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DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNING TECHNIQUEIRJET Journal
1) The document discusses a method for detecting diabetic retinal disease using integrated shallow convolutional neural networks, which can improve classification accuracy by 3% on small datasets compared to other CNN techniques.
2) It aims to classify retinal images to detect diabetic retinopathy through shallow CNNs, focusing on cases with limited labelled training data, as deep CNNs typically require large datasets for high accuracy.
3) Experimental results show the proposed approach reduces time cost to around 30% of the smallest dataset tested, which is 10% of the original dataset, while maintaining classification accuracy compared to other integrated CNN learning algorithms.
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 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.
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This document discusses using a convolutional neural network to classify retinal images. Specifically, it aims to develop a system to distinguish between different retinal diseases using fundus images. The system would extract retinal features from the images like the retina, optic nerve and lesions. It then uses a CNN to detect multiple retinal diseases in fundus photographs from a structured analysis database. The CNN is trained on publicly available retinal image datasets. Neural networks have been found to effectively capture disease-specific color and texture features to enable automated diagnosis similar to human experts. The document also provides background on related work using deep learning and CNNs for tasks like lesion detection and classification of retinal diseases from fundus images.
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...cscpconf
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.
Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to
screen abnormalities through retinal fundus images by clinicians. However, limited number of
well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a
big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy
severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images
in the training set, we also automatically segment the abnormal patches with an occlusion test,
and model the transformations and deterioration process of DR. Our results can be widely used
for fast diagnosis of DR, medical education and public-level healthcare propagation.
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.
FEATURE EXTRACTION TO DETECT AND CLASSIFY DIABETIC RETINOPATHY USING FUNDAL I...IRJET Journal
This document summarizes research on using feature extraction to detect and classify diabetic retinopathy from fundus images. It discusses using gray-level co-occurrence matrix (GLCM) textural features to extract microaneurysms and exudates from retinal images. The proposed method involves pre-processing images, extracting GLCM features, and classifying diabetic retinopathy. It showed better performance than previous methods on a real-time dataset. The document also reviews several prior studies on blood vessel segmentation, feature-based classification, and automated detection of eye diseases from retinal images to diagnose diabetic retinopathy early.
MALARIAL PARASITES DETECTION IN THE BLOOD CELL USING CONVOLUTIONAL NEURAL NET...IRJET Journal
This document describes a study that uses a convolutional neural network (CNN) to detect malaria parasites in blood cell images. The researchers collected over 27,000 labeled images of parasitized and uninfected blood cells from an online source. They preprocessed the images using median filtering to remove noise. Then they trained and validated a CNN model on 80% of the dataset, achieving an accuracy of 95.34% at detecting the presence or absence of malaria parasites in individual blood cell images. The goal of the study was to develop an automated method for malaria detection as an alternative to existing diagnostic techniques that require skilled microscopists or have limitations in detecting low parasite levels.
DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNING TECHNIQUEIRJET Journal
1) The document discusses a method for detecting diabetic retinal disease using integrated shallow convolutional neural networks, which can improve classification accuracy by 3% on small datasets compared to other CNN techniques.
2) It aims to classify retinal images to detect diabetic retinopathy through shallow CNNs, focusing on cases with limited labelled training data, as deep CNNs typically require large datasets for high accuracy.
3) Experimental results show the proposed approach reduces time cost to around 30% of the smallest dataset tested, which is 10% of the original dataset, while maintaining classification accuracy compared to other integrated CNN learning algorithms.
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 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.
RETINAL IMAGE CLASSIFICATION USING NEURAL NETWORK BASED ON A CNN METHODSIRJET Journal
This document discusses using a convolutional neural network to classify retinal images. Specifically, it aims to develop a system to distinguish between different retinal diseases using fundus images. The system would extract retinal features from the images like the retina, optic nerve and lesions. It then uses a CNN to detect multiple retinal diseases in fundus photographs from a structured analysis database. The CNN is trained on publicly available retinal image datasets. Neural networks have been found to effectively capture disease-specific color and texture features to enable automated diagnosis similar to human experts. The document also provides background on related work using deep learning and CNNs for tasks like lesion detection and classification of retinal diseases from fundus images.
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...cscpconf
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.
Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to
screen abnormalities through retinal fundus images by clinicians. However, limited number of
well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a
big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy
severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images
in the training set, we also automatically segment the abnormal patches with an occlusion test,
and model the transformations and deterioration process of DR. Our results can be widely used
for fast diagnosis of DR, medical education and public-level healthcare propagation.
