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.
Shap Analysis Based Gastric Cancer DetectionIRJET Journal
This document proposes a novel deep learning framework to detect gastric cancer from endoscopic images of the stomach. The framework uses a patch-based analysis where features are extracted from image patches and evaluated for cancer risk. A bag-of-features technique is then applied to the extracted features from selected patches for analysis. Experimental results show the proposed framework can effectively and efficiently detect gastric cancer from images and identify minute lesions. It achieves higher accuracy than other models using the same dataset. The framework is also more accurate than existing methods for gastric cancer detection.
A DEEP LEARNING-BASED ACCURATE DRUG DETECTION, IDENTIFICATION AND CONFIRMATIO...IJCSEA Journal
The document describes a deep learning-based mechanism for accurate drug detection, identification and confirmation in medication dispensing packages for inpatients. The mechanism uses the YOLO object detection algorithm to detect and identify drugs in images of packages. A QR code on each package provides drug information that is compared to detections to confirm accuracy. Different warning sounds are generated for different incorrect detection situations. The mechanism achieved accurate detection and identification of drugs in a test dataset to help prevent medication errors.
A DEEP LEARNING-BASED ACCURATE DRUG DETECTION, IDENTIFICATION AND CONFIRMATIO...IJCSEA Journal
In this study, a deep learning based accurate drug detection, identification and confirmation mechanism
(DLADDICM) for medication dispensing package is proposed for inpatients. In this proposed
DLADDICM, a medication dispensing package with a printed QR code is photo taken and drugs in the
image are detected and identified using a deep learning object detection algorithm, namely You Only Look
Once (YOLO). The QR code information is deciphered and compared with the detected drugs to confirm
the correctness of the medication dispensing. If there are mismatch situation(s), the computer with the
proposed DLADDICM will generate different warning sound in responding to different incorrect
situations. A data set with 30 drugs form the National Library of Medicine of NIH, USA is used for testing
the DLADDICM using the object tracking and detection deep learning algorithm YOLOv3. Experimental
results shown that the DLADDICM can detect and identify the incorrect drugs and generate the
appropriate warning sound for the incorrect drug in pharma package for further human inspection. The
experimental results also exhibits that by using the AI-enabled mechanism an accurate, safer, healthier
with precision medication environment for the medical industries could also be achieved.
A DEEP LEARNING-BASED ACCURATE DRUG DETECTION, IDENTIFICATION AND CONFIRMATIO...IJCSEA Journal
In this study, a deep learning based accurate drug detection, identification and confirmation mechanism
(DLADDICM) for medication dispensing package is proposed for inpatients. In this proposed
DLADDICM, a medication dispensing package with a printed QR code is photo taken and drugs in the
image are detected and identified using a deep learning object detection algorithm, namely You Only Look
Once (YOLO). The QR code information is deciphered and compared with the detected drugs to confirm
the correctness of the medication dispensing. If there are mismatch situation(s), the computer with the
proposed DLADDICM will generate different warning sound in responding to different incorrect
situations. A data set with 30 drugs form the National Library of Medicine of NIH, USA is used for testing
the DLADDICM using the object tracking and detection deep learning algorithm YOLOv3. Experimental
results shown that the DLADDICM can detect and identify the incorrect drugs and generate the
appropriate warning sound for the incorrect drug in pharma package for further human inspection. The
experimental results also exhibits that by using the AI-enabled mechanism an accurate, safer, healthier
with precision medication environment for the medical industries could also be achieved.
Predictions And Analytics In Healthcare: Advancements In Machine LearningIRJET Journal
This document discusses advancements in machine learning and predictive analytics for healthcare. It begins with an introduction discussing how technologies like machine learning and artificial intelligence can help researchers and doctors achieve goals faster when integrated with healthcare. The document then reviews literature on challenges with analyzing big healthcare data due to issues like data variety, speed and volume. It discusses different machine learning algorithms that have been used for disease prediction and diagnosis, including decision trees, random forests, bagging and boosting. The methodology section outlines the use of an ensemble approach, combining multiple models to improve overall accuracy. Technologies implemented in this work include Python libraries like Pandas, NumPy and Scikit-learn for data processing and modeling, along with Flask and AWS for web app deployment. The
Journal of Otolaryngology-Head and Neck Surgery Face and Content Validity of ...Jordan Lewis
This document describes the development and validation of a novel web-based otoscopy simulator called OtoTrain. Experts in otolaryngology evaluated the simulator and found that it demonstrated good face validity and content validity based on survey responses. The simulator was found to be superior to traditional education methods for learning otoscopy skills. Suggested improvements included adding haptic feedback and increasing the focus on pneumatic otoscopy techniques.
