7 research outputs found
An end-to-end smart IoT-Driven navigation for social distance monitoring framework for Covid-19 using deep convolutional neural networks in Deep Learning
No full textThe extraordinary worldwide spread of the COVID-19 coronavirus pandemic has considerably boosted the development of new Internet-of-things (IoT)-based strategies to stop, stop, monitor, or foresee virus propagation among people. These technologies are being used more often for a variety of practical purposes that include enhanced safety, discipline, and control. The novel coronavirus creates a global pandemic and affects a human, besides a huge death rate. As there is no proper medication, at least the spread should be stopped or it should be minimized. One way is to avoid physical contact. People should maintain a minimum distance to avoid direct contact. This paper presents detecting the social distance between pedestrians in public places. The idea is to take live videos from the camera we set up in a public place and create bounding boxes for each person in the video. If two or more people are close to each other violating social distance, then we put a red box around them otherwise we put a green box. The proposed framework's experimental results exhibit good results in monitoring the distance among humans in public areas compared to the existing methods
Energy Efficient TDMA and Secure Based MAC Protocol for WSN Using AQL Coding and ASGWI Clustering
No full textResource management system database maintenance in cloud computing
No full textThe Resource Management System (RMS) is a comprehensive solution designed to optimize resource allocation, enhance project efficiency, and streamline customer interactions within a dynamic business environment. The system encompasses four key components: projects, resources, customers, and business units. It offers a range of functionalities including create, update, retrieve, and delete operations to facilitate seamless operations and data management. The RMS is built using Spring Boot (Java) for the backend, providing a robust and scalable foundation. The front end is developed using React, ensuring a modern and user-friendly interface. Data is managed in a MySQL database, offering reliability and data integrity. Communication between frontend and backend is achieved through RESTful APIs. Resource Management system typically manage and allocate non-human resources and as well human resources throughout an organization, However, if we choose to pursue human resources management, we will recruit, hire, train, manage employees and staff which is also a variation of resources. It provides comprehensive solution designed to optimize resource allocation, enhance project efficiency, and streamline customer interactions within a dynamic business environment
A brain tumor identification using fully convolution neural networks in the deep learning
No full textWe used post dispensation to flat out the segmentations generated via our model. And the beautiful meaning of the medical image analysis and in the direction of enhancing the identification of brain tumors MRI is considered to be outstanding within the current time towards the increased need to qualify with reliable information using semantic segmentation. CNN is being used to detect brain tumors efficiently and precisely. In evaluating and recognizing tumors, in its place of with 2D detection and dice cutting, we can use 3Dimension segmentation for identification, which makes it additionally precise. Similar algorithms' effort better for unlike sub regions are the fusion of some of the best algorithms that can produce a high-quality result in complete segmentation with the aid of FCN. Medical imaging is an area of increasing interest because there is a growing need for automated, fast, and efficient diagnostics to provide imaging capabilities and better quality compared to human eyes. Brain tumors, which are the second largest cause of death due to cancer-related diseases in males aged between 20 and 39, and the fifthlargest cause of cancer, have caused death in females aged in the same category. 1 Introductio
Retraction Notice: A brain tumor identification using convolution neural network in the deep learning
No full textWe take a zero tolerance to any situation where fraudulent research is published in our journals. As a result, this article has been retracted by the Publisher because it is suspected to be a nonsensical computer-generated publication with a number of tortured phrases and irrelevant references.
Additional measures have been implemented to prevent these issues from reoccurring.
EDP Sciences is extremely grateful to anonymous whistleblowers and the Problematic Paper Screene
RETRACTED: A brain tumor identification using convolution neural network in the deep learning
No full textWe take a zero tolerance to any situation where fraudulent research is published in our journals. As a result, this article has been retracted by the Publisher because it is suspected to be a nonsensical computer-generated publication with a number of tortured phrases and irrelevant references.
Additional measures have been implemented to prevent these issues from reoccurring.
EDP Sciences is extremely grateful to anonymous whistleblowers and the Problematic Paper Screene
Moving object detection using modified GMM based background subtraction
No full textAcademics have become increasingly interested in creating cutting-edge technologies to enhance Intelligent Video Surveillance (IVS) performance in terms of accuracy, speed, complexity, and deployment. It has been noted that precise object detection is the only way for IVS to function well in higher level applications including event interpretation, tracking, classification, and activity recognition. Through the use of cutting-edge techniques, the current study seeks to improve the performance accuracy of object detection techniques based on Gaussian Mixture Models (GMM). It is achieved by developing crucial phases in the object detecting process. In this study, it is discussed how to model each pixel as a mixture of Gaussians and how to update the model using an online k-means approximation. The adaptive mixture model's Gaussian distributions are then analyzed to identify which ones are more likely to be the product of a background process. Each pixel is categorized according to whether the background model is thought to include the Gaussian distribution that best depicts it
