This document presents a method for detecting and classifying acute ischemic strokes in CT scan images. The method involves pre-processing images using median filtering and skull stripping. Features like mean, entropy, and gray-level co-occurrence matrix values are then extracted. Naive Bayes and k-nearest neighbor classifiers are used to classify images as normal or stroke with 92% accuracy. The k-NN classifier takes longer (8.80 seconds) to process images compared to the Naive Bayes classifier (5.85 seconds). The method accurately detects stroke regions in images and can help in early diagnosis and treatment of ischemic strokes.
This document discusses automatic segmentation of white matter from brain fMRI images. It presents a 3-step solution: 1) preprocessing raw images using histogram-based double thresholding to remove noise, 2) estimating a threshold value for segmentation using Otsu's algorithm, and 3) performing binary segmentation of images based on the calculated threshold. Currently, white matter segmentation is done manually, which is time-consuming. The proposed automatic method could help address this issue.
This document summarizes a study that used artificial neural networks (ANN) to segment MRI brain images into gray matter, white matter, and cerebrospinal fluid in order to analyze and classify three neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and epilepsy. Real MRI data from patients with these diseases was preprocessed, features were extracted using Gabor filters, and ANN was used to classify tissues. The ANN approach achieved 96.13% accuracy for Alzheimer's classification, 93.26% for Parkinson's, and 91.33% for epilepsy. The study demonstrated that ANN is effective for automated brain tissue segmentation and shows potential for assisting in diagnosis of neurological diseases.
Cerebral infarction classification using multiple support vector machine with...journalBEEI
Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.
This document presents a system for classifying electrocardiogram (ECG) signals using a convolutional neural network (CNN). The system first preprocesses ECG data, then uses a CNN classifier to identify different types of arrhythmias without requiring complex feature engineering. The CNN model is optimized using techniques like batch normalization, Xavier initialization, data augmentation, and dropout. The system was tested on ECG datasets and achieved classification accuracy of 93.3%, sensitivity of 96%, and specificity of 100%, showing improvements over existing methods.
IRJET- Arrhythmia Detection using One Dimensional Convolutional Neural NetworkIRJET Journal
1) The document discusses using a 1D convolutional neural network to detect different types of arrhythmias from electrocardiogram (ECG) signals.
2) It proposes a novel wavelet domain multiresolution convolutional neural network approach that avoids complicated heartbeat detection techniques and heavy manual feature engineering.
3) The approach segments ECG signals, applies a discrete cosine transform to select coefficients, and uses a CNN for classification and arrhythmia monitoring. It detects five types of arrhythmias from one-lead ECG signals.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
An Automatic ROI of The Fundus Photography IJECEIAES
The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photography’s of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as dark background, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.
Hybrids Otsu Method, Feature region and Mathematical Morphology for Calculati...TELKOMNIKA JOURNAL
Hemorrhage in the brain is a process of pathological culture from the tissues of the brain with the
strength of the external mechanical, which cause physical disorders, cognitive function, and psychosocial
support. Brain bleeding can cause was bruised, network torn, bleeding and brain damage or
death.Segmentation techniques can be done with the Scanner computed tomography images (CT-scan) to
detect the abnormalities or bleeding of the brain which occurs in the brain. This research describes the
taking of an area of the brain bleeding on each image slice CT-scan and reconstruction 3D, to visualize the
image of the 3D and calculate the volume of the brain bleeding. Extraction of bleeding area of the brain is
based on a hybrids of Otsu algorithm, morphological features algorithm algorithm and an area of bleeding.
For the reconstruction of 3D area on the area of bleeding from a slice 2D is done by using a linear
interpolation approach.
This document discusses automatic segmentation of white matter from brain fMRI images. It presents a 3-step solution: 1) preprocessing raw images using histogram-based double thresholding to remove noise, 2) estimating a threshold value for segmentation using Otsu's algorithm, and 3) performing binary segmentation of images based on the calculated threshold. Currently, white matter segmentation is done manually, which is time-consuming. The proposed automatic method could help address this issue.
