This document presents a study that aims to detect COVID-19 from chest x-rays using deep learning models. The researchers collected chest x-ray images of COVID-19 patients, people with pneumonia, and healthy individuals. They used a transfer learning approach with the Inception V3 model to extract features from the x-ray images. Additionally, they developed a customized deep neural network (DNN) for classification. The model was trained on 80% of the dataset and evaluated on the remaining 20%. Results showed the Inception V3 model achieved high accuracy, sensitivity, and F1-score for detecting COVID-19, pneumonia, and normal cases from chest x-rays. This deep learning approach holds promise for fast and accurate COVID-
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.
Imagine a vitamin pill-sized camera that could travel through your body taking pictures, helping diagnose a problem which doctor previously would have found only through surgery.
Image processing in lung cancer screening and treatmentWookjin Choi
The document discusses image processing techniques for lung cancer screening and treatment. It covers topics like lung segmentation, nodule detection, computer-aided diagnosis, image-guided radiotherapy, and quantitative assessment of tumor response. Lung segmentation is used to isolate the lungs from other organs in CT images. Nodule detection algorithms then aim to find potential cancerous nodules. Computer-aided diagnosis systems analyze extracted features of nodules to determine if they are malignant or benign. Image-guided radiotherapy utilizes 4D CT and gating to account for tumor motion during treatment. Quantitative metrics like standardized uptake value are used to assess tumor response in PET imaging.
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.
Lung Cancer Detection using transfer learning.pptx.pdfjagan477830
Lung cancer is one of the deadliest cancers worldwide. However, the early detection of lung cancer significantly improves survival rate. Cancerous (malignant) and noncancerous (benign) pulmonary nodules are the small growths of cells inside the lung. Detection of malignant lung nodules at an early stage is necessary for the crucial prognosis.
A new classification model for covid 19 based on convolutional neural networksAboul Ella Hassanien
This document proposes a new convolutional neural network model based on AlexNet to classify CT chest scans into five categories: normal lung, COVID-19, viral pneumonia, bacterial pneumonia, and mycoplasma pneumonia. The model was trained on a dataset of 5000 CT images across the five categories. Experimental results showed the model achieved over 99% accuracy in classifying the different pneumonia types based on CT scans after 9 epochs of training. The authors conclude the proposed model is effective at distinguishing between the five chest CT image types but further optimization may improve performance.
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.
Imagine a vitamin pill-sized camera that could travel through your body taking pictures, helping diagnose a problem which doctor previously would have found only through surgery.
Image processing in lung cancer screening and treatmentWookjin Choi
The document discusses image processing techniques for lung cancer screening and treatment. It covers topics like lung segmentation, nodule detection, computer-aided diagnosis, image-guided radiotherapy, and quantitative assessment of tumor response. Lung segmentation is used to isolate the lungs from other organs in CT images. Nodule detection algorithms then aim to find potential cancerous nodules. Computer-aided diagnosis systems analyze extracted features of nodules to determine if they are malignant or benign. Image-guided radiotherapy utilizes 4D CT and gating to account for tumor motion during treatment. Quantitative metrics like standardized uptake value are used to assess tumor response in PET imaging.
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.
Lung Cancer Detection using transfer learning.pptx.pdfjagan477830
Lung cancer is one of the deadliest cancers worldwide. However, the early detection of lung cancer significantly improves survival rate. Cancerous (malignant) and noncancerous (benign) pulmonary nodules are the small growths of cells inside the lung. Detection of malignant lung nodules at an early stage is necessary for the crucial prognosis.
A new classification model for covid 19 based on convolutional neural networksAboul Ella Hassanien
This document proposes a new convolutional neural network model based on AlexNet to classify CT chest scans into five categories: normal lung, COVID-19, viral pneumonia, bacterial pneumonia, and mycoplasma pneumonia. The model was trained on a dataset of 5000 CT images across the five categories. Experimental results showed the model achieved over 99% accuracy in classifying the different pneumonia types based on CT scans after 9 epochs of training. The authors conclude the proposed model is effective at distinguishing between the five chest CT image types but further optimization may improve performance.
