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An Extensive Overview of Machine Learning for Signal Processing: Development, Uses, and Future Prospects

Charan Singh

Abstract


Machine learning has found a surprising number of uses in problem solving and enabling computerization across several domains. This is mostly because to the explosion in information accessibility, significant improvements in machine learning techniques, and advancements in registering skills. Unquestionably, ML has been used to address a variety of ordinary yet intricate problems that arise in executives' and signal processing activities. Different summaries of machine learning exist for specific domains within signal processing, such as explicit advancements. This analysis is distinctive since it shows how different machine learning techniques are applied in distinct areas of signal processing related to different system advancements. Currently, will benefit from an in-depth discussion of the unique learning ideal models and machine learning techniques applied to important signal processing problems, such as productivity, signal to noise ratio, and bit error rate estimate. In addition, this paper illustrates the limitations, provides tidbits of information, and explores challenges and opportunities to advance machine learning in signal processing. This makes it a timely commitment to the implications of machine learning for signal processing, which is expanding the limits of different systems and activities.


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