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An integrated framework for 3 d modeling, object detection, and pose estimati...
I3E Technologies,
#23/A, 2nd Floor SKS Complex, Opp. Bus Stand, Karur-639 001.
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NON-IEEE PROJECTS AVAILABLE,
APPLICATION BASED PROJECTS AVAILABLE,
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PhD Tool MATLAB Projects Tutorial
Significant Tools under MATLAB
Recent Topics under MATLAB
Major PhD Toolbox Support
25 17 dec16 13743 28032-1-sm(edit)
An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
Integrative Networks Centric Bioinformatics
These slides present several of our integrative bioinformatics webservers:
www.arraymining.net
www.pathexpand.net
www.enrichnet.net
www.topogsa.net
Towards Prediction of Platinum Treatment Response in Ovarian Cancer using Mac...
Towards Prediction of Platinum Treatment Response in Ovarian Cancer using Mac...Antoaneta Vladimirova
ASHG 2017 PresentationIRJET - Breast Cancer Prediction using Supervised Machine Learning Algorithms...
https://www.irjet.net/archives/V7/i4/IRJET-V7I4396.pdf
Impact of Classification Algorithms on Cardiotocography Dataset for Fetal Sta...
Impact of Classification Algorithms on Cardiotocography Dataset for Fetal Sta...BRNSSPublicationHubI
AJCSERecommended
An integrated framework for 3 d modeling, object detection, and pose estimati...
I3E Technologies,
#23/A, 2nd Floor SKS Complex, Opp. Bus Stand, Karur-639 001.
IEEE PROJECTS AVAILABLE,
NON-IEEE PROJECTS AVAILABLE,
APPLICATION BASED PROJECTS AVAILABLE,
MINI-PROJECTS AVAILABLE.
PhD Tool MATLAB Projects Tutorial
Significant Tools under MATLAB
Recent Topics under MATLAB
Major PhD Toolbox Support
25 17 dec16 13743 28032-1-sm(edit)
An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
Integrative Networks Centric Bioinformatics
These slides present several of our integrative bioinformatics webservers:
www.arraymining.net
www.pathexpand.net
www.enrichnet.net
www.topogsa.net
Towards Prediction of Platinum Treatment Response in Ovarian Cancer using Mac...
Towards Prediction of Platinum Treatment Response in Ovarian Cancer using Mac...Antoaneta Vladimirova
ASHG 2017 PresentationIRJET - Breast Cancer Prediction using Supervised Machine Learning Algorithms...
https://www.irjet.net/archives/V7/i4/IRJET-V7I4396.pdf
Impact of Classification Algorithms on Cardiotocography Dataset for Fetal Sta...
Impact of Classification Algorithms on Cardiotocography Dataset for Fetal Sta...BRNSSPublicationHubI
AJCSEDeep learning methods applied to physicochemical and toxicological endpoints
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
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An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcifications’ patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-C Means clustering and feature extraction techniques using texture based segmentation and genetic algorithm for detecting and diagnosing micro calcifications’ patterns in digital mammograms.We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features, such as entropy, standard deviation, and number of pixels, is the best combination to distinguish a benign micro calcification pattern from one that is malignant. A fuzzy C Means technique in conjunction with three features was used to detect a micro calcification pattern and a neural network to classify it into benign/malignant. The system was developed on a Windows platform. It is an easy to use intelligent system that gives the user options to diagnose, detect, enlarge, zoom, and measure distances of areas in digital mammograms. The present study focused on the investigation of the application of artificial intelligence and data mining techniques to the prediction models of breast cancer. The artificial neural network, decision tree,Fuzzy C Means, and genetic algorithm were used for the comparative studies and the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 699 records acquired from the breast cancer patients at the MIAS database, 9 predictor variables, and 1 outcome variable were incorporated for the data analysis followed by the 10-fold cross-validation. The results revealed that the accuracies of Fuzzy C Means were 0.9534 (sensitivity 0.98716 and specificity 0.9582), the decision tree model 0.9634 (sensitivity 0.98615, specificity 0.9305), the neural network model 0.96502 (sensitivity 0.98628, specificity 0.9473), the genetic algorithm model 0.9878 (sensitivity 1, specificity 0.9802). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.9612. The predicted outcome of the Fuzzy C Means model was higher than that of the neural network model but no significant difference was observed. The average predicted accuracy of the decision tree model was 0.9635 which was the lowest of all 4 predictive models. The standard deviation of the 10-fold cross-validation was rather unreliable. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible. Keywords: Fuzzy C Means, Decision Tree Induction, Genetic algorithm, data mining, breast cancer, rule discovery.
