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AI and Deep Learning for Ear Infection Detection ~xRay Pixy

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Learn how AI and deep learning revolutionize ear infection detection, enabling accurate, fast, and automated diagnosis using advanced image processing and machine learning techniques. Video Chapters: Ear Infection Detection using AI and DL 00:00 Introduction 00:14 My Experience with Ear Infections 01:15 Topics Covered 02:24 Ear Infections 02:48 Ear Infection Signs 03:55 Ear Infection Preventions 04:29 Ear Infection Types 05:19 Ear Infection Causes 06:14 How Bacteria and Fungus Grow in Ear 07:26 My Mistakes 08:49 Doctors Advise after Ear Infection 09:45 Ear Infection Common Symptoms 10:37 Automated Ear Infection Detection with Deep Learning AI 15:09 Smartphone Otoscopes 16:04 Conclusion Ear fungus, also known as otomycosis , is a fungal infection of the outer ear canal. If an ear infection is not treated on time, it can lead to serious complications.  Hearing Loss – Persistent infections can damage the eardrum and middle ear structures, leading to partial or permanent hearing loss....

Nicholl - Lee Nicholl (NLN) Line Clipping in Computer Graphics

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 In the Cohen-Sutherland method, multiple intersections may be calculated along the path of a single line before an intersection on the clipping rectangle or line is completely rejected.  In Nicholl - Lee Nicholl Line Clipping, Extra intersection calculations are eliminated.  ADVANTAGE OF Nicholl - Lee Nicholl (NLN) Line Clipping 1.) The  number of clipping points is reduced to one, compared to other algorithms that may require two or more clipping.  2.)  Nicholl - Lee Nicholl (NLN) Line Clipping is FAST. Clipping algorithms for Circle, Curved boundary clipping regions are SLOWER because intersections calculations involve nonlinear curve equations.  3.)    Nicholl - Lee Nicholl (NLN) Line Clipping avoids multiple clipping of Line segments.  4.) As Compare to Cohen Sutherland and Liang-Barsy  Nicholl - Lee Nicholl Line Clipping performed fewer comparisons.    How Nicholl - Lee Nicholl Line Clipping works?  Nicholl...
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