This document summarizes a study that used artificial neural networks (ANN) to segment MRI brain images into gray matter, white matter, and cerebrospinal fluid in order to analyze and classify three neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and epilepsy. Real MRI data from patients with these diseases was preprocessed, features were extracted using Gabor filters, and ANN was used to classify tissues. The ANN approach achieved 96.13% accuracy for Alzheimer's classification, 93.26% for Parkinson's, and 91.33% for epilepsy. The study demonstrated that ANN is effective for automated brain tissue segmentation and shows potential for assisting in diagnosis of neurological diseases.
Cerebral infarction classification using multiple support vector machine with...journalBEEI
Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.
This document presents a system for classifying electrocardiogram (ECG) signals using a convolutional neural network (CNN). The system first preprocesses ECG data, then uses a CNN classifier to identify different types of arrhythmias without requiring complex feature engineering. The CNN model is optimized using techniques like batch normalization, Xavier initialization, data augmentation, and dropout. The system was tested on ECG datasets and achieved classification accuracy of 93.3%, sensitivity of 96%, and specificity of 100%, showing improvements over existing methods.
IRJET- Arrhythmia Detection using One Dimensional Convolutional Neural NetworkIRJET Journal
1) The document discusses using a 1D convolutional neural network to detect different types of arrhythmias from electrocardiogram (ECG) signals.
2) It proposes a novel wavelet domain multiresolution convolutional neural network approach that avoids complicated heartbeat detection techniques and heavy manual feature engineering.
3) The approach segments ECG signals, applies a discrete cosine transform to select coefficients, and uses a CNN for classification and arrhythmia monitoring. It detects five types of arrhythmias from one-lead ECG signals.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
An Automatic ROI of The Fundus Photography IJECEIAES
The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photography’s of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as dark background, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.
Hybrids Otsu Method, Feature region and Mathematical Morphology for Calculati...TELKOMNIKA JOURNAL
Hemorrhage in the brain is a process of pathological culture from the tissues of the brain with the
strength of the external mechanical, which cause physical disorders, cognitive function, and psychosocial
support. Brain bleeding can cause was bruised, network torn, bleeding and brain damage or
death.Segmentation techniques can be done with the Scanner computed tomography images (CT-scan) to
detect the abnormalities or bleeding of the brain which occurs in the brain. This research describes the
taking of an area of the brain bleeding on each image slice CT-scan and reconstruction 3D, to visualize the
image of the 3D and calculate the volume of the brain bleeding. Extraction of bleeding area of the brain is
based on a hybrids of Otsu algorithm, morphological features algorithm algorithm and an area of bleeding.
For the reconstruction of 3D area on the area of bleeding from a slice 2D is done by using a linear
interpolation approach.
This document presents an expert system based on fuzzy logic for fault detection and diagnosis of Doubly Fed Induction Generators (DFIGs). The system uses fuzzy inference rules based on root mean square values of stator currents to determine the operating state of the DFIG. Simulations in Matlab/Simulink demonstrate the effectiveness of the proposed strategy in accurately detecting and diagnosing faults like short circuits and open circuits. The results show the fuzzy logic approach can distinguish between fault severities like dangerous versus normal short circuits.
Performance analysis of ecg qrs complex detection using morphological operatorsIAEME Publication
The QRS complex detection is one of the most essential tasks in ECG analysis. This paper
presents an algorithm of QRS complex detection using morphological operators. The proposed
algorithm utilizes the dilation-erosion mathematical morphology filtering to suppress the background
noise and remove the baseline drift. Then the modulus accumulation is used to enhance the signal
and improve signal-to-noise ratio. The performance of the algorithm is evaluated with MIT-BIH
arrhythmia database and wearable ECG Data. The algorithm gets the high detection rate and high
speed.
Clustering of medline documents using semi supervised spectral clusteringeSAT Journals
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
This paper proposes using a deep learning model with 1D convolutional layers and fully-connected layers for ECG classification. The model is tested on a dataset containing single-lead ECG recordings classified into 4 categories. The deep learning model achieves 86% accuracy on the validation set, outperforming traditional machine learning approaches that rely on hand-crafted features. While deep learning has potential for ECG classification, further work is needed to compare architectures and optimize model performance.
