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.
IRJET- Feature Selection and Classifier Accuracy of Data Mining AlgorithmsIRJET Journal
This document discusses using data mining algorithms and feature selection techniques to improve classifier accuracy for chronic kidney disease prediction. It analyzes the J48 and Naive Bayes classifiers on different combinations of attributes from a chronic kidney disease dataset, ranked by information gain. The classifiers were tested on attribute combinations from highest to lowest ranked attributes. J48 achieved the highest accuracy of 96.75% using highly ranked attributes, demonstrating the benefit of feature selection for improving classifier performance.
A Non-Invasive Blood Glucose Monitoring Device using Red Laser LightIRJET Journal
This document describes a non-invasive blood glucose monitoring device that uses red laser light. The device passes a 650nm wavelength red laser through a human finger to analyze the transmitted and absorbed blood samples to determine glucose level without drawing blood. The hardware implementation includes a laser transmitter, phototransistor receiver, and microcontroller to calculate glucose levels from the voltage output and display results. Testing showed a relationship between glucose concentration levels and voltage values. The non-invasive method provides pain-free glucose measurement but has lower accuracy compared to invasive techniques.
IRJET- Counting of RBCS and WBCS using Image Processing TechniqueIRJET Journal
This document describes a method for counting red blood cells (RBCs) and white blood cells (WBCs) from blood sample images using image processing techniques. The key steps include: 1) acquiring blood sample images, 2) enhancing the images through techniques like histogram equalization, 3) segmenting the images to identify RBCs and WBCs, and 4) applying detection and counting algorithms to obtain the cell counts. The proposed automated image analysis method provides a faster, more cost-effective alternative to manual counting methods currently used in medical laboratories.
This document discusses 10 highly cited papers from the International Journal on Cybernetics & Informatics. It provides brief summaries of 3 papers:
1. The first paper discusses using data mining classification algorithms like Naive Bayes and Support Vector Machine to predict kidney disease. It finds SVM performs better based on accuracy and runtime.
2. The second paper focuses on the design and analysis of Current Starved Ring Voltage Controlled Oscillators for PLL applications. It designs a CSVCO in CMOS technology with a frequency range of 53MHz to 2.348GHz and power consumption of 848uW.
3. The third paper presents a method to recognize vehicle license plates using morphology operations and a neural network
IRJET- Prediction and Classification of Cardiac ArrhythmiaIRJET Journal
This document discusses a study that used an ensemble classifier to predict and classify cardiac arrhythmia. The study used a dataset from the UCI machine learning repository. Feature selection was performed using an extra trees classifier and data preprocessing including normalization and imputing missing values. An ensemble classifier combining logistic regression, SVM, random forest and gradient boosting models was implemented and achieved 90% accuracy in predicting arrhythmia, outperforming other machine learning algorithms. The ensemble approach combined the strengths of different models for improved performance in cardiac arrhythmia classification.
IRJET- Heart Failure Risk Prediction using Trained Electronic Health RecordIRJET Journal
The document describes a study that uses an electronic health record and the K-dimensional tree classifier to predict the risk of heart failure. The study aims to use more risk factors from a patient's electronic health record to more accurately predict heart failure risk compared to previous methods. The proposed method involves preprocessing the electronic health record data, using an admin module to input patient details, and applying the K-dimensional tree classifier to partition the data and determine the risk level. The results show that the K-dimensional tree approach can reliably predict heart failure risk. Future work could analyze each heart failure risk factor and predict the level of risk as high or low.
IRJET- Diabetic Retinopathy Stage Classification using CNNIRJET Journal
This document describes a study that used convolutional neural networks (CNNs) to classify diabetic retinopathy (DR) into five stages of severity based on color fundus retinal images. Three CNN models (VGG16, AlexNet, and InceptionNet V3) were trained individually on a dataset of 500 retinal images. The models achieved accuracies of 63.23%, 73.3%, and 80.1% respectively when used alone. The study found that concatenating the features from all three CNNs resulted in the highest classification accuracy of 80.1%. The CNN approach can help automate DR stage classification, which is important for evaluating and treating the disease.
Heartbeat Rate Measurement from Facial VideoIRJET Journal
This document proposes a method to measure heartbeat rate from facial videos using computer vision techniques. It involves detecting the face, tracking facial landmarks over time, analyzing trajectories of landmarks like the mouth and eyebrows, removing noise using PCA, and calculating heart rate from peak frequencies in the signal. The method aims to provide a convenient non-contact way to measure heart rate compared to traditional ECG methods. It was tested on publicly available datasets and shown to achieve accurate results comparable to ECG. The proposed approach could be useful for applications in healthcare, fitness tracking, and more.
IRJET- Feature Selection and Classifier Accuracy of Data Mining AlgorithmsIRJET Journal
This document discusses using data mining algorithms and feature selection techniques to improve classifier accuracy for chronic kidney disease prediction. It analyzes the J48 and Naive Bayes classifiers on different combinations of attributes from a chronic kidney disease dataset, ranked by information gain. The classifiers were tested on attribute combinations from highest to lowest ranked attributes. J48 achieved the highest accuracy of 96.75% using highly ranked attributes, demonstrating the benefit of feature selection for improving classifier performance.
