1) The document discusses using deep learning and machine learning models to detect Covid-19 from chest x-ray images with a high accuracy.
2) Specifically, it evaluates using convolutional neural networks (CNNs) which are well-suited for medical image classification tasks since they can learn spatial relationships within images.
3) Previous studies that developed CNN and other models for Covid detection from chest x-rays are reviewed, finding classification accuracies from 87-99% depending on the dataset and model used.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
Covid 19 diagnosis using x-ray images and deep learningShamik Tiwari
Researchers developed a convolutional neural network (CNN) model to classify chest X-ray images into three classes: positive for COVID-19, normal, or viral pneumonia. The model was trained on these image sets and achieved 94% accuracy on the training data and 96% on the validation data. When tested, the model achieved 94% accuracy in classifying chest X-ray images into the three classes. The goal was to create a faster and less complex model than previous approaches for detecting COVID-19 in chest images using artificial intelligence.
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
Transfer Learning for the Detection and Classification of traditional pneumon...Yusuf Brima
A presentation of my MSc in Mathematical Sciences thesis at the African Institute of Mathematical Sciences (AIMS), Rwanda. This presentation explores the application of Deep Transfer Learning towards the diagnosis and classification of traditional pneumonia and pneumonia induced from COVID-19 using chest X-ray images.
Eye tracking is the process of measuring either the point of gaze or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
Covid 19 diagnosis using x-ray images and deep learningShamik Tiwari
Researchers developed a convolutional neural network (CNN) model to classify chest X-ray images into three classes: positive for COVID-19, normal, or viral pneumonia. The model was trained on these image sets and achieved 94% accuracy on the training data and 96% on the validation data. When tested, the model achieved 94% accuracy in classifying chest X-ray images into the three classes. The goal was to create a faster and less complex model than previous approaches for detecting COVID-19 in chest images using artificial intelligence.
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
Transfer Learning for the Detection and Classification of traditional pneumon...Yusuf Brima
A presentation of my MSc in Mathematical Sciences thesis at the African Institute of Mathematical Sciences (AIMS), Rwanda. This presentation explores the application of Deep Transfer Learning towards the diagnosis and classification of traditional pneumonia and pneumonia induced from COVID-19 using chest X-ray images.
Eye tracking is the process of measuring either the point of gaze or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement.
This document describes a student project to develop a driver drowsiness detection system using OpenCV and Python. It includes approval from an internal examiner, declarations by the student, and certificates of completion. The system detects drowsiness based on eye closure and yawning detection using facial landmark tracking and thresholds on eye and mouth aspect ratios. Experimental results showed the system could successfully detect drowsiness and provide alerts when thresholds were exceeded.
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
NCCR 2020: Conference Of Very Important Disease (COVID-19) | 24 - 26 August 2020
Young Investigator Awards Presentation
Kim-Ann Git1, Aida binti Abdul Aziz2, Lau Kiew Siong3, Lau Song Lung3, Preetvinder Singh a/l Dheer Singh4, Tan Ying Sern5, Eric Chung6
1-Selayang Hospital
2-Sungai Buloh Hospital
3-Sarawak General Hospital
4-Hospital Raja Permaisuri Bainun
5-Taiping Hospital
6-University of Malaya Medical Centre
https://doi.org/10.5281/zenodo.4004461
This document describes a deep learning approach for detecting diabetic retinopathy using OCT images. It discusses the proposed system which will use OCT images and apply classification algorithms to identify the level of infection. The model will be trained on datasets of infected images to accurately detect regions of infection and the condition level. Image processing techniques like median filtering and edge detection will be used along with statistical data extraction and supervised training to identify clusters and classify images. Results will be compared to evaluate the machine learning models. The system aims to automate diabetic retinopathy detection to improve efficiency over conventional methods.
This document discusses color image processing and different color models. It begins with an introduction and then covers color fundamentals such as brightness, hue, and saturation. It describes common color models like RGB, CMY, HSI, and YIQ. Pseudo color processing and full color image processing are explained. Color transformations between color models are also discussed. Implementation tips for interpolation methods in color processing are provided. The document concludes with thanks to the head of the computer science department.
IRJET- Glaucoma Detection using Convolutional Neural NetworkIRJET Journal
This document describes a study that used a convolutional neural network (CNN) to detect glaucoma from eye images. The researchers:
1) Collected a database of 100 eye images, with 50 normal and 50 glaucoma cases, for training and testing the CNN model.
