Career recommendation system aims to offer direction and assist students in selecting engineering streams with the help of a WebApp. Nowadays, there are more educational courses available, making it easier for students to pick courses that interest them. However, in the 22nd century, more than half of the youths do not exercise their freedom of choice and make wellinformed decisions. A number of factors contribute to this. One of the main reasons is the lack of awareness of all the available options. The other well-known hindrance is the family pressure of following a well-known or previously followed path when it comes to choosing a career. This results in candidates ending up in the wrong fields and dropping out of college midway through the course. As a result, time and money are wasted. Moreover, it may happen that a deserving candidate was denied a seat in an institution since its capacity was full at the start of the course. The proposed WebApp aims to develop a system that would suggest a course based on certain fundamental information about the student such as academic performance, extracurricular activities, personal interests and aims. The WebApp will try to mimic the role of a career counselor, use a chatbot to interact with the student and recommend to them the branches of engineering that align with their interests the most, by making use of Machine Learning to provide an unbiased recommendation
The advent of satellite technology has made it possible to continuously monitor and manage forest fires, which pose a serious hazard to people and other living things. Smoke in the air indicates the presence of forest wildfires. Fire detection is essential in fire alarm systems for preventing damage and other fire catastrophes that have an impact on society. It's crucial to effectively identify fire from visual settings to prevent large-scale fires. An efficient method of a machine learning based Inception-v3 based on transfer learning is developed to increase the accuracy of fire detection. It trains satellite images to classify datasets into fire and non-fire images, generates a confusion matrix to determine the framework's effectiveness, and then uses local binary patterns to extract the fire-occurring region from satellite images. This method lowers the rate of false detection.
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