The news is the most crucial resource for the general population to learn about what is occurring across the world. Even if newspapers remain a reliable source of news, social media is currently the next frontier in news. Regular individuals may simply alter the news to produce fake news since these social networks are so accessible. These fictitious news stories may be utilized for both political and commercial gains. It may be used as a vehicle to stir up neighborhood animosity, which is detrimental to society. In order to mitigate its impacts, it is crucial to recognize fake news. A platform that can validate and classify news is currently unavailable. In this essay, a technique is presented for figuring out whether or not news is reliable in the present. To train the features that were retrieved from the data using natural language processing techniques, this system makes use of ML classifiers including Decision Tree, Random Forest (RF), and Logistic Regression (LR). We evaluate each classifier’s performance using a variety of parameters. The best classifier will provide the outcome for real-time news prediction.

Face Recognition (FR) and surveillance video analytics are well-defined and solved problems in the applications of Computer Vision. FR aims to identify an already known person in a given image. Surveillance video analytics seeks to identify the occurrence of abnormal events or things in public places. But, recognizing the movements of most wanted criminals or suspects in public areas using FR systems with unclear surveillance video inputs is a very challenging problem. This work analyses the performance of three existing popular machine learning-based FR systems. They are (i) Viola–Jones detector, (ii) HOG-based FR, and (iii) PCA-based FR. This work analyzes the performance of these FR models on two different datasets. One is a benchmark dataset that has only the frontal view of the faces of various subjects. Another dataset we created with 10000 images. These images are collected from 50 subjects. From each subject, 200 images are taken from various angles. This work observes that the above models will improve their performance from 7 to 10% in terms of accuracy by training them on the proposed dataset.