2) Pre-processed the images using Gaussian blur to remove noise before classification.
3) Trained a CNN on the images and tested it on a separate set of 100 images, achieving 97% accuracy, 96% precision, and 98% recall.
4) Concluded that CNNs provide an effective technique for early glaucoma detection that could help save vision.
Chest X-ray Pneumonia Classification with Deep LearningBaoTramDuong2
This document discusses using deep learning models to classify chest x-ray images as either normal or pneumonia. It obtained a dataset of over 5,800 pediatric chest x-rays from a Chinese hospital. Various deep learning models were explored, including multilayer perceptrons, convolutional neural networks, and transfer learning with VGG16, which achieved 92% validation accuracy. The document recommends future work such as distinguishing between viral and bacterial pneumonia and combining models with SVM. It also discusses recommendations to reduce childhood pneumonia prevalence.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
Glaucoma Detection in Retinal Images Using Image Processing Techniques: A SurveyEswar Publications
Glaucoma is a disease associated with human eyes and second conducting movement o fblindness across the globe if
eyes are not treated at preliminary stage. Glaucoma normally occurs with increased intra-ocular pressure (IOP) in eyes and gradually damagesthe vision field of eyes. The term ocular-hypertension is related to those people in whom IOP increases consistently and does not damage the optic nerve. Glaucoma has different types such as open-angle, close-angle, congenital, normal tension and etcetera. Normal tension glaucoma affects vision field and damages optic nerve as well. The term angle means the distance between iris and cornea; if this distance is large it is referred to as open-angle glaucoma and similarly if the distance between iris and cornea is short than this is called close-angle glaucoma. Open-angle glaucoma is common as compared to close-angle glaucoma. Close-angle glaucoma is very painful and affects vision field of eyes quickly as compared to open-angle glaucoma. In this
paper, the state of the art CAD systems and image processing methods are studied and compared systematically in terms of their classification accuracy, methodology approach, sensitivity and specificity. The comparison results indicate that the accuracy of these CAD systems and image processing methods is not up to the mark.
The document summarizes Jini technology, which provides a simple infrastructure for delivering services like applications, databases, and printing across a network. It discusses Jini's history, goals of enabling universal access to shared resources. The key components are services, lookup services for discovery, leasing to manage access time, and security. Jini architecture uses lookup services for registration and discovery to simplify adding and removing network services and devices.
Virtual Mouse using hand gesture recognitionMuktiKalsekar
This project is to develop a Virtual Mouse using Hand Gesture Recognition. Hand gestures are the most effortless and natural way of communication. The aim is to perform various operations of the cursor. Instead of using more expensive sensors, a simple web camera can identify the gesture and perform the action. It helps the user to interact with a computer without any physical or hardware device to control mouse operation.
Diabetic retinopathy is a leading cause of blindness that can be detected through automated analysis of fundus images. The document proposes using support vector machines to build a model that can robustly detect four key features of diabetic retinopathy - hard exudates, soft exudates, microaneurysms, and hemorrhages. The model is trained on a standardized set of fundus images and achieves over 95% accuracy on classification, providing an affordable solution to diagnose a disease affecting many people.
IRJET- Driver Drowsiness Detection & Identification of Alcoholic System using...IRJET Journal
This document summarizes a research paper on detecting driver drowsiness and identifying drunk driving using computer vision techniques. The paper proposes using a camera and sensors placed in a Raspberry Pi to monitor the driver. If the system detects the driver is sleeping based on eye closure analysis or detects alcohol on the driver's breath, it will capture an image, send alerts and location data to a server, and cut off the vehicle's motor. The system is intended to help prevent accidents caused by drowsy or intoxicated driving.
This document discusses using deep learning and convolutional neural networks to detect diabetic retinopathy through analyzing fundus images. It proposes a CNN model trained on a public Kaggle dataset to classify images based on the severity of retinopathy. The CNN architecture would automatically diagnose retinopathy without user input. The document outlines modules for an app, including uploading images, displaying results, and providing doctor referrals. It aims to address the growing problem of vision loss from diabetic retinopathy worldwide.
Number Plate Recognition (NPR) is a computer vision technology that captures images of vehicles using a camera. It extracts the vehicle's number plate to identify the owner's details by matching it to a database. The system works by capturing images, preprocessing them, detecting the number plate using YOLO, recognizing the characters, and outputting the results to a database. It has benefits like saving time, reducing errors, and aiding in tracking criminals. Potential future improvements include enhancing plate recognition for different fonts/sizes and speeding up the system.