Brain fingerprinting technology uses electrical brain wave responses to determine what information is stored in a person's brain. It was invented by Lawrence Farewell and measures the MERMER (Memory and Encoding Related Multifaceted Electroencephalographic) signal, specifically the P300 brain response. The technique presents stimuli on a screen while measuring brain waves through electrodes on the scalp to analyze the brain's response and determine if information about a certain event is present or absent in the subject's mind. The technology has applications in national security, medicine, and solving crimes.
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.
💬 Free Powerpoint Template Coronavirus (Covid-19) for DownloadPresentationLoad
PowerPoint presentation and free PPT template with valuable information and important rules of conduct on the coronavirus (SARS-CoV-2).
Download free: https://www.presentationload.com/coronavirus-free-ppt-template.html
With simple measures, you too can help protect yourself and others from COVID-19 and influenza infections. We have therefore prepared a free template with hygiene and behavior tips according to WHO recommendations, which you can print out and also distribute digitally.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict diseases based on patient health data. Specifically, it proposes using a k-means machine learning algorithm to analyze structured and unstructured patient data stored in a healthcare dataset. This would allow the system to predict diseases and outbreaks with greater accuracy than existing methods. The k-means algorithm is applied to cluster patient data, including symptoms from sensors and medical records, to identify patterns and deliver predictive results. The goal is to enable early disease prediction and prevention through analysis of big healthcare data using machine learning.
Lung Cancer Detection Using Convolutional Neural NetworkIRJET Journal
This document describes a study that uses a convolutional neural network (CNN) to classify lung cancer in CT scans. The CNN model is trained on a dataset of 1018 patient CT scans containing annotations of lung nodules as benign or malignant. The CNN architecture includes convolution layers to extract features, max pooling layers to reduce computations, dropout layers to prevent overfitting, and fully connected layers to classify scans. The model achieves a 65% accuracy on the training set at detecting cancer in new CT scans. The CNN is integrated into a web application to allow doctors to efficiently analyze scans for lung cancer.
The document discusses a pill camera, which is a capsule endoscopy device used to examine the digestive tract. It is about the size of a pill and contains a camera, lights, batteries, and transmitter. Patients swallow the capsule, which takes pictures as it passes through the digestive system. The images are transmitted to a data recorder and can be reviewed later by doctors. The capsule offers a non-invasive alternative to traditional endoscopy for examining the small intestine. Key benefits include increased patient comfort and ability to capture images of hard to reach areas of the digestive tract.
Google's Project Loon aims to provide internet access to rural and remote areas using high-altitude balloons. Balloons float in the stratosphere, carrying communications equipment and solar panels. They are moved using winds at different altitudes to position them over desired locations. People on the ground connect to the balloon network using special antennas. Signals hop between balloons and back to the ground, providing internet speeds comparable to 3G. The balloons are designed to operate autonomously for months at a time in the stratosphere's harsh conditions.
Application of-image-segmentation-in-brain-tumor-detectionMyat Myint Zu Thin
This document discusses applications of image segmentation in brain tumor detection. It begins by defining brain tumors and different types. It then discusses various image segmentation methods that can be used for brain tumor segmentation, including k-means clustering, region-based watershed algorithm, region growing, and active contour methods. It demonstrates how these methods can be implemented in Python for segmenting tumors from MRI images. The document also discusses computer-aided diagnosis systems and the roles of artificial intelligence and machine learning in medical image analysis and cancer diagnosis using image processing.
The Blue Brain project aims to create a virtual brain through detailed computer simulations. It seeks to reverse engineer the brain by simulating a cortical column of rat neurons using supercomputers. The goal is to understand how human intelligence and memory works at the neuronal level. If successful, it could lead to cures for neurological diseases and development of artificial general intelligence capable of human-level thought. However, issues around privacy, security and human dependence on technology remain challenges.
