This paper proposes a method for automatic brain tumour segmentation using multi-modality magnetic resonance images and support vector machine models. It combines structural MR images with diffusion tensor imaging data to create an integrated tissue profile for brain tumours. Texture features are extracted from normal and tumour regions and used to train an SVM classifier to differentiate between healthy and tumour tissues. The method is tested on real brain MRI data and achieves accurate classification results, demonstrating its potential for assessing tumour growth and computer-guided surgery.
Comparative Study on Medical Image Classification TechniquesINFOGAIN PUBLICATION
This brief study compares the proposed RGSA algorithm with other recent methods by several experiments to indicate that proposed 3DGLCM and SGLDM with SVM classifier is more efficient and accurate. The accuracy results of this study imply how well their experimental results were found to give more accurate results of classifying tumors. The center of interest for this study was made on supervised classification approaches on 2D MRI images of brain tumors. This paper gives the comparative study of various approaches that was used to identify the tumor cells with classifiers.
One of the most dangerous disease occurring these days i.e. brain tumor can be detected by MRI images. Biomedical imaging and medical image processing that plays a vital role for MRI images has now become the most challenging field in engineering and technology. A detailed information about the anatomy can be showed through MRI images, that helps in monitoring the disease and is beneficial for the diagnosis as it consists of a high tissue contrast and have fewer artifacts. For tracking the disease and to proceed its treatment, MRI images plays a key role. It is having several advantages over other imaging techniques and is an important step for post-processing of medical images. However, having a large amount of data for manual analysis can sometimes proved to be an obstacle in the way of its effective use. In this paper, the introduction of image processing and the details of image segmentation techniques such as image preprocessing, feature extraction, image enhancement and classification of tumor processes, and how image segmentation can be applied to all Other available imaging modalities that are different from one another. This paper provides the survey on various methods used for image segmentation that have been applied for MRI images, that detects the tumor by segmenting the brain images into constituent parts. Also the advantages and disadvantages of Image segmentation is discussed using the various approaches of image segmentation of MRI brain images.
GRADE CATEGORIZATION OF TUMOUR CELLS WITH STANDARD AND REFERENTIAL FRONTIER A...pharmaindexing
This document summarizes a research paper that proposes a new method for classifying brain tumor grades using image processing techniques. The method involves preprocessing MRI images to isolate the tumor region using thresholding and image subtraction. The tumor area is then segmented into four quadrants. Standard points mark the initial tumor location, while growth points registered in later images indicate tumor expansion over time. Comparing growth point changes across patient images at different stages allows calculating the tumor growth rate, aiding pathologists in diagnosis and treatment recommendations.
Implementation of Medical Image Analysis using Image Processing TechniquesYogeshIJTSRD
Clinical imaging is playing a fundamental limit in assessment and patching of affliction and discovering tumors and finding of threatening cells in less than ideal stage. As a standard system for perceiving bone features, is minute pictures were used. These photos are secured by using small radiography, where it expected to reiterated, drawn out and work raised measure. This method cant recognize the destructive cells because of the presence of uproar in the photos. Hence there is a necessity for automated and strong strategies to finish the image planning examination. As a first stage, the most fundamental piece of picture planning is to denoising without barging in on the diagnostics information during the clearing of commotion. The past collaboration disposes of the uproar and present fog in the image. To get precise picture getting ready, we have executed fragile and hard breaking point with various coefficients and to check the edge Visu wither was used. It was found that the Wavelet deionsing gadget was a helpful resource for picture improvement. In the gathering, our proposed work was connected with pre planning methodology to wipe out the noise and to get smooth pictures. This collaboration will help with improving the idea of the image and besides take out the fake areas. To recognize the presence of bone illness and to choose its stage, K infers estimation was used and thusly to get smooth picture, edge division measure was performed. Miss. Kode Keerthi | Mr. Parasurama N "Implementation of Medical Image Analysis using Image Processing Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39893.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39893/implementation-of-medical-image-analysis-using-image-processing-techniques/miss-kode-keerthi
Brain Tumor Segmentation and Volume Estimation from T1-Contrasted and T2 MRIsCSCJournals
This document presents a novel, fully automatic method for brain tumor segmentation and volume estimation using T1-contrasted and T2 MRI scans. The method involves 5 main steps: 1) preprocessing images using anisotropic diffusion filtering, 2) segmenting tumor regions using k-means clustering, 3) combining segmented regions using logical and morphological operations, 4) applying temporal smoothing for error checking, and 5) measuring the tumor volume. The method was tested on brain volumes from a public dataset and compared to 5 other state-of-the-art algorithms, outperforming them in accuracy and closeness to actual tumor volumes.
IRJET- Detection and Classification of Breast Cancer from Mammogram ImageIRJET Journal
This document discusses techniques for detecting and classifying breast cancer from mammogram images. It begins with an introduction to breast cancer and the importance of early detection using mammography. The proposed system utilizes the MIAS mammogram dataset and performs preprocessing, segmentation, feature extraction using texture analysis, and classification using multilayer perceptron. K-fold cross validation is used to evaluate the model. The goal is to develop an automated system for mammogram analysis to improve early detection of breast cancer.
This document presents an automatic brain tumor detection and segmentation scheme using MRI images. The proposed method involves 4 main stages: 1) preprocessing to reduce noise and improve image quality, 2) feature extraction of shape, intensity, and texture features, 3) tumor segmentation, and 4) post-processing including regularization and constraints. The method aims to identify and segment tumor portions of brain images successfully and with less time than manual methods. Evaluation results suggest it outperforms other peer methods in accuracy metrics.
Comparative Study on Medical Image Classification TechniquesINFOGAIN PUBLICATION
This brief study compares the proposed RGSA algorithm with other recent methods by several experiments to indicate that proposed 3DGLCM and SGLDM with SVM classifier is more efficient and accurate. The accuracy results of this study imply how well their experimental results were found to give more accurate results of classifying tumors. The center of interest for this study was made on supervised classification approaches on 2D MRI images of brain tumors. This paper gives the comparative study of various approaches that was used to identify the tumor cells with classifiers.
One of the most dangerous disease occurring these days i.e. brain tumor can be detected by MRI images. Biomedical imaging and medical image processing that plays a vital role for MRI images has now become the most challenging field in engineering and technology. A detailed information about the anatomy can be showed through MRI images, that helps in monitoring the disease and is beneficial for the diagnosis as it consists of a high tissue contrast and have fewer artifacts. For tracking the disease and to proceed its treatment, MRI images plays a key role. It is having several advantages over other imaging techniques and is an important step for post-processing of medical images. However, having a large amount of data for manual analysis can sometimes proved to be an obstacle in the way of its effective use. In this paper, the introduction of image processing and the details of image segmentation techniques such as image preprocessing, feature extraction, image enhancement and classification of tumor processes, and how image segmentation can be applied to all Other available imaging modalities that are different from one another. This paper provides the survey on various methods used for image segmentation that have been applied for MRI images, that detects the tumor by segmenting the brain images into constituent parts. Also the advantages and disadvantages of Image segmentation is discussed using the various approaches of image segmentation of MRI brain images.
GRADE CATEGORIZATION OF TUMOUR CELLS WITH STANDARD AND REFERENTIAL FRONTIER A...pharmaindexing
This document summarizes a research paper that proposes a new method for classifying brain tumor grades using image processing techniques. The method involves preprocessing MRI images to isolate the tumor region using thresholding and image subtraction. The tumor area is then segmented into four quadrants. Standard points mark the initial tumor location, while growth points registered in later images indicate tumor expansion over time. Comparing growth point changes across patient images at different stages allows calculating the tumor growth rate, aiding pathologists in diagnosis and treatment recommendations.