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.
FEATURE EXTRACTION TO DETECT AND CLASSIFY DIABETIC RETINOPATHY USING FUNDAL I...IRJET Journal
This document summarizes research on using feature extraction to detect and classify diabetic retinopathy from fundus images. It discusses using gray-level co-occurrence matrix (GLCM) textural features to extract microaneurysms and exudates from retinal images. The proposed method involves pre-processing images, extracting GLCM features, and classifying diabetic retinopathy. It showed better performance than previous methods on a real-time dataset. The document also reviews several prior studies on blood vessel segmentation, feature-based classification, and automated detection of eye diseases from retinal images to diagnose diabetic retinopathy early.
MALARIAL PARASITES DETECTION IN THE BLOOD CELL USING CONVOLUTIONAL NEURAL NET...IRJET Journal
This document describes a study that uses a convolutional neural network (CNN) to detect malaria parasites in blood cell images. The researchers collected over 27,000 labeled images of parasitized and uninfected blood cells from an online source. They preprocessed the images using median filtering to remove noise. Then they trained and validated a CNN model on 80% of the dataset, achieving an accuracy of 95.34% at detecting the presence or absence of malaria parasites in individual blood cell images. The goal of the study was to develop an automated method for malaria detection as an alternative to existing diagnostic techniques that require skilled microscopists or have limitations in detecting low parasite levels.
Rapid detection of diabetic retinopathy in retinal images: a new approach usi...IJECEIAES
The challenge of early detection of diabetic retinopathy (DR), a leading cause of vision loss in working-age individuals in developed nations, was addressed in this study. Current manual analysis of digital color fundus photographs by clinicians, although thorough, suffers from slow result turnaround, delaying necessary treatment. To expedite detection and improve treatment timeliness, a novel automated detection system for DR was developed. This system utilized convolutional neural networks. Visual geometry group 16-layer network (VGG16), a pre-trained deep learning model, for feature extraction from retinal images and the synthetic minority over-sampling technique (SMOTE) to handle class imbalance in the dataset. The system was designed to classify images into five categories: normal, mild DR, moderate DR, severe DR, and proliferative DR (PDR). Assessment of the system using the Kaggle diabetic retinopathy dataset resulted in a promising 93.94% accuracy during the training phase and 88.19% during validation. These results highlight the system's potential to enhance DR diagnosis speed and efficiency, leading to improved patient outcomes. The study concluded that automation and artificial intelligence (AI) could play a significant role in timely and efficient disease detection and management.
PSO-HRVSO: Segmentation of Retinal Vessels Through Homomorphic Filtering Enha...sipij
The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading
cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due
to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels
significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It
is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate
medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust
vessel segmentation, addressing prevalent challenges identified in existing literature.
PSO-HRVSO: SEGMENTATION OF RETINAL VESSELS THROUGH HOMOMORPHIC FILTERING ENHA...sipij
The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading
cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due
to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels
significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It
is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate
medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust
vessel segmentation, addressing prevalent challenges identified in existing literature.
The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging
PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels
are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage.
The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and
STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis
of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868,
0.8576, and 0.9831 for the STARE dataset, respectively.
The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when
compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without
incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that
the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative
procedures.
Early Stage Detection of Alzheimer’s Disease Using Deep LearningIRJET Journal
This document presents a study on early detection of Alzheimer's disease using deep learning techniques. The authors build an end-to-end framework for classifying MRI images into four stages of Alzheimer's - mild demented, very mild demented, moderate demented, and non-demented. They use transfer learning with pre-trained models like VGG16 and ResNet50, achieving accuracies of 95% and 84% respectively. A custom CNN model is also developed, achieving 93% accuracy. The models are trained on a dataset of 6400 MRI images from Kaggle. A web application is also created for remote analysis and detection of Alzheimer's stage to help doctors and patients.
Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using convolutional neural networks to detect breast cancer from images. It begins with an abstract stating that breast cancer starts as uncontrolled growth of breast cells that can form tumors. Early detection at the first stage allows for curing. The proposed approach uses a convolutional neural network to take input images, perform preprocessing, compare to a database of cancer images, and detect cancer along with its stage to recommend treatment. It discusses using CNN algorithms inspired by the visual cortex to perform image recognition like humans. The document provides definitions of CNNs and deep learning, technologies used like image processing, and concludes that detecting and treating cancer early at its first stage is preferable.
