Machine learning has been proven to be a game-changing technology in every domain since the late 20th century. There have been many advancements in healthcare not only for the diagnosis of disease but advanced in the prognosis of the diseases. Artificial intelligence/machine learning (AI/ML) has progressed a lot in the medical domain in just a couple of decades and played a very important role in exploring human data to understand human body behavior better than ever before, for predicting and classifying all kinds of medical images or videos. A recent and best-used application is detecting COVID-19 by just checking the chest x-ray in a very accurate manner that can be used without human presence and stop the spread of the virus resulting in fewer doctors getting affected. It is known as generative adversarial networks. Some of the types of GANs used for differentiate domains without human supervision and many such mutations of GANs are useful in the health sector. This is simply a quick review of various technologies that will become more in-depth as time goes on.

In the past decade, it is proven that satellite image classification using an object-based technique is better than the standard pixel-based technique. With the increasing need for classifying multispectral satellite images for urban planning, the accuracy of the classification becomes a significant performance parameter. Object-based classification (OBC) is a technique in which group of pixels having similar spectral properties, called objects, are generated using image segmentation and then these objects are classified based on their attributes. In this paper, the combination of a multiclass AdaBoost algorithm with extra trees classifier (ETC) is proposed with higher prediction accuracy for the OBC of the urban fringe area. The performance of the AdaBoost algorithm is found to be better in terms of classification accuracy than benchmarked SVM and RF classifiers for OBC. These classification methods were applied to IRS-R2 LISS IV data. The AdaBoosted extra trees classifier (ABETC) has demonstrated the highest accuracy with overall accuracy (OA) of 88.47% and a kappa coefficient of 0.85. The computational time of the ABETC is found to be much smaller than the RF algorithm. In detail, the sensitivity of the classifiers was investigated using stratified random sampling with various sample sizes.

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