KNOWLEDGE BASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST CANCER
In this paper, we study the performance criterion of machine learning tools in classifying breast cancer. We compare the data mining tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART. We used both binary and multi class data sets namely WBC, WDBC and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to accuracy, precision, sensitivity and specificity in detecting breast cancer
sec dc.pptx
import pygame
pygame.init() #initializes the Pygame
from pygame.locals import* #import all modules from Pygame
screen = pygame.display.set_mode((798,600))
#changing title of the game window
pygame.display.set_caption('Racing Beast')
#changing the logo
logo = pygame.image.load('car game/logo.jpeg')
pygame.display.set_icon(logo)
run = True
while run:
for event in pygame.event.get():
if event.type == pygame.QUIT:
run = False
if event.type == pygame.KEYDOWN:
if event.key == K_RIGHT:
maincarX += 5
if event.key == K_LEFT:
maincarX -= 5
#CHANGING COLOR WITH RGB VALUE, RGB = RED, GREEN, BLUE
screen.fill((255,0,0))
IRJET - Detection and Classification of Brain Tumor
https://irjet.net/archives/V7/i3/IRJET-V7I3535.pdf
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Cervical cancer is the highest rate of incidence after breast cancer, gastric cancer, colorectal cancer, thyroid cancer among all malignant that occurs to females ; also it is the most prevalent cancer among female genital cancers. Manual cervical cancer diagnosis methods are costly and sometimes result inaccurate diagnosis caused by human error but machine assisted classification system can reduce financial costs and increase screening accuracy. In this research article, we have developed multi class cervical classification system by using Pap Smear Images according to the WHO descriptive Classification of Cervical Histology. Then, this system classifies the cell of the Pap Smear image into anyone of five types of the classes of normal cell, mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma in situ (CIS) by using individual and Combining individual feature extraction method with the classification technique. In this paper three Feature Extraction methods were used: From that three, two were individual feature extraction method namely Enriched Rough Set Texton Co-Occurrence Matrix (ERSTCM) and Enriched Micro Structure Descriptor (EMSD) and the remained one was combining individual feature extraction method namely concatenated feature extraction method (CFE). The CFE method represents all the individual feature extraction methods of ERSTCM & EMSD features are combining together to one feature to assess their joint performance. Then these three feature extraction methods are tested over Fuzzy Logic based Hybrid Kernel Support Vector Machine (FL-HKSVM) Classifier. This Examination was conducted over a set of single cervical cell based pap smear images. The dataset contains five classes of images, with a total of 952 images. The distribution of number of images per class is not uniform. Then the performance was evaluated in both the individual and combining individual feature extraction method with the classification techniques by using the statistical parameters of sensitivity, specificity & accuracy. Hence the resultant values of the statistical parameters described in individual feature extraction method with the classification technique, proposed EMSD+FLHKSVM Classifier had given the better results than the other ERSTCM+FLHKSVM Classifier and combining individual feature extraction method with the classification technique described, proposed CFE+FLHKSVM Classifier had given the better results than other EMSD+FLHKSVM & ERSTCM+FLHKSVM classifiers.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Microarray Data Classification Using Support Vector Machine
DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
Clustering and Classification of Cancer Data Using Soft Computing Technique
Clustering and classification of cancer data has been used with success in field of medical side. In
this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of
the result. this paper address the problem of learning to classify the cancer data with two different method and
information derived from the training and testing .various soft computing based classification and show the
comparison of classification technique and classification of this health care data .this paper present the
accuracy of the result in cancer data.
Lec13: Clustering Based Medical Image Segmentation Methods
Clustering – K-means
– FCM (fuzzyc-means)
– SMC (simple membership based clustering) – AP(affinity propagation)
– FLAB(fuzzy locally adaptive Bayesian)
– Spectral Clustering Methods
ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Computer Aided System for Detection and Classification of Breast Cancer
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...
Optimization problems are dominantly being solved using Computational Intelligence. One of
the issues that can be addressed in this context is problems related to attribute subset selection
evaluation. This paper presents a computational intelligence technique for solving the
optimization problem using a proposed model called Modified Genetic Search Algorithms
(MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting
and deleting bad candidate chromosomes, thereby reducing the number of individual
chromosomes from search space and subsequent iterations in next generations. This paper aims
to show that Rotation forest ensembles are useful in the feature selection method. The base
classifier is multinomial logistic regression method integrated with Haar wavelets as projection
filter and reproducing the ranks of each features with 10 fold cross validation method. It also
discusses the main findings and concludes with promising result of the proposed model. It
explores the combination of MGSA for optimization with Naïve Bayes classification. The result
obtained using proposed model MGSA is validated mathematically using Principal Component
Analysis. The goal is to improve the accuracy and quality of diagnosis of Breast cancer disease
with robust machine learning algorithms. As compared to other works in literature survey,
experimental results achieved in this paper show better results with statistical inferenc
A survey on heart stroke prediction
In this project, we predict heart stroke or not based on 10 features using Machine learning algorithms.
