This document summarizes a research paper that proposes using neural networks to detect kidney stones from CT scan images. The researchers aim to improve detection accuracy by using discrete wavelet transforms to extract features from the images, as well as a gray-level co-occurrence matrix and watershed algorithm. They train neural networks on sample CT images that have been diagnosed for kidney stones. The proposed method is meant to provide automatic, accurate detection of kidney stones to help with diagnosis and treatment.
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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.
IRJET - Deep Multiple Instance Learning for Automatic Detection of Diabetic R...IRJET Journal
This document describes a proposed method for using deep multiple instance learning to automatically detect diabetic retinopathy in retinal images. Diabetic retinopathy is a complication of diabetes that can cause vision loss or blindness. The proposed method treats retinal images as "bags" containing "instances" of image patches. A deep learning model is trained using only image-level labels to both detect diabetic retinopathy images and identify lesions within images. The model first preprocesses images to normalize factors like scale and illumination. It then segments lesions and extracts features before classifying images using convolutional neural networks. The goal is to provide explicit locations of lesions to aid clinicians while leveraging large datasets typically required for deep learning.
Survey on Automatic Kidney Lesion Detection using Deep LearningIRJET Journal
This document discusses several studies that have used deep learning algorithms to detect kidney lesions from medical imaging data. It summarizes the key findings of 10 research papers. The studies demonstrate that deep learning methods like convolutional neural networks, deep belief networks, and LSTM models can accurately detect and classify kidney lesions from CT, MRI, ultrasound and other medical imaging modalities. Some studies achieved over 98% accuracy. However, the document also notes limitations like the need for large labelled datasets and potential for bias. Overall, the document emphasizes the potential for deep learning to improve kidney lesion detection and diagnosis of renal illnesses but highlights the need for further research.
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This document summarizes a research paper that aims to detect diabetic retinopathy using machine learning. It begins with an introduction to diabetic retinopathy and the need for early detection. It then discusses existing methods for detection that use features like SURF, MSER and morphological operations. The paper proposes a methodology using deep learning techniques like convolutional neural networks to classify retinal images as healthy or indicating diabetic retinopathy. This involves collecting and preprocessing images, training and evaluating a model, and potentially optimizing the model for accurate detection of the condition.
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This document presents a method for detecting diabetic retinopathy by analyzing fundus images and calculating the cup-to-disc ratio of the optic disc. The researchers first take fundus images of the retina using a camera. They then use image processing techniques like thresholding and morphological operations to extract and measure the area of the optic disc and cup. By calculating the ratio of the cup area to disc area, they can determine the severity of diabetic retinopathy as normal, mild, or severe. This automatic analysis of fundus images and cup-to-disc ratios could help doctors better track the progression of diabetic retinopathy and control vision loss.
IRJET - Automatic Detection of Diabetic Retinopathy in Retinal ImageIRJET Journal
This document discusses techniques for the automatic detection of diabetic retinopathy in retinal images. Diabetic retinopathy is a major cause of blindness that can be detected by analyzing changes in the retina through digital image processing of retinal photographs. The proposed system applies techniques like image enhancement, segmentation, feature extraction and classification to retinal images in order to detect features associated with diabetic retinopathy like exudates, hemorrhages and microaneurysms. It aims to provide early and accurate detection of diabetic retinopathy, which can help prevent vision loss and blindness if treated early. A review of existing techniques is provided and the proposed system is outlined in blocks, describing preprocessing, feature extraction and classification steps to automatically analyze retinal images
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This document summarizes a research paper that proposes a deep learning approach for detecting kidney stones using CT images. The paper first discusses how kidney stones are currently diagnosed using CT scans and the issues with existing detection methods. It then proposes a system that uses deep learning algorithms for image segmentation, followed by CNN classification and wavelet transformation for precise kidney stone detection. This aims to overcome limitations of prior methods, such as level set techniques requiring significant data. The document provides background on kidney stones and outlines the existing challenges, proposed solution, and potential advantages of the deep learning-based approach.
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This document summarizes various methods for detecting cataracts. It discusses five different cataract detection methods proposed in previous research: 1) a mobile system using texture analysis and k-NN classification, 2) fundus image processing using histogram equalization, 3) a tri-training method that generates three classifiers, 4) analysis of automatic detection of nuclear and cortical cataracts using fundus images, and 5) enhanced texture features to classify cataractous and non-cataractous lenses. The document also reviews literature on diabetic retinopathy detection and classification. It concludes that while challenges remain, recent applications have potential for early cataract detection and classification.
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.