Implementation of Medical Image Analysis using Image Processing TechniquesYogeshIJTSRD
Clinical imaging is playing a fundamental limit in assessment and patching of affliction and discovering tumors and finding of threatening cells in less than ideal stage. As a standard system for perceiving bone features, is minute pictures were used. These photos are secured by using small radiography, where it expected to reiterated, drawn out and work raised measure. This method cant recognize the destructive cells because of the presence of uproar in the photos. Hence there is a necessity for automated and strong strategies to finish the image planning examination. As a first stage, the most fundamental piece of picture planning is to denoising without barging in on the diagnostics information during the clearing of commotion. The past collaboration disposes of the uproar and present fog in the image. To get precise picture getting ready, we have executed fragile and hard breaking point with various coefficients and to check the edge Visu wither was used. It was found that the Wavelet deionsing gadget was a helpful resource for picture improvement. In the gathering, our proposed work was connected with pre planning methodology to wipe out the noise and to get smooth pictures. This collaboration will help with improving the idea of the image and besides take out the fake areas. To recognize the presence of bone illness and to choose its stage, K infers estimation was used and thusly to get smooth picture, edge division measure was performed. Miss. Kode Keerthi | Mr. Parasurama N "Implementation of Medical Image Analysis using Image Processing Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39893.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39893/implementation-of-medical-image-analysis-using-image-processing-techniques/miss-kode-keerthi
Brain Tumor Segmentation and Volume Estimation from T1-Contrasted and T2 MRIsCSCJournals
This document presents a novel, fully automatic method for brain tumor segmentation and volume estimation using T1-contrasted and T2 MRI scans. The method involves 5 main steps: 1) preprocessing images using anisotropic diffusion filtering, 2) segmenting tumor regions using k-means clustering, 3) combining segmented regions using logical and morphological operations, 4) applying temporal smoothing for error checking, and 5) measuring the tumor volume. The method was tested on brain volumes from a public dataset and compared to 5 other state-of-the-art algorithms, outperforming them in accuracy and closeness to actual tumor volumes.
IRJET- Detection and Classification of Breast Cancer from Mammogram ImageIRJET Journal
This document discusses techniques for detecting and classifying breast cancer from mammogram images. It begins with an introduction to breast cancer and the importance of early detection using mammography. The proposed system utilizes the MIAS mammogram dataset and performs preprocessing, segmentation, feature extraction using texture analysis, and classification using multilayer perceptron. K-fold cross validation is used to evaluate the model. The goal is to develop an automated system for mammogram analysis to improve early detection of breast cancer.
This document presents an automatic brain tumor detection and segmentation scheme using MRI images. The proposed method involves 4 main stages: 1) preprocessing to reduce noise and improve image quality, 2) feature extraction of shape, intensity, and texture features, 3) tumor segmentation, and 4) post-processing including regularization and constraints. The method aims to identify and segment tumor portions of brain images successfully and with less time than manual methods. Evaluation results suggest it outperforms other peer methods in accuracy metrics.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
A Re-Learning Based Post-Processing Step For Brain Tumor Segmentation From Mu...CSCJournals
We propose a brain tumor segmentation method from multi-spectral MRI images. The method is based on classification and uses Multiple Kernel Learning (MKL) which jointly selects one or more kernels associated to each feature and trains SVM (Support Vector Machine).
First, a large set of features based on wavelet decomposition is computed on a small number of voxels for all types of images. The most significant features from the feature base are then selected and a classifier is then learned. The images are segmented using the trained classifier on the selected features. In our framework, a second step called re-learning is added. It consists in training again a classifier from a reduced part of the training set located around the segmented tumor in the first step. A fusion of both segmentation procures the final results.
Our algorithm was tested on the real data provided by the challenge of Brats 2012. This dataset includes 20 high-grade glioma patients and 10 low-grade glioma patients. For each patient, T1, T2, FLAIR, and post-Gadolinium T1 MR images are available. The results show good performances of our method.
This document discusses image reconstruction techniques for detecting and segmenting tumor cells in brain images. It begins with an introduction to image reconstruction and its applications in medicine. The existing methods for brain tumor detection using MRI are reviewed. The proposed method involves preprocessing MRI images, enhancing contrast, dividing images into quadrants, analyzing pixel intensities and entropy to identify the quadrant most likely containing a tumor, applying thresholding and segmentation techniques like watershed to that quadrant to identify and mark the tumor boundaries, and analyzing features of detected tumors. Results are shown of tumor detection and segmentation using watershed segmentation, morphological operators, and feature identification to classify tumors as benign or malignant. The conclusion is that the proposed method provides faster and more targeted tumor detection compared to existing
Performance Analysis of SVM Classifier for Classification of MRI ImageIRJET Journal
This document discusses using support vector machines (SVM) to classify MRI brain images as normal, benign tumor, or malignant tumor. Key steps include preprocessing images using median and Gaussian filters, extracting features using gray level co-occurrence matrix (GLCM) analysis, and training and testing an SVM classifier on the extracted features to classify new MRI images. The methodology first segments regions of interest in the images using k-means clustering, then extracts GLCM texture features from those regions to train and test the SVM for tumor classification.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
This document presents a proposed method for automatic brain tumor tissue detection in T1-weighted MR images. The method uses a four-step process: segmentation, morphological operations, feature extraction, and classification. In the training section, MRI images are preprocessed and features are extracted using gray-level co-occurrence matrix (GLCM). The features are then used to train a classifier to detect and classify tumors as normal, abnormal, benign, or malignant. In the testing section, input MRI images also undergo preprocessing, feature extraction with GLCM, and then the trained classifier detects, segments, and classifies any tumor tissues found in the images. The goal is to automatically localize and diagnose brain tumor masses in MRI scans.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
Comparative Study on Cancer Images using Watershed Transformationijtsrd
Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An enhanced liver stages classification in 3 d ct and 3d-us images using glrl...Bathshebaparimala
This document proposes a method to classify liver stages in 3D CT and 3D US images using texture feature extraction and 3D convolutional neural networks (CNNs). Gray level run length matrix (GLRLM) is used to extract texture features from segmented liver regions. A 3D CNN is then used for two stages of classification: first to classify images as normal or abnormal, and second to classify abnormal images into stages of liver disease (fatty liver, compensated cirrhosis, decompensated cirrhosis, hepatocellular carcinoma). The method is implemented using TensorFlow and Keras in Python. Results show that 3D CT provides higher classification accuracy than 3D US.
This paper primarily focuses on to employ a novel approach to classify the brain tumor and its area. The Tumor is an uncontrolled enlargement of tissues in any portion of the human body. Tumors are of several types and have some different characteristics. According to their characteristics some of them are avoidable and some are unavoidable. Brain tumor is serious and life threatening issues now days, because of today’s hectic lifestyle. Medical imaging play important role to diagnose brain tumor .In this study an automated system has been proposed to detect and calculate the area of tumor. For proposed system the experiment carried out with 150 T1 weighted MRI images. The edge based segmentation, watershed segmentation has applied for tumor, and watershed segmentation has used to extract abnormal cells from the normal cells to get the tumor identification of involved and noninvolved areas so that the radiologist differentiate the affected area. The experiment result shows tumor extraction and area of tumor find the weather it is benign and malignant.
