A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin & Eosin Images of Colon Cancer
•
0 likes•498 views
This document presents a novel hierarchical clustering algorithm for color image segmentation of Haematoxylin and Eosin (H&E) stained images of colon cancer. The algorithm aims to classify differences between benign and malignant tumor cells. It involves reading an input color image, coarse representation using 25 bins, hierarchical clustering to segment the image into classes, and outputting the segmented image. Intermediate steps include filtering, normalization, and segmentation of nuclei into a separate image in MATLAB for real-time simulation. Results show the algorithm improves color segmentation accuracy over previous k-means clustering approaches and provides features for region of interest classification of benign and malignant tissues.
Report
Share
Report
Share
Download to read offline
![IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 11, Issue 5 (May. - Jun. 2013), PP 79-83
www.iosrjournals.org
www.iosrjournals.org 79 | Page
A Hierarchical Clustering Algorithm Based Computer Aided
Molecular Modeling with Haematoxylin & Eosin Images of Colon
Cancer
K. Satish Reddy1
, Shaik Basha2
, B. Prashanth3
, K. Sridhar Reddy4
1,2,3,4
Department of ECE, Vardhaman College of Engineering, Hyderabad 501218 , India.
Abstract: Color Image segmentation plays a crucial role in many medical imaging applications. In this paper
we present a novel algorithm for computer aided molecular modeling in MATLAB R2012, a real time simulation
environment. This manuscript presents a novel algorithm for color image segmentation of Haematoxilin and
Eosin images of colon cancer. With help of this algorithm we can classify the differences between benign and
malignant tumor cells.
Keywords: - Color segmentation, Hierarchical Clustering, Real Time Simulation Environment, Molecular
Modeling.
I. INTRODUCTION
In this manuscript we compare the existing k-means clustering based Computer Aided Molecular
Modeling approach of H & E (Haematoxilin & Eosin) images of Colon Cancer as illustrated in [1] with the
current hierarchical clustering based approach. Ideally image segmentation is defined as the separation of an
image into non-overlapping, constituent regions which are homogeneous with respect to some characteristics
such as intensity or texture [2]. In present research work, we developed computer image-processing techniques
for classifying colon cancer and non-malignant tissue in digital histological images acquired from tissue sections
captured using scanning electronic microscope (SEM) acquired with high resolution charged coupled device
(CCD) camera from regions of interest (ROI) marked by pathologists on colon tissue sections [5].
II. DESIGN OF ALGORITHM
The different steps involved in the design of algorithm are explained as follows:
a. The input color image will be coarsely represented using 25 bins.
b. Coarse representation uses the spatial information from a Histogram based windowing process.
c. Hierarchical clustering is used to cluster the coarse image data.
d. Parameters Used
Input Color Image
Number of bins for coarse representation
Window size for histogram processing
Number of classes
Output segmented image
The coding part is illustrated as shown in the below code snippet, this coding part is done in MATLAB
R2012 - a real time simulation environment [6].
%Clear Memory & Command Window
clc;
clear all;
close all;
%Parameters for the Segmentation
nBins=5;
winSize=7;
nClass=6;
%Read Input Image
x = imread('DSS.PNG');
figure;imshow(x);title('Input Image');
%Segmentation
outImg = colImgSeg(x, nBins,winSize,nClass);
%Displaying Output](https://image.slidesharecdn.com/n01157983-140429012315-phpapp02/85/A-Hierarchical-Clustering-Algorithm-Based-Computer-Aided-Molecular-Modeling-with-Haematoxylin-Eosin-Images-of-Colon-Cancer-1-320.jpg)
![A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin
www.iosrjournals.org 80 | Page
figure;imshow(outImg);title('Image labled by cluster index');
%colormap('default');
%Image segmentation using Hierarchical clustering algorithm
xr = x(:, :, 1);
xg = x(:, :, 2);
xb = x(:, :, 3);
figure , imshow(xr, [ ]);title('objects in cluster1');
figure ,imshow(xg, [ ]);title('objects in cluster2');
figure ,imshow(xb, [ ]);title('objects in cluster3');
se = strel('disk', 3);
f = imopen(xg, se);
%figure ,imshow(f, [ ]);
xgd = im2double(f);
w = fspecial('average');
xgd2 = imfilter(xgd, w, 'replicate');
%figure ,imshow(xgd2, [ ]);
wg = fspecial('gaussian',[9 9], 1.8);
xgd3 = imfilter(xgd2, wg,'conv', 'replicate');
%figure ,imshow(xgd3, [ ]);
wm = fspecial('average',[69 69]);
xgb = imfilter(xgd3,wm, 'replicate');
%figure ,imshow(xgb, [ ]);
d = 255*xgd3-255*xgb;
%figure ,imshow(d, [ ]);
dmat = mat2gray(d);
isc = im2uint8(dmat);
%figure ,imshow(isc, [ ]);
ginmax = max(max(isc));
[m, n] = size(isc);
giimax = max(isc);
for i = 1:m
for j = 1:n
g = double(isc(i,j))+128-ginmax;
if g<0
ih(i,j) = 0;
elseif g>255
ih(i,j) = 255;
else
ih(i,j) = g;
end
end
end
figure ,imshow(ih, [ ]);
title('blue nuclei');
Modeling steps involved are from reading the image to segmentation of the nuclei into a separate image [3].
Further there are also intermediate steps that are involved between reading the image and segmenting the nuclei
into a separate image in MATLAB real-time simulation environment [4].](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Ensemble Classifications of Wavelets based GLCM Texture Feature from MR Human...
This paper presents an automatic image analysis of multi-model views of MR brain using ensemble classifications of wavelets based texture feature. Primarily, an input MR image has pre-processed for an enhancement process. Then, the pre-processed image is decomposed into different frequency sub-band image using 2D stationary and discrete wavelet transform. The GLCM texture feature information is extracted from the above low-frequency sub band image of 2D discrete and stationary wavelet transform. The extracted texture features are given as an input to ensemble classifiers of Gentle Boost and Bagged Tree classifiers to recognize the appropriate image samples. Image abnormality has extracted from the recognized abnormal image samples of classifiers using multi-level Otsu thresholding. Finally, the performance of two ensemble classifiers performance has analyzed using sensitivity, specificity, accuracy, and MCC measures of two different wavelet based GLCM texture features. The resultant proposed feature extraction technique achieves the maximum level of accuracy is 90.70% with the fraction of 0.78 MCC value.
Copy Move Forgery Detection Using GLCM Based Statistical Features
The features Gray Level Co-occurrence Matrix (GLCM) are mostly explored in Face Recognition and
CBIR. GLCM technique is explored here for Copy-Move Forgery Detection. GLCMs are extracted from all
the images in the database and statistics such as contrast, correlation, homogeneity and energy are
derived. These statistics form the feature vector. Support Vector Machine (SVM) is trained on all these
features and the authenticity of the image is decided by SVM classifier. The proposed work is evaluated on
CoMoFoD database, on a whole 1200 forged and processed images are tested. The performance analysis
of the present work is evaluated with the recent methods.
3D Riesz-wavelet Based Covariance Descriptors for Texture Classi cation of Lu...
