Face Detection System
This research paper gives an ideal way of detecting and recognizing human faces using OpenCV, and python which is part of Machine learning. This report contains the ways in which Machine learning, an important part of the computer science field can be used to determine the face using several libraries in OpenCV along with python. This System will contain a proposed system which will help in detecting the human face in real time. This implementation can be used at various platforms in machines and smartphones, and several software applications.
Automatic Attendance through Face recognition system
The management of the attendance can be a great burden on the teachers if it is done by hand. To resolve this problem, a smart and auto attendance management system is being utilised. By utilising this framework, the problem of proxies and students being marked present even though they are not physically present can easily be solved. This system marks the attendance using a live video stream. The frames are extracted from video using OpenCV. The main implementation steps used in this type of system are face detection and recognizing the detected face, for which dlib is used. After these, the connection of recognized faces ought to be conceivable by comparing with the database containing student’s faces. This model will be a successful technique to manage the attendance of students.
Lung cancer detection
Lung Cancer detection at an earlier stage has become a very important and needy one for a human being. Early detection helps several patients with the best chance of recovery. Timely diagnosis and determination to the type of lung cancer has important clinical significance. The features which are used for the detection of Lung Cancer are collected from the Computed Tomography (CT scan) images. Input as sympotoms will be given and output will tell that person have cancer or not. Machine Learning is an emerging technique that allows us to increase the accuracy of the result. In this project, we have implemented cancer detection using ML.
Potato disease detection
The world population is increasing at a rapid rate, and so is the demand for food. With the advancements in agricultural technology, combined with the use of artificial intelligence to predict diseases in crops, it becomes important to make relevant research to ensure sustainable agricultural development. Agriculture is the cultivation of land and raising of crops to produce food. It is a form of subsistence farming that has been practised for thousands of years. With the advancement in technology and the use of artificial intelligence in diagnosing plant diseases, it becomes important to make pertinent research toward sustainable agricultural development. In the past, diagnosis of plant diseases was done by observing the symptoms of a diseased plant. However, with the advent of artificial intelligence and its application to agriculture, it has become important to make pertinent research in this area. With the use of image processing and deep learning algorithms, it is now possible to diagnose a plant disease by using a picture taken from a smartphone. The advancement of agricultural technology has made it possible to grow more crops with limited resources. Potato is one of the staple foods that are widely consumed, becoming the 4th staple food consumed throughout the world. Also, the world demand for potato is increasing significantly, primarily due to the world pandemic coronavirus. However, potato diseases are the leading cause of the decline in the quality and quantity of the harvest. Inappropriate classification and late detection of the disease’s type will drastically worsen the plant conditions. Fortunately, several diseases in potato plants can be identified based on leaf conditions. However, the increase in the population of the world is constantly increasing the demand for more food. One way to meet this demand is to use artificial intelligence in diagnosing plant diseases and using that information to more efficiently grow food. Machine Learning as Deep Learning is applied for the work done.
Email spam detection
Email Spam has become a major problem nowadays, with Rapid growth of internet users, Email spams are also increasing. People are using them for illegal and unethical conducts, phishing and fraud. Sending malicious links through spam emails which can harm our system and can also seek in into your system. Creating a fake profile and email account is much easier for the spammers, they pretend like a genuine person in their spam emails, these spammers target those people who are not aware about these frauds. So, it is necessary to Identify those spam mails which are fraudulent. This project will identify spam by using techniques of machine learning, this project will discuss the machine learning algorithms and apply all these algorithms on our data sets and the best algorithm is selected for the email spam detection having best precision and accuracy.
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