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.
SVM Classifiers at it Bests in Brain Tumor Detection using MR Imagesijtsrd
This paper presents some case study frameworks to limelight SVM classifiers as most efficient one compared to existing classifiers like Otsu, k-means and fuzzy c-means. In general, Computed Tomography (CT) and Magnetic Resonance Imaging (MR) are more dominant imaging technique for any brain lesions detection like brain tumor, Alzheimer' disease and so on. MR imaging takes a lead technically for imaging medical images due to its possession of large spatial resolution and provides better contrast for the soft tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The usual method used for classification of lesions in brain images consists of pre-processing, feature extraction, feature reduction and classification. Early detection of the tumor region without much time lapse in computation can be achieved by using efficient SVM classifier model. Brain tumor grade classifications with the assistance of morphologically selected features are extracted and tumor classification is attained using SVM classifier. The assessment of SVM classifications are evaluated through metrics termed as sensitivity, exactness and accuracy of segmentation. These measures are then compared with existing methods to exhibit the SVM classifier as significant classifier model. Dr. R Manjunatha Prasad | Roopa B S"SVM Classifiers at it Bests in Brain Tumor Detection using MR Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18372.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18372/svm-classifiers-at-it-bests-in-brain-tumor-detection-using-mr-images/dr-r-manjunatha-prasad
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
This document discusses gamma knife radiosurgery for treating brain tumors. It begins with an introduction to gamma knives and their use in radiosurgery. It then discusses the types of brain tumors and various treatment methods, focusing on gamma knife radiosurgery. The mechanism of gamma knife radiosurgery is explained, involving targeting radiation from multiple sources precisely on the tumor. Serial MRI studies on patients show that temporary tumor enlargement within 2 years often leads to later regression, and gamma knife radiosurgery can effectively treat tumors up to 4cm in size. The conclusion is that gamma knife radiosurgery is an effective treatment for brain tumors, though cystic components can make tumor volumes unpredictable.
An Approach for Study and Analysis of Brain Tumor Using Soft Approachjournal ijrtem
Abstract: As of late, picture preparing is one among quickly developing innovation, rising as a center digging zone and a fascinating subject basically in restorative field. Determination of malady, for example, mind cist, Cancer, Diabetes and so forth is brought out through this innovation. Late studies demonstrate that around 600,000 individuals experience the ill effects of mind cist. From Magnetic reverberation pictures (MRI) , manual restriction and division of cists in mind is blunder inclined and tedious. Picture preparing is exceptionally valuable method to call attention to and remove the suspicious ranges from MRI and CT check therapeutic pictures. With this inspiration in this work, Fuzzy C Means (Potential K-implies) bunching is proposed for MRI cerebrum picture division. Prior to the division the Haralick strategy is advanced for highlight annihilation which will enhance the division exactness. A compelling classifier Support Vector Machines (SVM) is utilized to naturally identify the cist from MRI cerebrum picture. Under boisterous or terrible power standardization conditions this methodology turns out to be more vigorous and deliver better results utilizing high determination pictures. Keywords: Potential K Means, Haralic Feature, Magnetic Resonance Image, Support Vector Machine
This document discusses gamma knife radiosurgery for treating brain tumors. It begins with an introduction to gamma knife radiosurgery, noting that it focuses low-dose gamma radiation from multiple sources precisely on the tumor target. It then discusses using gamma knife to treat various types of brain tumors. The remainder of the document details a study on using gamma knife to treat vestibular schwannomas, including the patient selection and treatment method, follow-up MR imaging, analysis of images showing tumor control rates and volume changes, and conclusions that gamma knife is effective for tumors up to 4cm and short-term enlargement often leads to later regression.
Brain Tumor Segmentation Based on SFCM using Neural NetworkIRJET Journal
This document describes a proposed system for brain tumor segmentation using neural networks. The system involves 4 phases: 1) Preprocessing MRI images using dual-tree complex wavelet transforms for feature extraction. 2) Spatial fuzzy C-means clustering to classify tissues into normal, tumor core and edema classes. 3) Extracting features using the dual-tree complex wavelet transforms. 4) Classifying the features using a backpropagation neural network to identify normal and abnormal brain tissues. The goal is to automatically and accurately segment brain tumors from MRI images to aid diagnosis and reduce analysis time for radiologists. The system was tested on real patient MRI data and achieved accurate segmentation results.
iaetsd Modern e aid to dementia patientsIaetsd Iaetsd
This document discusses the development of an assistive technology device for dementia patients using RF transmitter and receiver modules. The device is intended to help dementia patients locate commonly used household items by attaching RF receivers to the items and activating them using an RF remote control. The system would use low-power RF signals transmitted at 434MHz between an RF transmitter connected to a microcontroller and RF receivers connected to small speakers. This would allow caregivers to remotely activate the speakers on lost items to help patients relocate them. The document provides details on the RF modules, microcontrollers, and encoder/decoder ICs used in the system. It aims to enhance independence for dementia patients while reducing caregiver burden.
Iaetsd emergency recovery control unit using microcontrollerIaetsd Iaetsd
This document describes an emergency recovery control unit that monitors the heart rate of a driver using a photoplethysmograph sensor and PIC microcontroller. If the heart rate is abnormal, it sends an SMS alert to medical experts or family members. It then provides first aid like oxygen and defibrillation. The system aims to detect heart issues early to save lives and prevent car accidents. It monitors heart rate, processes the data, communicates via SMS, and offers first aid treatments using components like a PPG sensor, PIC microcontroller, LCD display, and GPS.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
A Re-Learning Based Post-Processing Step For Brain Tumor Segmentation From Mu...CSCJournals
We propose a brain tumor segmentation method from multi-spectral MRI images. The method is based on classification and uses Multiple Kernel Learning (MKL) which jointly selects one or more kernels associated to each feature and trains SVM (Support Vector Machine).
First, a large set of features based on wavelet decomposition is computed on a small number of voxels for all types of images. The most significant features from the feature base are then selected and a classifier is then learned. The images are segmented using the trained classifier on the selected features. In our framework, a second step called re-learning is added. It consists in training again a classifier from a reduced part of the training set located around the segmented tumor in the first step. A fusion of both segmentation procures the final results.
Our algorithm was tested on the real data provided by the challenge of Brats 2012. This dataset includes 20 high-grade glioma patients and 10 low-grade glioma patients. For each patient, T1, T2, FLAIR, and post-Gadolinium T1 MR images are available. The results show good performances of our method.
