Full metadata
Title
Signal Processing and Machine Learning Techniques Towards Various Real-World Applications
Description
Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.
Date Created
2018
Contributors
- Dutta, Arindam (Author)
- Bliss, Daniel W (Thesis advisor)
- Berisha, Visar (Committee member)
- Richmond, Christ (Committee member)
- Corman, Steven (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
184 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.51580
Level of coding
minimal
Note
Doctoral Dissertation Electrical Engineering 2018
System Created
- 2019-02-01 07:00:52
System Modified
- 2021-08-26 09:47:01
- 3 years 2 months ago
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