Voice based Application as Medicine Spotter for Visually ImpairedIRJET Journal
This paper proposes a voice-based mobile application to help visually impaired individuals identify their medicines independently. The application uses image processing and text recognition on photos of medicine packages taken with the phone's camera to identify the medicine name. It then checks the user's prescription to determine if it is time to take that medicine and outputs the quantity to take via voice. This allows visually impaired people to identify their medicines without assistance and ensure they are taking the correct dose at the right time. The proposed application is designed to be easy for visually impaired users to operate via voice input and output.
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.
Shap Analysis Based Gastric Cancer DetectionIRJET Journal
This document proposes a novel deep learning framework to detect gastric cancer from endoscopic images of the stomach. The framework uses a patch-based analysis where features are extracted from image patches and evaluated for cancer risk. A bag-of-features technique is then applied to the extracted features from selected patches for analysis. Experimental results show the proposed framework can effectively and efficiently detect gastric cancer from images and identify minute lesions. It achieves higher accuracy than other models using the same dataset. The framework is also more accurate than existing methods for gastric cancer detection.
A DEEP LEARNING-BASED ACCURATE DRUG DETECTION, IDENTIFICATION AND CONFIRMATIO...IJCSEA Journal
The document describes a deep learning-based mechanism for accurate drug detection, identification and confirmation in medication dispensing packages for inpatients. The mechanism uses the YOLO object detection algorithm to detect and identify drugs in images of packages. A QR code on each package provides drug information that is compared to detections to confirm accuracy. Different warning sounds are generated for different incorrect detection situations. The mechanism achieved accurate detection and identification of drugs in a test dataset to help prevent medication errors.
A DEEP LEARNING-BASED ACCURATE DRUG DETECTION, IDENTIFICATION AND CONFIRMATIO...IJCSEA Journal
In this study, a deep learning based accurate drug detection, identification and confirmation mechanism
(DLADDICM) for medication dispensing package is proposed for inpatients. In this proposed
DLADDICM, a medication dispensing package with a printed QR code is photo taken and drugs in the
image are detected and identified using a deep learning object detection algorithm, namely You Only Look
Once (YOLO). The QR code information is deciphered and compared with the detected drugs to confirm
the correctness of the medication dispensing. If there are mismatch situation(s), the computer with the
proposed DLADDICM will generate different warning sound in responding to different incorrect
situations. A data set with 30 drugs form the National Library of Medicine of NIH, USA is used for testing
the DLADDICM using the object tracking and detection deep learning algorithm YOLOv3. Experimental
results shown that the DLADDICM can detect and identify the incorrect drugs and generate the
appropriate warning sound for the incorrect drug in pharma package for further human inspection. The
experimental results also exhibits that by using the AI-enabled mechanism an accurate, safer, healthier
with precision medication environment for the medical industries could also be achieved.
A DEEP LEARNING-BASED ACCURATE DRUG DETECTION, IDENTIFICATION AND CONFIRMATIO...IJCSEA Journal
In this study, a deep learning based accurate drug detection, identification and confirmation mechanism
(DLADDICM) for medication dispensing package is proposed for inpatients. In this proposed
DLADDICM, a medication dispensing package with a printed QR code is photo taken and drugs in the
image are detected and identified using a deep learning object detection algorithm, namely You Only Look
Once (YOLO). The QR code information is deciphered and compared with the detected drugs to confirm
the correctness of the medication dispensing. If there are mismatch situation(s), the computer with the
proposed DLADDICM will generate different warning sound in responding to different incorrect
situations. A data set with 30 drugs form the National Library of Medicine of NIH, USA is used for testing
the DLADDICM using the object tracking and detection deep learning algorithm YOLOv3. Experimental
results shown that the DLADDICM can detect and identify the incorrect drugs and generate the
appropriate warning sound for the incorrect drug in pharma package for further human inspection. The
experimental results also exhibits that by using the AI-enabled mechanism an accurate, safer, healthier
with precision medication environment for the medical industries could also be achieved.