This document discusses image reconstruction techniques for detecting and segmenting tumor cells in brain images. It begins with an introduction to image reconstruction and its applications in medicine. The existing methods for brain tumor detection using MRI are reviewed. The proposed method involves preprocessing MRI images, enhancing contrast, dividing images into quadrants, analyzing pixel intensities and entropy to identify the quadrant most likely containing a tumor, applying thresholding and segmentation techniques like watershed to that quadrant to identify and mark the tumor boundaries, and analyzing features of detected tumors. Results are shown of tumor detection and segmentation using watershed segmentation, morphological operators, and feature identification to classify tumors as benign or malignant. The conclusion is that the proposed method provides faster and more targeted tumor detection compared to existing
Performance Analysis of SVM Classifier for Classification of MRI ImageIRJET Journal
This document discusses using support vector machines (SVM) to classify MRI brain images as normal, benign tumor, or malignant tumor. Key steps include preprocessing images using median and Gaussian filters, extracting features using gray level co-occurrence matrix (GLCM) analysis, and training and testing an SVM classifier on the extracted features to classify new MRI images. The methodology first segments regions of interest in the images using k-means clustering, then extracts GLCM texture features from those regions to train and test the SVM for tumor classification.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
This document presents a proposed method for automatic brain tumor tissue detection in T1-weighted MR images. The method uses a four-step process: segmentation, morphological operations, feature extraction, and classification. In the training section, MRI images are preprocessed and features are extracted using gray-level co-occurrence matrix (GLCM). The features are then used to train a classifier to detect and classify tumors as normal, abnormal, benign, or malignant. In the testing section, input MRI images also undergo preprocessing, feature extraction with GLCM, and then the trained classifier detects, segments, and classifies any tumor tissues found in the images. The goal is to automatically localize and diagnose brain tumor masses in MRI scans.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
Comparative Study on Cancer Images using Watershed Transformationijtsrd
Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An enhanced liver stages classification in 3 d ct and 3d-us images using glrl...Bathshebaparimala
This document proposes a method to classify liver stages in 3D CT and 3D US images using texture feature extraction and 3D convolutional neural networks (CNNs). Gray level run length matrix (GLRLM) is used to extract texture features from segmented liver regions. A 3D CNN is then used for two stages of classification: first to classify images as normal or abnormal, and second to classify abnormal images into stages of liver disease (fatty liver, compensated cirrhosis, decompensated cirrhosis, hepatocellular carcinoma). The method is implemented using TensorFlow and Keras in Python. Results show that 3D CT provides higher classification accuracy than 3D US.
This paper primarily focuses on to employ a novel approach to classify the brain tumor and its area. The Tumor is an uncontrolled enlargement of tissues in any portion of the human body. Tumors are of several types and have some different characteristics. According to their characteristics some of them are avoidable and some are unavoidable. Brain tumor is serious and life threatening issues now days, because of today’s hectic lifestyle. Medical imaging play important role to diagnose brain tumor .In this study an automated system has been proposed to detect and calculate the area of tumor. For proposed system the experiment carried out with 150 T1 weighted MRI images. The edge based segmentation, watershed segmentation has applied for tumor, and watershed segmentation has used to extract abnormal cells from the normal cells to get the tumor identification of involved and noninvolved areas so that the radiologist differentiate the affected area. The experiment result shows tumor extraction and area of tumor find the weather it is benign and malignant.
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.
SVM Classifiers at it Bests in Brain Tumor Detection using MR Imagesijtsrd
This paper presents some case study frameworks to limelight SVM classifiers as most efficient one compared to existing classifiers like Otsu, k-means and fuzzy c-means. In general, Computed Tomography (CT) and Magnetic Resonance Imaging (MR) are more dominant imaging technique for any brain lesions detection like brain tumor, Alzheimer' disease and so on. MR imaging takes a lead technically for imaging medical images due to its possession of large spatial resolution and provides better contrast for the soft tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The usual method used for classification of lesions in brain images consists of pre-processing, feature extraction, feature reduction and classification. Early detection of the tumor region without much time lapse in computation can be achieved by using efficient SVM classifier model. Brain tumor grade classifications with the assistance of morphologically selected features are extracted and tumor classification is attained using SVM classifier. The assessment of SVM classifications are evaluated through metrics termed as sensitivity, exactness and accuracy of segmentation. These measures are then compared with existing methods to exhibit the SVM classifier as significant classifier model. Dr. R Manjunatha Prasad | Roopa B S"SVM Classifiers at it Bests in Brain Tumor Detection using MR Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18372.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18372/svm-classifiers-at-it-bests-in-brain-tumor-detection-using-mr-images/dr-r-manjunatha-prasad
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
This document discusses gamma knife radiosurgery for treating brain tumors. It begins with an introduction to gamma knives and their use in radiosurgery. It then discusses the types of brain tumors and various treatment methods, focusing on gamma knife radiosurgery. The mechanism of gamma knife radiosurgery is explained, involving targeting radiation from multiple sources precisely on the tumor. Serial MRI studies on patients show that temporary tumor enlargement within 2 years often leads to later regression, and gamma knife radiosurgery can effectively treat tumors up to 4cm in size. The conclusion is that gamma knife radiosurgery is an effective treatment for brain tumors, though cystic components can make tumor volumes unpredictable.
An Approach for Study and Analysis of Brain Tumor Using Soft Approachjournal ijrtem
Abstract: As of late, picture preparing is one among quickly developing innovation, rising as a center digging zone and a fascinating subject basically in restorative field. Determination of malady, for example, mind cist, Cancer, Diabetes and so forth is brought out through this innovation. Late studies demonstrate that around 600,000 individuals experience the ill effects of mind cist. From Magnetic reverberation pictures (MRI) , manual restriction and division of cists in mind is blunder inclined and tedious. Picture preparing is exceptionally valuable method to call attention to and remove the suspicious ranges from MRI and CT check therapeutic pictures. With this inspiration in this work, Fuzzy C Means (Potential K-implies) bunching is proposed for MRI cerebrum picture division. Prior to the division the Haralick strategy is advanced for highlight annihilation which will enhance the division exactness. A compelling classifier Support Vector Machines (SVM) is utilized to naturally identify the cist from MRI cerebrum picture. Under boisterous or terrible power standardization conditions this methodology turns out to be more vigorous and deliver better results utilizing high determination pictures. Keywords: Potential K Means, Haralic Feature, Magnetic Resonance Image, Support Vector Machine
This document discusses gamma knife radiosurgery for treating brain tumors. It begins with an introduction to gamma knife radiosurgery, noting that it focuses low-dose gamma radiation from multiple sources precisely on the tumor target. It then discusses using gamma knife to treat various types of brain tumors. The remainder of the document details a study on using gamma knife to treat vestibular schwannomas, including the patient selection and treatment method, follow-up MR imaging, analysis of images showing tumor control rates and volume changes, and conclusions that gamma knife is effective for tumors up to 4cm and short-term enlargement often leads to later regression.
Brain Tumor Segmentation Based on SFCM using Neural NetworkIRJET Journal
This document describes a proposed system for brain tumor segmentation using neural networks. The system involves 4 phases: 1) Preprocessing MRI images using dual-tree complex wavelet transforms for feature extraction. 2) Spatial fuzzy C-means clustering to classify tissues into normal, tumor core and edema classes. 3) Extracting features using the dual-tree complex wavelet transforms. 4) Classifying the features using a backpropagation neural network to identify normal and abnormal brain tissues. The goal is to automatically and accurately segment brain tumors from MRI images to aid diagnosis and reduce analysis time for radiologists. The system was tested on real patient MRI data and achieved accurate segmentation results.
iaetsd Modern e aid to dementia patientsIaetsd Iaetsd
This document discusses the development of an assistive technology device for dementia patients using RF transmitter and receiver modules. The device is intended to help dementia patients locate commonly used household items by attaching RF receivers to the items and activating them using an RF remote control. The system would use low-power RF signals transmitted at 434MHz between an RF transmitter connected to a microcontroller and RF receivers connected to small speakers. This would allow caregivers to remotely activate the speakers on lost items to help patients relocate them. The document provides details on the RF modules, microcontrollers, and encoder/decoder ICs used in the system. It aims to enhance independence for dementia patients while reducing caregiver burden.
