake is diminished. Learning happens in the shrouded layer where information is summed and weighted with factual capacities to create an anticipated worth that is then passed on to the yield layer as a rule with a direct exchange work. Example acknowledgment is refined as loads are balanced with each go of information through the system design. To display nonlinear connections, the neurons in the shrouded layer must utilize nonlinear measurable capacities. In particular, it has been appeared whenever summed determined data sources are changed into a yield utilizing a nonlinear factual capacity, the outcome is a model with genuine nonlinear parameters
This procedure is influenced by the sort of measurable exchange capacity utilized and the number of neurons in the shrouded layer. Accordingly, extraordinary neural system designs or potentially diverse exchange capacities can deliver particularly various outcomes. Moreover, it ought to be noticed that neural systems are not limited to one shrouded layer, and occasions where at least two concealed layers are more qualified to mapping nonlinearities in information are normal. All the more explicitly, one concealed layer probably won't be adequate to demonstrate complex nonlinear connections with the goal that at least two shrouded layers are important to precisely demonstrate connections among factors. It is the obligation of the analyst to show that the number of shrouded layers in an ANN is suitable for the current issue.
Objective of the Book
This general approach to using ANNs requires researchers to think differently about data analysis. Most important, it requires them to examine long–standing assumptions about the nature of the relationships among variables including the assumption of linearity. Thus, we begin the discussion of ANNs with the issue of nonlinearity in organizational research and then move on to consider how neural networks can be useful in modeling nonlinear relationships. So basically this book aims to discuss about neural network from the bottom level. The main focus of this book is the applications of Neural Network with data applicability.
Target Audience
Broader audiences of this book will widely vary from individuals, Statisticians, researchers, scientists, academics, students, libraries and development practitioners. This book will generate tremendous impetus in terms of solutions for diagnosing various non parametric and non linear problems using ANN, thus will have highly acceptable scholarly value and at the same time potentially contribute to this very specific sector of research. This book will be utilized in places such Stock Markets, Financial Matters Solving, Classifiable type problems, library reference, research in computer field, teaching etc.
Recommended topics include, but are not limited to, the following:
Introduction
Artificial Neural Network
Purpose of ANN
Parameters of ANN
ANN benefits
Weight Assignment in ANN
Design of ANN
Applications of ANN
Real time 4 to 5 case study on ANN
Latest Technology and Future Trends
Submission Procedure
Researchers and practitioners are invited to submit on or before August 18, 2019 a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by August 30, 2018 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by December 30, 2018 and all interested authors must consult the guidelines for manuscript submissions athttp://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.
Note: There are no submission or acceptance fees for manuscripts submitted to this book publication. All manuscripts are accepted based on a double-blind peer review editorial process.
All proposals should be submitted through the eEditorial Discovery®TM online submission manager.
Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the “Information Science Reference” (formerly Idea Group Reference), “Medical Information Science Reference,” “Business Science Reference,” and “Engineering Science Reference” imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit www.igi-global.com. This publication is anticipated to be released in 2019.
Important Dates
August 18, 2018: Chapter Proposal Submission Deadline
August 30 , 2018: Notification of Acceptance
October 31, 2018: Full Chapter Submission
December 14 2018: Review Results Returned
January 25, 2019: Final Acceptance Notification
February 8, 2019: Final Chapter Submission
Inquiries can be forwarded to
Hiral R. Patel
Assistant Professor, Department of Computer Science
Ganpat University, India
[email protected]
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