3D Riesz-wavelet Based Covariance Descriptors for Texture Classi cation of Lu...Institute of Information Systems (HES-SO)
In this paper we present a novel technique for characterizing and classifying 3D textured volumes belonging to different lung tissues in 3D CT images.We build a volume based 3D descripton, robust to changes of size, rigid spatial transformations and texture variability, thanks to the integration of Riesz-wavelet features within a Covariance-based descriptor formulation. 3D Riesz features characterize the morphology of tissue density thanks to their response to changes in intensity in CT images. These features are encoded in a Covariancebased descripton formulation: this provides a compact and flexible representation thanks to the use of feature variations rather than dense features themselves, and adds robustness to spatial changes. Furthermore, the particular symmetric definite positive matrix form of these descriptors causes them to lay in a Riemannian manifold. Thus, descriptors can be compared with analytical measures, and accurate techniques from Machine Learning and clustering can be adapted to their spatial domain. Additionally we present a classification model following a “Bag of Covariance Descriptors” paradigm in order to distinguish three different nodule tissue types in CT: solid, ground-glass opacity (GGO), and healthy. Classification accuracy is estimated based on an acquired dataset of 95 patients with manually delineated ground truth by radiology specialists in 3D. The promising outcomes of the presented method support a future aim for automated lung nodule detection and computerized diagnosis assistance applications.Texture-Based Computational Models of Tissue in Biomedical Images: Initial Ex...
Texture-Based Computational Models of Tissue in Biomedical Images: Initial Ex...Institute of Information Systems (HES-SO)
One approach to computerized histopathology image analysis is to leverage the multi-scale texture information resulting from single nuclei appearance to entire cell populations. In this talk, we will introduce a novel framework for learning highly adaptive texture-based local models of biomedical tissue. I will discuss our initial experience with the differentiation of brain tumor types in digital histopathology.Digital image processing
The document discusses digital image processing and provides an overview of the topic. It defines an image and digital image processing. The purpose of image processing is to enhance images or extract useful information from them. Key techniques discussed include image representation, preprocessing, enhancement, restoration, analysis, reconstruction, and compression. Applications mentioned include medical imaging, astronomy, radar imaging, and more.
Regenerating face images from multi-spectral palm images using multiple fusio...
This paper established a relationship between multi-spectral palm images and a face image based
on multiple fusion methods. The first fusion method to be considered is a feature extraction between different
multi-spectral palm images, where multi-spectral CASIA database was used. The second fusion method to
be considered is a score fusion between two parts of an output face image. Our method suggests that both
right and left hands are used, and that each hand aims to produce a significant part of
a face image by using a Multi-Layer Perceptron (MLP) network. This will lead to the second fusion part to
reconstruct the full-face image, in order to examine its appearance. This topology provided interesting results
of Equal Error Rate (EER) equal to 1.99%.
An Automatic ROI of The Fundus Photography
The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photography’s of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as dark background, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.
An Efficient System for Cancer Detection using Digital Mammograms
Breast cancer can be considered one of the most dangerous types of cancer among women. Early detection of breast cancer leads to significant improvements in treatment. Digital mammograms are one of the most effective means for detecting breast cancer in early stages. In this paper, an efficient system based on performing professional pre-processing phase and on applying Discrete Cosine Transform (DCT) for features extraction, and support vector machine has been used for classification into benign and malignant. We have used Mias data set for experimentation purpose. We tune the coefficients of DCT to get the best sensitivity, specificity, positive predicitivity, and accuracy results. We reach 100% performance rate in some cases.
Recommended
Ensemble Classifications of Wavelets based GLCM Texture Feature from MR Human...
This paper presents an automatic image analysis of multi-model views of MR brain using ensemble classifications of wavelets based texture feature. Primarily, an input MR image has pre-processed for an enhancement process. Then, the pre-processed image is decomposed into different frequency sub-band image using 2D stationary and discrete wavelet transform. The GLCM texture feature information is extracted from the above low-frequency sub band image of 2D discrete and stationary wavelet transform. The extracted texture features are given as an input to ensemble classifiers of Gentle Boost and Bagged Tree classifiers to recognize the appropriate image samples. Image abnormality has extracted from the recognized abnormal image samples of classifiers using multi-level Otsu thresholding. Finally, the performance of two ensemble classifiers performance has analyzed using sensitivity, specificity, accuracy, and MCC measures of two different wavelet based GLCM texture features. The resultant proposed feature extraction technique achieves the maximum level of accuracy is 90.70% with the fraction of 0.78 MCC value.
Copy Move Forgery Detection Using GLCM Based Statistical Features
The features Gray Level Co-occurrence Matrix (GLCM) are mostly explored in Face Recognition and
CBIR. GLCM technique is explored here for Copy-Move Forgery Detection. GLCMs are extracted from all
the images in the database and statistics such as contrast, correlation, homogeneity and energy are
derived. These statistics form the feature vector. Support Vector Machine (SVM) is trained on all these
features and the authenticity of the image is decided by SVM classifier. The proposed work is evaluated on
CoMoFoD database, on a whole 1200 forged and processed images are tested. The performance analysis
of the present work is evaluated with the recent methods.
3D Riesz-wavelet Based Covariance Descriptors for Texture Classi cation of Lu...
3D Riesz-wavelet Based Covariance Descriptors for Texture Classi cation of Lu...Institute of Information Systems (HES-SO)
In this paper we present a novel technique for characterizing and classifying 3D textured volumes belonging to different lung tissues in 3D CT images.We build a volume based 3D descripton, robust to changes of size, rigid spatial transformations and texture variability, thanks to the integration of Riesz-wavelet features within a Covariance-based descriptor formulation. 3D Riesz features characterize the morphology of tissue density thanks to their response to changes in intensity in CT images. These features are encoded in a Covariancebased descripton formulation: this provides a compact and flexible representation thanks to the use of feature variations rather than dense features themselves, and adds robustness to spatial changes. Furthermore, the particular symmetric definite positive matrix form of these descriptors causes them to lay in a Riemannian manifold. Thus, descriptors can be compared with analytical measures, and accurate techniques from Machine Learning and clustering can be adapted to their spatial domain. Additionally we present a classification model following a “Bag of Covariance Descriptors” paradigm in order to distinguish three different nodule tissue types in CT: solid, ground-glass opacity (GGO), and healthy. Classification accuracy is estimated based on an acquired dataset of 95 patients with manually delineated ground truth by radiology specialists in 3D. The promising outcomes of the presented method support a future aim for automated lung nodule detection and computerized diagnosis assistance applications.Texture-Based Computational Models of Tissue in Biomedical Images: Initial Ex...
Texture-Based Computational Models of Tissue in Biomedical Images: Initial Ex...Institute of Information Systems (HES-SO)
One approach to computerized histopathology image analysis is to leverage the multi-scale texture information resulting from single nuclei appearance to entire cell populations. In this talk, we will introduce a novel framework for learning highly adaptive texture-based local models of biomedical tissue. I will discuss our initial experience with the differentiation of brain tumor types in digital histopathology.Digital image processing
The document discusses digital image processing and provides an overview of the topic. It defines an image and digital image processing. The purpose of image processing is to enhance images or extract useful information from them. Key techniques discussed include image representation, preprocessing, enhancement, restoration, analysis, reconstruction, and compression. Applications mentioned include medical imaging, astronomy, radar imaging, and more.
Regenerating face images from multi-spectral palm images using multiple fusio...