Visible watermarking within the region of non interest of medical images base...csandit
Transfer of medical information amongst various hospitals and diagnostic centers for mutual
availability of diagnostic and therapeutic case studies is a very common process. Watermarking
is adding “ownership” information in multimedia contents to verify signal integrity, prove
authenticity and achieve control over the copy process. Distortion in Region of Interest (ROI) of
a bio-medical image caused by watermarking may lead to wrong diagnosis and treatment.
Therefore, proper selection of Region of Non-Interest (RONI) in a medical image is very crucial
for adding watermark. First part of the present work proposes proper selection of Region of
Non-Interest based on Fuzzy C-Means segmentation and Harris corner detection, to improve
retention of diagnostic value lost in embedding ownership information. The second part of the
work presents watermark embedding in the selected area of RONI based on alpha blending
technique. In this approach, the generated watermarked image having an acceptable level of
imperceptibility and distortion is compared to the original image. The Peak Signal to Noise
Ratio (PSNR) of the original image vs. watermarked image is calculated to prove the efficacy of
the proposed method.
Analysis electrocardiogram signal using ensemble empirical mode decomposition...IAEME Publication
This document discusses techniques for analyzing electrocardiogram (ECG) signals that are noisy and non-stationary. It compares the Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Discrete Wavelet Transform (DWT) for denoising ECG signals, finding that EEMD performs best by preserving true waveform features while eliminating noise. It also analyzes normal and abnormal (atrial fibrillation) ECG signals using parametric (periodogram, capon, time-varying autoregressive) and non-parametric (S-transform, smoothed pseudo affine Wigner distributions) time-frequency techniques, determining that the periodogram technique provides the best resolution
This document presents a method for automatically classifying CT brain images according to different types of head trauma. The method involves three main steps: 1) preprocessing images to segment potential hemorrhage regions, 2) extracting features from each region like size, shape, location, 3) classifying each region and overall image using machine learning. The method was tested on 35 CT brain images and achieved an average accuracy of 93% in classifying potential hemorrhage regions into categories like epidural hemorrhage, subdural hemorrhage, and intracerebral hemorrhage.
This document summarizes a research paper that proposes a novel method for separating clumped particles in microscopic images. The method uses an iterative hypothesis and verification technique. It generates hypotheses about particle boundaries and colors, then verifies the hypotheses using measures like boundary distance. This allows it to detect non-circular particle shapes, unlike previous methods using circle/ellipse detection. The technique is tested on blood cell images and achieves 98% accuracy in particle counting, higher than other methods.
Binary operation based hard exudate detection and fuzzy based classification ...IJECEIAES
Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.
Ensemble Classifications of Wavelets based GLCM Texture Feature from MR Human...rahulmonikasharma
This paper presents an automatic image analysis of multi-model views of MR brain using ensemble classifications of wavelets based texture feature. Primarily, an input MR image has pre-processed for an enhancement process. Then, the pre-processed image is decomposed into different frequency sub-band image using 2D stationary and discrete wavelet transform. The GLCM texture feature information is extracted from the above low-frequency sub band image of 2D discrete and stationary wavelet transform. The extracted texture features are given as an input to ensemble classifiers of Gentle Boost and Bagged Tree classifiers to recognize the appropriate image samples. Image abnormality has extracted from the recognized abnormal image samples of classifiers using multi-level Otsu thresholding. Finally, the performance of two ensemble classifiers performance has analyzed using sensitivity, specificity, accuracy, and MCC measures of two different wavelet based GLCM texture features. The resultant proposed feature extraction technique achieves the maximum level of accuracy is 90.70% with the fraction of 0.78 MCC value.
MULTIPLE SCLEROSIS DIAGNOSIS WITH FUZZY C-MEANScscpconf
Magnetic resonance imaging (MRI) can support and substitute clinical information in the
diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm
for MS lesion segmentation. We revisit the modification of properties of fuzzy c means
algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply
canny contraction principle, and establish a relationship between MS lesions and edge
detection. For the special case of FCM, we derive a sufficient condition for fixed lesions,
allowing identification of them as (local) minima of the objective function.
