Towards applicability of artificial intelligence in healthcare, banking and education sector

Computational and artificial intelligence work together to create machine learning. As a model, they look to the human mind, from which they hope to create intelligent machines that can deal with real-world issues. For the design and deployment of intelligent tools it includes neuro-computing and fuzzy logic as well as genetic computing. It also includes probabilistic reasoning, including genetic algorithms, belief networks, and learning theory. Classification of medical and genomic data is a major challenge in biomedical informatics. A patient's disease status can be predicted using small medical data classification. This is in addition to a reduction in dimension, i.e. using feature extraction or feature selection methods to derive reduced feature sets and then classifying them with effective classifiers. [1] [2] The term data refers to a field of study that uses data mining, machine learning, and statistics to analyze data gathered specifically from the teaching and learning process. It's a great area for data mining because the data is so readily available. In the teaching-learning process, scientific criteria and objective data are used to monitor and judge the overall quality of teaching and learning. Student performance can be forecasted and students at risk identified using educational data mining. Important concerns in learning patterns of different groups of students can be identified, increasing pass out rates can be increased using educational data mining, and subject curriculum renewal can be optimized using educational data mining. For example, fraud detection, customer satisfaction improvement, and identifying a financial product's target audience are all possible uses for machine learning in banking.[3][4][5] This article provides an investigation over various applications of machine learning in healthcare, education and banking sector.