Iaetsd emergency recovery control unit using microcontrollerIaetsd Iaetsd
This document describes an emergency recovery control unit that monitors the heart rate of a driver using a photoplethysmograph sensor and PIC microcontroller. If the heart rate is abnormal, it sends an SMS alert to medical experts or family members. It then provides first aid like oxygen and defibrillation. The system aims to detect heart issues early to save lives and prevent car accidents. It monitors heart rate, processes the data, communicates via SMS, and offers first aid treatments using components like a PPG sensor, PIC microcontroller, LCD display, and GPS.
4 iaetsd detecting linear structures within the aster satellite image by effe...Iaetsd Iaetsd
This document discusses methods for detecting linear structures in ASTER satellite images through image preprocessing techniques. It proposes using the Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) method and Isotropic Undecimated Wavelet transform (IUWT) for contrast enhancement and effective denoising of ASTER images. BPDFHE preserves brightness while enhancing contrast. IUWT is used to segment texture features like lineaments. The combination of BPDFHE for preprocessing and IUWT for segmentation provides better results for extracting linear structures and intersections from ASTER dataset images compared to other methods.
The document discusses the potential of nanorobots for cancer treatment. It describes how nanorobots could detect cancer cells in early stages using various nanotechnology tools and destroy the cancerous cells while leaving healthy cells unharmed. This would provide a more targeted treatment compared to surgery and chemotherapy, reducing side effects and allowing faster recovery for patients. The document outlines key considerations for designing nanorobots for medical applications like size, structure, communication methods, and biocompatibility.
iaetsd Second level security using intrusion detection and avoidance systemIaetsd Iaetsd
This document proposes a system that provides second level security for applications running on PCs connected via a LAN. The system includes an intrusion detection component that monitors login attempts. If an unauthorized user exceeds a threshold for incorrect login attempts, the server is notified and sends an alert message to the administrator's mobile phone via GSM modem. The PC where the intrusion attempt occurred is then automatically shut down, preventing further access attempts. The system aims to protect applications from unauthorized access while alerting administrators of intrusion attempts.
iaetsd Secured multiple keyword ranked search over encrypted databasesIaetsd Iaetsd
This document proposes a Robust Key-Aggregate Cryptosystem (RKAC) that allows flexible and efficient assignment of decryption rights for encrypted data stored in cloud storage. The RKAC produces constant-sized ciphertexts such that a constant-sized aggregate decryption key can decrypt any subset of ciphertexts. This allows the data owner to share access to selected encrypted files by sending a single small aggregate key to authorized users, without decrypting the files themselves or distributing individual keys. The RKAC is described as providing a secure and flexible method for sharing encrypted data stored in the cloud.
Iirdem a novel approach for enhancing security in multi cloud environmentIaetsd Iaetsd
This document discusses security issues in multi-cloud environments and proposes a novel approach called UEG-16 (User-End Generated 16 character key code) to enhance security. The approach aims to provide clients anonymity about passwords to cloud hosts by having clients generate their own 16 character security codes instead of using passwords handled by third parties. This reduces the role of third parties and increases security. The document then provides background on cloud computing and discusses some common security issues like shared access between tenants, virtualization exploits, authentication and access control challenges, availability risks if redundancy is not under a client's control, and unclear data ownership policies in cloud contracts.
iaetsd Easy tax a user friendly mobile applicationIaetsd Iaetsd
The document describes an Android mobile application called "Easy Tax" that was designed to make paying taxes easier. It includes a tax calculator, information on current tax rules and regulations, ways to minimize taxes through investments, and links to tax department websites and forms. The application aims to overcome issues with existing tax apps like bugs, offline access, and complex interfaces. Screenshots of the Easy Tax app user interface are provided, showing features like the home screen, tax calculator, office locators, and a help line. The conclusion states that the app helps users understand and pay taxes while supporting the tax department.
iaetsd Vehicle monitoring and security systemIaetsd Iaetsd
The document describes a Vehicle Monitoring and Security System (VMSS) that uses GPS and GSM technologies to track vehicles and ensure passenger security. The system tracks a vehicle's location using a GPS module and sends the location data via GSM to a base station. It also allows passengers to activate an alarm if emergency help is needed. The system supports monitoring vehicles throughout a journey by transmitting passenger and vehicle IDs along with location to the base station as the vehicle picks up and drops off passengers at different stages.
Iirdem screen less displays – the imminent vanguardIaetsd Iaetsd
This document discusses screenless display technology, which involves displaying information without using a physical screen. It describes three main types of screenless displays: visual images like holograms, retinal displays that project images directly onto the retina, and synaptic interfaces that transmit visual information directly to the brain. The document outlines the working principles and potential applications of screenless displays, such as in virtual reality systems. It also discusses the structure and implementation of retinal displays specifically.
iirdem CRASH IMPACT ATTENUATOR (CIA) FOR AUTOMOBILES WITH THE ADVOCATION OF M...Iaetsd Iaetsd
This document describes a proposed crash impact attenuation system for automobiles that uses mechatronic systems. The system includes an accident prediction system using ultrasound sensors to monitor vehicle surroundings and detect potential collisions. It also includes a crash absorption system with components like a pneumatic cylinder attached to the vehicle chassis that can push and pull a shock energy absorber upon detection of an imminent crash by the microcontroller. This proposed system aims to reduce crash impacts and potentially save lives by fully absorbing crash forces through controlled actuation of the absorber components.
iaetsd Preserving private multi keyword searching with ranking by anonymous i...Iaetsd Iaetsd
This document discusses privacy-preserving multi-keyword ranked search over encrypted cloud data. It proposes assigning anonymous IDs to cloud users to hide their identities from the cloud service provider and better protect sensitive data on the cloud. The system allows data owners to encrypt and outsource data to the cloud for storage. It then builds a searchable index to allow authorized users to search for keywords without learning the content. The cloud server ranks search results based on relevance but hides information about important documents. Previous works focused on search and privacy but revealed user identities. The proposed system addresses this by anonymizing user IDs to maintain privacy under two threat models.
Iaetsd experimental investigation on self compacting fiber reinforced concret...Iaetsd Iaetsd
- The document discusses using self-compacting fiber reinforced concrete (SCFRC) for rigid pavements.
- SCFRC provides good compressive and tensile strength, making it suitable for rigid pavements. An experimental investigation tested different fiber types in SCFRC and evaluated strength properties.
- A rigid pavement was designed and cast using SCFRC according to IRC methods. Core cutting tests were performed on pavement samples to evaluate strength and durability.
Iaetsd io t based advanced smart health care systemIaetsd Iaetsd
This document proposes an IoT-based smart health care system called the Smart Hospital System (SHS). The SHS uses technologies like RFID, wireless sensor networks, and smart mobile devices to automatically monitor patients, medical staff, and devices in hospitals. It collects environmental and physiological data in real-time using a hybrid sensing network. The data is sent to a control center where it can be accessed locally and remotely through a web interface. A prototype was implemented that demonstrated tracking patients and responding to emergencies like falls. The system aims to improve healthcare efficiency while reducing costs.
Iaetsd implementation of lsb image steganography system using edge detectionIaetsd Iaetsd
This document proposes an image steganography system that uses edge detection, LZW compression, and hybrid encryption methods. It first encrypts the secret image using AES and ECC encryption. It then compresses the encrypted image using LZW compression. Next, it detects edges in the cover image using Canny edge detection. It then embeds the compressed encrypted image into the cover image by modifying the least significant bits of edge pixels. To decode, it extracts the embedded image, decompresses it, and decrypts it using ECC and AES decryption, recovering the original secret image. Evaluation results show the proposed method provides better security compared to existing methods while maintaining high quality of the stego image.
