Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having  HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications

Ensemble-Based Machine Learning Approach for Real-Time Person Counting in an Instant Attendance System

Real-time attendance systems have become indispensable in various domains, including educational institutions and workplaces, as they automate attendance tracking and improve efficiency. This paper introduces a robust real-time attendance system that combines OpenCV and the You Only Look Once (YOLO) model. By integrating computer vision and deep learning techniques, the system achieves accurate and rapid face detection and recognition. Our proposed system utilizes OpenCV, a powerful computer vision library, to capture video streams from cameras. The YOLO model, a cutting-edge real-time object detection algorithm, is employed to identify and localize faces within the video frames. Thanks to YOLO's efficiency, the system ensures real-time processing, enabling seamless attendance recording. To enhance accuracy, the system employs a two-step approach consisting of face detection and face recognition. During the face detection phase, the YOLO model detects bounding boxes around faces. Subsequently, the system matches these detected faces against a pre-existing database of enrolled individuals using face recognition techniques. To improve performance, transfer learning techniques are applied to fine-tune the YOLO model on a diverse dataset containingvarious face images. This adaptation process ensures high precision and recall rates, even in challenging conditions such as varying lighting and occlusion. Experimental results demonstrate the effectiveness of the proposed real-time attendance system, achieving a high accuracy rate suitable for practical applications. Its real-time performance allows for seamless integration into existing attendance management workflows, resulting in time savings and improved administrative processes. The core intention of this paperwork is to develop a GUI page using Python programming, which will display the number of students who are present and number of students who are absent along with the number of students when section details are entered manually. In addition to this, pictures of the students who are present are displayed on the framework. It is called the real-time face detection which is very beneficial for managingacademic activity