IRJET - Deep Multiple Instance Learning for Automatic Detection of Diabetic R...IRJET Journal
This document describes a proposed method for using deep multiple instance learning to automatically detect diabetic retinopathy in retinal images. Diabetic retinopathy is a complication of diabetes that can cause vision loss or blindness. The proposed method treats retinal images as "bags" containing "instances" of image patches. A deep learning model is trained using only image-level labels to both detect diabetic retinopathy images and identify lesions within images. The model first preprocesses images to normalize factors like scale and illumination. It then segments lesions and extracts features before classifying images using convolutional neural networks. The goal is to provide explicit locations of lesions to aid clinicians while leveraging large datasets typically required for deep learning.
Survey on Automatic Kidney Lesion Detection using Deep LearningIRJET Journal
This document discusses several studies that have used deep learning algorithms to detect kidney lesions from medical imaging data. It summarizes the key findings of 10 research papers. The studies demonstrate that deep learning methods like convolutional neural networks, deep belief networks, and LSTM models can accurately detect and classify kidney lesions from CT, MRI, ultrasound and other medical imaging modalities. Some studies achieved over 98% accuracy. However, the document also notes limitations like the need for large labelled datasets and potential for bias. Overall, the document emphasizes the potential for deep learning to improve kidney lesion detection and diagnosis of renal illnesses but highlights the need for further research.
Diabetic Retinopathy detection using Machine learningIRJET Journal
This document summarizes a research paper that aims to detect diabetic retinopathy using machine learning. It begins with an introduction to diabetic retinopathy and the need for early detection. It then discusses existing methods for detection that use features like SURF, MSER and morphological operations. The paper proposes a methodology using deep learning techniques like convolutional neural networks to classify retinal images as healthy or indicating diabetic retinopathy. This involves collecting and preprocessing images, training and evaluating a model, and potentially optimizing the model for accurate detection of the condition.
Optical Disc Detection, Localization and Glaucoma Identification of Retinal F...IRJET Journal
This document presents a method for detecting diabetic retinopathy by analyzing fundus images and calculating the cup-to-disc ratio of the optic disc. The researchers first take fundus images of the retina using a camera. They then use image processing techniques like thresholding and morphological operations to extract and measure the area of the optic disc and cup. By calculating the ratio of the cup area to disc area, they can determine the severity of diabetic retinopathy as normal, mild, or severe. This automatic analysis of fundus images and cup-to-disc ratios could help doctors better track the progression of diabetic retinopathy and control vision loss.
A SURVEY ON KIDNEY STONE DETECTION USING IMAGE PROCESSING AND DEEP LEARNINGIRJET Journal
The document describes a study on detecting kidney stones using image processing and deep learning techniques. The researchers preprocessed CT and MRI scan images of kidney stones using techniques like gray level co-occurrence matrix for feature extraction. They then trained a convolutional neural network (CNN) model on the images to classify kidney stones. The CNN model consisted of convolution, ReLU, pooling and fully connected layers. The trained model could accurately detect and classify kidney stones in input images. The researchers concluded that combining image processing and deep learning was an effective method for automated kidney stone detection.
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IRJET- Unsupervised Detecting and Locating of Gastrointestinal AnomaliesIRJET Journal
This document discusses unsupervised machine learning techniques for detecting and locating gastrointestinal anomalies. It begins with an introduction to gastrointestinal diseases and the need for accurate assessment. Commonly used techniques for detection include supervised learning, semi-supervised learning, and unsupervised learning. The paper focuses on unsupervised techniques, which analyze images without human labeling to detect abnormalities. The methodology section describes preprocessing steps and the analysis of color, orientation, and intensity mappings to identify affected regions. The results demonstrate thresholding, histograms, color space conversions, and bounding boxes to highlight anomalies. The conclusion emphasizes that unsupervised learning can provide accurate detection without requiring extensive human effort for labeling.
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.
IRJET - Survey on Chronic Kidney Disease Prediction System with Feature Selec...IRJET Journal
The document describes a study that developed a Chronic Kidney Disease Prediction System (CKDPS) using machine learning techniques. Researchers collected a dataset of 400 patients with 25 attributes related to chronic kidney disease and applied feature selection and feature extraction algorithms. They then trained various machine learning models on the data, finding that a Random Forest classifier achieved the highest accuracy of 95% at predicting chronic kidney disease. The developed CKDPS system is intended to help doctors and medical experts easily predict chronic kidney disease in patients.
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- Retinal Structure Segmentation using Adaptive Fuzzy ThresholdingIRJET Journal
This document proposes a system for segmenting different structures in retinal images using adaptive fuzzy thresholding and mathematical morphology. It aims to segment retinal vessels, the optic disc, and exudate lesions from one retinal image with high accuracy. The system first extracts the region of interest for each structure, then uses adaptive fuzzy thresholding for coarse segmentation followed by mathematical morphology operations for refinement. It is evaluated on four benchmark datasets and shown to achieve competitive performance compared to other state-of-the-art segmentation systems.