A Non-Invasive Blood Glucose Monitoring Device using Red Laser LightIRJET Journal
This document describes a non-invasive blood glucose monitoring device that uses red laser light. The device passes a 650nm wavelength red laser through a human finger to analyze the transmitted and absorbed blood samples to determine glucose level without drawing blood. The hardware implementation includes a laser transmitter, phototransistor receiver, and microcontroller to calculate glucose levels from the voltage output and display results. Testing showed a relationship between glucose concentration levels and voltage values. The non-invasive method provides pain-free glucose measurement but has lower accuracy compared to invasive techniques.
IRJET- Counting of RBCS and WBCS using Image Processing TechniqueIRJET Journal
This document describes a method for counting red blood cells (RBCs) and white blood cells (WBCs) from blood sample images using image processing techniques. The key steps include: 1) acquiring blood sample images, 2) enhancing the images through techniques like histogram equalization, 3) segmenting the images to identify RBCs and WBCs, and 4) applying detection and counting algorithms to obtain the cell counts. The proposed automated image analysis method provides a faster, more cost-effective alternative to manual counting methods currently used in medical laboratories.
This document discusses 10 highly cited papers from the International Journal on Cybernetics & Informatics. It provides brief summaries of 3 papers:
1. The first paper discusses using data mining classification algorithms like Naive Bayes and Support Vector Machine to predict kidney disease. It finds SVM performs better based on accuracy and runtime.
2. The second paper focuses on the design and analysis of Current Starved Ring Voltage Controlled Oscillators for PLL applications. It designs a CSVCO in CMOS technology with a frequency range of 53MHz to 2.348GHz and power consumption of 848uW.
3. The third paper presents a method to recognize vehicle license plates using morphology operations and a neural network
IRJET- Prediction and Classification of Cardiac ArrhythmiaIRJET Journal
This document discusses a study that used an ensemble classifier to predict and classify cardiac arrhythmia. The study used a dataset from the UCI machine learning repository. Feature selection was performed using an extra trees classifier and data preprocessing including normalization and imputing missing values. An ensemble classifier combining logistic regression, SVM, random forest and gradient boosting models was implemented and achieved 90% accuracy in predicting arrhythmia, outperforming other machine learning algorithms. The ensemble approach combined the strengths of different models for improved performance in cardiac arrhythmia classification.
IRJET- Heart Failure Risk Prediction using Trained Electronic Health RecordIRJET Journal
The document describes a study that uses an electronic health record and the K-dimensional tree classifier to predict the risk of heart failure. The study aims to use more risk factors from a patient's electronic health record to more accurately predict heart failure risk compared to previous methods. The proposed method involves preprocessing the electronic health record data, using an admin module to input patient details, and applying the K-dimensional tree classifier to partition the data and determine the risk level. The results show that the K-dimensional tree approach can reliably predict heart failure risk. Future work could analyze each heart failure risk factor and predict the level of risk as high or low.
IRJET- Diabetic Retinopathy Stage Classification using CNNIRJET Journal
This document describes a study that used convolutional neural networks (CNNs) to classify diabetic retinopathy (DR) into five stages of severity based on color fundus retinal images. Three CNN models (VGG16, AlexNet, and InceptionNet V3) were trained individually on a dataset of 500 retinal images. The models achieved accuracies of 63.23%, 73.3%, and 80.1% respectively when used alone. The study found that concatenating the features from all three CNNs resulted in the highest classification accuracy of 80.1%. The CNN approach can help automate DR stage classification, which is important for evaluating and treating the disease.
Heartbeat Rate Measurement from Facial VideoIRJET Journal
This document proposes a method to measure heartbeat rate from facial videos using computer vision techniques. It involves detecting the face, tracking facial landmarks over time, analyzing trajectories of landmarks like the mouth and eyebrows, removing noise using PCA, and calculating heart rate from peak frequencies in the signal. The method aims to provide a convenient non-contact way to measure heart rate compared to traditional ECG methods. It was tested on publicly available datasets and shown to achieve accurate results comparable to ECG. The proposed approach could be useful for applications in healthcare, fitness tracking, and more.
IRJET- Counting of RBCS and WBCS using Image Processing TechniqueIRJET Journal
This document describes a study that aims to develop an image processing-based system to automatically detect and count red blood cells (RBCs) and white blood cells (WBCs) from microscopic blood sample images. The proposed system involves steps of image acquisition, pre-processing, image enhancement, image segmentation, post-processing, and a counting algorithm. The researchers evaluate different methodologies for automated cell counting and achieve 91% accuracy in detecting and distinguishing RBCs and WBCs. The developed system provides a low-cost and effective alternative to manual counting methods.
IRJET- Automated Detection of Diabetic Retinopathy using Deep LearningIRJET Journal
This document discusses using convolutional neural networks (CNNs) to automatically detect diabetic retinopathy from fundus images. It aims to classify images into multiple severity levels, including early stages of the disease. The authors train and test CNN models on two datasets containing over 35,000 retinal images total. While CNNs achieve high accuracy for binary classification, performance decreases with additional severity classes, particularly for mild or early-stage disease. The authors explore techniques like data augmentation and transfer learning to improve CNN performance on multi-class classification of diabetic retinopathy severity levels from fundus images.