Predictions And Analytics In Healthcare: Advancements In Machine LearningIRJET Journal
This document discusses advancements in machine learning and predictive analytics for healthcare. It begins with an introduction discussing how technologies like machine learning and artificial intelligence can help researchers and doctors achieve goals faster when integrated with healthcare. The document then reviews literature on challenges with analyzing big healthcare data due to issues like data variety, speed and volume. It discusses different machine learning algorithms that have been used for disease prediction and diagnosis, including decision trees, random forests, bagging and boosting. The methodology section outlines the use of an ensemble approach, combining multiple models to improve overall accuracy. Technologies implemented in this work include Python libraries like Pandas, NumPy and Scikit-learn for data processing and modeling, along with Flask and AWS for web app deployment. The
Journal of Otolaryngology-Head and Neck Surgery Face and Content Validity of ...Jordan Lewis
This document describes the development and validation of a novel web-based otoscopy simulator called OtoTrain. Experts in otolaryngology evaluated the simulator and found that it demonstrated good face validity and content validity based on survey responses. The simulator was found to be superior to traditional education methods for learning otoscopy skills. Suggested improvements included adding haptic feedback and increasing the focus on pneumatic otoscopy techniques.
Voice based Application as Medicine Spotter for Visually ImpairedIRJET Journal
This paper proposes a voice-based mobile application to help visually impaired individuals identify their medicines independently. The application uses image processing and text recognition on photos of medicine packages taken with the phone's camera to identify the medicine name. It then checks the user's prescription to determine if it is time to take that medicine and outputs the quantity to take via voice. This allows visually impaired people to identify their medicines without assistance and ensure they are taking the correct dose at the right time. The proposed application is designed to be easy for visually impaired users to operate via voice input and output.
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTIONijaia
Rapid technological growth has made Artificial Intelligence (AI) and application of robots common among
human lives. The advancements undertaken to make designs with human similarities or adaptations to the
society are elaborated in detail. The increasing manufacturing and use of robots for industrial purposes
have been related to their operating mechanisms. The experiments and laboratory testing of these devices
is analysed in form tables to show the statistical side of the technology. This report explains the
technological aspects and laboratory experiments that have been advanced to increase knowledge on these
digital technologies. This study aims to present an overview of two developing technologies: artificial
intelligence (AI) and robots and their potential applications. The product variety is a primary
characteristic of each of these specialties. In addition, they may be described as disruptive, facilitating,
and transdisciplinary.
This document outlines a seminar on plant phenotyping. It begins with introductions to concepts like genotype, phenotype, plant phenomics, and the phenotyping bottleneck. It then covers topics like levels of plant phenotyping, controlled environment vs field phenotyping, various imaging technologies (e.g. visible, thermal, spectral, fluorescence, 3D), and platforms for controlled environment and field phenotyping. Methods for root and whole plant phenotyping are also discussed. The document concludes with sections on data integration and sharing in plant phenomics.
“Detection of Diseases using Machine Learning”IRJET Journal
This document describes a machine learning-based disease prediction system. The system was developed as a web application using the Flask framework. It uses logistic regression and random forest classifiers trained on disease-related health parameters to predict diseases. The system allows users to login and submit their health details, generates a prediction report, and stores all user data in a MySQL database for admin access and record keeping. The goal is to help doctors detect diseases earlier and improve healthcare system quality by leveraging machine learning models.
March 2024 - Top 10 Read Articles in Computer Networks & CommunicationsIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Face and liveness detection with criminal identification using machine learni...IAESIJAI
In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.
Adverse Events Following Immunization: Reporting standardization, Automatic C...Melanie Courtot
Analysis of spontaneous reports of Adverse Events Following Immunization (AEFIs) is an important way to identify potential problems in vaccine safety and efficacy and summarize experience for dissemination to health care authorities. The Adverse Event Reporting Ontology (AERO) we are building plays a role in increasing accuracy and quality of reporting, ultimately enhancing response time to adverse event signals.
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.
IRJET - Prediction of Autistic Spectrum Disorder based on Behavioural Fea...IRJET Journal
This document summarizes a research paper that aims to predict autism spectrum disorder (ASD) based on behavioral features using machine learning. The researchers collected ASD screening data from different age groups to develop and evaluate neural network models for predicting ASD. They achieved up to 90% accuracy in predicting ASD. The researchers concluded that machine learning is a promising approach for ASD prediction but noted limitations like lack of large datasets. They plan to improve the models by collecting more data from various sources.