A study on techniques to detect and classify acute lymphoblastic leukemia usi...IRJET Journal
This document summarizes 14 research papers on techniques for detecting and classifying acute lymphoblastic leukemia (ALL) using machine learning and deep learning methods. The papers describe various approaches including using convolutional neural networks to analyze peripheral blood smear images, applying machine learning algorithms to clinical data to identify ALL, and developing mobile apps to assist caregivers of ALL patients. Most papers achieved high accuracy rates of over 95% for detecting and classifying ALL. The document concludes that preprocessing images is an important first step, and that pre-trained CNN models with modifications are effective for detecting and classifying ALL in images.
A Survey on techniques for Diabetic Retinopathy Detection & ClassificationIRJET Journal
This document summarizes research on techniques for detecting and classifying diabetic retinopathy through analysis of fundus images. Diabetic retinopathy is a complication of diabetes that can lead to vision loss if undetected. Recent studies have used deep learning techniques like convolutional neural networks to automatically detect features in fundus images and classify levels of diabetic retinopathy more accurately than manual screening. The document reviews 21 recent papers that tested techniques including CNNs, support vector machines, ensemble models and more to detect microaneurysms and other lesions associated with diabetic retinopathy in fundus images. Many achieved high accuracy above 90% in detecting and diagnosing diabetic retinopathy levels from mild to severe.
Sepsis Prediction Using Machine LearningIRJET Journal
This document presents research on using machine learning models to predict sepsis. It first provides background on sepsis and discusses the need for early detection. The researchers collected data on vital signs, laboratory tests, and demographics from electronic health records. They performed exploratory data analysis and feature selection before using various classifiers like random forest, KNN, naive Bayes, and XGBoost to classify sepsis. The random forest classifier achieved the best performance with 96.23% accuracy. The study demonstrated that machine learning can enable early and accurate sepsis detection compared to traditional methods.
IRJET -An Automatated Learning Approach for Detection of Diabetic Retinopathy...IRJET Journal
This document presents a review of existing methods for detecting diabetic retinopathy (DR) using fundus images and proposes a new automated learning approach using deep learning. Existing methods are discussed, including those using artificial neural networks, modified AlexNet architecture, convolutional neural networks, and detection of retinal changes in longitudinal fundus images. The proposed method uses contrast limited adaptive histogram equalization for segmentation followed by a deep belief network for classification of DR stages. It aims to provide an optimal and automated solution for classifying DR severity levels using fundus images to help ophthalmologists detect the condition early. The model will be trained and evaluated on a publicly available dataset from Kaggle to classify images as healthy, normal, mild, moderate, severe
An automated severity classification model for diabetic retinopathyIRJET Journal
This document presents a study on developing an automated severity classification model for diabetic retinopathy using deep learning techniques. The proposed model uses a modified DenseNet169 architecture with a Convolutional Block Attention Module to classify retinal images into different severity categories of diabetic retinopathy. The model was trained on the Kaggle Asia Pacific Tele-Ophthalmology Society dataset and achieved state-of-the-art performance, accurately classifying 82% of images for severity grading. The lightweight model requires less time and complexity compared to other methods, making it suitable for automated diagnosis of diabetic retinopathy severity.
IRJET- A Survey on Medical Image Interpretation for Predicting PneumoniaIRJET Journal
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
Detection of Macular Edema by using Various Techniques of Feature Extraction ...IRJET Journal
This document presents a review of techniques for automatically detecting diabetic retinopathy by analyzing color fundus images. Diabetic retinopathy occurs when blood vessels in the retina are damaged from diabetes, and can lead to vision loss if left untreated. The document discusses existing work on feature extraction and classification methods for detecting signs of diabetic retinopathy like exudates and macular edema. It proposes a new method that focuses on extracting texture features from the region around the macula in order to accurately detect high-risk macular edema cases.
Application of deep learning methods for automated analysis of retinal struct...IJECEIAES
This article examines a current area of research in the field of ophthalmology the use of deep learning methods for automated analysis of retinal structures. This work explores the use of deep learning methods such as EfficientNet and DenseNet for the automated analysis of retinal structures in ophthalmology. EfficientNet, originally proposed to balance between accuracy and computational efficiency, and DenseNet, based on dense connections between layers, are considered as tools for identifying and classifying retina features. Automated analysis includes identifying pathologies, assessing the degree of their development and, possibly, diagnosing various eye diseases. Experiments are performed on a dataset containing a variety of images of retinal structures. Results are evaluated using metrics of accuracy, sensitivity, and specificity. It is expected that the proposed deep learning methods can significantly improve the automated analysis of retinal images, which is important for the diagnosis and monitoring of eye diseases. As a result, the article highlights the significance and promise of using deep learning methods in ophthalmology for automated analysis of retinal structures. These methods help improve the early diagnosis, treatment and monitoring of eye diseases, which can ultimately lead to improved healthcare quality and improved patient lives.