This paper established a relationship between multi-spectral palm images and a face image based
on multiple fusion methods. The first fusion method to be considered is a feature extraction between different
multi-spectral palm images, where multi-spectral CASIA database was used. The second fusion method to
be considered is a score fusion between two parts of an output face image. Our method suggests that both
right and left hands are used, and that each hand aims to produce a significant part of
a face image by using a Multi-Layer Perceptron (MLP) network. This will lead to the second fusion part to
reconstruct the full-face image, in order to examine its appearance. This topology provided interesting results
of Equal Error Rate (EER) equal to 1.99%.
An Automatic ROI of The Fundus Photography
The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photography’s of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as dark background, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.
An Efficient System for Cancer Detection using Digital Mammograms
Breast cancer can be considered one of the most dangerous types of cancer among women. Early detection of breast cancer leads to significant improvements in treatment. Digital mammograms are one of the most effective means for detecting breast cancer in early stages. In this paper, an efficient system based on performing professional pre-processing phase and on applying Discrete Cosine Transform (DCT) for features extraction, and support vector machine has been used for classification into benign and malignant. We have used Mias data set for experimentation purpose. We tune the coefficients of DCT to get the best sensitivity, specificity, positive predicitivity, and accuracy results. We reach 100% performance rate in some cases.
Design Issues for Search Engines and Web Crawlers: A Review
This document provides a review of design issues for search engines and web crawlers. It discusses how search engines use web crawlers to collect web documents for storage and indexing from the growing World Wide Web. The three main parts of a search engine are the crawler, indexer, and query engine. Crawler-based search engines create listings automatically using algorithms while human-powered directories rely on human organization. Designing efficient search engines and crawlers faces challenges from the diversity of web documents and changing user behaviors. Web crawlers prioritize URLs to download pages and extract new links to update search engine databases.
Prevalence of Cleft Lip and Palate in Abuja Nigeria (From December2000 to Dec...
The prevalence of cleft lip and palate in University of Abuja Teaching Hospital from December 2000 to December 2010 was investigated in this works and was found to be 3.2 in every 1,000 live birth. This is very high as compared to reports from earlier works but was suspected to be due to the smile Train Project which is a private sector driven initiative that takes up the burden of paying for cleft lip and palate repair. Left sides clefts of the lip and palate were found to be almost twice that of right sides cleft of the lip and palate. This was in keeping with reports from earlier works. This study shows there is need for further studies in the field of cleft lip palate in all regions of the country
A010230105
This document summarizes research on developing an online model-based control system for a photovoltaic (PV) converter unit to track the maximum power point under varying conditions like partial shading. It presents a new model that uses a logarithmic equation to predict the maximum power point voltage based on irradiance and temperature measurements. The model was tested in simulations where it accurately adjusted the PV voltage to match the predicted maximum power point voltage in response to changes in irradiance and temperature. This online model-based approach shows potential for improving PV power extraction under non-uniform conditions like partial shading.
P01725105109
This document summarizes research on script and language identification techniques for handwritten document images. It discusses the challenges of identifying scripts in handwritten versus printed documents. It then reviews various approaches that have been used, which are generally categorized as structure-based (using features like character geometry) or visual appearance-based (using texture features). Specific techniques discussed include those using linear discriminant analysis, K-nearest neighbors, neural networks, support vector machines, Gabor filters, and steerable pyramids. The document analyzes and compares the performance of different methods. It notes that better pre-processing and larger, standardized datasets are still needed to fully evaluate the techniques.
B017150823
This document discusses a proposed method for offline signature verification called OSPCV (Off-line Signature Verification using Principal Component Variances). The method extracts two features from signatures - pixel density and center of gravity distance. It then uses Principal Component Analysis to analyze the features and train a model using signature samples. When a new "test" signature is analyzed, it extracts the same two features and compares them to the trained model to determine if the signature is genuine or a forgery. The researchers believe this method provides better accuracy than existing offline signature verification systems, especially in differentiating between genuine and skilled forgery signatures. It aims to overcome challenges from intra-personal and inter-personal signature variations.
I017235662
This document discusses using particle swarm optimization to improve the k-prototype clustering algorithm. The k-prototype algorithm clusters data with both numeric and categorical attributes but can get stuck in local optima. The proposed method uses particle swarm optimization, a global optimization technique, to guide the k-prototype algorithm towards better clusterings. Particle swarm optimization models potential solutions as particles that explore the search space. It is integrated with k-prototype clustering to avoid locally optimal solutions and produce better clusterings. The method is tested on standard benchmark datasets and shown to outperform traditional k-modes and k-prototype clustering algorithms.
E017163033
1) The document reviews cross-platform mobile application development and IDEs. It discusses tools like PhoneGap and Titanium that allow developing applications using a single codebase that can be deployed to multiple platforms like Android and iOS.
2) It proposes developing an IDE that allows coding an app once in a language like Java or Objective-C, then deploying it to other platforms through automated code translation. A mathematical model is presented to conceptualize the system.
3) The review finds that tools like PhoneGap based on technologies like HTML5, CSS, and jQuery offer a way to build cross-platform web apps, but generator-based tools producing native apps from a common codebase are not yet mature.
Spectral Studies of Some Transition Metal Ion complexes with 4-[(E)-(Ferrocen...
Several new complexes of some transition metal ions with organometallic compounds were derived from ferrocencecarboxylaldehyde and 4-amino-2-hydroxy pyridine. These organometallic complexes were investigated by using some analytical techniques like infrared, mass spectra, electronic spectra, electron spin resonance, thermal analysis, magnetic moment, conductivity and antimicrobial activity. From the obtained elemental analysis data, organometallic compounds complexes shows 1:2 [M: L] ratio and general formula of the complex is [ML2.2H2O]. These complexes revealed a non-electrolytic nature. The magnetic moment values of the complexes exhibited the paramagnetic as well as diamagnetic in nature. The coordination behavior of the metal ions towards to the investigated organometallic compounds takes place through >C=N- and –OH groups. The electronic spectral data shows that all the complexes are covalent in nature, octahedral structure with co-ordination number six. Organometallic compound complexes were loses two water molecules subjected to simultaneous thermo gravimetric analysis, to study their decomposition mechanism and thermal stability. Mass spectra of the organometallic compound and their complexes are matched with theoretical values of the masses. The prepared organometallic compounds and their metal complexes were screened for their antibacterial activity against some bacterial species, Escherichia coli, Pseudomonas aeruginosa and Staphylococcus Pyogones. The Antimicrobial activity data show the metal complexes to be more active than the parent organometallic compounds.
C017521927
This document proposes a new scheme for representing signed numbers using ternary logic and developing semiconductor optical amplifiers for wavelength-encoded optical ternary half adders. It discusses using ternary (1, 0, 1) instead of binary to represent numbers, allowing for higher information storage capacity. The authors propose implementing an all-optical ternary half adder circuit using wavelength conversion via nonlinear polarization rotation in semiconductor optical amplifiers. This scheme aims to overcome limitations of prior work such as intensity loss and requirements for precise beam control.
A Cost Estimation Model For Reuse Based Software Program
This document presents a cost estimation model for a software reuse program. It proposes estimating costs at multiple levels - component, domain, application, and corporate. Costs include development, overhead, integration, and maintenance. The model is demonstrated using a hypothetical software company that develops reusable components from 2007-2010. Component size, availability, strategies, scale, standardization, and complexity are identified as factors that affect costs. Equations are provided to estimate costs at each level based on variables like component size, salary, reuse metrics, and probability of component availability. Graphs show relationships between various cost-affecting factors and total application costs.
