The document describes a proposed real-time system to monitor social distancing using computer vision and deep learning techniques. The system would use a camera to detect individuals and calculate distances between them in order to identify instances where social distancing guidelines are breached. When a breach is detected, an audio-visual cue would be emitted to alert individuals without identifying or saving personal data. The system aims to help reduce the spread of COVID-19 while respecting privacy and avoiding overreach. It outlines the technical approach including camera calibration, region of interest definition, object detection using YOLOv3, distance calculation techniques, and system architecture at a high level.
A Social Distancing Monitoring System Using OpenCV to Ensure Social Distancin...IRJET Journal
This document presents a social distancing monitoring system that uses OpenCV and a YOLO object detection model to monitor social distancing in public areas. The system processes video frames to detect people and calculate the distance between them. It will sound an alert if the distance between any two people is less than the configured social distancing limit. The system is designed to be installed in locations with moderate crowd sizes like banks, ATMs, shops, and offices to help enforce social distancing and reduce the spread of COVID-19 and other diseases. It works by loading a pre-trained YOLO model, detecting people in frames, finding their centroids, and calculating distances to identify violations of the social distancing parameter.
A Survey on Person Detection for Social Distancing and Safety Violation Alert...IRJET Journal
This document discusses methods for monitoring social distancing using video surveillance and deep learning techniques. It describes how faster R-CNN, single shot detector (SSD) and YOLO v3 deep learning models can be used to detect people in video frames and calculate the distance between individuals to determine if social distancing guidelines are being followed. If distances between people are found to be unsafe, the system can send alerts or cautions. The methodology is intended to help prevent the spread of COVID-19 by monitoring adherence to social distancing and triggering warnings if safety violations are detected.
This document describes a social distance detection system to help identify areas with high violations of COVID-19 guidelines. The system uses YOLO v3 for object detection, OpenCV for distance calculation, and a mask classifier to differentiate humans and detect social distancing violations and lack of masks. It records violations to predict disease hotspots. The system was able to accurately detect humans, measure distances between them, and identify situations that violate social distancing or lack of mask guidelines to help control the spread of contagious diseases like COVID-19.
Real Time Social Distance Detector using Deep learningIRJET Journal
The document describes a real-time social distance detector using deep learning. The researchers created a model called SocialdistancingNet-19 that can identify people in frames from video and label them as safe or dangerous based on whether they are more than a certain distance threshold from others. They used a pre-trained YOLO v3 object detection model to detect people and then calculated the distance between centroids of detected objects to assess social distancing compliance. The model achieved 92.8% accuracy on test data. It is intended to automatically monitor social distancing in public spaces using video surveillance to help reduce the spread of COVID-19.
Covid Face Mask Detection Using Neural NetworksIRJET Journal
The document describes a study that developed a convolutional neural network (CNN) model using the MobileNetV2 architecture to detect if people in images are wearing face masks properly, improperly, or not at all. The model was trained on a dataset containing these three classes and achieved an accuracy of 97.25% for classifying images. The developed model can be implemented in real-world applications like public transportation stations, hospitals, offices, and schools to help monitor mask compliance and reduce the spread of COVID-19.
Social Distance Monitoring and Mask Detection Using Deep Learning TechniquesIRJET Journal
The document describes a proposed system to monitor social distancing using computer vision and deep learning techniques. The system uses the YOLOv5 object detection model to identify people in video feeds from surveillance cameras. It then analyzes the distances between detected individuals to determine if they are following social distancing guidelines. Image preprocessing and feature extraction techniques are used to improve detection accuracy. The system is intended to help enforce social distancing protocols during the COVID-19 pandemic and reduce disease transmission.
A FALL DETECTION SMART WATCH USING IOT AND DEEP LEARNINGIRJET Journal
The document describes a proposed fall detection smartwatch system using IoT and deep learning. It aims to enable smartwatches and algorithms to detect falls in smart homes. The proposed system, IMEFD-ODCNN, uses data collection, preprocessing, feature extraction using SqueezeNet, parameter tuning using SSO, and classification using SSOA-VAE. Video frames are preprocessed and features extracted before the SSOA-VAE classifier identifies falls. If a fall is detected, an alert is sent to the patient and caregiver for immediate assistance. The system aims to remotely monitor elderly people and help doctors treat patients by providing health data and history.