IRJET- Detection of White Blood Sample Cells using CNNIRJET Journal
This document presents a study that uses a convolutional neural network (CNN) to classify four types of white blood cells (WBCs) from microscope images of blood samples. The CNN model achieved 81% accuracy on a dataset of 15,000 labeled cell images. The CNN framework segments individual cells from images and extracts features to classify each cell as one of four types: neutrophils, lymphocytes, eosinophils, or monocytes. This automated classification approach using deep learning techniques could help diagnose blood-related diseases by reducing the time and expertise required for manual classification of cells under a microscope.
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REASIJCI JOURNAL
Retina images are obtained from the fundus camera a
nd graded by skilled professionals. However there i
s
considerable shortage of expert observers has encou
raged computer assisted monitoring. Evaluation of
blood vessels network plays an important task in a
variety of medical diagnosis. Manifestations of
numerous vascular disorders, such as diabetic retin
opathy, depend on detection of the blood vessels
network. In this work the fundus RGB image is used
for obtaining the traces of blood vessels and areas
of
blood vessels are used for detection of Diabetic Re
tinopathy (DR). The algorithm developed uses
morphological operation to extract blood vessels. M
ainly two steps are used: firstly enhancement opera
tion
is applied to original retina image to remove noise
and increase contrast of retinal blood vessels. Se
condly
morphology operations are used to take out blood ve
ssels. Experiments are conducted on publicly availa
ble
DIARETDB1 database. Experimental results obtained b
y using gray-scale images have been presented.
Heart Disease Prediction using Machine Learning Algorithmijtsrd
Nowadays, Heart disease has become dangerous to a human being, it effects very badly to human body. If anyone is suffering from heart disease, then it leads to blood clotting. Heart disease prediction is very difficult task to predict in the field of medical science. Affiliation has predicted that 12 million people fail horrendously every year as a result of heart disease. In this paper, we propose a k Nearest Neighbors Algorithm KNN way to deal with improve the exactness of heart determination. We show that k Nearest Neighbors Algorithm KNN have better accuracy than random forest algorithm for viewing heart disease. The k Nearest Neighbors Algorithm give more precise and exact outcome . We have taken 13 attributes in the dataset and a target attribute, by applying machine learning we achieved 84 accuracy in the heart disease detection. Ravi Kumar Singh | Dr. A Rengarajan "Heart Disease Prediction using Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38358.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38358/heart-disease-prediction-using-machine-learning-algorithm/ravi-kumar-singh
IRJET- Automatic Detection of Diabetic Retinopathy using R-CNNIRJET Journal
This document presents research on using Regional Convolutional Neural Networks (R-CNN) to automatically detect diabetic retinopathy from fundus images. The researchers trained an R-CNN model on 130 fundus images and tested it on 110 images. It classified the images into two groups: with diabetic retinopathy and without. The R-CNN approach segmented the whole image and focused classification only on regions of interest. This was found to be more efficient and accurate than regular CNN in terms of speed and accuracy. The R-CNN model achieved an accuracy of approximately 93.8% on the test images. Challenges included the computational complexity which required moving to more powerful systems for training the neural network model.
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
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
Classification of cardiac vascular disease from ecg signals for enhancing mod...hiij
“Why to be in frustration we will do new creation f
or salvation”. Based on these words we grapes your
attention towards saving a life of a heart patient
with the use of ECG in Public Health Care Center by
transmitting ECG signals to nearby hospital server.
In this paper we analyze the abnormalities found i
n the
ECG signals by identifying the Normal, Bradycardia
Arrhythmia, Tachycardia Arrhythmia and Ischemia
signal using the method of Neuro Fuzzy Classifier.
Daubechies Wavelet Transforms is used for feature
extraction and Adaptive Neuro Fuzzy Inference Syste
m (ANFIS) is used for classification. The compressi
on
algorithm is performed by using Huffman coding.
BSS_M KMZ Presentation - A Gamechanger in Medical Diagnostics Vikki Choudhry
BSS Materiel Limited and KMZ Holdings LLC presentation on Zeptronic Diagnostics - A Gamechanger in Medical Diagnostics
System and Method for Diagnostic Analysis of Human Body Systems, Organs and Cells.
This document discusses using data mining classifiers and attribute reduction techniques to predict chronic kidney disease (CKD) more accurately and efficiently. It first provides background on CKD and the need for early detection. It then discusses data mining, classification algorithms, attribute selection filters and wrappers. The document analyzes several studies that predicted CKD using techniques like decision trees, SVM and Naive Bayes. It describes the dataset used from the UCI repository and evaluation metrics. The results section compares J48, Decision Tree and IBK classifiers with and without attribute reduction using CfsSubsetEval, ClassifierSubsetEval and WrapperSubsetEval. Attribute reduction improved accuracy, especially for IBK which achieved 100% accuracy with 72% fewer attributes.
IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...IRJET Journal
This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
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.
Development of computer vision medical recognition systems in MoscowSmartCity Moscow
The document summarizes the current state of computer vision medical recognition systems in Moscow and future plans for development. Moscow has integrated over 700 health facilities into its citywide medical information system, collecting data from over 9 million unique patients. This data is used to develop computer vision systems for recognizing diseases like lung cancer and cardiovascular issues in medical images. The lung cancer recognition system can currently identify 86.5% of tumors in test images. For heart issues, ECG data is being analyzed to improve early detection of conditions. Future plans include expanding recognition capabilities to additional organs and diseases.