M010127983
This document discusses the future scope of wind energy in India. It begins by providing background on India's growing population and economy, and increasing energy demands. Wind energy provides an opportunity to meet these demands through a renewable source. The document then discusses current sources of wind energy production in India, including coastal regions and large wind farms. It explores future opportunities for offshore wind turbines and wind turbines placed along highways. Overall the document argues that wind energy will play a major role in India's energy future by providing a sustainable and domestic source of power.
F012515059
1) The document presents the results of a linear and non-linear analysis of reinforced concrete frames with members of varying inertia (non-prismatic beams) for buildings ranging from G+2 to G+10 storeys.
2) Both bare frames and frames with infill walls were analyzed considering different beam cross-sections - prismatic, linear haunch, parabolic haunch, and stepped haunch.
3) The linear analysis was performed using ETABS and considered parameters like fundamental time period, base shear, and top storey displacement. The non-linear analysis used pushover analysis in SAP2000 to determine effective time period, effective stiffness, and hinge formation patterns.
H010344552
This document describes a PIC microcontroller and PC-based system using multiple gas sensors and artificial intelligence techniques for gas identification. Five commercial gas sensors are used to detect methane, carbon monoxide, and LPG at different concentrations and temperatures. The microcontroller collects analog voltage output from the sensors. Artificial neural networks are trained on the sensor data to identify gases based on patterns in responses to varying parameters like concentration, temperature, and load resistance. Experimental results show the sensors have different sensitivities to different gases and temperatures. The neural network approach can accurately predict gas concentrations online based on the sensor behavior patterns extracted during experiments. This system improves gas detection sensitivity and selectivity with high accuracy.
G0423538
The document discusses modifications to the Vigenère cipher encryption algorithm. It proposes using random numbers, punctuation marks, and mathematical symbols for the encryption key instead of characters. Random numbers would also be used to spread out the encrypted message. The modifications are intended to make the cipher more secure against brute force attacks by increasing the size of the key space. An implementation of the modified Vigenère cipher in C programming language is presented along with diagrams comparing the encryption and decryption processes between the original and modified algorithms.
E010132529
This document summarizes spatial scalable video compression using H.264. It discusses previous video compression standards like H.261 and H.263. It then describes the key components of the H.264 encoder and decoder, including prediction models, spatial models and entropy encoding. Simulation results comparing parameters like PSNR, CSNR and MSE between encoded and decoded video using H.264 are presented. The paper concludes that H.264 provides 31-35% improved efficiency and bit rate reduction over previous standards.
G010334554
This document summarizes an FPGA implementation of a trained neural network. It describes implementing a 3-2-1 multilayer perceptron network on an FPGA for a fault identification application. The key modules implemented include multiply-accumulate, truncation, sigmoid and linear activation functions. Resource utilization is low, with the entire integrated network using only 2.2% of FPGA slices. Simulation results match manual calculations, demonstrating the network accurately classifies faults.
A010130104
This document summarizes a research paper on modeling driver behavior using smartphone sensors. It describes using sensors like accelerometers, GPS, and heart rate monitors on a smartphone to detect aggressive or reckless driving behavior in real-time. If dangerous behavior is identified, the system would send alerts by GSM to emergency contacts, caregivers, or medical providers. The goal is to increase road safety by monitoring drivers' behavior and alerting them or others to risk.
G0953643
1. The document discusses privacy preservation techniques for knowledge discovery from published data. It aims to anonymize data before release to limit disclosure risk while maximizing usefulness.
2. Three types of information disclosure are defined: membership, identity, and attribute disclosure. Identity disclosure occurs when a record is linked to an individual, while attribute disclosure reveals sensitive attributes about individuals.
3. Data anonymization techniques aim to prevent these disclosures. Explicit identifiers are first removed, then techniques like generalization and suppression are used to anonymize quasi-identifiers to prevent identity and attribute disclosures.
J012256367
The document experimentally investigates enhancing the performance of a domestic refrigerator by adding a shell and tube heat exchanger after the condenser. Ammonia is used as the cooling fluid in the heat exchanger to further subcool the refrigerant. Testing showed the coefficient of performance increased 18.4% with the additional heat exchanger due to increased refrigeration effect and lower operating pressures and temperatures. Graphs compare the heat rejection, refrigeration effect, power input, and COP between the original and modified systems.
L012438186
This document describes a method for automatically extracting coastlines from satellite images. The method involves the following steps:
1. Pre-processing Landsat TM, ETM+, and Landsat 8 images through layer stacking, registration, and calibration.
2. Performing unsupervised classification or edge detection on infrared bands or band ratios to identify coastline edges.
3. Converting the raster coastline edges into vector layers using GIS tools.
4. Applying editing tools to enhance the extracted coastlines.
The method was tested on Landsat images of the Egyptian Mediterranean coast from different years. Band 5 and ratios of band 2 to band 4 or 5 produced the clearest coastline edges. The
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...
We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.
IRJET- Detection and Classification of Skin Diseases using Different Colo...
This document discusses methods for detecting and classifying skin diseases using image processing techniques. It first presents an abstract that outlines how image processing has played a major role in identifying skin diseases by techniques like filtering, segmentation, feature extraction and edge detection. It then reviews literature on different skin disease detection systems using these image processing methods. The proposed methodology extracts features from input skin disease images using two color phase models: HSV and LAB. These features are then classified using a k-nearest neighbor algorithm to identify the disease. Results show the HSV model achieved higher accuracy than LAB in detecting and classifying five common diseases.
More Related Content
Viewers also liked
Design Issues for Search Engines and Web Crawlers: A Review
This document provides a review of design issues for search engines and web crawlers. It discusses how search engines use web crawlers to collect web documents for storage and indexing from the growing World Wide Web. The three main parts of a search engine are the crawler, indexer, and query engine. Crawler-based search engines create listings automatically using algorithms while human-powered directories rely on human organization. Designing efficient search engines and crawlers faces challenges from the diversity of web documents and changing user behaviors. Web crawlers prioritize URLs to download pages and extract new links to update search engine databases.
Prevalence of Cleft Lip and Palate in Abuja Nigeria (From December2000 to Dec...
The prevalence of cleft lip and palate in University of Abuja Teaching Hospital from December 2000 to December 2010 was investigated in this works and was found to be 3.2 in every 1,000 live birth. This is very high as compared to reports from earlier works but was suspected to be due to the smile Train Project which is a private sector driven initiative that takes up the burden of paying for cleft lip and palate repair. Left sides clefts of the lip and palate were found to be almost twice that of right sides cleft of the lip and palate. This was in keeping with reports from earlier works. This study shows there is need for further studies in the field of cleft lip palate in all regions of the country
A010230105
This document summarizes research on developing an online model-based control system for a photovoltaic (PV) converter unit to track the maximum power point under varying conditions like partial shading. It presents a new model that uses a logarithmic equation to predict the maximum power point voltage based on irradiance and temperature measurements. The model was tested in simulations where it accurately adjusted the PV voltage to match the predicted maximum power point voltage in response to changes in irradiance and temperature. This online model-based approach shows potential for improving PV power extraction under non-uniform conditions like partial shading.
