Covid-19 virus has moved the world to a static state. This has confused the education field in conducting the end examinations. The school education has declared the results of a few classes as pass without conducting the end examinations. But, this may not be possible for all the education system, especially for final year college student results. For this reason, the prediction of result based on their previous performance is essential. Prediction can be effectively achieved by using machine learning algorithms. Machine learning automatically learns and improves from example and experience. Supervised machine learning algorithms Logistic regression and SVM are used for this work. The data set of 1460 students result of a college is considered for the study. Finally, the trained machine predicts accurately whether the student is eligible to acquire the degree or not and the same is viewed in the college portal.

Nature has a huge role to maintain the stability in the environment. But, natural disasters damage the environment, affect the life cycle and decline the lifetime of living beings. Nature is destroyed by different disasters namely earthquake, fire, flood, landslide, air pollution and so on. Among these, forest fire is one of the foremost dangerous natural disasters which cause several serious issues like biodiversity loss, global warming, fuel wood loss and air pollution in the environment. Therefore, prediction of fire occurring in forest plays a crucial role to save lots of the environment. Thus, researchers focused on different technologies with different methodologies for predicting the fire that occurred in forest, as early as possible. Moreover, smoke is the focal point for fire, some of the researchers pay their attention on detecting the smoke of the forest using different technologies. Therefore, this paper gives a summary for effectively detecting the smoke and fire in forest.

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