Covid Mask Detection and Social Distancing Using Raspberry piIRJET Journal
This document describes a system that uses computer vision and machine learning to detect if individuals are wearing masks and maintaining proper social distancing in public places. The system uses a Raspberry Pi connected to a USB camera to take photos and video. Convolutional neural network models like CNN and YOLO are used to analyze the images, detect faces, and determine if masks are being worn correctly. If individuals are not wearing masks or social distancing, the system will provide an alert or sound from a connected speaker. The goal is to help enforce mask and distancing guidelines without needing human monitoring, in order to reduce virus spread during the COVID-19 pandemic.
A Social Distancing Monitoring System Using OpenCV to Ensure Social Distancin...IRJET Journal
This document presents a social distancing monitoring system that uses OpenCV and a YOLO object detection model to monitor social distancing in public areas. The system processes video frames to detect people and calculate the distance between them. It will sound an alert if the distance between any two people is less than the configured social distancing limit. The system is designed to be installed in locations with moderate crowd sizes like banks, ATMs, shops, and offices to help enforce social distancing and reduce the spread of COVID-19 and other diseases. It works by loading a pre-trained YOLO model, detecting people in frames, finding their centroids, and calculating distances to identify violations of the social distancing parameter.
A Survey on Person Detection for Social Distancing and Safety Violation Alert...IRJET Journal
This document discusses methods for monitoring social distancing using video surveillance and deep learning techniques. It describes how faster R-CNN, single shot detector (SSD) and YOLO v3 deep learning models can be used to detect people in video frames and calculate the distance between individuals to determine if social distancing guidelines are being followed. If distances between people are found to be unsafe, the system can send alerts or cautions. The methodology is intended to help prevent the spread of COVID-19 by monitoring adherence to social distancing and triggering warnings if safety violations are detected.
This document describes a social distance detection system to help identify areas with high violations of COVID-19 guidelines. The system uses YOLO v3 for object detection, OpenCV for distance calculation, and a mask classifier to differentiate humans and detect social distancing violations and lack of masks. It records violations to predict disease hotspots. The system was able to accurately detect humans, measure distances between them, and identify situations that violate social distancing or lack of mask guidelines to help control the spread of contagious diseases like COVID-19.
Real Time Social Distance Detector using Deep learningIRJET Journal
The document describes a real-time social distance detector using deep learning. The researchers created a model called SocialdistancingNet-19 that can identify people in frames from video and label them as safe or dangerous based on whether they are more than a certain distance threshold from others. They used a pre-trained YOLO v3 object detection model to detect people and then calculated the distance between centroids of detected objects to assess social distancing compliance. The model achieved 92.8% accuracy on test data. It is intended to automatically monitor social distancing in public spaces using video surveillance to help reduce the spread of COVID-19.
Covid Face Mask Detection Using Neural NetworksIRJET Journal
The document describes a study that developed a convolutional neural network (CNN) model using the MobileNetV2 architecture to detect if people in images are wearing face masks properly, improperly, or not at all. The model was trained on a dataset containing these three classes and achieved an accuracy of 97.25% for classifying images. The developed model can be implemented in real-world applications like public transportation stations, hospitals, offices, and schools to help monitor mask compliance and reduce the spread of COVID-19.
Social Distance Monitoring and Mask Detection Using Deep Learning TechniquesIRJET Journal
The document describes a proposed system to monitor social distancing using computer vision and deep learning techniques. The system uses the YOLOv5 object detection model to identify people in video feeds from surveillance cameras. It then analyzes the distances between detected individuals to determine if they are following social distancing guidelines. Image preprocessing and feature extraction techniques are used to improve detection accuracy. The system is intended to help enforce social distancing protocols during the COVID-19 pandemic and reduce disease transmission.