P01725105109
This document summarizes research on script and language identification techniques for handwritten document images. It discusses the challenges of identifying scripts in handwritten versus printed documents. It then reviews various approaches that have been used, which are generally categorized as structure-based (using features like character geometry) or visual appearance-based (using texture features). Specific techniques discussed include those using linear discriminant analysis, K-nearest neighbors, neural networks, support vector machines, Gabor filters, and steerable pyramids. The document analyzes and compares the performance of different methods. It notes that better pre-processing and larger, standardized datasets are still needed to fully evaluate the techniques.
B017150823
This document discusses a proposed method for offline signature verification called OSPCV (Off-line Signature Verification using Principal Component Variances). The method extracts two features from signatures - pixel density and center of gravity distance. It then uses Principal Component Analysis to analyze the features and train a model using signature samples. When a new "test" signature is analyzed, it extracts the same two features and compares them to the trained model to determine if the signature is genuine or a forgery. The researchers believe this method provides better accuracy than existing offline signature verification systems, especially in differentiating between genuine and skilled forgery signatures. It aims to overcome challenges from intra-personal and inter-personal signature variations.
I017235662
This document discusses using particle swarm optimization to improve the k-prototype clustering algorithm. The k-prototype algorithm clusters data with both numeric and categorical attributes but can get stuck in local optima. The proposed method uses particle swarm optimization, a global optimization technique, to guide the k-prototype algorithm towards better clusterings. Particle swarm optimization models potential solutions as particles that explore the search space. It is integrated with k-prototype clustering to avoid locally optimal solutions and produce better clusterings. The method is tested on standard benchmark datasets and shown to outperform traditional k-modes and k-prototype clustering algorithms.
E017163033
1) The document reviews cross-platform mobile application development and IDEs. It discusses tools like PhoneGap and Titanium that allow developing applications using a single codebase that can be deployed to multiple platforms like Android and iOS.
2) It proposes developing an IDE that allows coding an app once in a language like Java or Objective-C, then deploying it to other platforms through automated code translation. A mathematical model is presented to conceptualize the system.
3) The review finds that tools like PhoneGap based on technologies like HTML5, CSS, and jQuery offer a way to build cross-platform web apps, but generator-based tools producing native apps from a common codebase are not yet mature.
Spectral Studies of Some Transition Metal Ion complexes with 4-[(E)-(Ferrocen...
Several new complexes of some transition metal ions with organometallic compounds were derived from ferrocencecarboxylaldehyde and 4-amino-2-hydroxy pyridine. These organometallic complexes were investigated by using some analytical techniques like infrared, mass spectra, electronic spectra, electron spin resonance, thermal analysis, magnetic moment, conductivity and antimicrobial activity. From the obtained elemental analysis data, organometallic compounds complexes shows 1:2 [M: L] ratio and general formula of the complex is [ML2.2H2O]. These complexes revealed a non-electrolytic nature. The magnetic moment values of the complexes exhibited the paramagnetic as well as diamagnetic in nature. The coordination behavior of the metal ions towards to the investigated organometallic compounds takes place through >C=N- and –OH groups. The electronic spectral data shows that all the complexes are covalent in nature, octahedral structure with co-ordination number six. Organometallic compound complexes were loses two water molecules subjected to simultaneous thermo gravimetric analysis, to study their decomposition mechanism and thermal stability. Mass spectra of the organometallic compound and their complexes are matched with theoretical values of the masses. The prepared organometallic compounds and their metal complexes were screened for their antibacterial activity against some bacterial species, Escherichia coli, Pseudomonas aeruginosa and Staphylococcus Pyogones. The Antimicrobial activity data show the metal complexes to be more active than the parent organometallic compounds.
C017521927
This document proposes a new scheme for representing signed numbers using ternary logic and developing semiconductor optical amplifiers for wavelength-encoded optical ternary half adders. It discusses using ternary (1, 0, 1) instead of binary to represent numbers, allowing for higher information storage capacity. The authors propose implementing an all-optical ternary half adder circuit using wavelength conversion via nonlinear polarization rotation in semiconductor optical amplifiers. This scheme aims to overcome limitations of prior work such as intensity loss and requirements for precise beam control.
A Cost Estimation Model For Reuse Based Software Program
This document presents a cost estimation model for a software reuse program. It proposes estimating costs at multiple levels - component, domain, application, and corporate. Costs include development, overhead, integration, and maintenance. The model is demonstrated using a hypothetical software company that develops reusable components from 2007-2010. Component size, availability, strategies, scale, standardization, and complexity are identified as factors that affect costs. Equations are provided to estimate costs at each level based on variables like component size, salary, reuse metrics, and probability of component availability. Graphs show relationships between various cost-affecting factors and total application costs.
M010127983
This document discusses the future scope of wind energy in India. It begins by providing background on India's growing population and economy, and increasing energy demands. Wind energy provides an opportunity to meet these demands through a renewable source. The document then discusses current sources of wind energy production in India, including coastal regions and large wind farms. It explores future opportunities for offshore wind turbines and wind turbines placed along highways. Overall the document argues that wind energy will play a major role in India's energy future by providing a sustainable and domestic source of power.
F012515059
1) The document presents the results of a linear and non-linear analysis of reinforced concrete frames with members of varying inertia (non-prismatic beams) for buildings ranging from G+2 to G+10 storeys.
2) Both bare frames and frames with infill walls were analyzed considering different beam cross-sections - prismatic, linear haunch, parabolic haunch, and stepped haunch.
3) The linear analysis was performed using ETABS and considered parameters like fundamental time period, base shear, and top storey displacement. The non-linear analysis used pushover analysis in SAP2000 to determine effective time period, effective stiffness, and hinge formation patterns.
H010344552
This document describes a PIC microcontroller and PC-based system using multiple gas sensors and artificial intelligence techniques for gas identification. Five commercial gas sensors are used to detect methane, carbon monoxide, and LPG at different concentrations and temperatures. The microcontroller collects analog voltage output from the sensors. Artificial neural networks are trained on the sensor data to identify gases based on patterns in responses to varying parameters like concentration, temperature, and load resistance. Experimental results show the sensors have different sensitivities to different gases and temperatures. The neural network approach can accurately predict gas concentrations online based on the sensor behavior patterns extracted during experiments. This system improves gas detection sensitivity and selectivity with high accuracy.
G0423538
The document discusses modifications to the Vigenère cipher encryption algorithm. It proposes using random numbers, punctuation marks, and mathematical symbols for the encryption key instead of characters. Random numbers would also be used to spread out the encrypted message. The modifications are intended to make the cipher more secure against brute force attacks by increasing the size of the key space. An implementation of the modified Vigenère cipher in C programming language is presented along with diagrams comparing the encryption and decryption processes between the original and modified algorithms.
E010132529
This document summarizes spatial scalable video compression using H.264. It discusses previous video compression standards like H.261 and H.263. It then describes the key components of the H.264 encoder and decoder, including prediction models, spatial models and entropy encoding. Simulation results comparing parameters like PSNR, CSNR and MSE between encoded and decoded video using H.264 are presented. The paper concludes that H.264 provides 31-35% improved efficiency and bit rate reduction over previous standards.
G010334554
This document summarizes an FPGA implementation of a trained neural network. It describes implementing a 3-2-1 multilayer perceptron network on an FPGA for a fault identification application. The key modules implemented include multiply-accumulate, truncation, sigmoid and linear activation functions. Resource utilization is low, with the entire integrated network using only 2.2% of FPGA slices. Simulation results match manual calculations, demonstrating the network accurately classifies faults.