Personal identity verification based ECG biometric using non-fiducial features IJECEIAES
Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.
Day by day the scope & use of the electronics concepts in bio-medical field is increasing step by step. In this paper the review of newly developed concepts is done for the monitoring of the ECG signal. This paper also reviews a power and area efficient electrocardiogram (ECG) acquisition and signal processing application sensor node. Further the study of IoT frame work for ECG monitoring has been carried out. Ms. Dhanashri Yamagekar | Dr. Pradip Bhaskar"Real time ECG Monitoring: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7065.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/7065/real-time-ecg-monitoring-a-review/ms-dhanashri-yamagekar
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.
Utilizing ECG Waveform Features as New Biometric Authentication Method IJECEIAES
In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.
IRJET- Brain Tumor Segmentation and Detection using F-TransformIRJET Journal
The document presents a method for brain tumor segmentation and detection using F-transform. There are two main stages: detection and segmentation. In detection, asymmetry between the left and right hemispheres of the brain is analyzed to detect abnormalities. In segmentation, F-transform is used for edge detection followed by morphological operations to extract the tumor region. The method was able to accurately segment and detect brain tumors in MRI images and showed improved speed over other techniques.
IRJET- An Effective Brain Tumor Segmentation using K-means ClusteringIRJET Journal
This document presents a study on using k-means clustering for brain tumor segmentation from MRI images. It begins with an introduction to brain strokes and current segmentation techniques. It then describes the fuzzy c-means clustering algorithm and its limitations. The proposed method is to use k-means clustering for tumor segmentation, with preprocessing of MRI images followed by k-means clustering. Experimental results on brain MRI images show that k-means clustering can effectively segment tumors, with clearer edges compared to traditional algorithms like fuzzy c-means.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Automated segmentation and classification technique for brain strokeIJECEIAES
Difussion-Weighted Imaging (DWI) plays an important role in the diagnosis of brain stroke by providing detailed information regarding the soft tissue contrast in the brain organ. Conventionally, the differential diagnosis of brain stroke lesions is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). The type of stroke lesions consists of acute ischemic, sub-acute ischemic, chronic ischemic and acute hemorrhage. For segmentation, fuzzy c-Means (FCM) and active contour is proposed to segment the lesion’s region. FCM is implemented with active contour to separate the cerebral spinal fluid (CSF) with the hypointense lesion. Pre-processing is applied to the DWI for image normalization, background removal and image enhancement. The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). The average results for the Jaccard index, DC, FPR and FNR are 0.55, 0.68, 0.23 and 0.23, respectively. First statistical order method is applied to the segmentation result to obtain the features for the classifier input. For classification technique, bagged tree classifier is proposed to classify the type of stroke. The accuracy results for the classification is 90.8%. Based on the results, the proposed technique has potential to segment and classify brain stroke lesion from DWI images.
Development of algorithm for identification of maligant growth in cancer usin...IJECEIAES
The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The various algorithms had been proposed earlier by many researchers within the past, but the accuracy of prediction is usually a challenging task. During this work, a man-made neural networ based methodology is proposed to seek out the irregular growth of lung tissues. Higher probability of detection is taken as a goal to urge an automatic tool, with great accuracy. The best feature sets derived from Haralick Gray level co occurrence Matrix and used because the dimension reduction way for feeding neural network. During this work, a binary Binary classifier neural network has been proposed to spot the traditional images out of all the images. The potential of the proposed neural network has been quantitatively computed using confusion matrix and located in terms of accuracy.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
IRJET- Image Processing for Brain Tumor Segmentation and ClassificationIRJET Journal
This document presents a method for segmenting and classifying brain tumors in MR images using image processing techniques. It involves pre-processing images using adaptive histogram equalization, extracting features using discrete wavelet transform (DWT) and principal component analysis (PCA) for dimension reduction. Texture and statistical features are then extracted and classifiers like support vector machine (SVM), K-nearest neighbors (KNN) and neural networks are used to classify tumors as benign, malignant or pituitary. The method is evaluated on a brain tumor dataset containing MR images of different tumor types and shows promise for automatic brain tumor segmentation and classification.