A FALL DETECTION SMART WATCH USING IOT AND DEEP LEARNINGIRJET Journal
The document describes a proposed fall detection smartwatch system using IoT and deep learning. It aims to enable smartwatches and algorithms to detect falls in smart homes. The proposed system, IMEFD-ODCNN, uses data collection, preprocessing, feature extraction using SqueezeNet, parameter tuning using SSO, and classification using SSOA-VAE. Video frames are preprocessed and features extracted before the SSOA-VAE classifier identifies falls. If a fall is detected, an alert is sent to the patient and caregiver for immediate assistance. The system aims to remotely monitor elderly people and help doctors treat patients by providing health data and history.
Covid Mask Detection and Social Distancing Using Raspberry piIRJET Journal
This document describes a system that uses computer vision and machine learning to detect if individuals are wearing masks and maintaining proper social distancing in public places. The system uses a Raspberry Pi connected to a USB camera to take photos and video. Convolutional neural network models like CNN and YOLO are used to analyze the images, detect faces, and determine if masks are being worn correctly. If individuals are not wearing masks or social distancing, the system will provide an alert or sound from a connected speaker. The goal is to help enforce mask and distancing guidelines without needing human monitoring, in order to reduce virus spread during the COVID-19 pandemic.
Social Distancing Detection, Monitoring and Management Using OpenCVIRJET Journal
This document proposes a system to detect social distancing violations using computer vision and deep learning algorithms. The system would identify individuals in video frames using a YOLOv3 model, calculate distances between detected individuals, and classify the risk level based on social distancing guidelines. It transforms frames into a bird's eye view to standardize distance measurements. The proposed system aims to help monitor social distancing and slow the spread of COVID-19 by identifying groups that are too close together. It achieved 92.8% precision in social distancing classification during testing.
Face Mask and Social Distance DetectionIRJET Journal
1) The document describes a project that uses computer vision techniques like convolutional neural networks and YOLO to detect face masks and social distancing in video feeds.
2) It trains models using OpenCV, TensorFlow and Keras to identify if people in frames are wearing masks or not, and to check if social distancing protocols are being followed.
3) The system is meant to help enforce COVID safety protocols at locations like schools, businesses and public transit by monitoring mask usage and physical distancing.
Face Mask Detection and Contactless Body Temperature SensingIRJET Journal
This document presents a proposed system to detect if individuals are wearing face masks and measure their body temperature using CCTV cameras at grocery stores. The system aims to help reduce the spread of COVID-19 by notifying store owners if an unmasked individual or someone with a high temperature is detected. It uses computer vision and deep learning techniques like convolutional neural networks to identify faces and determine if a mask is worn correctly. An infrared thermometer would also measure temperatures and alert authorities by email if thresholds are exceeded. The researchers hope this system can be implemented in other public areas to enforce mask and distancing rules and control viral transmission through early detection of potential cases.
Designing of Application for Detection of Face Mask and Social Distancing Dur...IRJET Journal
This document proposes a system to detect whether people are wearing face masks and maintaining social distancing during the COVID-19 pandemic using computer vision algorithms. The system uses YOLO v3 for object detection to detect people and faces in frames. A CNN model is then used to classify whether faces are wearing masks or not. Social distancing is measured by calculating the Euclidean distance between detected face boxes. The system is intended to help enforce COVID safety protocols and reduce cases by automatically monitoring compliance. It analyzes video frames to label faces as masked or unmasked and issue notifications if people are too close. The proposed application aims to assist governments in controlling the pandemic through machine learning-based social distancing and mask detection.
This document summarizes a research paper on developing a system to detect whether individuals are wearing face masks using CCTV cameras in public places like grocery stores. The system uses convolutional neural networks (CNN) for face detection and mask detection in images from the cameras. If someone is detected without a mask, an alert is sent to store owners. The goal is to help reduce the spread of COVID-19 by enforcing mask rules and making people aware of the importance of masks for health and safety. The proposed system could be expanded for use in other public areas like malls and universities to monitor mask compliance through IoT-connected cameras.