Assessing Effectiveness of Information Presentation Using Wearable Augmented ...CSCJournals
Technological intervention that supports data transfer of sending summary of the patient vitals through the transfer of care would be a great benefit to the trauma care department. This paperfocuses on presenting the effectiveness of information presentation on using wearable augmented reality devices to improve human decision making during transfer of care for surgicaltrauma, and to improve user experience and reduce cognitive workload. The results of this experiment can make significant contributions to design guidelines for information presentation on small form factors especially in time critical decision-making scenarios.This could potentially help medical responders in the trauma care center to prepare for treatment materials such asmedicines, diagnostic procedures, bringing in specialized doctors or consulting the advice of experienced doctors and calling in support staff as required, and so on.
RETINAL IMAGE CLASSIFICATION USING NEURAL NETWORK BASED ON A CNN METHODSIRJET Journal
This document discusses using a convolutional neural network to classify retinal images. Specifically, it aims to develop a system to distinguish between different retinal diseases using fundus images. The system would extract retinal features from the images like the retina, optic nerve and lesions. It then uses a CNN to detect multiple retinal diseases in fundus photographs from a structured analysis database. The CNN is trained on publicly available retinal image datasets. Neural networks have been found to effectively capture disease-specific color and texture features to enable automated diagnosis similar to human experts. The document also provides background on related work using deep learning and CNNs for tasks like lesion detection and classification of retinal diseases from fundus images.
영상기반 딥러닝 의료 분야 응용 (KIST 김영준) - 2017 대한의료영상학회 발표Youngjun Kim
This document summarizes deep learning applications in medical imaging at the Korea Institute of Science and Technology. It begins with an introduction to deep learning and examples of medical applications such as disease diagnosis from medical images using convolutional neural networks. It then discusses KIST's research applying deep learning to tasks such as rotator cuff tear diagnosis from MRI and surgical planning software. The document concludes by noting some of KIST's publications in using deep learning for medical image analysis and registration.
From unimodal image classification to integrative multimodal deep learning pipelines in disease classification, disease management and predictive personalised healthcare.
Virtual reality in health care by Rabeendra Basnetरविन्द्र बस्नेत
Virtual Reality in Healthcare in terms of preventive, curative and restorative and rehabilitative purpose in the physical, virtual, Ambient and Augmented Reality through computer generation enviroments.
Personal Identifiability of User Tracking Data During Observation of 360 Degree VR Video
Abstract
Virtual reality (VR) is a technology that is gaining traction in the consumer market. With it comes an unprecedented ability to track body motions. These body motions are diagnostic of personal identity, medical conditions, and mental states. Previous work has focused on the identifiability of body motions in idealized situations in which some action is chosen by the study designer. In contrast, our work tests the identifiability of users under typical VR viewing circumstances, with no specially designed identifying task. Out of a pool of 511 participants, the system identifies 95% of users correctly when trained on less than 5 min of tracking data per person. We argue these results show nonverbal data should be understood by the public and by researchers as personally identifying data.
This study assessed the feasibility of using Google Glass to record first-person video of medical procedures, compared to traditional third-person recordings. Seven medical trainees performed simulated central venous catheter insertions, which were recorded from both the trainee's perspective using Google Glass and an observer's perspective using a mounted camera. Videos were assessed by three expert raters using checklists and rating scales to evaluate procedural skills. First-person recordings yielded a significantly higher checklist score compared to third-person, but rating scale scores were similar between perspectives. Inter-rater reliability was also similar for both recording methods. First-person video may improve visualization of specific tasks on checklists, while maintaining comparable overall evaluations to traditional third-person video.
This document provides a summary of Ruida Cheng's background and experience. It includes her education, programming skills, work experience at NIH and IBM, publications, and areas of research interest including parallel processing, machine learning, and 3D rendering. At NIH, she has led numerous medical imaging projects applying techniques such as deep learning, segmentation, registration, and visualization. Her work involves developing algorithms and software for tasks like MRI prostate segmentation.
DEEP FACIAL DIAGNOSIS: DEEP TRANSFER LEARNING FROM FACE RECOGNITION TO FACIAL...IRJET Journal
This document discusses using deep learning techniques like transfer learning and convolutional neural networks (CNNs) to perform computer-aided facial diagnosis of diseases from photographs. Specifically, it explores diagnosing single diseases like beta-thalassemia and multiple diseases including beta-thalassemia, hyperthyroidism, Down syndrome, and leprosy. The researchers achieve over 90% accuracy on these facial diagnosis tasks using deep transfer learning from pre-trained face recognition models, outperforming traditional machine learning methods. This shows promise for non-invasive disease screening using facial analysis with deep learning.
1) The document discusses the concept of a "digital phenotype", which refers to aspects of a person's interactions with technology that can provide diagnostic or prognostic insights into their health conditions.