Computer Graphics 471 Project Report FinalAli Ahmed
This document is a project report for a 3D bowling game created by a team of students for a computer graphics course. It includes sections on the team's methodology, implementation details of the game engine, modeling, visual effects, and results. Key elements developed include a modular game engine, procedural modeling of the ball and pins with textures, shadows, reflections, collision detection, and animation sequences. The report discusses completed and uncompleted elements, and includes screenshots showcasing the results.
ABSTRACT
Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider.
For securing user data from such attacks a new paradigm called fog computing can be used. Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined network This technique can monitor the user activity to identify the legitimacy and prevent from any unauthorized user access. Here we have discussed this paradigm for preventing misuse of user data and securing information.
CONCLUSION
This proposal of monitoring data access patterns by profiling user behavior to determine if and when a malicious insider illegitimately accesses someone’s documents in a Cloud service. Decoy documents stored in the Cloud alongside the user’s real data also serve as sensors to detect illegitimate access. Once unauthorized data access or exposure is suspected, and later verified, with challenge questions for instance, this inundate the malicious insider with bogus information in order to dilute the user’s real data. Such preventive attacks that rely on disinformation technology could provide unprecedented levels of security in the Cloud and in social networks.
Applications of Digital image processing in Medical FieldAshwani Srivastava
This document discusses different types of electromagnetic radiation used for imaging. It describes digital images as composed of pixels and notes that digital image processing involves manipulating digital images on a computer. It outlines different levels of image processing from low-level tasks like noise reduction to mid-level tasks like segmentation to high-level tasks like image analysis. It provides examples of imaging applications using gamma rays, X-rays, ultraviolet light, microwaves, radio waves, and magnetic resonance imaging.
This document presents a convolutional neural network model to detect pneumonia from chest x-ray images. The model is trained from scratch on a dataset of over 5,800 chest x-ray images categorized into pneumonia and normal images. The model uses preprocessing like resizing and normalization, data augmentation, and a custom sequential CNN model with convolutional and pooling layers to extract features and classify images. Evaluation metrics like precision, recall, accuracy and F1 score are used to analyze the trained model's performance at detecting pneumonia from chest x-rays. The proposed system aims to help diagnose pneumonia early and assist medical professionals, especially in remote areas.
Predicting Covid-19 pneumonia Severity on Chest x-ray with deep learningIRJET Journal
This document discusses using deep learning models to analyze chest x-ray images to predict the severity of COVID-19 pneumonia. It analyzed chest x-ray images of COVID-19 patients and healthy individuals using InceptionV3, Xception, and ResNet models. The Xception model achieved the highest accuracy rate of 97.97% at detecting COVID-19 in chest x-ray images compared to the other models. The study aimed to develop a method for classifying COVID-19 patients using chest x-rays but did not claim any medical diagnostic accuracy. It discussed using deep learning on medical images to help address issues caused by the COVID-19 pandemic.
This document describes a student project to develop a driver drowsiness detection system using OpenCV and Python. It includes approval from an internal examiner, declarations by the student, and certificates of completion. The system detects drowsiness based on eye closure and yawning detection using facial landmark tracking and thresholds on eye and mouth aspect ratios. Experimental results showed the system could successfully detect drowsiness and provide alerts when thresholds were exceeded.
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
NCCR 2020: Conference Of Very Important Disease (COVID-19) | 24 - 26 August 2020
Young Investigator Awards Presentation
Kim-Ann Git1, Aida binti Abdul Aziz2, Lau Kiew Siong3, Lau Song Lung3, Preetvinder Singh a/l Dheer Singh4, Tan Ying Sern5, Eric Chung6
1-Selayang Hospital
2-Sungai Buloh Hospital
3-Sarawak General Hospital
4-Hospital Raja Permaisuri Bainun
5-Taiping Hospital
6-University of Malaya Medical Centre
https://doi.org/10.5281/zenodo.4004461
This document describes a deep learning approach for detecting diabetic retinopathy using OCT images. It discusses the proposed system which will use OCT images and apply classification algorithms to identify the level of infection. The model will be trained on datasets of infected images to accurately detect regions of infection and the condition level. Image processing techniques like median filtering and edge detection will be used along with statistical data extraction and supervised training to identify clusters and classify images. Results will be compared to evaluate the machine learning models. The system aims to automate diabetic retinopathy detection to improve efficiency over conventional methods.
