Enhanced Method in Artificial Intelligence and Machine Learning for Enhanced Computer Vision Application - Deepali Virmani (International Journal of Intelligent Systems and Applications in Engineering, 2024)
For computers to be able to see, researchers are studying computer vision. Even the most general computer vision problems include drawing conclusions about the environment from images. It draws from a variety of disciplines and might be considered a subfield of AI and ML, which employ both generalizable and domain-specific learning strategies. The use of techniques from other fields, such as computer science and engineering, can make interdisciplinary research appear disorganized. A sophisticated ensemble of generic machine learning algorithms can be needed to handle a different visual problem than a hand-crafted statistical technique. Modern science has revolutionized computer vision. Exciting and often chaotic, frontier areas often have few trustworthy authorities. Some theories work in theory but not in practice, while many excellent ideas are theoretically unfounded. A lot of the developed world is spread out and can look like it's out of reach. These days, deep learning, machine learning, and computer vision all work well. The cornerstones of any school are its teaching and learning programmes. Students' actions and presence in class are closely observed alongside their academic progress. Classroom monitoring, emotion recognition, appraisal, and real-time attendance tracking were some of the computer vision applications studied in this research. A wide range of viewpoints have explored computer vision. Digital picture processing, pattern identification, machine learning, and computer graphics are all a part of it, in addition to raw data recording. Because of its extensive application, many researchers include it into a wide range of disciplines.