2) Previous research has found correlations between depressive symptom severity and certain location-based smartphone sensor data, such as increased location variance and disrupted circadian rhythms.
3) This study replicates previous findings using GPS smartphone sensor data collected from 48 college students over 10 weeks, finding significant correlations between depressive symptoms and location variance, entropy, and circadian movement patterns. The relationships were stronger when analyzing weekend versus weekday data.
Virtual reality, augmented reality, and ambient reality have various applications in healthcare, including surgical procedures, medical therapy, patient education, medical training, data visualization, skill enhancement, and architectural design. However, many current applications are limited by technological issues such as delays in overlaying real-time images, side effects from prolonged headset use, and limitations of computing power that require tradeoffs between realism and interactivity. Further research is needed to address these problems.
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.
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTIONijaia
Rapid technological growth has made Artificial Intelligence (AI) and application of robots common among
human lives. The advancements undertaken to make designs with human similarities or adaptations to the
society are elaborated in detail. The increasing manufacturing and use of robots for industrial purposes
have been related to their operating mechanisms. The experiments and laboratory testing of these devices
is analysed in form tables to show the statistical side of the technology. This report explains the
technological aspects and laboratory experiments that have been advanced to increase knowledge on these
digital technologies. This study aims to present an overview of two developing technologies: artificial
intelligence (AI) and robots and their potential applications. The product variety is a primary
characteristic of each of these specialties. In addition, they may be described as disruptive, facilitating,
and transdisciplinary.
This document outlines a seminar on plant phenotyping. It begins with introductions to concepts like genotype, phenotype, plant phenomics, and the phenotyping bottleneck. It then covers topics like levels of plant phenotyping, controlled environment vs field phenotyping, various imaging technologies (e.g. visible, thermal, spectral, fluorescence, 3D), and platforms for controlled environment and field phenotyping. Methods for root and whole plant phenotyping are also discussed. The document concludes with sections on data integration and sharing in plant phenomics.
“Detection of Diseases using Machine Learning”IRJET Journal
This document describes a machine learning-based disease prediction system. The system was developed as a web application using the Flask framework. It uses logistic regression and random forest classifiers trained on disease-related health parameters to predict diseases. The system allows users to login and submit their health details, generates a prediction report, and stores all user data in a MySQL database for admin access and record keeping. The goal is to help doctors detect diseases earlier and improve healthcare system quality by leveraging machine learning models.
March 2024 - Top 10 Read Articles in Computer Networks & CommunicationsIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Face and liveness detection with criminal identification using machine learni...IAESIJAI
In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.
Adverse Events Following Immunization: Reporting standardization, Automatic C...Melanie Courtot
Analysis of spontaneous reports of Adverse Events Following Immunization (AEFIs) is an important way to identify potential problems in vaccine safety and efficacy and summarize experience for dissemination to health care authorities. The Adverse Event Reporting Ontology (AERO) we are building plays a role in increasing accuracy and quality of reporting, ultimately enhancing response time to adverse event signals.
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.
IRJET - Prediction of Autistic Spectrum Disorder based on Behavioural Fea...IRJET Journal
This document summarizes a research paper that aims to predict autism spectrum disorder (ASD) based on behavioral features using machine learning. The researchers collected ASD screening data from different age groups to develop and evaluate neural network models for predicting ASD. They achieved up to 90% accuracy in predicting ASD. The researchers concluded that machine learning is a promising approach for ASD prediction but noted limitations like lack of large datasets. They plan to improve the models by collecting more data from various sources.
Assessing Effectiveness of Information Presentation Using Wearable Augmented ...CSCJournals
Technological intervention that supports data transfer of sending summary of the patient vitals through the transfer of care would be a great benefit to the trauma care department. This paperfocuses on presenting the effectiveness of information presentation on using wearable augmented reality devices to improve human decision making during transfer of care for surgicaltrauma, and to improve user experience and reduce cognitive workload. The results of this experiment can make significant contributions to design guidelines for information presentation on small form factors especially in time critical decision-making scenarios.This could potentially help medical responders in the trauma care center to prepare for treatment materials such asmedicines, diagnostic procedures, bringing in specialized doctors or consulting the advice of experienced doctors and calling in support staff as required, and so on.