The Combination of Steganography and Cryptography for Medical Image ApplicationsIJAAS Team
To give more security for the biomedical images for the patient betterment as well privacy for the patient highly confidently patient image report can be placed in database. If unknown persons like hospital staffs, relatives and third parties like intruder trying to see the report it has in the form of hidden state in another image. The patient detail like MRI image has been converted into any form of steganography. Then, encrypt those image by using proposed cryptography algorithm and place in the database.
This study uses logistic regression to analyze factors that contribute to the risk of acute coronary syndrome (ACS) using a dataset of 319 patients from two cardiac hospitals in Karachi, Pakistan. The dependent variable is diagnosis of ACS, and there are 14 independent variables including age, blood pressure, heart rate, smoking status, and other medical factors. Wald statistics are calculated to test the significance of each variable, finding smoking to have the highest prevalence in increasing ACS risk. The logistic regression model correctly classified 37.1% of patients without ACS and 88.7% of patients with ACS. However, 30.1% of cases were incorrectly classified, indicating room for improving the model's predictive ability.
Analysis of Blood Samples Using Anfis ClassificationIRJET Journal
This document analyzes blood samples using an Adaptive Neuro Fuzzy Inference System (ANFIS) classification to detect different types of leukemia. It discusses preprocessing input images, segmenting blood cells, extracting features, and using ANFIS to classify samples as acute myeloid leukemia, chronic myeloid leukemia, acute lymphocytic leukemia, or chronic lymphocytic leukemia. The methodology includes detecting leukemia cells through structural element analysis and connected component labeling. Texture features and statistical measures from a gray-level co-occurrence matrix are extracted for ANFIS training and classification. Results show the system can reliably classify leukemia types from microscopic images in a cost-effective manner.
Prediction of Heart Disease Using Data Mining Techniques- A ReviewIRJET Journal
This document reviews the use of data mining techniques to predict heart disease. It discusses how medical data sets contain a large amount of patient diagnosis, medication, and detail data that can be mined using techniques like classification algorithms to extract useful patterns and predict trends. Specifically, it explores using classification algorithms like decision trees, Naive Bayes, and neural networks on a data set of 603 records with 76 attributes related to heart health to predict heart disease.
IRJET -Analysis of Ophthalmic System Applications using Signal ProcessingIRJET Journal
This document discusses using signal processing and image analysis techniques to develop objective measures for evaluating ophthalmic diseases like diabetic retinopathy. It analyzes various algorithms that have been used to measure features in retinal images like vessel widths, tortuosity, and network topography. While some algorithms have been widely accepted for use in adults, their applicability to neonates and conditions like retinopathy of prematurity requires further validation. Advances in imaging technologies may help provide more robust outcome measures for clinical diagnosis and research on ophthalmic diseases.
IRJET- A System to Detect Heart Failure using Deep Learning TechniquesIRJET Journal
This document proposes a system to detect heart failure using deep learning techniques. The system uses a boosted decision tree to initially detect the probability that a patient is prone to heart failure. If the probability is over 50%, the patient's ECG recordings are passed to a convolutional neural network (CNN) for more accurate detection of heart failure. The CNN is trained on a dataset of 60,000 ECG recordings. The system also aims to detect the subtype of heart failure using an SVM algorithm trained on data distinguishing systolic vs diastolic heart failure. The overall goal is to accurately detect heart failure at early stages to improve outcomes.
IRJET - Prediction of Risk Factor of the Patient with Hepatocellular Carcinom...IRJET Journal
This document discusses using machine learning to predict the risk factor of patients with hepatocellular carcinoma (HCC or liver cancer) based on medical test results. It involves collecting patient data, preprocessing the data, feature selection to identify key predictive features, and using machine learning algorithms like support vector machines (SVM) and random forests. The best model achieved 95% accuracy using SVM with 5 selected features to classify patients as high or low risk, where high risk means less than one year lifetime. The system could help predict survival time and guide treatment decisions for liver cancer patients.
Heart Disease Prediction Using Multi Feature and Hybrid ApproachIRJET Journal
This document presents a study that uses a hybrid machine learning model to predict heart disease. The study uses a dataset of 303 patient records containing 14 medical features. Several classification algorithms are trained on the data, including logistic regression, Gaussian Naive Bayes, linear support vector classification, K-nearest neighbors, decision tree, and random forest. The linear support vector classification model achieved the best results with 90.78% accuracy, 96.87% precision, 83.78% sensitivity, 89.85% F1 score, and 90.60% ROC. The proposed hybrid model combines the results of the different algorithms to more accurately predict heart disease compared to previous studies.
IRJET- Counting of RBCS and WBCS using Image Processing TechniqueIRJET Journal
This document describes a study that aims to develop an image processing-based system to automatically detect and count red blood cells (RBCs) and white blood cells (WBCs) from microscopic blood sample images. The proposed system involves steps of image acquisition, pre-processing, image enhancement, image segmentation, post-processing, and a counting algorithm. The researchers evaluate different methodologies for automated cell counting and achieve 91% accuracy in detecting and distinguishing RBCs and WBCs. The developed system provides a low-cost and effective alternative to manual counting methods.