IRJET - Classification of Cancer Images using Deep LearningIRJET Journal
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Glaucoma Detection using Deep Learning (1).pptxnoyarav597
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Performance Comparison Analysis for Medical Images Using Deep Learning Approa...IRJET Journal
This document discusses and compares several deep learning approaches for analyzing medical images, specifically chest x-rays. It first provides an abstract that outlines comparing existing technologies for analyzing chest x-rays using deep learning. It then reviews literature on models like convolutional neural networks (CNN), fully convolutional networks (FCN), lookup-based convolutional neural networks (LCNN), and deep cascade of convolutional neural networks (DCCNN) that have been applied to tasks like image segmentation, classification, and quality assessment of medical images. The document compares the performance of these models on different medical image datasets based on accuracy metrics.
This document discusses a student project on using machine learning to detect diabetic retinopathy. It includes an introduction on diabetic retinopathy and how machine learning can help with early detection. It then discusses the existing and proposed systems, objectives, implementation details using technologies like OpenCV and CNNs, and a conclusion on how the project aims to enhance diabetic retinopathy diagnosis.
IRJET- Blood Vessel Segmentation in Retinal Images using MatlabIRJET Journal
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Human recognition system based on retina vascular network characteristicscbnaikodi
This document proposes a human recognition system based on retina vascular network characteristics. The system uses fundus images of a person's retina as input. It performs pre-processing such as histogram equalization and edge detection using techniques like Sobel and Prewitt filters. It then compares the processed retina image to images stored in a database. If a matching image is found, the person is authorized, otherwise they are unauthorized. The system can accurately authenticate individuals and has applications in security environments like banking, military, and government that require high security.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
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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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The challenge of early detection of diabetic retinopathy (DR), a leading cause of vision loss in working-age individuals in developed nations, was addressed in this study. Current manual analysis of digital color fundus photographs by clinicians, although thorough, suffers from slow result turnaround, delaying necessary treatment. To expedite detection and improve treatment timeliness, a novel automated detection system for DR was developed. This system utilized convolutional neural networks. Visual geometry group 16-layer network (VGG16), a pre-trained deep learning model, for feature extraction from retinal images and the synthetic minority over-sampling technique (SMOTE) to handle class imbalance in the dataset. The system was designed to classify images into five categories: normal, mild DR, moderate DR, severe DR, and proliferative DR (PDR). Assessment of the system using the Kaggle diabetic retinopathy dataset resulted in a promising 93.94% accuracy during the training phase and 88.19% during validation. These results highlight the system's potential to enhance DR diagnosis speed and efficiency, leading to improved patient outcomes. The study concluded that automation and artificial intelligence (AI) could play a significant role in timely and efficient disease detection and management.
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cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due
to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels
significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It
is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate
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The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading
cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due
to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels
significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It
is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate
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The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging
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The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and
STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis
of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868,
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The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when
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IRJET -An Automatated Learning Approach for Detection of Diabetic Retinopathy...IRJET Journal
This document presents a review of existing methods for detecting diabetic retinopathy (DR) using fundus images and proposes a new automated learning approach using deep learning. Existing methods are discussed, including those using artificial neural networks, modified AlexNet architecture, convolutional neural networks, and detection of retinal changes in longitudinal fundus images. The proposed method uses contrast limited adaptive histogram equalization for segmentation followed by a deep belief network for classification of DR stages. It aims to provide an optimal and automated solution for classifying DR severity levels using fundus images to help ophthalmologists detect the condition early. The model will be trained and evaluated on a publicly available dataset from Kaggle to classify images as healthy, normal, mild, moderate, severe
An automated severity classification model for diabetic retinopathyIRJET Journal
This document presents a study on developing an automated severity classification model for diabetic retinopathy using deep learning techniques. The proposed model uses a modified DenseNet169 architecture with a Convolutional Block Attention Module to classify retinal images into different severity categories of diabetic retinopathy. The model was trained on the Kaggle Asia Pacific Tele-Ophthalmology Society dataset and achieved state-of-the-art performance, accurately classifying 82% of images for severity grading. The lightweight model requires less time and complexity compared to other methods, making it suitable for automated diagnosis of diabetic retinopathy severity.