This document presents an expert system based on fuzzy logic for fault detection and diagnosis of Doubly Fed Induction Generators (DFIGs). The system uses fuzzy inference rules based on root mean square values of stator currents to determine the operating state of the DFIG. Simulations in Matlab/Simulink demonstrate the effectiveness of the proposed strategy in accurately detecting and diagnosing faults like short circuits and open circuits. The results show the fuzzy logic approach can distinguish between fault severities like dangerous versus normal short circuits.
Performance analysis of ecg qrs complex detection using morphological operatorsIAEME Publication
The QRS complex detection is one of the most essential tasks in ECG analysis. This paper
presents an algorithm of QRS complex detection using morphological operators. The proposed
algorithm utilizes the dilation-erosion mathematical morphology filtering to suppress the background
noise and remove the baseline drift. Then the modulus accumulation is used to enhance the signal
and improve signal-to-noise ratio. The performance of the algorithm is evaluated with MIT-BIH
arrhythmia database and wearable ECG Data. The algorithm gets the high detection rate and high
speed.
Clustering of medline documents using semi supervised spectral clusteringeSAT Journals
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
This paper proposes using a deep learning model with 1D convolutional layers and fully-connected layers for ECG classification. The model is tested on a dataset containing single-lead ECG recordings classified into 4 categories. The deep learning model achieves 86% accuracy on the validation set, outperforming traditional machine learning approaches that rely on hand-crafted features. While deep learning has potential for ECG classification, further work is needed to compare architectures and optimize model performance.
Visible watermarking within the region of non interest of medical images base...csandit
Transfer of medical information amongst various hospitals and diagnostic centers for mutual
availability of diagnostic and therapeutic case studies is a very common process. Watermarking
is adding “ownership” information in multimedia contents to verify signal integrity, prove
authenticity and achieve control over the copy process. Distortion in Region of Interest (ROI) of
a bio-medical image caused by watermarking may lead to wrong diagnosis and treatment.
Therefore, proper selection of Region of Non-Interest (RONI) in a medical image is very crucial
for adding watermark. First part of the present work proposes proper selection of Region of
Non-Interest based on Fuzzy C-Means segmentation and Harris corner detection, to improve
retention of diagnostic value lost in embedding ownership information. The second part of the
work presents watermark embedding in the selected area of RONI based on alpha blending
technique. In this approach, the generated watermarked image having an acceptable level of
imperceptibility and distortion is compared to the original image. The Peak Signal to Noise
Ratio (PSNR) of the original image vs. watermarked image is calculated to prove the efficacy of
the proposed method.
Analysis electrocardiogram signal using ensemble empirical mode decomposition...IAEME Publication
This document discusses techniques for analyzing electrocardiogram (ECG) signals that are noisy and non-stationary. It compares the Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Discrete Wavelet Transform (DWT) for denoising ECG signals, finding that EEMD performs best by preserving true waveform features while eliminating noise. It also analyzes normal and abnormal (atrial fibrillation) ECG signals using parametric (periodogram, capon, time-varying autoregressive) and non-parametric (S-transform, smoothed pseudo affine Wigner distributions) time-frequency techniques, determining that the periodogram technique provides the best resolution
This document presents a method for automatically classifying CT brain images according to different types of head trauma. The method involves three main steps: 1) preprocessing images to segment potential hemorrhage regions, 2) extracting features from each region like size, shape, location, 3) classifying each region and overall image using machine learning. The method was tested on 35 CT brain images and achieved an average accuracy of 93% in classifying potential hemorrhage regions into categories like epidural hemorrhage, subdural hemorrhage, and intracerebral hemorrhage.
This document summarizes a research paper that proposes a novel method for separating clumped particles in microscopic images. The method uses an iterative hypothesis and verification technique. It generates hypotheses about particle boundaries and colors, then verifies the hypotheses using measures like boundary distance. This allows it to detect non-circular particle shapes, unlike previous methods using circle/ellipse detection. The technique is tested on blood cell images and achieves 98% accuracy in particle counting, higher than other methods.
