Fake News Identification for Web Scrapped Data

Majority of the people get affected with misleading stories spread through different posts on social media and forward them assuming that it is a fact. Nowadays, Social media is used as a weapon to create havoc in the society by spreading fake news. Such havoc can be controlled by using machine-learning algorithms. Various methods of machine learning and deep learning techniques are used to identify false stories. There is a need for identification and controlling of fake news posts that have increased in alarming rate. Here we use Passive-Aggressive Classifier for fake news identification. Two datasets, Kaggle fake news dataset and as well as dynamically web scrapped dataset from politifact.com website. We achieved 88.66% accuracy using Passive Aggressive Classifier

Monitoring Social Distancing Using OpenCv

The paper proposes a method for social separating identification based on deep understanding of how to measure the gap between people in order to mitigate the impact of the COVID-19 pandemic. By evaluating with the aid of videos as feedback, the position instrument was developed to make people aware of the importance of keeping a safe distance from one another. The input video outline from the camera has been used as details, along with a free and open source object location system based on YOLOv3. Calculation that was used to determine walker recognition. After that, the input frame outline was modified to elevated perspective for distance estimation in the 2-Dimensional plane. The RED edge and line represent the range between individuals being measured and a part of the rebellious pairing of individuals during the showcase. The proposed strategy is accepted using a pre-recorded feedback frame of people walking around the city on foot. This result demonstrates how the presented methodology can make decisions about social removing estimates for a large number of people in the input picture. As the discovery apparatus was gradually introduced, this developed technique evolved as well

Technology from traditional knowledge - Vrikshayurveda-based expert system for diagnosis and management of plant diseases

Vrikshayurveda (An ancient Indian science of plant life) includes complete plant-life knowledge compendium of plant physiology, horticulture, pathology, and treatment. Though translation of the manuscript is available, the knowledge contained in the translation is not easily accessible to ordinary farmers who want answers to their specific problems or researchers who want references for specific topics without having to read the complete book. This research work proposes to convert the knowledge in the manuscript form to an expert system form which can provide the solutions to specific queries from the farmers and agriculture stakeholders. A rule based expert system using backward chaining Expert System is developed. The database in this design has ten diseases. The evaluation is done for all the dataset. The results are compatible with the expert's diagnosis. Thus the users can get comprehensive information on Vriksha-Ayurvedic expertise on all elements of disease and plant protection