IRJET- A Survey on Medical Image Interpretation for Predicting PneumoniaIRJET Journal
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
Detection of Macular Edema by using Various Techniques of Feature Extraction ...IRJET Journal
This document presents a review of techniques for automatically detecting diabetic retinopathy by analyzing color fundus images. Diabetic retinopathy occurs when blood vessels in the retina are damaged from diabetes, and can lead to vision loss if left untreated. The document discusses existing work on feature extraction and classification methods for detecting signs of diabetic retinopathy like exudates and macular edema. It proposes a new method that focuses on extracting texture features from the region around the macula in order to accurately detect high-risk macular edema cases.
Application of deep learning methods for automated analysis of retinal struct...IJECEIAES
This article examines a current area of research in the field of ophthalmology the use of deep learning methods for automated analysis of retinal structures. This work explores the use of deep learning methods such as EfficientNet and DenseNet for the automated analysis of retinal structures in ophthalmology. EfficientNet, originally proposed to balance between accuracy and computational efficiency, and DenseNet, based on dense connections between layers, are considered as tools for identifying and classifying retina features. Automated analysis includes identifying pathologies, assessing the degree of their development and, possibly, diagnosing various eye diseases. Experiments are performed on a dataset containing a variety of images of retinal structures. Results are evaluated using metrics of accuracy, sensitivity, and specificity. It is expected that the proposed deep learning methods can significantly improve the automated analysis of retinal images, which is important for the diagnosis and monitoring of eye diseases. As a result, the article highlights the significance and promise of using deep learning methods in ophthalmology for automated analysis of retinal structures. These methods help improve the early diagnosis, treatment and monitoring of eye diseases, which can ultimately lead to improved healthcare quality and improved patient lives.
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.
Glaucoma Detection using Deep Learning (1).pptxnoyarav597
The document proposes using deep learning to detect glaucoma from images. Glaucoma causes blindness if undetected, so early detection is important. A CNN model will be trained on a dataset of 100,000+ eye images labeled as having glaucoma, not having glaucoma, or ungradable. Spatial attention algorithms will be used to increase the CNN's accuracy. Previous methods used Mask R-CNN, LeNet, and other techniques, but were expensive or did not generalize well. The proposed method aims to accurately detect glaucoma while avoiding high computational costs.
Performance Comparison Analysis for Medical Images Using Deep Learning Approa...IRJET Journal
This document discusses and compares several deep learning approaches for analyzing medical images, specifically chest x-rays. It first provides an abstract that outlines comparing existing technologies for analyzing chest x-rays using deep learning. It then reviews literature on models like convolutional neural networks (CNN), fully convolutional networks (FCN), lookup-based convolutional neural networks (LCNN), and deep cascade of convolutional neural networks (DCCNN) that have been applied to tasks like image segmentation, classification, and quality assessment of medical images. The document compares the performance of these models on different medical image datasets based on accuracy metrics.
This document discusses a student project on using machine learning to detect diabetic retinopathy. It includes an introduction on diabetic retinopathy and how machine learning can help with early detection. It then discusses the existing and proposed systems, objectives, implementation details using technologies like OpenCV and CNNs, and a conclusion on how the project aims to enhance diabetic retinopathy diagnosis.
IRJET- Blood Vessel Segmentation in Retinal Images using MatlabIRJET Journal
This document summarizes research on blood vessel segmentation in retinal images using MATLAB. It discusses using stationary wavelet transforms and neural networks to enhance vessels and classify pixels. The research aims to implement an effective algorithm using morphological processing and segmentation techniques to detect retinal vessels and exudates. It reviews related work applying techniques like fuzzy segmentation, matched filtering, and image mining. The document concludes that analyzing retinal vessels and exudates can help detect diseases early by comparing vessel states, and the presented algorithm effectively detects retinal blood vessels.
Human recognition system based on retina vascular network characteristicscbnaikodi
This document proposes a human recognition system based on retina vascular network characteristics. The system uses fundus images of a person's retina as input. It performs pre-processing such as histogram equalization and edge detection using techniques like Sobel and Prewitt filters. It then compares the processed retina image to images stored in a database. If a matching image is found, the person is authorized, otherwise they are unauthorized. The system can accurately authenticate individuals and has applications in security environments like banking, military, and government that require high security.
Similar to A Deep Learning Approach for the Detection and Identification of Neovascularization in Fundus Images (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.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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.
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.
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.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
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.
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%.
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.
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
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