The document discusses the history and working of digital cameras. It explains that digital cameras trace their origins to inventions like the camera obscura in ancient times and developments in photography in the 18th-19th centuries. A key development was the invention of the first digital camera by Steve Sasson in 1975. The document then describes the basic components and working of digital cameras, including how light is focused onto sensors using lenses, how sensors convert light into digital signals, and how these signals are processed and stored as digital images. It also discusses different types of digital cameras and image sensors like CCD and CMOS.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
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.
A pill camera is a capsule-sized device used in endoscopy to record images of the digestive tract. It contains a tiny camera that takes pictures after being swallowed. The primary use is to examine the small intestine, which other endoscopy methods cannot access well. The capsule transmits images wirelessly to an external receiver as it passes through the tract. Images are then reviewed by doctors to diagnose problems, though the capsule cannot treat any issues found. While generally safe, there is a small risk of the capsule being retained in the body for an extended time. It works using electromagnetic waves and has protections to safely pass through the digestive system.
PPT of 6th sense tech. Jagdeep Singh Sidhujagdeepsidhu
The document describes the Sixth Sense technology, a wearable gestural interface developed by Pranav Mistry in 2009. It allows users to access digital information about the physical world by projecting it onto surfaces and interacting through natural hand gestures. The system uses a camera, projector, and mirror connected to a smartphone to recognize objects, gestures, and surfaces and display related data seamlessly overlaid on the physical world. Some applications mentioned include using gestures to draw, access maps and photos, and interact with projected interfaces on surfaces like palms or walls. Educational and other potential uses are also discussed.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
The document discusses the "camera in a pill", or capsule endoscopy. It provides a high-level overview of the technology, including that a pill-sized camera is swallowed to take images of the small intestine as it passes through. The camera transmits over 56,000 images wirelessly to a sensor array and data recorder worn on the body. The images can later be reviewed by a doctor on specialized software to diagnose conditions of the small intestine.
Rainbow Technology enables high-density data storage of up to 450 GB on paper by encoding digital data as colored shapes and symbols printed at high resolution. It uses principles where color combinations can be converted to and from numeric values to store and retrieve data. To read the data, the paper is scanned and special software decodes the color patterns back into the original digital files. This technique could revolutionize portable storage if it can reliably distinguish enough colors at a high enough resolution to achieve hundreds of gigabytes of storage capacity from a single sheet of paper.
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.
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.
Brain fingerprinting technology uses electrical brain wave responses to determine what information is stored in a person's brain. It was invented by Lawrence Farewell and measures the MERMER (Memory and Encoding Related Multifaceted Electroencephalographic) signal, specifically the P300 brain response. The technique presents stimuli on a screen while measuring brain waves through electrodes on the scalp to analyze the brain's response and determine if information about a certain event is present or absent in the subject's mind. The technology has applications in national security, medicine, and solving crimes.
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.
💬 Free Powerpoint Template Coronavirus (Covid-19) for DownloadPresentationLoad
PowerPoint presentation and free PPT template with valuable information and important rules of conduct on the coronavirus (SARS-CoV-2).
Download free: https://www.presentationload.com/coronavirus-free-ppt-template.html
With simple measures, you too can help protect yourself and others from COVID-19 and influenza infections. We have therefore prepared a free template with hygiene and behavior tips according to WHO recommendations, which you can print out and also distribute digitally.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict diseases based on patient health data. Specifically, it proposes using a k-means machine learning algorithm to analyze structured and unstructured patient data stored in a healthcare dataset. This would allow the system to predict diseases and outbreaks with greater accuracy than existing methods. The k-means algorithm is applied to cluster patient data, including symptoms from sensors and medical records, to identify patterns and deliver predictive results. The goal is to enable early disease prediction and prevention through analysis of big healthcare data using machine learning.