This document discusses color image processing and different color models. It begins with an introduction and then covers color fundamentals such as brightness, hue, and saturation. It describes common color models like RGB, CMY, HSI, and YIQ. Pseudo color processing and full color image processing are explained. Color transformations between color models are also discussed. Implementation tips for interpolation methods in color processing are provided. The document concludes with thanks to the head of the computer science department.
IRJET- Glaucoma Detection using Convolutional Neural NetworkIRJET Journal
This document describes a study that used a convolutional neural network (CNN) to detect glaucoma from eye images. The researchers:
1) Collected a database of 100 eye images, with 50 normal and 50 glaucoma cases, for training and testing the CNN model.
2) Pre-processed the images using Gaussian blur to remove noise before classification.
3) Trained a CNN on the images and tested it on a separate set of 100 images, achieving 97% accuracy, 96% precision, and 98% recall.
4) Concluded that CNNs provide an effective technique for early glaucoma detection that could help save vision.
Chest X-ray Pneumonia Classification with Deep LearningBaoTramDuong2
This document discusses using deep learning models to classify chest x-ray images as either normal or pneumonia. It obtained a dataset of over 5,800 pediatric chest x-rays from a Chinese hospital. Various deep learning models were explored, including multilayer perceptrons, convolutional neural networks, and transfer learning with VGG16, which achieved 92% validation accuracy. The document recommends future work such as distinguishing between viral and bacterial pneumonia and combining models with SVM. It also discusses recommendations to reduce childhood pneumonia prevalence.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
Glaucoma Detection in Retinal Images Using Image Processing Techniques: A SurveyEswar Publications
Glaucoma is a disease associated with human eyes and second conducting movement o fblindness across the globe if
eyes are not treated at preliminary stage. Glaucoma normally occurs with increased intra-ocular pressure (IOP) in eyes and gradually damagesthe vision field of eyes. The term ocular-hypertension is related to those people in whom IOP increases consistently and does not damage the optic nerve. Glaucoma has different types such as open-angle, close-angle, congenital, normal tension and etcetera. Normal tension glaucoma affects vision field and damages optic nerve as well. The term angle means the distance between iris and cornea; if this distance is large it is referred to as open-angle glaucoma and similarly if the distance between iris and cornea is short than this is called close-angle glaucoma. Open-angle glaucoma is common as compared to close-angle glaucoma. Close-angle glaucoma is very painful and affects vision field of eyes quickly as compared to open-angle glaucoma. In this
paper, the state of the art CAD systems and image processing methods are studied and compared systematically in terms of their classification accuracy, methodology approach, sensitivity and specificity. The comparison results indicate that the accuracy of these CAD systems and image processing methods is not up to the mark.
The document summarizes Jini technology, which provides a simple infrastructure for delivering services like applications, databases, and printing across a network. It discusses Jini's history, goals of enabling universal access to shared resources. The key components are services, lookup services for discovery, leasing to manage access time, and security. Jini architecture uses lookup services for registration and discovery to simplify adding and removing network services and devices.
Virtual Mouse using hand gesture recognitionMuktiKalsekar
This project is to develop a Virtual Mouse using Hand Gesture Recognition. Hand gestures are the most effortless and natural way of communication. The aim is to perform various operations of the cursor. Instead of using more expensive sensors, a simple web camera can identify the gesture and perform the action. It helps the user to interact with a computer without any physical or hardware device to control mouse operation.
Diabetic retinopathy is a leading cause of blindness that can be detected through automated analysis of fundus images. The document proposes using support vector machines to build a model that can robustly detect four key features of diabetic retinopathy - hard exudates, soft exudates, microaneurysms, and hemorrhages. The model is trained on a standardized set of fundus images and achieves over 95% accuracy on classification, providing an affordable solution to diagnose a disease affecting many people.
IRJET- Driver Drowsiness Detection & Identification of Alcoholic System using...IRJET Journal
This document summarizes a research paper on detecting driver drowsiness and identifying drunk driving using computer vision techniques. The paper proposes using a camera and sensors placed in a Raspberry Pi to monitor the driver. If the system detects the driver is sleeping based on eye closure analysis or detects alcohol on the driver's breath, it will capture an image, send alerts and location data to a server, and cut off the vehicle's motor. The system is intended to help prevent accidents caused by drowsy or intoxicated driving.