IRJET- Automated Detection of Diabetic Retinopathy using Deep LearningIRJET Journal
This document discusses using convolutional neural networks (CNNs) to automatically detect diabetic retinopathy from fundus images. It aims to classify images into multiple severity levels, including early stages of the disease. The authors train and test CNN models on two datasets containing over 35,000 retinal images total. While CNNs achieve high accuracy for binary classification, performance decreases with additional severity classes, particularly for mild or early-stage disease. The authors explore techniques like data augmentation and transfer learning to improve CNN performance on multi-class classification of diabetic retinopathy severity levels from fundus images.
IRJET- Detection of White Blood Sample Cells using CNNIRJET Journal
This document presents a study that uses a convolutional neural network (CNN) to classify four types of white blood cells (WBCs) from microscope images of blood samples. The CNN model achieved 81% accuracy on a dataset of 15,000 labeled cell images. The CNN framework segments individual cells from images and extracts features to classify each cell as one of four types: neutrophils, lymphocytes, eosinophils, or monocytes. This automated classification approach using deep learning techniques could help diagnose blood-related diseases by reducing the time and expertise required for manual classification of cells under a microscope.
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REASIJCI JOURNAL
Retina images are obtained from the fundus camera a
nd graded by skilled professionals. However there i
s
considerable shortage of expert observers has encou
raged computer assisted monitoring. Evaluation of
blood vessels network plays an important task in a
variety of medical diagnosis. Manifestations of
numerous vascular disorders, such as diabetic retin
opathy, depend on detection of the blood vessels
network. In this work the fundus RGB image is used
for obtaining the traces of blood vessels and areas
of
blood vessels are used for detection of Diabetic Re
tinopathy (DR). The algorithm developed uses
morphological operation to extract blood vessels. M
ainly two steps are used: firstly enhancement opera
tion
is applied to original retina image to remove noise
and increase contrast of retinal blood vessels. Se
condly
morphology operations are used to take out blood ve
ssels. Experiments are conducted on publicly availa
ble
DIARETDB1 database. Experimental results obtained b
y using gray-scale images have been presented.
Heart Disease Prediction using Machine Learning Algorithmijtsrd
Nowadays, Heart disease has become dangerous to a human being, it effects very badly to human body. If anyone is suffering from heart disease, then it leads to blood clotting. Heart disease prediction is very difficult task to predict in the field of medical science. Affiliation has predicted that 12 million people fail horrendously every year as a result of heart disease. In this paper, we propose a k Nearest Neighbors Algorithm KNN way to deal with improve the exactness of heart determination. We show that k Nearest Neighbors Algorithm KNN have better accuracy than random forest algorithm for viewing heart disease. The k Nearest Neighbors Algorithm give more precise and exact outcome . We have taken 13 attributes in the dataset and a target attribute, by applying machine learning we achieved 84 accuracy in the heart disease detection. Ravi Kumar Singh | Dr. A Rengarajan "Heart Disease Prediction using Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38358.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38358/heart-disease-prediction-using-machine-learning-algorithm/ravi-kumar-singh
IRJET- Automatic Detection of Diabetic Retinopathy using R-CNNIRJET Journal
This document presents research on using Regional Convolutional Neural Networks (R-CNN) to automatically detect diabetic retinopathy from fundus images. The researchers trained an R-CNN model on 130 fundus images and tested it on 110 images. It classified the images into two groups: with diabetic retinopathy and without. The R-CNN approach segmented the whole image and focused classification only on regions of interest. This was found to be more efficient and accurate than regular CNN in terms of speed and accuracy. The R-CNN model achieved an accuracy of approximately 93.8% on the test images. Challenges included the computational complexity which required moving to more powerful systems for training the neural network model.
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
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
Classification of cardiac vascular disease from ecg signals for enhancing mod...hiij
“Why to be in frustration we will do new creation f
or salvation”. Based on these words we grapes your
attention towards saving a life of a heart patient
with the use of ECG in Public Health Care Center by
transmitting ECG signals to nearby hospital server.
In this paper we analyze the abnormalities found i
n the
ECG signals by identifying the Normal, Bradycardia
Arrhythmia, Tachycardia Arrhythmia and Ischemia
signal using the method of Neuro Fuzzy Classifier.
Daubechies Wavelet Transforms is used for feature
extraction and Adaptive Neuro Fuzzy Inference Syste
m (ANFIS) is used for classification. The compressi
on
algorithm is performed by using Huffman coding.
BSS_M KMZ Presentation - A Gamechanger in Medical Diagnostics Vikki Choudhry
BSS Materiel Limited and KMZ Holdings LLC presentation on Zeptronic Diagnostics - A Gamechanger in Medical Diagnostics
System and Method for Diagnostic Analysis of Human Body Systems, Organs and Cells.