Retinal Vessel Segmentation in U-Net Using Deep LearningIRJET Journal
This document describes research on using a U-Net convolutional neural network for retinal blood vessel segmentation. It proposes a modified U-Net architecture with fewer parameters to segment vessels in retinal images. The model achieves high performance, outperforming manual segmentation by experts. It pre-processes training and test images before running them through the U-Net model in patches to generate vessel/non-vessel predictions pixel-by-pixel to extract the retinal vasculature. The model is tested on DRIVE dataset images and achieves high accuracy, recall, precision and F1-score, demonstrating its effectiveness at automated retinal vessel segmentation.
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.
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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.
PCOS Detect using Machine Learning AlgorithmsIRJET Journal
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RICE LEAF DISEASES CLASSIFICATION USING CNN WITH TRANSFER LEARNINGIRJET Journal
The document presents a study on classifying rice leaf diseases using convolutional neural networks (CNN) with transfer learning. Key points:
- The study developed a CNN model based on VGG-16 architecture to classify rice leaf diseases like blast, blight, and brown spot from a dataset of 1649 disease leaf images and 507 healthy leaf images.
- Transfer learning was used by keeping the earlier layers of pre-trained VGG-16 unchanged and fine-tuning the later layers for the new dataset, since the dataset was small.
- The proposed CNN model with transfer learning achieved a test accuracy of 92.46%, while a CNN model developed from scratch without transfer learning achieved only 74% accuracy, highlighting the benefit of
EYE DISEASE IDENTIFICATION USING DEEP LEARNINGIRJET Journal
This document presents a deep learning model for identifying eye diseases from images. The model was trained on datasets of five different eye conditions - conjunctivitis, cataracts, uveitis, bulging eyes, and crossed eyes. The model uses a convolutional neural network architecture with several convolutional and pooling layers. It achieves 96% accuracy on single-eye images and 92.31% accuracy on two-eye images. The authors conclude the model is effective and cost-efficient at classifying common eye diseases and recommending users seek treatment from ophthalmologists when needed.
Diagnosis Of Chronic Kidney Disease Using Machine LearningIRJET Journal
This document discusses a study that used machine learning techniques to diagnose chronic kidney disease (CKD). The study analyzed a dataset of 400 patients and 24 features related to CKD. Missing data was imputed using statistical methods. Recursive feature elimination was used to select important features. Four classification algorithms were tested - support vector machine, k-nearest neighbors, decision tree, and random forest. The random forest algorithm achieved the highest accuracy, precision and other performance measures at 100% for CKD diagnosis. The study aims to help doctors make early diagnoses of CKD to prevent kidney failure through the use of artificial intelligence techniques.
Ultrasound image segmentation through deep learning based improvised U-Netnooriasukmaningtyas
Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism , however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model.
IRJET- An Efficient Techniques in Digital Image Processing to Detect Glau...IRJET Journal
This document presents a method for detecting glaucoma using digital image processing techniques to segment retinal blood vessels. It begins with an abstract discussing how evaluating retinal blood vessels allows for early detection of eye diseases like glaucoma and diabetic retinopathy. The document then provides background on glaucoma and retinal blood vessels. It describes the proposed method, which uses filters, thresholding, segmentation, and a Gaussian mixture model to identify blood vessels in retinal images. Implementation details are discussed and results of the segmented vessels are shown. The method is concluded to provide high accuracy in segmenting retinal blood vessels, aiding in the detection of diseases like glaucoma.
This document describes a system for detecting microaneurysms in retinal images to aid in the diagnosis of diabetic retinopathy. It first discusses diabetic retinopathy and the need for automated detection systems. It then outlines the proposed system, which uses preprocessing like vessel enhancement, thresholding and morphological operations to detect microaneurysms. A neural network is used to classify pixels in retinal images into vessel and non-vessel after extracting 2D features. The algorithm is implemented in MATLAB and can detect microaneurysms with better accuracy and faster than previous methods. Public retinal image databases are also discussed to test and evaluate such algorithms.
Review on automated follicle identification for polycystic ovarian syndromejournalBEEI
This document reviews different methods that have been proposed for automated follicle identification in ultrasound images to diagnose polycystic ovarian syndrome (PCOS). Various image segmentation techniques are discussed, including watershed transform, region growing, edge-based, thresholding, clustering, and active contour methods. For each technique, example studies applying the method are described along with their performance evaluations in terms of metrics like recognition rate, misidentification rate, and computational time. The highest recognition rate of 87.5% was achieved using morphological operations with edge-based segmentation, but that study did not consider follicle size which is important for PCOS diagnosis. Overall, the review finds that accurate identification of small follicles remains a challenge and suggests further research is needed
A Survey on Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Im...IRJET Journal
This document summarizes a survey on methods for automatically detecting the true retinal area from scanning laser ophthalmoscope (SLO) images, which is an important first step for computer-aided diagnosis of retinal diseases. Distinguishing the retinal area from artifacts in SLO images is challenging. The proposed system uses a machine learning approach involving extracting features from super-pixels of the image, selecting important features, and training a classifier to identify the retinal area versus artifacts. This automated detection of the retinal area could help diagnose retinal disorders more efficiently and at scale compared to manual examination methods.