IRJET- Review on: A Wireless IoT System for Gait Detection in Stroke PatientIRJET Journal
This document summarizes a proposed wireless IoT system for gait detection in stroke patients. The system would use sensors embedded in a smart shoe to discreetly monitor a patient's insole pressure and acceleration during walking. The data collected from the shoe sensors and a smartphone's built-in sensors would be used to detect any abnormal or cautious gait patterns that could predict risk of falling. The system aims to warn patients about risky gaits and potentially prevent injuries. It discusses how IoT and wireless communication could help create a portable system to continuously monitor patients' gaits outside of a clinical setting.
A novel predictive model for capturing threats for facilitating effective soc...IJECEIAES
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
Design and development of a fuzzy explainable expert system for a diagnostic ...IJECEIAES
Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability.
A new system to detect coronavirus social distance violation IJECEIAES
This document proposes a new system to detect social distance violations using a smartphone. The system uses two Android applications - one uses the phone's camera to detect faces and estimate distances during calls, and one uses voice biometrics to differentiate the user's voice from others. Both applications perform real-time processing without collecting or sharing private user data. The system aims to help prevent the spread of COVID-19 by notifying users if social distancing guidelines are violated.
Non-contact, camera-based physiological measurements -
such as blood volume pulse and respiration rate - can now
be inferred by neural networks based on facial videos. This
technology has the potential to enable medical professionals
to make more informed telehealth decisions. Currently, this
software only runs on PCs, without a user interface. The neu-
ral network has a significant computational cost, making it
difficult to deploy on low-cost mobile devices. It also performs
poorly in varied environmental, sensor, personal, and contex-
tual conditions - such as darker skin tones. In this project,
we implement this neural network as an Android app that
runs in real-time; develop a more efficient architecture; evalu-
ate these architectures on older smartphones; and provide an
open-source, simple personalization pipeline to enable users to
calibrate the app. This all serves to make the technology more
democratic: making it available to as many users as possible,
while giving them the means to train and develop it further.
Comprehensive study: machine learning approaches for COVID-19 diagnosis IJECEIAES
Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.
AI-based Mechanism to Authorise Beneficiaries at Covid Vaccination Camps usin...IRJET Journal
The document presents a research on developing an AI-based facial recognition system to authorize beneficiaries at COVID-19 vaccination camps. It aims to create a deep learning model that allows individuals to register for vaccinations and book slots using real-time face recognition. This aims to make the process contactless and reduce infection risk at camps. The proposed system uses a CNN model that extracts facial features from images to encode them as hashes for identification. It achieved 98.34% accuracy in tests, making it effective for replacing identification methods requiring physical documents or contact. The system could help address issues like de-duplication of beneficiaries and ensuring compliance with safety protocols at crowded camps.
A Novel Method For Evaluation of Automation Dry Fog Disinfecting UnitIRJET Journal
This document presents a novel method for evaluating an automated dry fog disinfecting unit. The COVID-19 pandemic has increased interest in automation robots to conduct work in contaminated areas safely. The paper describes the design and development of a new affordable autonomous indoor sterilization robot that uses a wheeled mobile platform and hydrogen peroxide fogging device. A simulation analysis of the dry mist hydrogen peroxide sterilization model was conducted to study dispersal in an indoor environment. The efficacy of the created robot was tested in practical situations like hospitals, hotels, offices and laboratories, with positive results confirmed by an independent testing organization. The robot is aimed at autonomous indoor sanitization tasks to reduce human exposure to pathogens.
1) A novel face mask detection framework called FMD-Yolo is proposed to detect whether people are wearing masks correctly in public settings.
2) FMD-Yolo uses an improved feature extractor called Im-Res2Net-101 and an enhanced feature fusion method called En-PAN to thoroughly extract and merge multi-scale information from input images.
3) Experimental results on two public datasets show that FMD-Yolo achieves state-of-the-art precision of 92.0% and 88.4%, outperforming other detection methods. FMD-Yolo demonstrates superior performance for face mask detection.
FACE MASK DETECTION USING MACHINE LEARNING AND IMAGE PROCESSINGIRJET Journal
The document discusses a project that aims to develop a face mask detection system using machine learning and image processing. The system will first prepare a dataset with two classes: images of people with masks and without masks. It will then use MTCNN for face detection and EfficientNet for image classification to determine if a detected face has a mask or not. The system is intended to automatically identify people not wearing masks in public places to help prevent the spread of COVID-19 and reduce the need for manual monitoring. It is expected to classify faces in real-time video as with-mask or without-mask.