A010130104
This document summarizes a research paper on modeling driver behavior using smartphone sensors. It describes using sensors like accelerometers, GPS, and heart rate monitors on a smartphone to detect aggressive or reckless driving behavior in real-time. If dangerous behavior is identified, the system would send alerts by GSM to emergency contacts, caregivers, or medical providers. The goal is to increase road safety by monitoring drivers' behavior and alerting them or others to risk.
G0953643
1. The document discusses privacy preservation techniques for knowledge discovery from published data. It aims to anonymize data before release to limit disclosure risk while maximizing usefulness.
2. Three types of information disclosure are defined: membership, identity, and attribute disclosure. Identity disclosure occurs when a record is linked to an individual, while attribute disclosure reveals sensitive attributes about individuals.
3. Data anonymization techniques aim to prevent these disclosures. Explicit identifiers are first removed, then techniques like generalization and suppression are used to anonymize quasi-identifiers to prevent identity and attribute disclosures.
J012256367
The document experimentally investigates enhancing the performance of a domestic refrigerator by adding a shell and tube heat exchanger after the condenser. Ammonia is used as the cooling fluid in the heat exchanger to further subcool the refrigerant. Testing showed the coefficient of performance increased 18.4% with the additional heat exchanger due to increased refrigeration effect and lower operating pressures and temperatures. Graphs compare the heat rejection, refrigeration effect, power input, and COP between the original and modified systems.
L012438186
This document describes a method for automatically extracting coastlines from satellite images. The method involves the following steps:
1. Pre-processing Landsat TM, ETM+, and Landsat 8 images through layer stacking, registration, and calibration.
2. Performing unsupervised classification or edge detection on infrared bands or band ratios to identify coastline edges.
3. Converting the raster coastline edges into vector layers using GIS tools.
4. Applying editing tools to enhance the extracted coastlines.
The method was tested on Landsat images of the Egyptian Mediterranean coast from different years. Band 5 and ratios of band 2 to band 4 or 5 produced the clearest coastline edges. The
Viewers also liked (20)
Design Issues for Search Engines and Web Crawlers: A Review
Design Issues for Search Engines and Web Crawlers: A Review
Prevalence of Cleft Lip and Palate in Abuja Nigeria (From December2000 to Dec...
Prevalence of Cleft Lip and Palate in Abuja Nigeria (From December2000 to Dec...
Spectral Studies of Some Transition Metal Ion complexes with 4-[(E)-(Ferrocen...
Spectral Studies of Some Transition Metal Ion complexes with 4-[(E)-(Ferrocen...
A Cost Estimation Model For Reuse Based Software Program
A Cost Estimation Model For Reuse Based Software Program
Similar to A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin & Eosin Images of Colon Cancer
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...
We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.
IRJET- Detection and Classification of Skin Diseases using Different Colo...
This document discusses methods for detecting and classifying skin diseases using image processing techniques. It first presents an abstract that outlines how image processing has played a major role in identifying skin diseases by techniques like filtering, segmentation, feature extraction and edge detection. It then reviews literature on different skin disease detection systems using these image processing methods. The proposed methodology extracts features from input skin disease images using two color phase models: HSV and LAB. These features are then classified using a k-nearest neighbor algorithm to identify the disease. Results show the HSV model achieved higher accuracy than LAB in detecting and classifying five common diseases.
Classification of mammograms based on features extraction techniques using su...
Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the preprocessing stage. Secondly, in the segmentation phase, a hybrid bounding box and region growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the region of interest (ROI). In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), local binary patterns (LBP), and gray-level co-occurrence matrix (GLCM), Finally, support vector machine (SVM) has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the mammogram image analysis society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.
RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORK
The complementary DNA (cDNA) sequence considered the magic biometric technique for personal identification. Microarray image processing used for the concurrent genes identification. In this paper, we present a new method for cDNA recognition based on the artificial neural network (ANN). We have segmented the location of the spots in a cDNA microarray. Thus, a precise localization and segmenting of a spot are essential to obtain a more exact intensity measurement, leading to a more accurate gene expression measurement. The segmented cDNA microarray image resized and used as an input for the
proposed artificial neural network. For matching and recognition, we have trained the artificial neural
network. Recognition results are given for the galleries of cDNA sequences . The numerical results show
that, the proposed matching technique is an effective in the cDNA sequences process. The experimental
results of our matching approach using different databases shows that, the proposed technique is an effective matching performance.
Augmentation of Multimodal 3D Magnetic Resonance Imaging using Generative Adv...
This document proposes using a generative adversarial network (GAN) to augment a brain tumor MRI dataset with missing modalities. Specifically, it uses a modified 3D UNet architecture as the generator and PatchGAN discriminator. The generator takes 3 MRI modalities as input and generates the 4th missing modality. The discriminator evaluates the generated images for quality. Incorporating the generated modalities into the original dataset improves brain tumor segmentation accuracy compared to using only the original modalities. Evaluation metrics like MSE, PSNR, SSIM and Dice score indicate the GAN-generated modalities are of acceptable quality for augmenting the dataset and enhancing segmentation performance.
Finger vein identification system using capsule networks with hyperparameter ...
Safety and security systems are essential for personnel who need to be protected and valuables. The security and safety system can be supported using a biometric system to identify and verify permitted users or owners. Finger vein is one type of biometric system that has high-level security. The finger vein biometrics system has two primary functions: identification and verification. Safety and security technology development is often followed by hackers' development of science and technology. Therefore, the science and technology of safety and security need to be continuously developed. The paper proposes finger vein identification using capsule networks with hyperparameter tuning. The augmentation, convolution layer parameters, and capsule layers are optimized. The experimental results show that the capsule network with hyperparameter tuning successfully identifies the finger vein images. The system achieves an accuracy of 91.25% using the Shandong University machine learning and applications-homologous multimodal traits (SDUMLA-HMT) dataset.
MRI_processing
This document proposes a method for measuring the area of a brain tumor affected region using MRI image processing in MATLAB. The algorithm involves preprocessing the MRI image, detecting the tumor boundaries using edge detection, allowing the user to select the tumor region, and calculating the number of pixels and corresponding real-world area of the selected region. Calibration is required to convert pixels to real units based on an image of a known length scale. The method allows semi-automatic measurement of the tumor area.
Neural Network based Supervised Self Organizing Maps for Face Recognition
The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Face is one of the human biometrics for passive identification with uniqueness and stability. In this manuscript we present a new face based biometric system based on neural networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed method to a variety of datasets and show the results.
NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITION
The word biometrics refers to the use of physiological or biological characteristics of human to recognize
and verify the identity of an individual. Face is one of the human biometrics for passive identification with
uniqueness and stability. In this manuscript we present a new face based biometric system based on neural
networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the
proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed
method to a variety of datasets and show the results.
A Hybrid Approach for Classification of MRI Brain Tumors Using Genetic Algori...