RETINAL IMAGE CLASSIFICATION USING NEURAL NETWORK BASED ON A CNN METHODSIRJET Journal
This document discusses using a convolutional neural network to classify retinal images. Specifically, it aims to develop a system to distinguish between different retinal diseases using fundus images. The system would extract retinal features from the images like the retina, optic nerve and lesions. It then uses a CNN to detect multiple retinal diseases in fundus photographs from a structured analysis database. The CNN is trained on publicly available retinal image datasets. Neural networks have been found to effectively capture disease-specific color and texture features to enable automated diagnosis similar to human experts. The document also provides background on related work using deep learning and CNNs for tasks like lesion detection and classification of retinal diseases from fundus images.
영상기반 딥러닝 의료 분야 응용 (KIST 김영준) - 2017 대한의료영상학회 발표Youngjun Kim
This document summarizes deep learning applications in medical imaging at the Korea Institute of Science and Technology. It begins with an introduction to deep learning and examples of medical applications such as disease diagnosis from medical images using convolutional neural networks. It then discusses KIST's research applying deep learning to tasks such as rotator cuff tear diagnosis from MRI and surgical planning software. The document concludes by noting some of KIST's publications in using deep learning for medical image analysis and registration.
From unimodal image classification to integrative multimodal deep learning pipelines in disease classification, disease management and predictive personalised healthcare.
Virtual reality in health care by Rabeendra Basnetरविन्द्र बस्नेत
Virtual Reality in Healthcare in terms of preventive, curative and restorative and rehabilitative purpose in the physical, virtual, Ambient and Augmented Reality through computer generation enviroments.
Personal Identifiability of User Tracking Data During Observation of 360 Degree VR Video
Abstract
Virtual reality (VR) is a technology that is gaining traction in the consumer market. With it comes an unprecedented ability to track body motions. These body motions are diagnostic of personal identity, medical conditions, and mental states. Previous work has focused on the identifiability of body motions in idealized situations in which some action is chosen by the study designer. In contrast, our work tests the identifiability of users under typical VR viewing circumstances, with no specially designed identifying task. Out of a pool of 511 participants, the system identifies 95% of users correctly when trained on less than 5 min of tracking data per person. We argue these results show nonverbal data should be understood by the public and by researchers as personally identifying data.
This study assessed the feasibility of using Google Glass to record first-person video of medical procedures, compared to traditional third-person recordings. Seven medical trainees performed simulated central venous catheter insertions, which were recorded from both the trainee's perspective using Google Glass and an observer's perspective using a mounted camera. Videos were assessed by three expert raters using checklists and rating scales to evaluate procedural skills. First-person recordings yielded a significantly higher checklist score compared to third-person, but rating scale scores were similar between perspectives. Inter-rater reliability was also similar for both recording methods. First-person video may improve visualization of specific tasks on checklists, while maintaining comparable overall evaluations to traditional third-person video.
This document provides a summary of Ruida Cheng's background and experience. It includes her education, programming skills, work experience at NIH and IBM, publications, and areas of research interest including parallel processing, machine learning, and 3D rendering. At NIH, she has led numerous medical imaging projects applying techniques such as deep learning, segmentation, registration, and visualization. Her work involves developing algorithms and software for tasks like MRI prostate segmentation.
DEEP FACIAL DIAGNOSIS: DEEP TRANSFER LEARNING FROM FACE RECOGNITION TO FACIAL...IRJET Journal
This document discusses using deep learning techniques like transfer learning and convolutional neural networks (CNNs) to perform computer-aided facial diagnosis of diseases from photographs. Specifically, it explores diagnosing single diseases like beta-thalassemia and multiple diseases including beta-thalassemia, hyperthyroidism, Down syndrome, and leprosy. The researchers achieve over 90% accuracy on these facial diagnosis tasks using deep transfer learning from pre-trained face recognition models, outperforming traditional machine learning methods. This shows promise for non-invasive disease screening using facial analysis with deep learning.
1) The document discusses the concept of a "digital phenotype", which refers to aspects of a person's interactions with technology that can provide diagnostic or prognostic insights into their health conditions.
2) Previous research has found correlations between depressive symptom severity and certain location-based smartphone sensor data, such as increased location variance and disrupted circadian rhythms.
3) This study replicates previous findings using GPS smartphone sensor data collected from 48 college students over 10 weeks, finding significant correlations between depressive symptoms and location variance, entropy, and circadian movement patterns. The relationships were stronger when analyzing weekend versus weekday data.
Virtual reality, augmented reality, and ambient reality have various applications in healthcare, including surgical procedures, medical therapy, patient education, medical training, data visualization, skill enhancement, and architectural design. However, many current applications are limited by technological issues such as delays in overlaying real-time images, side effects from prolonged headset use, and limitations of computing power that require tradeoffs between realism and interactivity. Further research is needed to address these problems.