Lung Cancer Detection Using Convolutional Neural NetworkIRJET Journal
This document describes a study that uses a convolutional neural network (CNN) to classify lung cancer in CT scans. The CNN model is trained on a dataset of 1018 patient CT scans containing annotations of lung nodules as benign or malignant. The CNN architecture includes convolution layers to extract features, max pooling layers to reduce computations, dropout layers to prevent overfitting, and fully connected layers to classify scans. The model achieves a 65% accuracy on the training set at detecting cancer in new CT scans. The CNN is integrated into a web application to allow doctors to efficiently analyze scans for lung cancer.
The document discusses a pill camera, which is a capsule endoscopy device used to examine the digestive tract. It is about the size of a pill and contains a camera, lights, batteries, and transmitter. Patients swallow the capsule, which takes pictures as it passes through the digestive system. The images are transmitted to a data recorder and can be reviewed later by doctors. The capsule offers a non-invasive alternative to traditional endoscopy for examining the small intestine. Key benefits include increased patient comfort and ability to capture images of hard to reach areas of the digestive tract.
Google's Project Loon aims to provide internet access to rural and remote areas using high-altitude balloons. Balloons float in the stratosphere, carrying communications equipment and solar panels. They are moved using winds at different altitudes to position them over desired locations. People on the ground connect to the balloon network using special antennas. Signals hop between balloons and back to the ground, providing internet speeds comparable to 3G. The balloons are designed to operate autonomously for months at a time in the stratosphere's harsh conditions.
Application of-image-segmentation-in-brain-tumor-detectionMyat Myint Zu Thin
This document discusses applications of image segmentation in brain tumor detection. It begins by defining brain tumors and different types. It then discusses various image segmentation methods that can be used for brain tumor segmentation, including k-means clustering, region-based watershed algorithm, region growing, and active contour methods. It demonstrates how these methods can be implemented in Python for segmenting tumors from MRI images. The document also discusses computer-aided diagnosis systems and the roles of artificial intelligence and machine learning in medical image analysis and cancer diagnosis using image processing.
The Blue Brain project aims to create a virtual brain through detailed computer simulations. It seeks to reverse engineer the brain by simulating a cortical column of rat neurons using supercomputers. The goal is to understand how human intelligence and memory works at the neuronal level. If successful, it could lead to cures for neurological diseases and development of artificial general intelligence capable of human-level thought. However, issues around privacy, security and human dependence on technology remain challenges.
The document discusses the history and working of digital cameras. It explains that digital cameras trace their origins to inventions like the camera obscura in ancient times and developments in photography in the 18th-19th centuries. A key development was the invention of the first digital camera by Steve Sasson in 1975. The document then describes the basic components and working of digital cameras, including how light is focused onto sensors using lenses, how sensors convert light into digital signals, and how these signals are processed and stored as digital images. It also discusses different types of digital cameras and image sensors like CCD and CMOS.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
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.
A pill camera is a capsule-sized device used in endoscopy to record images of the digestive tract. It contains a tiny camera that takes pictures after being swallowed. The primary use is to examine the small intestine, which other endoscopy methods cannot access well. The capsule transmits images wirelessly to an external receiver as it passes through the tract. Images are then reviewed by doctors to diagnose problems, though the capsule cannot treat any issues found. While generally safe, there is a small risk of the capsule being retained in the body for an extended time. It works using electromagnetic waves and has protections to safely pass through the digestive system.
PPT of 6th sense tech. Jagdeep Singh Sidhujagdeepsidhu
The document describes the Sixth Sense technology, a wearable gestural interface developed by Pranav Mistry in 2009. It allows users to access digital information about the physical world by projecting it onto surfaces and interacting through natural hand gestures. The system uses a camera, projector, and mirror connected to a smartphone to recognize objects, gestures, and surfaces and display related data seamlessly overlaid on the physical world. Some applications mentioned include using gestures to draw, access maps and photos, and interact with projected interfaces on surfaces like palms or walls. Educational and other potential uses are also discussed.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
The document discusses the "camera in a pill", or capsule endoscopy. It provides a high-level overview of the technology, including that a pill-sized camera is swallowed to take images of the small intestine as it passes through. The camera transmits over 56,000 images wirelessly to a sensor array and data recorder worn on the body. The images can later be reviewed by a doctor on specialized software to diagnose conditions of the small intestine.