Detection of Breast Cancer using BPN Classifier in Mammograms
https://irjet.net/archives/V4/i3/IRJET-V4I3462.pdf
Using Artificial Neural Networks to Detect Multiple Cancers from a Blood Test
Research paper drafted during my 2 year internship with Oak Ridge National Laboratory illustrating the potential of artificial intelligence in cancer research.
Immunizing Image Classifiers Against Localized Adversary Attacks
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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Deep learning methods applied to physicochemical and toxicological endpoints
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
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An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcifications’ patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-C Means clustering and feature extraction techniques using texture based segmentation and genetic algorithm for detecting and diagnosing micro calcifications’ patterns in digital mammograms.We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features, such as entropy, standard deviation, and number of pixels, is the best combination to distinguish a benign micro calcification pattern from one that is malignant. A fuzzy C Means technique in conjunction with three features was used to detect a micro calcification pattern and a neural network to classify it into benign/malignant. The system was developed on a Windows platform. It is an easy to use intelligent system that gives the user options to diagnose, detect, enlarge, zoom, and measure distances of areas in digital mammograms. The present study focused on the investigation of the application of artificial intelligence and data mining techniques to the prediction models of breast cancer. The artificial neural network, decision tree,Fuzzy C Means, and genetic algorithm were used for the comparative studies and the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 699 records acquired from the breast cancer patients at the MIAS database, 9 predictor variables, and 1 outcome variable were incorporated for the data analysis followed by the 10-fold cross-validation. The results revealed that the accuracies of Fuzzy C Means were 0.9534 (sensitivity 0.98716 and specificity 0.9582), the decision tree model 0.9634 (sensitivity 0.98615, specificity 0.9305), the neural network model 0.96502 (sensitivity 0.98628, specificity 0.9473), the genetic algorithm model 0.9878 (sensitivity 1, specificity 0.9802). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.9612. The predicted outcome of the Fuzzy C Means model was higher than that of the neural network model but no significant difference was observed. The average predicted accuracy of the decision tree model was 0.9635 which was the lowest of all 4 predictive models. The standard deviation of the 10-fold cross-validation was rather unreliable. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible. Keywords: Fuzzy C Means, Decision Tree Induction, Genetic algorithm, data mining, breast cancer, rule discovery.
KNOWLEDGE BASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST CANCER
In this paper, we study the performance criterion of machine learning tools in classifying breast cancer. We compare the data mining tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART. We used both binary and multi class data sets namely WBC, WDBC and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to accuracy, precision, sensitivity and specificity in detecting breast cancer
sec dc.pptx
import pygame
pygame.init() #initializes the Pygame
from pygame.locals import* #import all modules from Pygame
screen = pygame.display.set_mode((798,600))
#changing title of the game window
pygame.display.set_caption('Racing Beast')
#changing the logo
logo = pygame.image.load('car game/logo.jpeg')
pygame.display.set_icon(logo)
run = True
while run:
for event in pygame.event.get():
if event.type == pygame.QUIT:
run = False
if event.type == pygame.KEYDOWN:
if event.key == K_RIGHT:
maincarX += 5
if event.key == K_LEFT:
maincarX -= 5
#CHANGING COLOR WITH RGB VALUE, RGB = RED, GREEN, BLUE
screen.fill((255,0,0))
IRJET - Detection and Classification of Brain Tumor
https://irjet.net/archives/V7/i3/IRJET-V7I3535.pdf
Multi Class Cervical Cancer Classification by using ERSTCM, EMSD & CFE method...
Cervical cancer is the highest rate of incidence after breast cancer, gastric cancer, colorectal cancer, thyroid cancer among all malignant that occurs to females ; also it is the most prevalent cancer among female genital cancers. Manual cervical cancer diagnosis methods are costly and sometimes result inaccurate diagnosis caused by human error but machine assisted classification system can reduce financial costs and increase screening accuracy. In this research article, we have developed multi class cervical classification system by using Pap Smear Images according to the WHO descriptive Classification of Cervical Histology. Then, this system classifies the cell of the Pap Smear image into anyone of five types of the classes of normal cell, mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma in situ (CIS) by using individual and Combining individual feature extraction method with the classification technique. In this paper three Feature Extraction methods were used: From that three, two were individual feature extraction method namely Enriched Rough Set Texton Co-Occurrence Matrix (ERSTCM) and Enriched Micro Structure Descriptor (EMSD) and the remained one was combining individual feature extraction method namely concatenated feature extraction method (CFE). The CFE method represents all the individual feature extraction methods of ERSTCM & EMSD features are combining together to one feature to assess their joint performance. Then these three feature extraction methods are tested over Fuzzy Logic based Hybrid Kernel Support Vector Machine (FL-HKSVM) Classifier. This Examination was conducted over a set of single cervical cell based pap smear images. The dataset contains five classes of images, with a total of 952 images. The distribution of number of images per class is not uniform. Then the performance was evaluated in both the individual and combining individual feature extraction method with the classification techniques by using the statistical parameters of sensitivity, specificity & accuracy. Hence the resultant values of the statistical parameters described in individual feature extraction method with the classification technique, proposed EMSD+FLHKSVM Classifier had given the better results than the other ERSTCM+FLHKSVM Classifier and combining individual feature extraction method with the classification technique described, proposed CFE+FLHKSVM Classifier had given the better results than other EMSD+FLHKSVM & ERSTCM+FLHKSVM classifiers.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
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DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
Clustering and Classification of Cancer Data Using Soft Computing Technique
Clustering and classification of cancer data has been used with success in field of medical side. In
this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of
the result. this paper address the problem of learning to classify the cancer data with two different method and
information derived from the training and testing .various soft computing based classification and show the
comparison of classification technique and classification of this health care data .this paper present the
accuracy of the result in cancer data.