Real Time Mask Detection Architecture for COVID PreventionIRJET Journal
The document presents a real-time mask detection architecture using deep learning to help prevent the spread of COVID-19. It describes collecting images of people with and without masks to train a convolutional neural network model. The trained model is then deployed using video streams from CCTV cameras to detect and identify in real-time if individuals are wearing a mask or not wearing a mask to help enforce social distancing and safety measures.
PREDICTION OF COVID-19 USING MACHINE LEARNING APPROACHESIRJET Journal
This document summarizes a research paper that used machine learning models to predict the spread of COVID-19. The researchers used various machine learning algorithms like SVM, random forest, decision tree, and linear regression on COVID-19 case data. SVM had the highest error in predictions, while random forest and decision tree performed best with lowest error. The models were developed using Python and deployed on cloud platforms. The study aimed to accurately predict COVID-19 trends to help governments respond better to the pandemic.
Covid-19 Data Analysis and VisualizationIRJET Journal
This document summarizes a research paper that analyzes COVID-19 data using machine learning algorithms. It first introduces the authors and provides an abstract describing the project's goal of gaining insights from COVID-19 data using Python and Tableau visualization tools. It then reviews related work applying models and algorithms to infectious disease data. The methodology section outlines the process used: collecting data from government websites, cleaning the data, performing data visualization, calculating accuracy of different algorithms (logistic regression, KNN, random forest, decision tree), and using the most accurate algorithm to predict if a person is COVID-19 positive based on symptoms.
Deep Learning Approaches for Diagnosis and Treatment of COVID-19IRJET Journal
The document discusses using deep learning approaches for diagnosing and treating COVID-19. It first provides background on deep learning and convolutional neural networks. It then discusses challenges in early COVID-19 diagnosis and the need for computer-assisted diagnosis methods. The paper reviews several existing studies that used deep learning on CT scans and X-rays to classify COVID-19. It proposes developing a COVID-19 diagnosis system using a lung CT image dataset and deep learning models. The system would be designed, implemented and tested to efficiently detect COVID-19 infections from CT scans.
Social Distancing Detector Management SystemIRJET Journal
This document describes a social distancing detector management system that uses computer vision and deep learning techniques. It aims to detect distances between people and warn them if they are not maintaining the recommended 6 feet distance, in order to help reduce the spread of COVID-19. The system uses a Raspberry Pi, OpenCV for image processing, and the YOLOv3 deep learning model trained on object detection. It works by detecting people in images or video frames using the YOLOv3 model, then calculates pixel distances between detected people and compares to a threshold to identify social distancing violations. The goal is to monitor social distancing and alert people to help slow the pandemic in places like schools, offices, and other areas where groups
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
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Non-contact, camera-based physiological measurements -
such as blood volume pulse and respiration rate - can now
be inferred by neural networks based on facial videos. This
technology has the potential to enable medical professionals
to make more informed telehealth decisions. Currently, this
software only runs on PCs, without a user interface. The neu-
ral network has a significant computational cost, making it
difficult to deploy on low-cost mobile devices. It also performs
poorly in varied environmental, sensor, personal, and contex-
tual conditions - such as darker skin tones. In this project,
we implement this neural network as an Android app that
runs in real-time; develop a more efficient architecture; evalu-
ate these architectures on older smartphones; and provide an
open-source, simple personalization pipeline to enable users to
calibrate the app. This all serves to make the technology more
democratic: making it available to as many users as possible,
while giving them the means to train and develop it further.
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Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.
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This document presents a novel method for evaluating an automated dry fog disinfecting unit. The COVID-19 pandemic has increased interest in automation robots to conduct work in contaminated areas safely. The paper describes the design and development of a new affordable autonomous indoor sterilization robot that uses a wheeled mobile platform and hydrogen peroxide fogging device. A simulation analysis of the dry mist hydrogen peroxide sterilization model was conducted to study dispersal in an indoor environment. The efficacy of the created robot was tested in practical situations like hospitals, hotels, offices and laboratories, with positive results confirmed by an independent testing organization. The robot is aimed at autonomous indoor sanitization tasks to reduce human exposure to pathogens.