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4. APPLICATIONS
• Liu H, Hu H, Zhou F, Yuan H. Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5. Fire. 2023;6(7). doi:10.3390/fire6070279
• Chin R, Catal C, Kassahun A. Plant disease detection using drones in precision agriculture. Precision Agriculture. 2023;24(5):1663-1682. doi:10.1007/s11119-
023-10014-y
• Koshta N, Devi Y, Chauhan C. Evaluating Barriers to the Adoption of Delivery Drones in Rural Healthcare Supply Chains: Preparing the Healthcare System
for the Future. IEEE Transactions on Engineering Management, Engineering Management, IEEE Transactions on, IEEE Trans Eng Manage. 2022;PP(99):1-
13. doi:10.1109/TEM.2022.3210121
• JANGRA V, SUNIL. A Semi-Autonomos Drone for Surveillance and Security. INCAS Bulletin. 2020;12(4):267-270. doi:10.13111/2066-8201.2020.12.4.25
7. MACHINE LEARNING MODEL
Hong S-J, Han Y, Kim S-Y, Lee A-Y, Kim G. Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery. Sensors (Basel, Switzerland). 2019;19(7).
doi:10.3390/s19071651
Bird detection results of Faster R-CNN Resnet 101 model. All birds are successfully detected regardless of the flying altitude of the birds.
8. You Only Look Once
(YOLO) is a state-of-the-
art, real-time object
detection algorithm.
• Residual blocks
• Bounding box
regression
• Intersection Over
Unions or IOU for short
• Non-Maximum
Suppression
What is a
YOLO?
.
9.
10. You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon, Santosh Divvala, Ross Girshick , Ali Farhadi. You Only Look Once: Unified, Real-Time Object Detection (University of Washington , Allen Institute
for AI , Facebook AI Research, 8 Jun 2015 ) https://arxiv.org/abs/1506.02640
Objective
• Introduces YOLO, a new approach to object detection.
Method
• Compared with F-CNN, DeepMultibox, Overfeat, MultiGrasp.
• No complex pipeline
• Less background errors
• YOLO tested with sample artwork and natural images from the internet. It is mostly accurate although it does
think one person is an airplane.
• Residual blocks -> Bounding box regression -> Intersection Over Unions or IOU for short -> Non-
Maximum Suppression
11. You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon, Santosh Divvala, Ross Girshick , Ali Farhadi. You Only Look Once: Unified, Real-Time Object Detection (University of Washington , Allen Institute
for AI , Facebook AI Research, 8 Jun 2015 ) https://arxiv.org/abs/1506.02640
12. Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification
Lu Tan, Tianran Huangfu, Liyao Wu, & Wenying Chen. (2021). Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification. BMC Medical
Informatics and Decision Making, 21(1), 1–11. https://0-doi.org.pacificatclassic.pacific.edu/10.1186/s12911-021-01691-8
Objective
• Identification of pills to ensure the safe administration of drugs to patients.
Method
• Trained each algorithm on a pill image dataset and analyzed the performance of the three models to
determine the best pill recognition model.
• The models were then used to detect difficult samples and it was compared the results.
• Faster learning algo and accurate result
13. Lu Tan, Tianran Huangfu, Liyao Wu, & Wenying Chen. (2021). Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification. BMC Medical
Informatics and Decision Making, 21(1), 1–11. https://0-doi.org.pacificatclassic.pacific.edu/10.1186/s12911-021-01691-8
Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification
14. Classification of Actors in an Animated Video using a Novel Yolo Framework in
Comparison with SVM Algorithm
V., S., & S., M. kumar. (2022). Classification of Actors in an Animated Video using a Novel Yolo Framework in Comparison with SVM Algorithm. Journal of
Pharmaceutical Negative Results, 13, 1566–1572. https://0-doi.org.pacificatclassic.pacific.edu/10.47750/pnr.2022.13.S04.187
Objective
• Classification of actors in an animated video using the novel YOLO framework in comparison with the SVM
algorithm.
Method
• Sample groups that are considered in the project can be classified into two, one for YOLO and other
for SVM.
• They are tested using 0.80 for G-power to determine the sample size and for t-test analysis.