Rainbow Technology enables high-density data storage of up to 450 GB on paper by encoding digital data as colored shapes and symbols printed at high resolution. It uses principles where color combinations can be converted to and from numeric values to store and retrieve data. To read the data, the paper is scanned and special software decodes the color patterns back into the original digital files. This technique could revolutionize portable storage if it can reliably distinguish enough colors at a high enough resolution to achieve hundreds of gigabytes of storage capacity from a single sheet of paper.
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.
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.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
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.
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.
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.
ARTIFICIAL INTELLIGENCE BASED COVID-19 DETECTION USING COMPUTED TOMOGRAPHY IM...IRJET Journal
This document summarizes an artificial intelligence system developed to detect COVID-19 in computed tomography (CT) images of the lungs. The system uses convolutional neural networks (CNNs) to extract features from segmented lung images and classify images as normal, COVID-19, or other lung diseases. Previous related work that used CNNs and other deep learning techniques on CT and X-ray images for COVID-19 detection is reviewed. The proposed system applies edge detection algorithms before training the CNN to enhance image contrast and improve COVID-19 detection accuracy. It also uses multi-image augmentation to increase the size and variability of the training dataset.
A deep learning approach for COVID-19 and pneumonia detection from chest X-r...IJECEIAES
There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.
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.
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.
Deep Learning Approaches for Diagnosis and Treatment of COVID-19IRJET Journal
The document discusses using deep learning approaches for diagnosing and treating COVID-19. It first provides background on deep learning and convolutional neural networks. It then discusses challenges in early COVID-19 diagnosis and the need for computer-assisted diagnosis methods. The paper reviews several existing studies that used deep learning on CT scans and X-rays to classify COVID-19. It proposes developing a COVID-19 diagnosis system using a lung CT image dataset and deep learning models. The system would be designed, implemented and tested to efficiently detect COVID-19 infections from CT scans.
Deep Learning for Pneumonia Diagnosis: A Comprehensive Analysis of CNN and Tr...IRJET Journal
This document summarizes a research paper that proposes using convolutional neural networks and transfer learning to accurately diagnose pneumonia from chest x-rays. The paper describes how pneumonia affects the lungs and the importance of early detection. It discusses how CNNs and transfer learning have been successfully used for medical image classification. The proposed model uses pre-trained CNN architectures like MobileNet, Inception, ResNet and EfficientNet applied to a dataset of chest x-rays to distinguish between normal and pneumonia cases. The model achieves highly accurate pneumonia detection, which could help improve 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%.
covid 19 detection using x ray based on neural networkArifuzzamanFaisal2
This document presents a comparative study of multiple neural network models for detecting COVID-19 from chest X-rays. It evaluates VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, ResNet50, and Xception on a dataset of 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Results show that DenseNet121 achieved the best performance with 99.48% accuracy, outperforming the other models in detecting and classifying COVID-19 and pneumonia cases from chest X-rays.
IRJET- Intelligent Prediction of Lung Cancer Via MRI Images using Morphologic...IRJET Journal
The document describes a proposed system to intelligently predict lung cancer using MRI images and morphological neural network analysis. The proposed system uses a three-stage approach: preprocessing MRI images, extracting features using wavelet decomposition and normalization, and classifying tissues as normal or abnormal using a morphological neural network with image pruning. This combination of morphological image processing and neural networks is intended to more efficiently classify cancer cells and identify affected regions than previous methods.