A Hybrid Approach for Classification of MRI Brain Tumors Using Genetic Algori...IJCSIS Research Publications
This document proposes a hybrid approach using genetic algorithm, K-nearest neighbor, and probabilistic neural network for classifying MRI brain tumors. It extracts texture features using gray level co-occurrence matrix from wavelet decomposed MRI images. A genetic algorithm is then used for feature selection to identify an optimal feature subset for classification. Finally, probabilistic neural network is used to classify tumors into seven types based on the selected features, achieving accurate classification results.An Evolutionary Dynamic Clustering based Colour Image Segmentation
We have presented a novel Dynamic Colour Image Segmentation (DCIS) System for colour image. In this paper, we have proposed an efficient colour image segmentation algorithm based on evolutionary approach i.e. dynamic GA based clustering (GADCIS). The proposed technique automatically determines the optimum number of clusters for colour images. The optimal number of clusters is obtained by using cluster validity criterion with the help of Gaussian distribution. The advantage of this method is that no a priori knowledge is required to segment the color image. The proposed algorithm is evaluated on well known natural images and its performance is compared to other clustering techniques. Experimental results show the performance of the proposed algorithm producing comparable segmentation results.
Dh3211231128
This document presents a method for speeding up fractal image compression using genetic algorithms. Fractal image compression explores self-similarity in images to compress them but the search for similar domain blocks is time-consuming. The proposed method uses genetic algorithms to more efficiently search for similar domain blocks. Experimental results on several test images show that the genetic algorithm approach can compress images faster than standard fractal compression with acceptable image quality and compression ratios. Key parameters like population size and error threshold that affect the genetic search are also analyzed.
Iaetsd an efficient way of detecting a numbers in car
This document proposes using genetic algorithms to detect numbers and characters in vehicle license plates from images. It describes an image processing method to capture license plate images with cameras, process the images to isolate the plate, and then use a genetic algorithm to accurately identify the plate numbers and characters. The genetic algorithm approach is said to provide higher accuracy than other methods. The document outlines the image processing steps including filtering, thresholding and morphological operations to extract potential license plate symbols from images. It then describes implementing a genetic algorithm with chromosome encoding to represent potential solutions, fitness functions to evaluate solutions, and genetic operators like crossover and mutation to evolve better solutions across generations and accurately detect license plate contents.
Prototype System to Detect Skin Cancer Through Images
This paper proposes the development of a software that performs the pre-diagnosis of malignant melanoma, spincellular carcinoma and basal-cell carcinoma. The software is divided into five modules, these being: digital imaging, analysis and processing, storage, feature extraction and classification by means of an Artificial Neural Network (ANN). The results shown the performance of the software for two different combination of activation functions in the network. With the use of spectroscopic techniques for the acquisition of images and the combination of non-linear and linear activation functions in the ANN, the software shows an effectiveness greater than 80%, concluding that it can be an effective tool as an aid in the diagnosis of cancer of skin.
A Review on the Brain Tumor Detection and Segmentation Techniques
This document reviews various techniques for brain tumor detection and segmentation from medical images. It summarizes 15 research papers published between 2012-2021. The papers propose and evaluate different methods using techniques like wavelet transforms, principal component analysis, kernel support vector machines, edge detection, thresholding, clustering, convolutional neural networks and deep learning. The methods are applied to MRI brain images and evaluated based on their accuracy, sensitivity and specificity in detecting and segmenting tumors. Accuracy rates between 91-100% are reported depending on the technique and dataset used. Overall the document provides an overview of the progress and various approaches for automated brain tumor detection and segmentation from medical images.
A new method of gridding for spot detection in microarray images
This document presents a new method for spot detection in microarray images. It begins with edge detection using an adaptive multi-structure morphological algorithm to effectively suppress noise while preserving image edges. Morphological filling is then used to fill holes in the binary image output from edge detection. Finally, an automatic spot detection algorithm is used to segment each sub-grid into individual spot regions based on intensity projection profiles along the rows and columns of the sub-grid. Fuzzy c-means clustering is then applied to separate spots from background pixels in each segmented region. The results show the method is fully automatic without needing human intervention or parameter presetting.
A new method of gridding for spot detection in microarray images
This document presents a new method for spot detection in microarray images. It begins with edge detection using an adaptive multi-structure morphological algorithm to effectively suppress noise while preserving image edges. Morphological filling is then used to fill holes in the binary image output from edge detection. Finally, an automatic spot detection algorithm is used to segment each sub-grid into individual spot regions by applying gridding based on the intensity projection profile of the sub-grid. Fuzzy c-means clustering is then used to segment each spot from the background pixels. The results show the method is fully automatic without needing human intervention or parameter presetting.
Development and Comparison of Image Fusion Techniques for CT&MRI Images
Image processing techniques primarily focus upon enhancing the quality of an image or a set ofimages to derive
the maximum information from them. Image Fusion is a technique of producing a superior quality image from a
set of available images. It is the process of combining relevant information from two or more images into a
single image wherein the resulting image will be more informative and complete than any of the input images. A
lot of research is being done in this field encompassing areas of Computer Vision, Automatic object detection,
Image processing, parallel and distributed processing, Robotics and remote sensing. This project paves way to
explain the theoretical and implementation issues of seven image fusion algorithms and the experimental results
of the same. The fusion algorithms would be assessed based on the study and development of some image
quality metrics
Hybrid Technique Based on N-GRAM and Neural Networks for Classification of Ma...
This document summarizes an experiment that used n-gram features extracted from mammographic images and classified the images using a neural network. Regions of interest from mammograms in the miniMIAS database were represented using n-gram models by treating pixel intensities like words. Three-gram and four-gram features were extracted and normalized. The features were input to an artificial neural network classifier to classify regions as normal or abnormal. Experiments varying n, grey levels, and background tissue showed the highest accuracy of 83.33% for classifying fatty background tissue using three-gram features reduced to 8 grey levels.
Iaetsd early detection of breast cancer
This document presents a computer-aided diagnosis (CAD) system for early detection of breast cancer using mammograms. The CAD system employs an image enhancement process including filtering, contrast stretching, and top hat transformation. Microcalcifications are then segmented from the images using thresholding. Features are extracted from the segmented regions and classified as benign or malignant using a support vector machine (SVM) classifier with a radial basis function kernel. The proposed CAD system was able to accurately detect cancerous tissue from mammograms to help radiologists make more informed diagnoses.
Similar to A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin & Eosin Images of Colon Cancer (20)
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...
IRJET- Detection and Classification of Skin Diseases using Different Colo...
IRJET- Detection and Classification of Skin Diseases using Different Colo...
Classification of mammograms based on features extraction techniques using su...
Classification of mammograms based on features extraction techniques using su...
RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORK
RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORK
Augmentation of Multimodal 3D Magnetic Resonance Imaging using Generative Adv...
Augmentation of Multimodal 3D Magnetic Resonance Imaging using Generative Adv...
Finger vein identification system using capsule networks with hyperparameter ...
Finger vein identification system using capsule networks with hyperparameter ...
Neural Network based Supervised Self Organizing Maps for Face Recognition
Neural Network based Supervised Self Organizing Maps for Face Recognition
NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITION
NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITION
A Hybrid Approach for Classification of MRI Brain Tumors Using Genetic Algori...
A Hybrid Approach for Classification of MRI Brain Tumors Using Genetic Algori...
