This paper presents a highly sensitive and selective InP/Si photodetector tailored for detecting 830 nm wavelength light. A comprehensive simulation analysis of the InP/Si heterostructure has been conducted to assess its optical performance across a wide spectrum of light. The optical characteristics of the resulting device are being evaluated by using a broad light source with an optical power of ~1 mW. The device's optical performance has been quantified in terms of responsivity and sensitivity. The responsivity of the proposed device has been measured under 830 nm illumination and bias voltages of -3 V, -2 V, and -1 V, yielding values of 2.4 mA/W, 1.9 mA/W, and 1.4 mA/W, respectively. These impressive performance metrics, coupled with the compact and low design complexity of the device, position it as a promising candidate for applications in the optoelectronics industry

The web and internet are very important to a very huge number of people and it has a large number of users. These users use these platforms for different purposes. There are many social media platforms that are available to these users. Any user can make a spread or post the news/message through these online social platforms. Even though the algorithms used by social media platforms are updated meticulously, they still are not efficient enough to filter out the fake news or make the essential information viral first where it is needed so that the information surrounding that specific region benefits the people living there before the news reaches out to the rest of the world. One of the biggest methods of fake news contribution is social bots. Social bots generate the content automatically and spread among the social media users. In this work, we propose an effective approach to detect fake news / false information using machine learning techniques. We provide a tool to detect fake news using Naive Bayes technique with high accuracy. We show the results on two data sets by using our tool.