This document discusses using data mining classifiers and attribute reduction techniques to predict chronic kidney disease (CKD) more accurately and efficiently. It first provides background on CKD and the need for early detection. It then discusses data mining, classification algorithms, attribute selection filters and wrappers. The document analyzes several studies that predicted CKD using techniques like decision trees, SVM and Naive Bayes. It describes the dataset used from the UCI repository and evaluation metrics. The results section compares J48, Decision Tree and IBK classifiers with and without attribute reduction using CfsSubsetEval, ClassifierSubsetEval and WrapperSubsetEval. Attribute reduction improved accuracy, especially for IBK which achieved 100% accuracy with 72% fewer attributes.
IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...IRJET Journal
This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
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.
Development of computer vision medical recognition systems in MoscowSmartCity Moscow
The document summarizes the current state of computer vision medical recognition systems in Moscow and future plans for development. Moscow has integrated over 700 health facilities into its citywide medical information system, collecting data from over 9 million unique patients. This data is used to develop computer vision systems for recognizing diseases like lung cancer and cardiovascular issues in medical images. The lung cancer recognition system can currently identify 86.5% of tumors in test images. For heart issues, ECG data is being analyzed to improve early detection of conditions. Future plans include expanding recognition capabilities to additional organs and diseases.
The Combination of Steganography and Cryptography for Medical Image ApplicationsIJAAS Team
To give more security for the biomedical images for the patient betterment as well privacy for the patient highly confidently patient image report can be placed in database. If unknown persons like hospital staffs, relatives and third parties like intruder trying to see the report it has in the form of hidden state in another image. The patient detail like MRI image has been converted into any form of steganography. Then, encrypt those image by using proposed cryptography algorithm and place in the database.
This study uses logistic regression to analyze factors that contribute to the risk of acute coronary syndrome (ACS) using a dataset of 319 patients from two cardiac hospitals in Karachi, Pakistan. The dependent variable is diagnosis of ACS, and there are 14 independent variables including age, blood pressure, heart rate, smoking status, and other medical factors. Wald statistics are calculated to test the significance of each variable, finding smoking to have the highest prevalence in increasing ACS risk. The logistic regression model correctly classified 37.1% of patients without ACS and 88.7% of patients with ACS. However, 30.1% of cases were incorrectly classified, indicating room for improving the model's predictive ability.
Analysis of Blood Samples Using Anfis ClassificationIRJET Journal
This document analyzes blood samples using an Adaptive Neuro Fuzzy Inference System (ANFIS) classification to detect different types of leukemia. It discusses preprocessing input images, segmenting blood cells, extracting features, and using ANFIS to classify samples as acute myeloid leukemia, chronic myeloid leukemia, acute lymphocytic leukemia, or chronic lymphocytic leukemia. The methodology includes detecting leukemia cells through structural element analysis and connected component labeling. Texture features and statistical measures from a gray-level co-occurrence matrix are extracted for ANFIS training and classification. Results show the system can reliably classify leukemia types from microscopic images in a cost-effective manner.
Prediction of Heart Disease Using Data Mining Techniques- A ReviewIRJET Journal
This document reviews the use of data mining techniques to predict heart disease. It discusses how medical data sets contain a large amount of patient diagnosis, medication, and detail data that can be mined using techniques like classification algorithms to extract useful patterns and predict trends. Specifically, it explores using classification algorithms like decision trees, Naive Bayes, and neural networks on a data set of 603 records with 76 attributes related to heart health to predict heart disease.
IRJET -Analysis of Ophthalmic System Applications using Signal ProcessingIRJET Journal
This document discusses using signal processing and image analysis techniques to develop objective measures for evaluating ophthalmic diseases like diabetic retinopathy. It analyzes various algorithms that have been used to measure features in retinal images like vessel widths, tortuosity, and network topography. While some algorithms have been widely accepted for use in adults, their applicability to neonates and conditions like retinopathy of prematurity requires further validation. Advances in imaging technologies may help provide more robust outcome measures for clinical diagnosis and research on ophthalmic diseases.
IRJET- A System to Detect Heart Failure using Deep Learning TechniquesIRJET Journal
This document proposes a system to detect heart failure using deep learning techniques. The system uses a boosted decision tree to initially detect the probability that a patient is prone to heart failure. If the probability is over 50%, the patient's ECG recordings are passed to a convolutional neural network (CNN) for more accurate detection of heart failure. The CNN is trained on a dataset of 60,000 ECG recordings. The system also aims to detect the subtype of heart failure using an SVM algorithm trained on data distinguishing systolic vs diastolic heart failure. The overall goal is to accurately detect heart failure at early stages to improve outcomes.
IRJET - Prediction of Risk Factor of the Patient with Hepatocellular Carcinom...IRJET Journal
This document discusses using machine learning to predict the risk factor of patients with hepatocellular carcinoma (HCC or liver cancer) based on medical test results. It involves collecting patient data, preprocessing the data, feature selection to identify key predictive features, and using machine learning algorithms like support vector machines (SVM) and random forests. The best model achieved 95% accuracy using SVM with 5 selected features to classify patients as high or low risk, where high risk means less than one year lifetime. The system could help predict survival time and guide treatment decisions for liver cancer patients.