15. Classification of Actors in an Animated Video using a Novel Yolo Framework in
Comparison with SVM Algorithm
V., S., & S., M. kumar. (2022). Classification of Actors in an Animated Video using a Novel Yolo Framework in Comparison with SVM Algorithm. Journal of
Pharmaceutical Negative Results, 13, 1566–1572. https://0-doi.org.pacificatclassic.pacific.edu/10.47750/pnr.2022.13.S04.187
ITERATIONS
(EPOCHS)
ACCURACY(%)
YOLO SVM
1 88.43 86.87
2 88.41 86.54
3 87.67 86.65
4 87.54 86.98
5 87.43 84.34
6 87.32 84.54
7 87.28 83.53
8 87.12 83.23
9 86.76 82.53
10 86.43 82.23
Video No.
Duration of
Video
Screen Time of
Tom
Screen Time of
Jerry
1 11 min 25 sec 276 sec 180 sec
2 9 min 10 sec 326 sec 129 sec
3 14 min 25 sec 600 sec 120 sec
4 8 min 45 sec 372 sec 147 sec
5 4 min 23 sec 135 sec 126 sec
Group N Mean
Std.
Deviation
Std. error
mean
Accuracy
YOLO 10 87.45 .63324 .20025
SVM 10
84.74 1.87293 .59227
Table 2. Screen time of Tom and Jerry calculated using the Classification count method.
Table 3. Consequences of institution records. Descriptive SPSS employs the unbiased
pattern test of Accuracy and Precision on the dataset.
Table 1. Accuracy achieved during evaluation of Screen time of an actor
using test and mapping dataset with YOLO algorithm and Comparison of
SVM algorithm for different iterations.
16. C onclusion
• YOLO Model perform better in
real time object detection
• High Speed Model training
• Detection accuracy high
• Open-source, multiple versions
available
• It can be used in drones for real
time object detection
17. References
1
.
. Joseph Redmon, Santosh Divvala, Ross Girshick , Ali Farhadi. You Only Look Once: Unified,
Real-Time Object Detection (University of Washington , Allen Institute for AI , Facebook AI
Research, 8 Jun 2015 ) https://arxiv.org/abs/1506.02640
2.
Lu Tan, Tianran Huangfu, Liyao Wu, & Wenying Chen. (2021). Comparison of RetinaNet, SSD,
and YOLO v3 for real-time pill identification. BMC Medical Informatics and Decision Making,
21(1), 1–11. https://0-doi.org.pacificatclassic.pacific.edu/10.1186/s12911-021-01691-8
3.
V., S., & S., M. kumar. (2022). Classification of Actors in an Animated Video using a Novel Yolo
Framework in Comparison with SVM Algorithm. Journal of Pharmaceutical Negative Results, 13,
1566–1572. https://0-doi.org.pacificatclassic.pacific.edu/10.47750/pnr.2022.13.S04.187
4.
Chin, R., Catal, C., & Kassahun, A. (2023). Plant disease detection using drones in precision
agriculture. Precision Agriculture, 24(5), 1663–1682. https://0-
doi.org.pacificatclassic.pacific.edu/10.1007/s11119-023-10014-y
5.
Koshta, N., Devi, Y., & Chauhan, C. (2022). Evaluating Barriers to the Adoption of Delivery
Drones in Rural Healthcare Supply Chains: Preparing the Healthcare System for the Future.
IEEE Transactions on Engineering Management, Engineering Management, IEEE Transactions
on, IEEE Trans. Eng. Manage, PP(99), 1–13. https://0-
doi.org.pacificatclassic.pacific.edu/10.1109/TEM.2022.3210121
6.
VISHAL JANGRA; SUNIL. A Semi-Autonomos Drone for Surveillance and Security. INCAS
Bulletin, [s. l.], v. 12, n. 4, p. 267–270, 2020. DOI 10.13111/2066-8201.2020.12.4.25. Disponível
em: https://0-research.ebsco.com.pacificatclassic.pacific.edu/linkprocessor/plink?id=85093a8e-
ab79-34ba-ad0e-65410347a06d. Acesso em: 19 out. 2023.
7.
Liu, H., Hu, H., Zhou, F., & Yuan, H. (2023). Forest Flame Detection in Unmanned Aerial
Vehicle Imagery Based on YOLOv5. Fire, 6(7).
https://0-doi.org.pacificatclassic.pacific.edu/10.3390/fire6070279
8.
Suk-Ju Hong, Yunhyeok Han, Sang-Yeon Kim, Ah-Yeong Lee, & Ghiseok Kim. (2019). Application
of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery. Sensors,
19(7), 1651. https://0-doi.org.pacificatclassic.pacific.edu/10.3390/s19071651