Suseta Datta

TL;DR: This paper represents the detection of human in a room on the basis of some identical features which has been done by using the artificial neural network with three data sets of training and testing with the help of a suitable algorithm from which 97% accuracy for detecting occupancy is being calculated.

TL;DR: In this paper , three different types of machine learning classifiers that may be used to categorize text patterns and discern emotions have been discussed, and the Light Gradient Boost Machine (LightGBM) classifier has given 93.2% mean accuracy based on repetition of 10 objects in 3 times.

TL;DR: In this article , a sizably voluminous heap of appraisals and assessments are culminated with online redirection information, and the evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilizing various natural language processing techniques based on positive, negative, and neutral tweets.

TL;DR: In this paper , the best fitted approach has been derived and modified using another ML approach which is giving almost 97% testing accuracy, the precision, recall, f1-score and training-testing accuracy have been calculated based on the confusion matrix for each applied approach.