In today's world, digitalization is becoming more popular in all aspects. One of the keen developments that have taken place in the 21st century in the field of computer science is artificial intelligence (AI). AI has a wide range of applications in the medical field. Imaging, on the other hand, has become an indispensable component of several fields in medicine, biomedical applications, biotechnology, and laboratory research by which images are processed and analyzed. Segmentation of images can be analyzed using both fuzzy logic and AI. As a result, AI and imaging, the tools and techniques of AI are useful for solving many biomedical problems and using a computer-based equipped hardware–software application for understanding images, researchers, and clinicians, thereby enhancing their ability to study, diagnose, monitor, understand, and treat medical disorders. 196Patient monitoring by automated data collection has created new challenges in the health sector for extracting information from raw data. For interpretation of data quickly and accurately the use of AI tools and statistical methods are required, for interpretation of high-frequency physiologic data. Technology supporting human motion analysis has advanced dramatically and yet its clinical application has not grown at the same pace. The issue of its clinical value is related to the length of time it takes to perform interpretation, cost, and the quality of the interpretation. Techniques from AI such as neural networks and knowledge-based systems can help overcome these limitations. In this chapter, we will discuss the key approaches using AI for biomedical applications and their wide range of applications in the medical field using AI. The different segmentation approaches their advantages and disadvantages. A short review of prognostic models and the usage of prognostic scores quantify the severity or intensity of diseases.
For communication, speech is one of the natural forms. A person's voice contains various parameters that convey information such as emotions, gender, attitude, health, and identity. Determination of these parameters will help to further develop the technology into a reliable and consistent means of identification using speaker recognition. Speaker recognition technologies have wide application areas especially in authentication; surveillance and forensic speaker recognition. In addition, speaker recognition refers to the automated method of identifying or confirming the identity of an individual based on his/her voice. Speech recognition strips out the personal differences to detect the words. Speaker recognition typically disregards the language and meaning to detect the physical person behind the speech. Speech recognition is language-dependent, while Speaker recognition is independent of language. In essence, voice biometrics provides speaker recognition rather than speech recognition. The most accepted form of identification for a human is his/her speech signal. Principally the speaker recognition is the computing task of validating a user's claimed identity 108using characteristics extracted from their voice. The speaker recognition process based on a speech signal is treated as one of the most exciting technologies of human recognition. For Speaker identification activities we mainly emphasize the physical features of signal. Speakers could be categorized as speaker identification and speaker verification. In speaker identification, the obtained features are compared with all the speaker's features which are stored in a voice model database and in speaker verification the obtained features are only compared with the stored features of the speaker he/she claimed to be. In this chapter the general principles of speaker recognition, methodology, and applications are discussed.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='translate(0 -400)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -600)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -800)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -1000)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -1200)'/%3e%3cpath d='M0 0h1400v800H0z' style='fill:%23010066'/%3e%3cpath d='M576 100c-166.146 0-301 134.406-301 300s134.854 300 301 300c60.027 0 115.955-17.564 162.927-47.783-27.353 9.44-56.71 14.602-87.271 14.602-147.327 0-266.897-119.172-266.897-266.01 0-146.837 119.57-266.01 266.897-266.01 32.558 0 63.746 5.815 92.602 16.468C696.217 118.91 638.305 100 576 100z' style='fill:%23fc0'/%3e%3cpath d='m914.286 471.429-99.538-53.25 29.43 108.982-66.575-91.165-20.77 110.96-20.428-111.024-66.857 90.96L699.314 418l-99.701 52.943 74.065-85.192-112.8 4.441 103.694-44.62-103.555-44.94L673.8 305.42 600 220l99.538 53.25-29.43-108.982 66.575 91.165 20.77-110.96 20.428 111.023 66.857-90.959L814.97 273.43l99.702-52.944-74.065 85.193 112.8-4.441-103.694 44.62 103.555 44.94-112.785-4.79z' style='fill:%23fc0' transform='matrix(1.2738 0 0 1.2423 -89.443 -29.478)'/%3e%3c/svg%3e)