This document summarizes a review article on using deep learning techniques to detect and diagnose COVID-19 using radiology images. The review analyzed 37 studies published between November 2019 and July 2020. The studies used deep learning models on CT scans and X-rays to improve the detection and diagnosis of COVID-19 compared to traditional methods. Deep learning was shown to increase sensitivity and specificity by extracting hidden features from radiology images. The models helped address limitations of current diagnostic methods by providing fast, low-cost analysis of images to help identify COVID-19 cases.
The document summarizes key information for radiographers on imaging patients with COVID-19, including:
- Medical imaging plays an important role in diagnosing and managing COVID-19, with chest X-rays, CT scans, lung ultrasounds, and MRI used.
- Safety protocols for decontaminating equipment and using proper PPE like gowns, gloves, and masks are crucial to protect patients and radiography staff.
- Guidelines from organizations recommend imaging only critically ill patients or when clinical decisions need to be made, to avoid cross-infection risks.
- Portable X-rays allow imaging in isolation rooms without transporting infectious patients, while CT scans have higher sensitivity but risk of cross
PNEUMONIA DIAGNOSIS USING CHEST X-RAY IMAGES AND CNNIRJET Journal
This document discusses using convolutional neural networks to diagnose pneumonia from chest x-ray images. Specifically, it summarizes several research papers that used CNN models like InceptionV3 to extract features from x-ray images and then trained classification algorithms like support vector machines, neural networks, and K-nearest neighbors to classify images as pneumonia or normal. The neural network model achieved 84.1% sensitivity while support vector machines obtained the highest AUC of 93.1%. In general, CNNs can accurately diagnose pneumonia from x-rays but training the models requires a large dataset and computing resources.
Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimizati...ijtsrd
The opinion of disease is important for Covid 19 as the antigen kit and RTPCR are unperfect and should be better for diagnosing such disease. Real Time Return Transcription real time converse transcription - polymerase chain . Healthcare practices include the collection of various sorts of patient data to help the physician diagnose the patients health. These data could be simple symptoms, first diagnosis by a doctor, or an in depth laboratory test. These data are therefore used for analyses only by a doctor, who subsequently uses his particular medical skills to found the ailment. In order to classify Covid 19 disease datasets such mild, middle and severe diseases, the proposed model utilizes the notion of controlled machine education and GWO optimization to regulate if the patient is affecting or not. An efficiency analysis is calculated and compared of disease data for both algorithms. The results of the simulations illustrate the effective nature and complexity of the data set for the grading techniques. Compared to SVM, the suggested model provides 7.8 percent improved prediction accuracy. The prediction accuracy is 8 better than the SVM. This results in an F1 score of 2 percent better than an SVM forecast. Swati Shilpi | Dr. Damodar Prasad Tiwari "Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid-19 Pandemic Crisis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46400.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/46400/health-risk-prediction-using-support-vector-machine-with-gray-wolf-optimization-in-covid19-pandemic-crisis/swati-shilpi
Automatic COVID-19 lung images classification system based on convolution ne...IJECEIAES
Coronavirus disease (COVID-19) still has disastrous effects on human life around the world. For fight that disease. Examination on the patients who have been sucked in quick and cheap way is necessary. Radiography is most effective step closer to this target. Chest X-ray is readily obtainable and cheap option. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral pneumonia from common viral pneumonia is difficult. In this study, X-ray images of 500, 500, 500, and 500 patients for healthy controls, typical viral pneumonia, bacterial pneumonia and COVID-19, were collected respectively. To our knowledge, this was the first quaternary classification study that also included classical viral pneumonia. To efficiently capture nuances in X-ray images, a new model was created by applying convolution neural network for accurate image classification. Our model outperformed to achieve an overall accuracy, sensitivity, specificity, F1-score, and area under curve (AUC) of 0.98, 0.97, 0.98, 0.97, and 0.99 respectively.
Similar to Detection Of Covid-19 From Chest X-Rays (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.