Binary operation based hard exudate detection and fuzzy based classification ...IJECEIAES
Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.
Ensemble Classifications of Wavelets based GLCM Texture Feature from MR Human...rahulmonikasharma
This paper presents an automatic image analysis of multi-model views of MR brain using ensemble classifications of wavelets based texture feature. Primarily, an input MR image has pre-processed for an enhancement process. Then, the pre-processed image is decomposed into different frequency sub-band image using 2D stationary and discrete wavelet transform. The GLCM texture feature information is extracted from the above low-frequency sub band image of 2D discrete and stationary wavelet transform. The extracted texture features are given as an input to ensemble classifiers of Gentle Boost and Bagged Tree classifiers to recognize the appropriate image samples. Image abnormality has extracted from the recognized abnormal image samples of classifiers using multi-level Otsu thresholding. Finally, the performance of two ensemble classifiers performance has analyzed using sensitivity, specificity, accuracy, and MCC measures of two different wavelet based GLCM texture features. The resultant proposed feature extraction technique achieves the maximum level of accuracy is 90.70% with the fraction of 0.78 MCC value.
MULTIPLE SCLEROSIS DIAGNOSIS WITH FUZZY C-MEANScscpconf
Magnetic resonance imaging (MRI) can support and substitute clinical information in the
diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm
for MS lesion segmentation. We revisit the modification of properties of fuzzy c means
algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply
canny contraction principle, and establish a relationship between MS lesions and edge
detection. For the special case of FCM, we derive a sufficient condition for fixed lesions,
allowing identification of them as (local) minima of the objective function.
Personal identity verification based ECG biometric using non-fiducial features IJECEIAES
Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.
Day by day the scope & use of the electronics concepts in bio-medical field is increasing step by step. In this paper the review of newly developed concepts is done for the monitoring of the ECG signal. This paper also reviews a power and area efficient electrocardiogram (ECG) acquisition and signal processing application sensor node. Further the study of IoT frame work for ECG monitoring has been carried out. Ms. Dhanashri Yamagekar | Dr. Pradip Bhaskar"Real time ECG Monitoring: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7065.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/7065/real-time-ecg-monitoring-a-review/ms-dhanashri-yamagekar
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.
Utilizing ECG Waveform Features as New Biometric Authentication Method IJECEIAES
In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.
IRJET- Brain Tumor Segmentation and Detection using F-TransformIRJET Journal
The document presents a method for brain tumor segmentation and detection using F-transform. There are two main stages: detection and segmentation. In detection, asymmetry between the left and right hemispheres of the brain is analyzed to detect abnormalities. In segmentation, F-transform is used for edge detection followed by morphological operations to extract the tumor region. The method was able to accurately segment and detect brain tumors in MRI images and showed improved speed over other techniques.
IRJET- An Effective Brain Tumor Segmentation using K-means ClusteringIRJET Journal
This document presents a study on using k-means clustering for brain tumor segmentation from MRI images. It begins with an introduction to brain strokes and current segmentation techniques. It then describes the fuzzy c-means clustering algorithm and its limitations. The proposed method is to use k-means clustering for tumor segmentation, with preprocessing of MRI images followed by k-means clustering. Experimental results on brain MRI images show that k-means clustering can effectively segment tumors, with clearer edges compared to traditional algorithms like fuzzy c-means.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Automated segmentation and classification technique for brain strokeIJECEIAES
Difussion-Weighted Imaging (DWI) plays an important role in the diagnosis of brain stroke by providing detailed information regarding the soft tissue contrast in the brain organ. Conventionally, the differential diagnosis of brain stroke lesions is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). The type of stroke lesions consists of acute ischemic, sub-acute ischemic, chronic ischemic and acute hemorrhage. For segmentation, fuzzy c-Means (FCM) and active contour is proposed to segment the lesion’s region. FCM is implemented with active contour to separate the cerebral spinal fluid (CSF) with the hypointense lesion. Pre-processing is applied to the DWI for image normalization, background removal and image enhancement. The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). The average results for the Jaccard index, DC, FPR and FNR are 0.55, 0.68, 0.23 and 0.23, respectively. First statistical order method is applied to the segmentation result to obtain the features for the classifier input. For classification technique, bagged tree classifier is proposed to classify the type of stroke. The accuracy results for the classification is 90.8%. Based on the results, the proposed technique has potential to segment and classify brain stroke lesion from DWI images.
