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A Hypothetical Study in Biomedical Based Artificial Intelligence Systems Using Machine Language (ML) Rudiments
| Content Provider | Scilit |
|---|---|
| Author | Devi, D. Renuka Sasikala, S. |
| Copyright Year | 2020 |
| Description | Book Name: Handbook of Artificial Intelligence in Biomedical Engineering |
| Abstract | Artificial intelligence (AI) is the recreation of the anthropological intelligence mechanism by machines and specific intelligence systems. These headways contain erudition, perceptive considerations and self-rectification. Some practices of AI include expert systems, speech recognition and machine vision and learning. Artificial astuteness or the intelligence is advancing dramatically, and it is already transforming our world socially, parsimoniously and politically. There have been paradigms of a shift in the way patients are clinically treated by doctors with AI-assisted excessive limits of data in their gauges. With the degree and dimensions of data raising up at a confounding pace, henceforth the conventional diagnostic approaches have been modernized and there has been a change in the clinical assertion making techniques. The intention of promising technological development in AI, leads to tackle critical health issues with quicker benefits even in shortcoming of the issue. On the same regards and notion machine learning (ML) is also trying to oversee the applications and possibilities of medical theories with assistive symptomatic services, which will drastically progress the accessibility and the accuracy of the medical services for the common man and mankind. In this 470chapter, we explore the fundamentals and application of ML in biomedical domain. We also discuss the research developments and challenges.Artificial intelligence is the recreation of the anthropological intelligence mechanism by machines and specific intelligence systems. Under research, most of the established Machine Language (ML) models and tools have explored the potential of prognosis, diagnosis, or differentiation of clinical groups, thus signifying capacity towards building computer-based decision support tools. In neuroscience, the decoding of brain's neural activity to infer intentions from brain measurements is a common problem. Machine learning methods are built to reach that objective and so to obtain accurate predictions, it is best to rely on such methods. The ML model for encoding, plots the signal against the function of external visual stimuli or any brain movements. A potential machine or deep learning system has the competence to adopt for rapid development in data accumulation and not only the textual data, but it also embraces and examines the image, genetic, and electrophysiology data. |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2020-0-12878-4&isbn=9781003045564&format=googlePreviewPdf |
| Ending Page | 492 |
| Page Count | 24 |
| Starting Page | 469 |
| DOI | 10.1201/9781003045564-21 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-12-28 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Handbook of Artificial Intelligence in Biomedical Engineering Medical Informatics Machine Learning Machine Language Anthropological Intelligence Specific Intelligence Intelligence Mechanism Machines and Specific |
| Content Type | Text |
| Resource Type | Chapter |