IRJET - A Smartphone ALS based Syringe System for Colorimetric Detection of C...IRJET Journal
This document describes a study that developed a smartphone-based system for detecting creatinine levels using a syringe and detection module attached to the smartphone's ambient light sensor. The syringe contained reagents to perform the Jaffe reaction for creatinine and was used to inject samples into the detection module. Standards were used to create a calibration curve relating creatinine concentration to changes in light intensity measured by the ambient light sensor. Results from the smartphone system agreed with those from a conventional spectrophotometer. The system provides a low-cost point-of-care method for measuring creatinine levels.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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This document discusses a study that used machine learning techniques to diagnose chronic kidney disease (CKD). The study analyzed a dataset of 400 patients and 24 features related to CKD. Missing data was imputed using statistical methods. Recursive feature elimination was used to select important features. Four classification algorithms were tested - support vector machine, k-nearest neighbors, decision tree, and random forest. The random forest algorithm achieved the highest accuracy, precision and other performance measures at 100% for CKD diagnosis. The study aims to help doctors make early diagnoses of CKD to prevent kidney failure through the use of artificial intelligence techniques.
Ultrasound image segmentation through deep learning based improvised U-Netnooriasukmaningtyas
Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism , however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model.
IRJET- An Efficient Techniques in Digital Image Processing to Detect Glau...IRJET Journal
This document presents a method for detecting glaucoma using digital image processing techniques to segment retinal blood vessels. It begins with an abstract discussing how evaluating retinal blood vessels allows for early detection of eye diseases like glaucoma and diabetic retinopathy. The document then provides background on glaucoma and retinal blood vessels. It describes the proposed method, which uses filters, thresholding, segmentation, and a Gaussian mixture model to identify blood vessels in retinal images. Implementation details are discussed and results of the segmented vessels are shown. The method is concluded to provide high accuracy in segmenting retinal blood vessels, aiding in the detection of diseases like glaucoma.
This document describes a system for detecting microaneurysms in retinal images to aid in the diagnosis of diabetic retinopathy. It first discusses diabetic retinopathy and the need for automated detection systems. It then outlines the proposed system, which uses preprocessing like vessel enhancement, thresholding and morphological operations to detect microaneurysms. A neural network is used to classify pixels in retinal images into vessel and non-vessel after extracting 2D features. The algorithm is implemented in MATLAB and can detect microaneurysms with better accuracy and faster than previous methods. Public retinal image databases are also discussed to test and evaluate such algorithms.
Review on automated follicle identification for polycystic ovarian syndromejournalBEEI
This document reviews different methods that have been proposed for automated follicle identification in ultrasound images to diagnose polycystic ovarian syndrome (PCOS). Various image segmentation techniques are discussed, including watershed transform, region growing, edge-based, thresholding, clustering, and active contour methods. For each technique, example studies applying the method are described along with their performance evaluations in terms of metrics like recognition rate, misidentification rate, and computational time. The highest recognition rate of 87.5% was achieved using morphological operations with edge-based segmentation, but that study did not consider follicle size which is important for PCOS diagnosis. Overall, the review finds that accurate identification of small follicles remains a challenge and suggests further research is needed
A Survey on Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Im...IRJET Journal
This document summarizes a survey on methods for automatically detecting the true retinal area from scanning laser ophthalmoscope (SLO) images, which is an important first step for computer-aided diagnosis of retinal diseases. Distinguishing the retinal area from artifacts in SLO images is challenging. The proposed system uses a machine learning approach involving extracting features from super-pixels of the image, selecting important features, and training a classifier to identify the retinal area versus artifacts. This automated detection of the retinal area could help diagnose retinal disorders more efficiently and at scale compared to manual examination methods.
IRJET - A Smartphone ALS based Syringe System for Colorimetric Detection of C...IRJET Journal
This document describes a study that developed a smartphone-based system for detecting creatinine levels using a syringe and detection module attached to the smartphone's ambient light sensor. The syringe contained reagents to perform the Jaffe reaction for creatinine and was used to inject samples into the detection module. Standards were used to create a calibration curve relating creatinine concentration to changes in light intensity measured by the ambient light sensor. Results from the smartphone system agreed with those from a conventional spectrophotometer. The system provides a low-cost point-of-care method for measuring creatinine levels.
Similar to Detection of Kidney Stone using Neural Network Classifier (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.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
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.
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.
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%.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
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.