Lec13: Clustering Based Medical Image Segmentation Methods
Clustering – K-means
– FCM (fuzzyc-means)
– SMC (simple membership based clustering) – AP(affinity propagation)
– FLAB(fuzzy locally adaptive Bayesian)
– Spectral Clustering Methods
ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Computer Aided System for Detection and Classification of Breast Cancer
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...
Optimization problems are dominantly being solved using Computational Intelligence. One of
the issues that can be addressed in this context is problems related to attribute subset selection
evaluation. This paper presents a computational intelligence technique for solving the
optimization problem using a proposed model called Modified Genetic Search Algorithms
(MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting
and deleting bad candidate chromosomes, thereby reducing the number of individual
chromosomes from search space and subsequent iterations in next generations. This paper aims
to show that Rotation forest ensembles are useful in the feature selection method. The base
classifier is multinomial logistic regression method integrated with Haar wavelets as projection
filter and reproducing the ranks of each features with 10 fold cross validation method. It also
discusses the main findings and concludes with promising result of the proposed model. It
explores the combination of MGSA for optimization with Naïve Bayes classification. The result
obtained using proposed model MGSA is validated mathematically using Principal Component
Analysis. The goal is to improve the accuracy and quality of diagnosis of Breast cancer disease
with robust machine learning algorithms. As compared to other works in literature survey,
experimental results achieved in this paper show better results with statistical inferenc
A survey on heart stroke prediction
In this project, we predict heart stroke or not based on 10 features using Machine learning algorithms.
Detection of Breast Cancer using BPN Classifier in Mammograms
https://irjet.net/archives/V4/i3/IRJET-V4I3462.pdf
Using Artificial Neural Networks to Detect Multiple Cancers from a Blood Test
Research paper drafted during my 2 year internship with Oak Ridge National Laboratory illustrating the potential of artificial intelligence in cancer research.
Similar to VA support ML (20)
Deep learning methods applied to physicochemical and toxicological endpoints
Deep learning methods applied to physicochemical and toxicological endpoints
A Novel Approach for Cancer Detection in MRI Mammogram Using Decision Tree In...
A Novel Approach for Cancer Detection in MRI Mammogram Using Decision Tree In...
KNOWLEDGE BASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST CANCER
KNOWLEDGE BASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST CANCER
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Multi Class Cervical Cancer Classification by using ERSTCM, EMSD & CFE method...
Multi Class Cervical Cancer Classification by using ERSTCM, EMSD & CFE method...
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Lec13: Clustering Based Medical Image Segmentation Methods
Lec13: Clustering Based Medical Image Segmentation Methods
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Immunizing Image Classifiers Against Localized Adversary Attacks
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Vaccine management system project report documentation..pdf
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Courier management system project report.pdf
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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WATER CRISIS and its solutions-pptx 1234
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Final project report on grocery store management system..pdf
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Dynamic Programming
Backtracking
Techniques for Graphs
Branch and Bound
HYDROPOWER - Hydroelectric power generation
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Cosmetic shop management system project report.pdf
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdf
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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VA support ML
- 3. “Be able to classify cancer promptly and by his more significant features 3
- 5. .data : all the numerical values of dataset (569 tuples) 2 .tsv files: .data and .names .names : the descriptions of the attributes (32 attributes) 5
- 6. 3. Clustering Analysis PCA and variance analysis
- 7. Barchart : Analysis of total variance Barchart 7 Place your screenshot here
- 8. Scatterplot Scatterplot : Analysis of possible clusters 8 Place your screenshot here
- 9. 4. Features selection Ratios’ change analysis
- 10. Histogramm : Value of ratio Histogramm 10
- 11. Linechart Linechart : History of variations 11
- 12. 5. Conclusion Machine Learning best model classification
- 13. “Clear separation benignant/malignant. Almost 90% of variance covered with 6 components. 13
- 14. “Using only some features make the ML algorithm accuracy’s grows up 14