Development of algorithm for identification of maligant growth in cancer usin...IJECEIAES
The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The various algorithms had been proposed earlier by many researchers within the past, but the accuracy of prediction is usually a challenging task. During this work, a man-made neural networ based methodology is proposed to seek out the irregular growth of lung tissues. Higher probability of detection is taken as a goal to urge an automatic tool, with great accuracy. The best feature sets derived from Haralick Gray level co occurrence Matrix and used because the dimension reduction way for feeding neural network. During this work, a binary Binary classifier neural network has been proposed to spot the traditional images out of all the images. The potential of the proposed neural network has been quantitatively computed using confusion matrix and located in terms of accuracy.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
IRJET- Image Processing for Brain Tumor Segmentation and ClassificationIRJET Journal
This document presents a method for segmenting and classifying brain tumors in MR images using image processing techniques. It involves pre-processing images using adaptive histogram equalization, extracting features using discrete wavelet transform (DWT) and principal component analysis (PCA) for dimension reduction. Texture and statistical features are then extracted and classifiers like support vector machine (SVM), K-nearest neighbors (KNN) and neural networks are used to classify tumors as benign, malignant or pituitary. The method is evaluated on a brain tumor dataset containing MR images of different tumor types and shows promise for automatic brain tumor segmentation and classification.
IRJET - ECG based Cardiac Arrhythmia Detection using a Deep Neural NetworkIRJET Journal
This paper presents a system for detecting cardiac arrhythmias based on electrocardiogram (ECG) signals using a deep neural network. ECG signals are first transformed into time-frequency spectrograms using short-time Fourier transform. These spectrograms are then used as input for a 2D convolutional neural network to classify five types of arrhythmias: normal beat, normal sinus rhythm, atrial fibrillation, supraventricular tachycardia, and atrial premature beat. The technique is evaluated on the MIT-BIH database and achieves 97% beat classification accuracy and perfect rhythm identification. Compared to other existing methods like SVM, RNN, RF and KNN, the deep learning approach provides better performance for E
A hybrid method for traumatic brain injury lesion segmentationIJECEIAES
Traumatic brain injuries are significant effects of disability and loss of life. Physicians employ computed tomography (CT) images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of hemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. The study is more challenging to unitize the AI field to collect brain hemorrhage by involving patient datasets employing CT scans images. We propose a novel technique free-form object model for brain injury CT image segmentation based on superpixel image processing that uses CT to analyzing brain injuries, quite challenging to create a high outstanding simple linear iterative clustering (SLIC) method. The method maintains a strategic distance of the segmentation image to reduced intensity boundaries. The segmentation image contains marked red hemorrhage to modify the free-form object model. The contour labelled by the red mark is the output from our free-form object model. We proposed a hybrid image segmentation approach based on the combined edge detection and dilation technique features. The approach diminishes computational costs, and the method accomplished 94.84% accuracy. The segmenting brain hemorrhage images are achieved in the clustered region to construct a free-form object model. The study also presents further directions on future research in this domain.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clust...IJECEIAES
Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain.
Segmentation and Classification of MRI Brain TumorIRJET Journal
This document presents a study comparing two techniques for detecting brain tumors in MRI images: level set segmentation and K-means segmentation. Features are extracted from the segmented tumors using discrete wavelet transform and gray level co-occurrence matrix. The features are then classified as benign or malignant using a support vector machine. The level set method and K-means method are evaluated based on accuracy, sensitivity, and specificity on a dataset of 41 MRI brain images. The level set method achieved slightly higher accuracy of 94.12% compared to the K-means method.