Heart Disease Prediction Using Multi Feature and Hybrid ApproachIRJET Journal
This document presents a study that uses a hybrid machine learning model to predict heart disease. The study uses a dataset of 303 patient records containing 14 medical features. Several classification algorithms are trained on the data, including logistic regression, Gaussian Naive Bayes, linear support vector classification, K-nearest neighbors, decision tree, and random forest. The linear support vector classification model achieved the best results with 90.78% accuracy, 96.87% precision, 83.78% sensitivity, 89.85% F1 score, and 90.60% ROC. The proposed hybrid model combines the results of the different algorithms to more accurately predict heart disease compared to previous studies.
IRJET-Automatic RBC And WBC Counting using Watershed Segmentation AlgorithmIRJET Journal
This document presents a method for automatically counting red blood cells (RBCs) and white blood cells (WBCs) using image processing techniques. It discusses the limitations of conventional manual counting methods and proposes a software-based watershed segmentation algorithm to segment and count blood cells from microscope images. The algorithm involves preprocessing the image, applying filters, segmenting cells using markers and boundaries, and counting the segmented cells. Experimental results found the automatic method took 14.43 seconds on average and achieved 94.58% accuracy, faster and more accurate than manual counting. This software-based solution provides a low-cost alternative for blood cell analysis in medical laboratories.
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.
IRJET- Diabetes Prediction using Machine LearningIRJET Journal
This document discusses predicting diabetes using machine learning algorithms. It analyzes the Pima Indian diabetes dataset using Support Vector Machine (SVM), K-Nearest Neighbors, and Decision Tree algorithms. SVM achieved the highest accuracy of 80% for predicting whether a patient has diabetes. Key features like glucose level and body mass index were most important for prediction. A GUI was created to allow users to enter patient data and predict diabetes status using the SVM model trained on the dataset.
IRJET- Automatic Detection of Diabetic Retinopathy LesionsIRJET Journal
This document describes a proposed method for automatically detecting lesions in retinal images that are indicative of diabetic retinopathy. The method uses image processing and machine learning techniques. It involves pre-processing retinal images by enhancing contrast and filtering noise. Lesions are then segmented using morphological operations and features like area of blood vessels and exudates are extracted. These features are classified using a sparse representative classifier to determine if lesions are present and classify image as normal or abnormal, indicating diabetic retinopathy. The goal is to accurately detect signs of diabetic retinopathy at early stages from retinal images to help with diagnosis and prevent vision loss.
LIFE EXPECTANCY PREDICTION FOR POST THORACIC SURGERYIRJET Journal
This document discusses using machine learning algorithms to predict life expectancy after thoracic surgery. Researchers used attribute ranking and selection methods to identify the most important attributes from a dataset of patient health records. They evaluated algorithms like logistic regression and random forest on the reduced dataset. Logistic regression achieved the highest prediction accuracy of 85%. The goal was to more accurately predict mortality risk based on a patient's underlying health issues and attributes related to lung cancer.
IRJET - Accuracy Prediction and Classification using Machine Learning Techniq...IRJET Journal
This document discusses using machine learning techniques to predict sepsis early using clinical data from ICU patients. It aims to develop a prediction system using neural networks to reduce sepsis-associated mortality. The methodology uses a probabilistic neural network with input, pattern, summation and output layers to classify patients as septic or non-septic based on 40 clinical variables from vital signs, labs and demographics. The results found this approach can efficiently predict sepsis onset, but future work is needed to handle missing data and improve efficiency.
Diagnosing Chronic Kidney Disease using Machine LearningIRJET Journal
This document summarizes a research paper that uses machine learning algorithms to diagnose chronic kidney disease. The researchers used the K-nearest neighbor imputation technique to fill missing values in a chronic kidney disease dataset containing 400 patient records. They then developed an integrated model using naive Bayes classifier and random forest algorithms to predict chronic kidney disease. The model was tested using various performance metrics like accuracy, precision, recall, F1-score, sensitivity, specificity and ROC. The results showed promising accuracy in early prediction of chronic kidney disease.
Design and Development of Prediction of Liver Disease, its Seriousness and Se...IRJET Journal
This document presents research on developing a machine learning model to predict liver disease and its severity. The researchers tested several machine learning algorithms on a dataset of over 16,000 liver patient records. AdaBoost Classifier performed best with 99% accuracy. The model predicts liver disease and severity based on medical attributes. A rule-based engine then predicts severity based on attributes and can be customized by doctors for different locations and conditions. The overall aim is to enable early detection of liver disease and proper treatment.
IRJET- Predicting Diabetes Disease using Effective Classification TechniquesIRJET Journal
This document discusses predicting diabetes disease using machine learning techniques. It begins with an abstract introducing diabetes mellitus and the importance of early detection. It then discusses the Pima Indian diabetes dataset that is commonly used for research. The document outlines the existing research which focuses mainly on one or two techniques, while the proposed research will take a more comprehensive approach, comparing multiple techniques. It describes evaluating classifiers like deep neural networks and support vector machines on the Pima Indian dataset. The best technique identified achieved 77.86% accuracy. Feature relevance is also analyzed to modify the dataset for future studies. The goal is to automate diabetes identification and help physicians detect the disease earlier.