This document discusses using deep learning and convolutional neural networks to detect diabetic retinopathy through analyzing fundus images. It proposes a CNN model trained on a public Kaggle dataset to classify images based on the severity of retinopathy. The CNN architecture would automatically diagnose retinopathy without user input. The document outlines modules for an app, including uploading images, displaying results, and providing doctor referrals. It aims to address the growing problem of vision loss from diabetic retinopathy worldwide.
Number Plate Recognition (NPR) is a computer vision technology that captures images of vehicles using a camera. It extracts the vehicle's number plate to identify the owner's details by matching it to a database. The system works by capturing images, preprocessing them, detecting the number plate using YOLO, recognizing the characters, and outputting the results to a database. It has benefits like saving time, reducing errors, and aiding in tracking criminals. Potential future improvements include enhancing plate recognition for different fonts/sizes and speeding up the system.
Computer Graphics 471 Project Report FinalAli Ahmed
This document is a project report for a 3D bowling game created by a team of students for a computer graphics course. It includes sections on the team's methodology, implementation details of the game engine, modeling, visual effects, and results. Key elements developed include a modular game engine, procedural modeling of the ball and pins with textures, shadows, reflections, collision detection, and animation sequences. The report discusses completed and uncompleted elements, and includes screenshots showcasing the results.
ABSTRACT
Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider.
For securing user data from such attacks a new paradigm called fog computing can be used. Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined network This technique can monitor the user activity to identify the legitimacy and prevent from any unauthorized user access. Here we have discussed this paradigm for preventing misuse of user data and securing information.
CONCLUSION
This proposal of monitoring data access patterns by profiling user behavior to determine if and when a malicious insider illegitimately accesses someone’s documents in a Cloud service. Decoy documents stored in the Cloud alongside the user’s real data also serve as sensors to detect illegitimate access. Once unauthorized data access or exposure is suspected, and later verified, with challenge questions for instance, this inundate the malicious insider with bogus information in order to dilute the user’s real data. Such preventive attacks that rely on disinformation technology could provide unprecedented levels of security in the Cloud and in social networks.
Applications of Digital image processing in Medical FieldAshwani Srivastava
This document discusses different types of electromagnetic radiation used for imaging. It describes digital images as composed of pixels and notes that digital image processing involves manipulating digital images on a computer. It outlines different levels of image processing from low-level tasks like noise reduction to mid-level tasks like segmentation to high-level tasks like image analysis. It provides examples of imaging applications using gamma rays, X-rays, ultraviolet light, microwaves, radio waves, and magnetic resonance imaging.
This document presents a convolutional neural network model to detect pneumonia from chest x-ray images. The model is trained from scratch on a dataset of over 5,800 chest x-ray images categorized into pneumonia and normal images. The model uses preprocessing like resizing and normalization, data augmentation, and a custom sequential CNN model with convolutional and pooling layers to extract features and classify images. Evaluation metrics like precision, recall, accuracy and F1 score are used to analyze the trained model's performance at detecting pneumonia from chest x-rays. The proposed system aims to help diagnose pneumonia early and assist medical professionals, especially in remote areas.
Predicting Covid-19 pneumonia Severity on Chest x-ray with deep learningIRJET Journal
This document discusses using deep learning models to analyze chest x-ray images to predict the severity of COVID-19 pneumonia. It analyzed chest x-ray images of COVID-19 patients and healthy individuals using InceptionV3, Xception, and ResNet models. The Xception model achieved the highest accuracy rate of 97.97% at detecting COVID-19 in chest x-ray images compared to the other models. The study aimed to develop a method for classifying COVID-19 patients using chest x-rays but did not claim any medical diagnostic accuracy. It discussed using deep learning on medical images to help address issues caused by the COVID-19 pandemic.
A Review Paper on Covid-19 Detection using Deep LearningIRJET Journal
This document reviews methods for detecting COVID-19 using deep learning techniques applied to chest X-rays and CT scans. It summarizes several research papers that have used convolutional neural networks and techniques like transfer learning to analyze medical images and accurately classify patients as COVID-19 positive or normal. The research shows these deep learning models can detect COVID-19 from images with high accuracy, even outperforming traditional PCR tests. Larger datasets are still needed to improve the models. Overall, the document concludes medical image analysis with deep learning is a promising approach for fast and effective COVID-19 detection.