Automatic ECG signal denoising and arrhythmia classification using deep learningIRJET Journal
This document presents a method for automatic ECG signal denoising and arrhythmia classification using deep learning. It proposes an automatic denoising method called LDAE that uses denoising autoencoders based on long short-term memory to remove noise from ECG signals. The denoised signals are then classified for different types of arrhythmias using a deep multilayer perceptron algorithm. The method achieves an average SNR of 27.5 and RMSE of 0.037 for signal reconstruction, demonstrating effective noise removal. For arrhythmia classification, the method obtains 98% accuracy, 98.57% precision, 97.25% recall, and 97.45% F1 score.
IRJET- Detection of Cataract by Statistical Features and ClassificationIRJET Journal
This document proposes a new method for detecting cataract using statistical features extracted from fundus images and classifying the severity using machine learning algorithms. The method involves pre-processing fundus images using adaptive histogram equalization, extracting statistical features like mean, entropy and area using thresholding, and classifying the features using K-means clustering and ANFIS to determine if the image shows normal, mild, or severe cataract. The method achieved high accuracy, sensitivity and specificity for cataract detection and classification.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
IRJET- Performance Analysis of Lung Disease Detection and ClassificationIRJET Journal
This document presents a study on the performance analysis of lung disease detection and classification using computed tomography (CT) scans. It begins with an introduction on the importance of early and accurate diagnosis of lung diseases. The study then describes the various steps involved - image acquisition, preprocessing, lung region extraction, identification of affected lung side, segmentation using thresholding and morphological methods, feature extraction of texture features, and classification using K-nearest neighbors. Performance metrics like accuracy, precision, sensitivity and specificity are evaluated. Finally, the study concludes that the proposed automatic system achieved accurate classification of segmented lung diseases.
IRJET- Texture Analysis and Fracture Identification of Bones X-Ray Images...IRJET Journal
This document presents a study on developing an image processing system for classifying bone fractures using X-ray images. The proposed system uses preprocessing, feature selection, feature extraction using GLCM, and classification with K-Means clustering and SVM. X-ray images of fractured and non-fractured bones are obtained, preprocessed to remove noise, and features are selected using DWT. GLCM is used to extract texture features like entropy, contrast and homogeneity. K-Means clustering groups similar features and SVM classifies images as fractured or non-fractured. The system achieves 90% accuracy in fracture detection and classification.
IRJET- Novel Approach for Detection of Brain Tumor :A ReviewIRJET Journal
1) The document discusses a novel approach for detecting brain tumors using MRI scans. It involves preprocessing scans to remove noise, segmenting images using K-means clustering, and classifying segments using SVM.
2) Current methods for detecting tumors are time-consuming for radiologists. The proposed automated method would classify MRI brain images as normal or abnormal to help radiologists.
3) The method involves preprocessing scans, segmenting images into clusters using K-means clustering, and classifying segments as normal or showing tumors using SVM classification. This could help detect tumors more accurately and efficiently.
Brain Tumor Detection and Classification using Adaptive BoostingIRJET Journal
1. The document describes a system for detecting and classifying brain tumors using MRI images.
2. The system uses techniques like preprocessing, segmentation using k-means clustering, feature extraction with discrete wavelet transform and principal component analysis for dimension reduction, and classification with decision trees and adaptive boosting.
3. Adaptive boosting combines multiple weak learners or decision trees into a strong classifier and focuses on misclassified examples to improve accuracy, achieving 100% accuracy for tumor detection and classification in the system.
IRJET - Lung Cancer Detection using GLCM and Convolutional Neural NetworkIRJET Journal
This document summarizes a research paper that proposes a method for detecting lung cancer using CT scan images with convolutional neural networks. The method involves preprocessing images using median filtering to remove noise, segmenting images using k-means clustering, extracting features using gray-level co-occurrence matrix, and classifying images using convolutional neural networks. The researchers achieved 96% accuracy in classifying tumors as malignant or benign, which is more accurate than traditional neural network methods.
Similar to IRJET- Acute Ischemic Stroke Detection and Classification (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.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
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.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.