1) A novel face mask detection framework called FMD-Yolo is proposed to detect whether people are wearing masks correctly in public settings.
2) FMD-Yolo uses an improved feature extractor called Im-Res2Net-101 and an enhanced feature fusion method called En-PAN to thoroughly extract and merge multi-scale information from input images.
3) Experimental results on two public datasets show that FMD-Yolo achieves state-of-the-art precision of 92.0% and 88.4%, outperforming other detection methods. FMD-Yolo demonstrates superior performance for face mask detection.
FACE MASK DETECTION USING MACHINE LEARNING AND IMAGE PROCESSINGIRJET Journal
The document discusses a project that aims to develop a face mask detection system using machine learning and image processing. The system will first prepare a dataset with two classes: images of people with masks and without masks. It will then use MTCNN for face detection and EfficientNet for image classification to determine if a detected face has a mask or not. The system is intended to automatically identify people not wearing masks in public places to help prevent the spread of COVID-19 and reduce the need for manual monitoring. It is expected to classify faces in real-time video as with-mask or without-mask.
Real Time Mask Detection Architecture for COVID PreventionIRJET Journal
The document presents a real-time mask detection architecture using deep learning to help prevent the spread of COVID-19. It describes collecting images of people with and without masks to train a convolutional neural network model. The trained model is then deployed using video streams from CCTV cameras to detect and identify in real-time if individuals are wearing a mask or not wearing a mask to help enforce social distancing and safety measures.
PREDICTION OF COVID-19 USING MACHINE LEARNING APPROACHESIRJET Journal
This document summarizes a research paper that used machine learning models to predict the spread of COVID-19. The researchers used various machine learning algorithms like SVM, random forest, decision tree, and linear regression on COVID-19 case data. SVM had the highest error in predictions, while random forest and decision tree performed best with lowest error. The models were developed using Python and deployed on cloud platforms. The study aimed to accurately predict COVID-19 trends to help governments respond better to the pandemic.
Covid-19 Data Analysis and VisualizationIRJET Journal
This document summarizes a research paper that analyzes COVID-19 data using machine learning algorithms. It first introduces the authors and provides an abstract describing the project's goal of gaining insights from COVID-19 data using Python and Tableau visualization tools. It then reviews related work applying models and algorithms to infectious disease data. The methodology section outlines the process used: collecting data from government websites, cleaning the data, performing data visualization, calculating accuracy of different algorithms (logistic regression, KNN, random forest, decision tree), and using the most accurate algorithm to predict if a person is COVID-19 positive based on symptoms.
Deep Learning Approaches for Diagnosis and Treatment of COVID-19IRJET Journal
The document discusses using deep learning approaches for diagnosing and treating COVID-19. It first provides background on deep learning and convolutional neural networks. It then discusses challenges in early COVID-19 diagnosis and the need for computer-assisted diagnosis methods. The paper reviews several existing studies that used deep learning on CT scans and X-rays to classify COVID-19. It proposes developing a COVID-19 diagnosis system using a lung CT image dataset and deep learning models. The system would be designed, implemented and tested to efficiently detect COVID-19 infections from CT scans.
Social Distancing Detector Management SystemIRJET Journal
This document describes a social distancing detector management system that uses computer vision and deep learning techniques. It aims to detect distances between people and warn them if they are not maintaining the recommended 6 feet distance, in order to help reduce the spread of COVID-19. The system uses a Raspberry Pi, OpenCV for image processing, and the YOLOv3 deep learning model trained on object detection. It works by detecting people in images or video frames using the YOLOv3 model, then calculates pixel distances between detected people and compares to a threshold to identify social distancing violations. The goal is to monitor social distancing and alert people to help slow the pandemic in places like schools, offices, and other areas where groups
Similar to NEW CORONA VIRUS DISEASE 2022: SOCIAL DISTANCING IS AN EFFECTIVE MEASURE (COVID-19) PANDEMIC (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.