Iaetsd enhancement of performance and security in bigdata processingIaetsd Iaetsd
This document discusses enhancing performance and security in big data processing. It proposes collecting sensitive data and encrypting it using proxy re-encryption before storing it in a NoSQL database for increased security. The encrypted data can then be decrypted and accessed by authorized external users. MapReduce is used to filter duplicate data during access.
Enhanced 3D Brain Tumor Segmentation Using Assorted Precision TrainingBIJIAM Journal
A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of nonessential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for threedimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.
IRJET- Analysis of Brain Tumor Classification by using Multiple Clustering Al...IRJET Journal
This document analyzes and compares multiple clustering algorithms for brain tumor classification using MRI and PET images. It first discusses using Gray Level Co-occurrence Matrix (GLCM) to extract texture features from the images. It then analyzes the performance of k-means clustering, fuzzy c-means, Gustafson-Kessel algorithm, and density-based spectral clustering for tumor detection. The Gustafson-Kessel algorithm was found to be the most efficient based on performance.
A Review on Multiclass Brain Tumor Detection using Convolutional Neural Netwo...IRJET Journal
This document summarizes a review on using convolutional neural networks and support vector machines for multiclass brain tumor detection. It begins by introducing the importance of accurate brain tumor diagnosis and segmentation. It then describes the proposed 5-stage approach: 1) applying linear contrast stretching for edge detection, 2) developing a CNN architecture for segmentation, 3) using transfer learning from MobileNetV2 for feature extraction, 4) selecting optimal features using entropy control, and 5) classifying tumors into categories using multi-class SVM. Related works applying deep learning and machine learning methods for brain tumor detection, segmentation and classification are also summarized.
Automated Intracranial Neoplasm Detection Using Convolutional Neural NetworksIRJET Journal
This document presents a study that uses convolutional neural networks to automatically detect intracranial neoplasms (brain tumors) from MRI scans. The researchers developed a CNN model that achieved 97.87% accuracy in identifying tumors. They used preprocessed MRI images to train and test the model for tumor detection. Convolutional neural networks are a type of deep learning that can provide efficient results for medical image classification tasks like tumor detection compared to traditional methods. The study demonstrates that CNNs are a promising approach for automated brain tumor identification from MRI scans.
IRJET - Brain Tumor Detection using Image Processing, ML & NLPIRJET Journal
This document presents a system for detecting brain tumors using image processing, machine learning, and natural language processing. The system applies preprocessing, filtering, and segmentation techniques to MRI images to extract features of the tumor such as shape, size, texture, and contrast. Machine learning algorithms are then used to classify tumors and detect their location. The system aims to make tumor detection more efficient and accurate compared to manual detection. It evaluates performance based on metrics like accuracy, sensitivity, specificity, and dice coefficient. The authors conclude the proposed approach can help timely and precise tumor detection and localization.
IRJET- Brain Tumor Detection using Image Processing, ML & NLPIRJET Journal
This document presents a system for detecting brain tumors using image processing, machine learning, and natural language processing. The system applies preprocessing, filtering, and segmentation techniques to MRI images to extract features of the tumor such as shape, size, texture, and contrast. Machine learning algorithms are then used to classify tumors and detect their location. The system aims to make tumor detection more efficient and accurate compared to manual detection. It evaluates performance based on metrics like accuracy, sensitivity, specificity, and dice coefficient. The authors conclude the proposed approach can help timely and precise tumor detection and localization.
An overview of automatic brain tumor detection frommagnetic resonance imagesMangesh Lingampalle
The document discusses several techniques for automatically detecting brain tumors from magnetic resonance (MR) images. It begins with an overview of MR imaging and challenges of manual tumor detection. Several existing techniques are then summarized, including thresholding-based methods, fuzzy classification with deformable models, using wavelets and statistics to segment tissues, feature extraction with Adaboost classification, and color-converted k-means clustering. The document proposes a technique using undecimated wavelet transform (UDWT) and Gabor filters for preprocessing, followed by morphological operations and parameter analysis to detect tumors. Automatic detection techniques could help address limitations of manual detection and improve diagnosis of brain tumors.
This document presents a genetic algorithm-based classification method for classifying different types of lung cancer in needle biopsy images. It first segments cell nuclei from biopsy images and extracts color, texture, and shape features from the nuclei. A dictionary learning approach is used to build discriminative subdictionaries for each feature type. In testing, features from an image are classified at the cell level and then fused at the image level via majority voting. The method achieves higher accuracy than using single features or existing classification methods, demonstrating its effectiveness in classifying lung cancer types in biopsy images.
Hybrid model for detection of brain tumor using convolution neural networksCSITiaesprime
The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisions because of this forecast. The proposed work creates a hybrid convolution neural networks (CNN) model using CNN for feature extraction and logistic regression (LR). The pre-trained model visual geometry group 16 (VGG16) is used for the extraction of features. To reduce the complexity and parameters to train we eliminated the last eight layers of VGG16. From this transformed model the features are extracted in the form of a vector array. These features fed into different machine learning classifiers like support vector machine (SVM), naïve bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.
BRAIN TUMOR’S DETECTION USING DEEP LEARNINGIRJET Journal
This document describes a study on detecting brain tumors using deep learning. The researchers created a brain tumor dataset from MR images and used techniques like CNN, NN, and KFOLDS validation to detect brain tumors in the images. KFOLDS validation improved the accuracy of the CNN model by testing it using various evaluation metrics. The proposed framework achieved good results, showing that KFOLDS validation is more effective than other models at independently focusing on the areas needed to identify brain tumors.
BRAIN TUMOUR DETECTION AND CLASSIFICATIONIRJET Journal
This document summarizes a method for detecting and classifying brain tumors using MRI images. A deep learning model based on ResNet152 is trained on labeled MRI images to identify different types of tumors. The model extracts features from MRI images and classifies tumors with 97% accuracy on one dataset and 96% accuracy on another. ResNet152 performed better than other models tested. The method provides automated tumor detection and classification to help with diagnosis and treatment planning in neurosurgery and radiation oncology.
This document discusses a study that proposes a framework for classifying brain tumors using an ensemble of deep features extracted from pre-trained convolutional neural networks (CNNs) and machine learning (ML) classifiers. The framework uses 13 pre-trained CNNs to extract deep features from magnetic resonance (MRI) brain images, which are then evaluated and the top 3 features selected using 9 ML classifiers. The selected features are concatenated to create an ensemble feature, which is classified using ML classifiers. The study evaluates this approach on 3 brain MRI datasets with different numbers of classes to classify tumors. Experimental results show that ensembling deep features can improve performance significantly, and support vector machines generally perform best, especially on larger datasets.