The document compares the performance of different machine learning models for detecting COVID-19 from CT scans, including single models like SVM, NB, MLP, CNN and ensemble models like AdaBoost and GBDT. Based on accuracy, precision, recall, F1-score and MCC metrics, the SVM model achieved the best performance with an accuracy of 99.2%, followed by CNN and AdaBoost. While MLP, NB and GBDT showed lower performance, CNN had the advantage of automatically detecting important image features.
INSIGHT ABOUT DETECTION, PREDICTION AND WEATHER IMPACT OF CORONAVIRUS (COVID-...ijaia
The document summarizes research using machine learning models to analyze the impact of weather factors on the COVID-19 pandemic and to detect COVID-19 from chest X-rays. It describes using decision tree regressors to determine that temperature, humidity, and sun exposure have 85.88% impact on COVID-19 spread and 91.89% impact on COVID-19 deaths. It also details using pre-trained convolutional neural networks like VGG16 and VGG19 on chest X-rays to classify images as normal, pneumonia, or COVID-19 with over 92% accuracy. Finally, it mentions using logistic regression to predict an individual's risk of death from COVID-19 based on attributes like age, gender, and location, achieving 94.
Deep Learning Approach for Unprecedented Lung Disease PrognosisIRJET Journal
This document summarizes a research project that developed a deep learning model using convolutional neural networks to classify and predict various lung diseases from chest x-ray images. The model was able to achieve a high test accuracy of 91% in distinguishing between normal, tuberculosis, pneumonia, and COVID-19 cases. The research involved collecting chest x-ray image datasets from public sources, preprocessing the data, designing and training a CNN model using TensorFlow, and evaluating the model's performance on test data. The study demonstrated the effectiveness of machine learning and deep learning techniques for automated lung disease detection and prognosis to help improve medical diagnoses and patient outcomes.
REVIEW ON COVID DETECTION USING X-RAY AND SYMPTOMSIRJET Journal
This document presents a review of detecting COVID-19 using chest X-rays and symptoms. It first provides background on the COVID-19 pandemic and discusses how artificial intelligence and deep learning are being used to classify medical images like chest X-rays to detect various diseases. The paper then reviews several existing studies that have used convolutional neural networks to achieve high accuracy (over 90%) in detecting COVID-19 in chest X-rays. It proposes a model that uses a CNN to analyze chest X-rays and a decision tree model to analyze reported symptoms, then integrates the results to diagnose whether a patient is COVID-19 positive or normal. The model aims to provide a low-cost and rapid method for COVID-19 detection.
Study and Analysis of Different CNN Architectures by Detecting Covid- 19 and ...IRJET Journal
This document discusses using various CNN architectures to detect Covid-19 and pneumonia from chest X-ray images. It analyzes eight CNN models - AlexNet, DenseNet121, MobileNet, ResNet50/101, VGG16/19, and Xception - on a dataset of over 9,000 chest X-ray images categorized into normal, Covid-19, and pneumonia classes. The ResNet-50 model achieved the optimal classification accuracy. The images were preprocessed and divided into training, validation, and test sets before inputting into the CNNs. Feature extraction and model training were then performed to classify the images and present results with associated probabilities.
This document summarizes a study that used machine learning and deep learning algorithms like support vector regression, polynomial regression, deep neural networks, and recurrent neural networks with long short-term memory to analyze the COVID-19 epidemic. The models were trained on real-time data from the Johns Hopkins dashboard to predict confirmed, recovered, and death cases worldwide and analyze the daily transmission behavior of the virus. The polynomial regression model yielded the lowest error in forecasting COVID-19 transmission compared to other approaches.
IRJET - Classification of Cancer Images using Deep LearningIRJET Journal
This document presents a methodology for classifying breast cancer histopathology images using deep learning. Specifically, it aims to classify images as either invasive ductal carcinoma (IDC) or non-IDC using a convolutional neural network (CNN) model. The proposed methodology involves preprocessing the images, building a CNN with convolutional, pooling and fully connected layers, training the model on labeled image data, and using the trained model to classify new images as IDC or non-IDC. The goal is to develop an automated system for early and accurate detection of breast cancer subtypes to improve diagnosis and patient outcomes.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Lung Cancer Detection using Convolutional Neural NetworkIRJET Journal
The document presents a study on detecting lung cancer using convolutional neural networks. Specifically, it uses the YOLO framework to accurately detect lung tumors and their locations in CT images. The proposed system first collects CT images and pre-processes them before training a YOLO object detection model. The trained model is then used to detect and localize tumors in test images and provide classification. Evaluation shows the model can successfully pinpoint tumors attached to blood vessels and distinguish between different types of lung cancer. The authors aim to improve the model through expanding the dataset and exploring updated deep learning techniques.