IRJET - Detection and Classification of Leukemia using Convolutional Neural N...IRJET Journal
This document describes a method for detecting and classifying leukemia using microscopic blood smear images. The method involves preprocessing images using techniques like median filtering for noise removal. Images then undergo segmentation using grayscaling, binarization, and adaptive thresholding. A convolutional neural network (CNN) is trained on a dataset of labeled images to classify images as acute, chronic, or normal leukemia. The CNN model provides accurate classification of leukemia types from blood smear images. Additionally, the results can be stored on a server using internet of things (IoT) for remote monitoring of patient data.
1 springer format chronic changed edit iqbal qcIAESIJEECS
In the present generation, majority of the people are highly affected by kidney diseases. Among them, chronic kidney is the most common life threatening disease which can be prevented by early detection.Histological grade in chronic kidney disease provides clinically important prognostic information. Therefore, machine learning techniques are applied on the information collected from previously diagnosed patients in order to discover the knowledge and patterns for making precise predictions.A large number of features exist in the raw data in which some may cause low information and error; hence feature selection techniques can be used to retrieve useful subset of features and to improve the computation performance. In this manuscript we use a set of Filter, Wrapper methods followed by Bagging and Boosting models with parameter tuning technique to classify chronic kidney disease.Capability of Bagging and Boosting classifiers are compared and the best ensemble classifier which attains high stability with better promising results is identified.
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.
AN EFFECTIVE PREDICTION OF CHRONIC KIDENY DISEASE USING DATA MINING CLASSIFIE...IRJET Journal
This document discusses using data mining classifiers and data sampling techniques to effectively predict chronic kidney disease. It analyzes the chronic kidney disease dataset from the UCI machine learning repository, which is imbalanced. It proposes applying various data sampling methods like SMOTE, ADASYN, SMOTE+Tomek Links, and K-means SMOTE to balance the dataset. Then, different data mining algorithms like decision trees, random forests, SVM, and KNN will be used on the sampled datasets to predict chronic kidney disease. The goal is to find the best performing combination of sampling technique and data mining algorithm based on accuracy, precision, sensitivity and specificity metrics.
A REVIEW PAPER ON THE DETECTION OF DIABETIC RETINOPATHYIRJET Journal
This document reviews different techniques for detecting diabetic retinopathy through automated methods. Diabetic retinopathy damages blood vessels in the retina and can cause vision loss if untreated. The review examines methods that use convolutional neural networks and deep learning models to classify images of the retina as normal, mild, moderate or proliferative diabetic retinopathy. These automated detection methods can analyze retina images faster and less expensively than manual examination. The review also evaluates the accuracy of different models, finding levels from 73% to 99% depending on the technique and dataset used. Overall, the document demonstrates that automated detection through deep learning is superior to manual detection for identifying diabetic retinopathy in a timely manner.
Heart Disease Prediction using Machine Learning AlgorithmsIRJET Journal
This document discusses using machine learning algorithms to predict heart disease based on patient attributes. It begins with an introduction describing heart disease as a major cause of death and the importance of early detection. It then discusses using machine learning techniques like logistic regression, backward elimination, and recursive feature elimination on a publicly available heart disease dataset to classify patients and evaluate the results. The goal is to help identify patients at high risk of heart disease so lifestyle changes can be made to reduce complications. Various machine learning algorithms are tested and evaluated to determine the best approach for heart disease prediction.
A novel salp swarm clustering algorithm for prediction of the heart diseasesnooriasukmaningtyas
Heart diseases have a severe impact on human life and health. Cardiovascular deaths and diseases have increased at a fast rate worldwide. The early prediction of these diseases is necessary to prevent deaths. Now a day; a considerable amount of medical information is available and collected as databases. An efficient technique is required to analyse this data and predict the disease. Clustering can help medical practitioners in diagnosis by classifying the patient’s data collected for a disease. Clustering techniques can analyse such data based on each patient-generated and predict disease. A new prediction model based on salp swarm algorithm and support vector machine is proposed in this research for predicting heart diseases. Salp swarm algorithm is used to select the useful features from the database. Support vector machine classifier is used to predict heart diseases. Results obtained are compared with the other algorithms available in the literature. It is observed that the proposed approach produces better results with accuracy 98.75% and 98.46% with the dataset 1 and 2, respectively. In addition to this, the algorithm converges in significantly less time in comparison to other algorithms. This algorithm might become a perfect supporting tool for medical practitioners.
HEART DISEASE PREDICTION RANDOM FOREST ALGORITHMSIRJET Journal
This document summarizes a research paper that proposes a machine learning model to predict heart disease using classification algorithms. The paper uses the Cleveland heart disease dataset to train and test decision tree, random forest, and a hybrid model combining the two. Experimental results showed the hybrid model achieved 88.7% accuracy in predicting heart disease, outperforming the individual algorithms. The paper aims to develop an effective heart disease prediction tool to assist healthcare professionals.
PARKINSON’S DISEASE DETECTION USING MACHINE LEARNINGIRJET Journal
This document discusses using machine learning to detect Parkinson's disease. It presents the results of several studies that used techniques like random forests, support vector machines, logistic regression, and neural networks. The best performing model was found to be random forest, achieving 97.43% accuracy, 96.55% precision, and 98.24% F1 score. The study concludes that machine learning shows promise for early detection of Parkinson's disease using features extracted from voice and image data.
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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.
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.
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.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
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%.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
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.