Tumor Detection from Brain MRI Image using Neural Network Approach: A ReviewIRJET Journal
This document reviews using neural networks to detect tumors in brain MRIs. It discusses how MRI is commonly used to diagnose soft tissue issues and analyze conditions like trauma and strokes. The paper proposes a methodology for brain tumor detection that includes image acquisition, pre-processing, enhancement, thresholding, and morphological operations using MATLAB. A neural network approach is also presented. The conclusions state that neural networks can help detect, classify, segment, and visualize brain tumors in MRI images with ease and accuracy.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
This document provides an overview of MRI techniques for imaging pediatric brain tumors and summarizes the MRI appearance of common pediatric brain tumor types. It discusses conventional MRI sequences as well as advanced techniques like diffusion tensor imaging, perfusion imaging, and magnetic resonance spectroscopy that provide microstructural, hemodynamic, and metabolic information. The document also notes the importance of spine imaging based on the suspected tumor histology to evaluate for cerebrospinal fluid dissemination of the tumor.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
Tumor Detection and Classification of MRI Brain Images using SVM and DNNijtsrd
The brain is one of the most complex organ in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low grade Grade 1 and 2 and malignant tumors or high grade Grade 3 and 4 . The proposed methodology aims to differentiate between normal brain and tumor brain Benign or Melign . The proposed method in this paper is automated framework for differentiate between normal brain and tumor brain. Then our method is used to predict the diseases accurately. Then these methods are used to predict the disease is affected or not by using a comparison method. These methodology are validated by a comprehensive set of comparison against competing and well established image registration methods, by using real medical data sets and classic measures typically employed as a benchmark by the medical imaging community our proposed method is mostly used in medical field. It is used to easily detect the diseases. We demonstrate the accuracy and effectiveness of the preset framework throughout a comprehensive set of qualitative comparisons against several influential state of the art methods on various brain image databases. Sanmathi. R | Sujitha. K | Susmitha. G | Gnanasekaran. S ""Tumor Detection and Classification of MRI Brain Images using SVM and DNN"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30192.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30192/tumor-detection-and-classification-of-mri-brain-images-using-svm-and-dnn/sanmathi-r
Identifying brain tumour from mri image using modified fcm and supportIAEME Publication
This document summarizes a research paper that proposes a technique for identifying brain tumors in MRI images. The technique involves 4 steps: 1) preprocessing the MRI image, 2) segmenting the image using a modified fuzzy C-means algorithm, 3) extracting features from the segmented regions like mean, standard deviation, and pixel orientation, and 4) classifying the image as tumorous or normal using support vector machine classification on the extracted features. The technique is evaluated on MRI brain images and achieves a testing accuracy of 93%, demonstrating its effectiveness at detecting brain tumors compared to other segmentation and classification methods.
IRJET- A Study on Brain Tumor Detection Algorithms for MRI ImagesIRJET Journal
This document discusses algorithms for detecting brain tumors in MRI images. It begins with an abstract that outlines the key stages of brain tumor detection using image processing techniques: pre-processing, segmentation, feature extraction, and classification. It then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. Specifically, it examines algorithms using Bayesian techniques, neural networks, clustering, and deep learning. The document proposes using a Spearman algorithm for segmentation combined with a convolutional neural network classifier to overcome limitations of other methods and provide more accurate tumor detection.
In recent years, the application of deep learning has demonstrated significant progress in various scientific subfields. Compared to other cutting-edge methods of processing and analysing images, deep learning algorithms performed significantly better. When applied in areas such as self-driving cars, where deep learning has been utilized, and the results are the best and most up-to-date currently available. In some situations, such as recognizing objects and playing games, deep learning performed significantly better than people did. One more industry that appears to have a lot to gain from deep learning is the medical field. There are a lot of patient records and data, and providing individualized care to each patient is becoming an increasingly important priority as a result. This indicates an immediate need for methods that are both efficient and reliable for processing and analyzing health informatics.
Similar to IIirdem mri brain tumour extraction by multi modality magnetic resonance images and support vector machine models (20)
iaetsd Survey on cooperative relay based data transmissionIaetsd Iaetsd
The document discusses cooperative relay based data transmission and proposes a system to select the most energy efficient relay node for a source node to transmit data through. It analyzes different cooperative relaying techniques like amplify-and-forward, decode-and-forward, and compress-and-forward. The proposed system aims to minimize the source node's cost for cooperation by selecting the relay node that provides the highest energy efficiency. This allows high data transmission over long distances with improved energy efficiency compared to direct transmission without a relay.
iaetsd Software defined am transmitter using vhdlIaetsd Iaetsd
This document discusses the design and implementation of an amplitude modulation (AM) software defined radio transmitter using an FPGA. It begins with an abstract describing the goals of the project. It then provides an overview of the system design, including discussion of the individual components like the microphone, analog to digital converter, digital to analog converter, carrier frequency generator, and antenna. It describes how these components will be implemented on the FPGA, including using behavioral modeling with VHDL. It also discusses designing filters and modulation/demodulation circuits. The overall summary is that this document outlines the goals and high-level system design for creating an AM transmitter using an FPGA that can transmit an audio signal by digitally modulating a carrier frequency.
iaetsd Health monitoring system with wireless alarmIaetsd Iaetsd
The document describes a health monitoring system with wireless alarm that detects a patient's heart rate and temperature. It consists of a sensor unit worn on the wrist that monitors vital signs and transmits data wirelessly to an alarm and display unit. This allows caregivers to be alerted quickly if a patient's condition changes, such as if their heart rate is too high or low. The system uses a microcontroller to process sensor readings from a pulse oximetry sensor and transmit data via RF to the receiving unit, which contains another microcontroller connected to an RF receiver and buzzer alarm. If an abnormal heart rate is detected, the system triggers an alarm to notify caregivers.
iaetsd Equalizing channel and power based on cognitive radio system over mult...Iaetsd Iaetsd
This document summarizes a research paper about equalizing power and channel allocation in a cognitive radio system using multiuser OFDM. It discusses how frequency spectrum is becoming scarce due to increased wireless usage, and how cognitive radio can help improve spectrum utilization by allowing unlicensed secondary users to access licensed bands opportunistically when primary users are not using them. The paper presents a system model for a cognitive radio network with one primary user and multiple secondary user pairs. It formulates the problem of allocating subcarriers and power to the secondary users while avoiding interference to the primary user.
iaetsd Economic analysis and re design of driver’s car seatIaetsd Iaetsd
The document discusses redesigning car seats to improve comfort. It notes that car seat design must balance comfort, safety, and health. Static comfort relates to the seat's form and support, while dynamic comfort considers vibration levels. The study reexamines existing car seat designs and proposes a novel design with improvements in form, features, usability, and comfort. A survey was also conducted to define important comfort factors like pain prevention to help guide future seat designs.
iaetsd Design of slotted microstrip patch antenna for wlan applicationIaetsd Iaetsd
This document describes the design and simulation of a slotted microstrip patch antenna for wireless local area network (WLAN) applications operating at 2.4 GHz. The antenna was designed on an FR-4 substrate with a dielectric constant of 4.2 and thickness of 1.6 mm. Simulation in HFSS showed the antenna has a voltage standing wave ratio of 1.88 at the resonant frequency, with omnidirectional radiation patterns. The compact size and simple design make this slotted patch antenna suitable for use in embedded wireless systems.
REVIEW PAPER- ON ENHANCEMENT OF HEAT TRANSFER USING RIBSIaetsd Iaetsd
This document reviews research on enhancing heat transfer using ribs mounted inside ducts. Various studies investigated ribs of different shapes, pitches, heights and angles. Continuous ribs, transverse ribs, angled ribs, and other rib configurations were examined. Most studies found that ribs increased turbulence and heat transfer compared to smooth ducts. Some key findings included V-shaped ribs providing better performance than other shapes, and certain rib pitches and angles performing better depending on parameters like Reynolds number. In general, ribs were found to effectively enhance heat transfer through boundary layer disruption and increased turbulence compared to smooth ducts.