Covid Detection Using Lung X-ray ImagesIRJET Journal
This document describes a study that used a deep learning model to detect COVID-19 in lung x-ray images. The researchers trained a VGG-16 convolutional neural network on a dataset of over 5,800 x-ray images of both COVID-19 and normal lungs. Data augmentation techniques were used to increase the size and variation of the training dataset. The model achieved 94% accuracy in distinguishing between COVID-19 and normal x-rays. This accurate and fast COVID-19 detection using deep learning could help reduce costs and diagnostic times compared to traditional testing methods.
Pneumonia Detection Using Deep Learning and Transfer LearningIRJET Journal
This document presents research on using deep learning and transfer learning techniques to detect pneumonia from chest x-ray images. The researchers trained several models, including CNNs, DenseNet, VGG-16, ResNet, and InceptionNet on a dataset of chest x-rays labeled as normal or pneumonia. The models achieved accuracy in detecting pneumonia of 89.6-97%, depending on the specific model. The researchers found that deep learning approaches like these have significant potential to improve the accuracy and efficiency of pneumonia diagnosis compared to traditional methods. Overall, the study demonstrated promising results for using machine and deep learning to classify medical images and detect health conditions like pneumonia.
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
An intelligent approach for detection of covid by analysing Xray and CT scan ...IRJET Journal
This document presents an intelligent approach to detect COVID-19 using X-ray and CT scan images. It discusses developing a deep learning model using a convolutional neural network (CNN) to analyze medical images and classify them as coming from COVID-19 positive or negative cases. The model would be integrated into a web application using Flask that allows users to upload images for rapid diagnosis. The goal is to address issues with traditional testing methods that can take days to get results, and to help reduce the spread of COVID-19 through faster detection. The document reviews several related studies applying deep learning to COVID-19 detection from medical images and discusses the materials and methodology used to develop and evaluate the proposed intelligent detection system.
This document discusses detecting pneumonia in chest X-rays using deep learning techniques. It begins by stating that pneumonia is a major cause of death worldwide, especially in children. The objective is to develop a deep learning framework to automatically diagnose pneumonia from chest X-rays to reduce human error. Various deep learning models like CNN, VGG-16 and MobileNetV2 are implemented and compared on a public dataset. VGG-16 achieved the highest accuracy of 94.3% among the models for detecting pneumonia. The document concludes that pneumonia can be identified and classified using deep learning models with VGG-16 performing best.
APPLICATION OF CNN MODEL ON MEDICAL IMAGEIRJET Journal
The document discusses using convolutional neural network (CNN) models to detect diseases from medical images such as chest X-rays. It describes how CNN models can be trained on large labeled datasets of chest X-rays to learn patterns and features that indicate diseases. The document then evaluates several CNN architectures - including VGG-16, ResNet, DenseNet, and InceptionNet - for classifying chest X-rays as normal or infected. It finds these models achieve high accuracy, with metrics like accuracy over 89% and AUC over 0.94. In conclusion, deep learning models show promising results for automated disease detection from medical images.
IMPLEMENTATION AND PERFORMANCE ANALYSIS OF X-RAY IMAGE FOR COVID-19 AFFECTED ...IRJET Journal
This document describes a study that implemented and evaluated deep learning models for detecting COVID-19 in chest X-ray images. The researchers trained convolutional neural network (CNN) models like VGG19 and U-Net on X-ray image data labeled as positive or negative for COVID-19. They analyzed the performance of different models and found that the proposed method achieved accurate detection of COVID-19 in X-rays without bias. The top-performing model could be useful for doctors in quickly diagnosing and responding to the COVID-19 pandemic.
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.
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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.
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A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
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Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
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A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
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Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
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Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
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A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
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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
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A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
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Review on studies and research on widening of existing concrete bridgesIRJET Journal
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React based fullstack edtech web applicationIRJET Journal
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A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
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A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
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Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
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Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
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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.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
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KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
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ACEP Magazine edition 4th launched on 05.06.2024Rahul
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CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.