A HYBRID AC/DC SOLAR POWERED STANDALONE SYSTEM WITHOUT INVERTER BASED ON LOAD...Iaetsd Iaetsd
This document discusses two methods for generating power from solar panels for a home without using inverters or batteries.
Method 1 proposes a hybrid AC/DC home grid system that shifts harmonic intensive loads to the DC side to reduce power conversion losses and isolates harmonic content. Solar power is fed to the home through a DC-DC converter, MPPT, and inverter to power AC loads, with a separate DC connection for DC loads.
Method 2 generates AC power directly from an array of solar cells connected in an alternating anti-parallel configuration, eliminating power losses from an inverter. Compatibility with residential loads is analyzed. This novel technique could remove the need for batteries and reduce overall cost.
The performance of
This document describes the fabrication of a dual power bike that can operate using either an internal combustion engine or electric motor. The goal is to improve fuel efficiency and reduce pollution by allowing electric-only operation in the city. The bike combines a petrol engine with a battery and electric motor, resulting in twice the fuel economy of a conventional bike. It works by using the electric motor powered by the battery for low-power city driving, and switching to the petrol engine for higher speeds or power needs. This hybrid system aims to lower costs and pollution compared to other vehicles.
This document discusses Blue Brain technology and the goal of creating an artificial brain using silicon chips. It aims to upload the contents of a natural human brain into a virtual brain. This would allow human intelligence, memories, and personalities to potentially persist after death through the virtual brain. The document outlines how nanobots could scan a human brain at a cellular level and transfer that information to a supercomputer to recreate the brain's structure and function virtually. It compares key aspects of natural and virtual brains, such as how inputs, interpretation, outputs, memory, and processing would theoretically work for a virtual brain modeled after the human brain.
iirdem The Livable Planet – A Revolutionary Concept through Innovative Street...Iaetsd Iaetsd
The document proposes an innovative street lighting and surveillance system using Internet of Things (IoT) and Li-Fi technologies. The system uses LED street lights equipped with sensors and cameras that can monitor traffic, detect crimes, and provide emergency assistance. Data from the lights would be transmitted using Li-Fi and stored in the cloud for analysis. This integrated system could save energy, reduce costs, and improve safety, traffic management, and emergency response capabilities in cities.
The document proposes a Surveillance Aided Robotic Bird (SARB) to improve on existing surveillance systems. SARB would be designed like a bird and equipped with cameras, including night vision, to monitor areas remotely. It would be powered by carbon nanotubes, allowing for wireless charging and extended flight time. SARB could track intruders under the control of image processing and fly between fixed points for charging. This would provide a more natural, mobile and energy efficient form of surveillance compared to static cameras.
iirdem Growing India Time Monopoly – The Key to Initiate Long Term Rapid GrowthIaetsd Iaetsd
This document discusses how small and medium enterprises can achieve long term rapid growth. It focuses on the concept of "time monopoly" which has two components - a competitive advantage over competitors in a niche market, and a time advantage where it takes competitors longer to catch up.
The literature review discusses different sources of competitive advantage according to Porter, including variety-based positioning, needs-based positioning, and access-based positioning. It also discusses the importance of fit between different business activities for achieving a competitive advantage.
The paper proposes five propositions for rapid growth. These include that all areas can enable or hinder growth; areas can be transformed from hindering to enabling growth; businesses need scalability; and time monopoly,
iirdem Design of Efficient Solar Energy Collector using MPPT AlgorithmIaetsd Iaetsd
This document discusses the design of an efficient solar energy collector using a Maximum Power Point Tracking (MPPT) algorithm. It aims to maximize solar energy output through the use of lenses to concentrate sunlight onto solar panels and an MPPT algorithm to track the optimal power point. The methodology involves designing a DC-DC boost converter, lens-based solar cell, and a microcontroller with driver circuit. Simulations and hardware implementation will analyze the solar array, boost converter, and verify the system collects more energy than a fixed panel system.
iirdem ADVANCING OF POWER MANAGEMENT IN HOME WITH SMART GRID TECHNOLOGY AND S...Iaetsd Iaetsd
1) The document describes a smart home energy management system that uses wireless sensor networks and ZigBee technology to monitor and control home appliances in real-time. Electrical parameters like voltage, current, and power consumption are measured.
2) The system allows flexible control of appliances based on consumer needs. Appliances can be monitored and controlled remotely or automatically based on power consumption thresholds.
3) Key features of the system include using a TRIAC circuit to control appliances without needing a microcontroller, and providing flexible control options to users for switching devices on/off according to their preferences. This allows improving consumer comfort while optimizing energy use.
iaetsd Shared authority based privacy preserving protocolIaetsd Iaetsd
This document proposes a Shared Authority based Privacy preserving Authentication protocol (SAPA) for handling privacy issues in cloud storage. SAPA achieves shared access authority through an anonymous access request matching mechanism. It applies attribute-based access control to allow users to reliably access their own data fields. It also uses proxy re-encryption to provide temporary authorized data sharing among multiple users. The goal is to preserve user privacy during data access and sharing in the cloud.
iaetsd Robots in oil and gas refineriesIaetsd Iaetsd
This document discusses attribute-based encryption in cloud computing with outsourced revocation. It proposes a pseudonym generation scheme for identity-based encryption and outsourced revocation in cloud computing. The scheme offloads most key generation operations to a Key Update Cloud Service Provider during key issuing and updating, leaving only simple operations for the Private Key Generator and users. It aims to reduce computation overhead at the Private Key Generator while using an untrusted cloud service provider.
iaetsd Modeling of solar steam engine system using parabolicIaetsd Iaetsd
The document describes the modeling and testing of a solar-steam engine system using a parabolic concentrator. The system focuses solar radiation onto a boiler to generate steam, which is then used to power an oscillating steam engine coupled to a generator to produce electricity. The parabolic dish has a diameter of 0.625m and focuses sunlight onto a 1L boiler. Testing showed the system could produce 9V with no load and 5.3V under load, demonstrating its potential for rural electrification applications.
iaetsd Isolation of cellulose from non conventional source and its chemical m...Iaetsd Iaetsd
This document describes isolating cellulose from the weed plant Prosopis juliflora and chemically modifying it into cellulose acetate. Conditions were optimized for isolating cellulose using sodium hydroxide and sodium chlorite treatments. The best isolation results used 50% sodium chlorite with 20% sodium hydroxide at 90°C for 120 minutes. Conditions for acetylating the cellulose into cellulose acetate were also optimized, with the best results at 100°C for 18 hours. Fourier transform infrared spectroscopy characterized the isolated cellulose and synthesized cellulose acetate.
iaetsd Effect of superconducting fault current limiter (sfcl) on triumphant i...Iaetsd Iaetsd
This document analyzes the effect of three types of superconducting fault current limiters (SFCL) - resistive, inductive, and bridge-type - on the transient recovery voltage (TRV) of circuit breakers. Computer simulations using the electromagnetic transients program were conducted on a test distribution system with a three-phase fault. The results show that the resistive and bridge-type SFCLs most effectively reduce both the TRV and rate of rise of recovery voltage seen by circuit breakers, allowing them to interrupt faults more reliably. The inductive SFCL limits fault current but increases TRV. The resistive SFCL provides the lowest TRV and most protection for circuit breakers during fault clearing.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
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.
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.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
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.