An Evolutionary Dynamic Clustering based Colour Image Segmentation
An Evolutionary Dynamic Clustering based Colour Image Segmentation
Iaetsd an efficient way of detecting a numbers in car
Iaetsd an efficient way of detecting a numbers in car
Prototype System to Detect Skin Cancer Through Images
Prototype System to Detect Skin Cancer Through Images
A Review on the Brain Tumor Detection and Segmentation Techniques
A Review on the Brain Tumor Detection and Segmentation Techniques
A new method of gridding for spot detection in microarray images
A new method of gridding for spot detection in microarray images
A new method of gridding for spot detection in microarray images
A new method of gridding for spot detection in microarray images
Development and Comparison of Image Fusion Techniques for CT&MRI Images
Development and Comparison of Image Fusion Techniques for CT&MRI Images
Hybrid Technique Based on N-GRAM and Neural Networks for Classification of Ma...
Hybrid Technique Based on N-GRAM and Neural Networks for Classification of Ma...
More from IOSR Journals
A011140104
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
M0111397100
This document analyzes the performance of various modulation schemes for achieving energy efficient communication over fading channels in wireless sensor networks. It finds that for long transmission distances, low-order modulations like BPSK are optimal due to their lower SNR requirements. However, as transmission distance decreases, higher-order modulations like 16-QAM and 64-QAM become more optimal since they can transmit more bits per symbol, outweighing their higher SNR needs. Simulations show lifetime extensions up to 550% are possible in short-range networks by using higher-order modulations instead of just BPSK. The optimal modulation depends on transmission distance and balancing the energy used by electronic components versus power amplifiers.
L011138596
This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
K011138084
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
J011137479
1) The document simulates and compares the performance of AODV and DSDV routing protocols in a mobile ad hoc network under three conditions: when users are fixed, when users move towards the base station, and when users move away from the base station.
2) The results show that both protocols have higher packet delivery and lower packet loss when users are either fixed or moving towards the base station, since signal strength is better in those scenarios. Performance degrades when users move away from the base station due to weaker signals.
3) AODV generally has better performance than DSDV, with higher throughput and packet delivery rates observed across the different user mobility conditions.
I011136673
This document describes the design and implementation of 4-bit QPSK and 256-bit QAM modulation techniques using MATLAB. It compares the two techniques based on SNR, BER, and efficiency. The key steps of implementing each technique in MATLAB are outlined, including generating random bits, modulation, adding noise, and measuring BER. Simulation results show scatter plots and eye diagrams of the modulated signals. A table compares the results, showing that 256-bit QAM provides better performance than 4-bit QPSK. The document concludes that QAM modulation is more effective for digital transmission systems.
G011134454
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
H011135565
This document studies the effects of dielectric superstrate thickness on microstrip patch antenna parameters. Three types of probes-fed patch antennas (rectangular, circular, and square) were designed to operate at 2.4 GHz using Arlondiclad 880 substrate. The antennas were tested with and without an Arlondiclad 880 superstrate of varying thicknesses. It was found that adding a superstrate slightly degraded performance by lowering the resonant frequency and increasing return loss and VSWR, while decreasing bandwidth and gain. Specifically, increasing the superstrate thickness or dielectric constant resulted in greater changes to the antenna parameters.
F011134043
This document describes a wireless environment monitoring system that utilizes soil energy as a sustainable power source for wireless sensors. The system uses a microbial fuel cell to generate electricity from the microbial activity in soil. Two microbial fuel cells were created using different soil types and various additives to produce different current and voltage outputs. An electronic circuit was designed on a printed circuit board with components like a microcontroller and ZigBee transceiver. Sensors for temperature and humidity were connected to the circuit to monitor the environment wirelessly. The system provides a low-cost way to power remote sensors without needing battery replacement and avoids the high costs of wiring a power source.
E011133639
1) The document proposes a model for a frequency tunable inverted-F antenna that uses ferrite material.
2) The resonant frequency of the antenna can be significantly shifted from 2.41GHz to 3.15GHz, a 31% shift, by increasing the static magnetic field placed on the ferrite material.
3) Altering the permeability of the ferrite allows tuning of the antenna's resonant frequency without changing the physical dimensions, providing flexibility to operate over a wide frequency range.
D011132635
This document summarizes a research paper that presents a speech enhancement method using stationary wavelet transform. The method first classifies speech into voiced, unvoiced, and silence regions based on short-time energy. It then applies different thresholding techniques to the wavelet coefficients of each region - modified hard thresholding for voiced speech, semi-soft thresholding for unvoiced speech, and setting coefficients to zero for silence. Experimental results using speech from the TIMIT database corrupted with white Gaussian noise at various SNR levels show improved performance over other popular denoising methods.
C011131925
This document reviews the design of an energy-optimized wireless sensor node that encrypts data for transmission. It discusses how sensing schemes that group nodes into clusters and transmit aggregated data can reduce energy consumption compared to individual node transmissions. The proposed node design calculates the minimum transmission power needed based on received signal strength and uses a periodic sleep/wake cycle to optimize energy when not sensing or transmitting. It aims to encrypt data at both the node and network level to further optimize energy usage for wireless communication.
B011130918
This document discusses group consumption modes. It analyzes factors that impact group consumption, including external environmental factors like technological developments enabling new forms of online and offline interactions, as well as internal motivational factors at both the group and individual level. The document then proposes that group consumption modes can be divided into four types based on two dimensions: vertical (group relationship intensity) and horizontal (consumption action period). These four types are instrument-oriented, information-oriented, enjoyment-oriented, and relationship-oriented consumption modes. Finally, the document notes that consumption modes are dynamic and can evolve over time.
A011130108
The document summarizes a study of different microstrip patch antenna configurations with slotted ground planes. Three antenna designs were proposed and their performance evaluated through simulation: a conventional square patch, an elliptical patch, and a star-shaped patch. All antennas were mounted on an FR4 substrate. The effects of adding different slot patterns to the ground plane on resonance frequency, bandwidth, gain and efficiency were analyzed parametrically. Key findings were that reshaping the patch and adding slots increased bandwidth and shifted resonance frequency. The elliptical and star patches in particular performed better than the conventional design. Three antenna configurations were selected for fabrication and measurement based on the simulations: a conventional patch with a slot under the patch, an elliptical patch with slots
I011125160
1) The document describes a study conducted to improve call drop rates in a GSM network through RF optimization.
2) Drive testing was performed before and after optimization using TEMS software to record network parameters like RxLevel, RxQuality, and events.
3) Analysis found call drops were occurring due to issues like handover failures between sectors, interference from adjacent channels, and overshooting due to antenna tilt.
4) Corrective actions taken included defining neighbors between sectors, adjusting frequencies to reduce interference, and lowering the mechanical tilt of an antenna.
5) Post-optimization drive testing showed improvements in RxLevel, RxQuality, and a reduction in dropped calls.
H011124050
This document describes the design of an intelligent autonomous wheeled robot that uses RF transmission for communication. The robot has two modes - automatic mode where it can make its own decisions, and user control mode where a user can control it remotely. It is designed using a microcontroller and can perform tasks like object recognition using computer vision and color detection in MATLAB, as well as wall painting using pneumatic systems. The robot's movement is controlled by DC motors and it uses sensors like ultrasonic sensors and gas sensors to navigate autonomously. RF transmission allows communication between the robot and a remote control unit. The overall aim is to develop a low-cost robotic system for industrial applications like material handling.
G011123539
This document reviews cryptography techniques to secure the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad-hoc networks. It discusses various types of attacks on AODV like impersonation, denial of service, eavesdropping, black hole attacks, wormhole attacks, and Sybil attacks. It then proposes using the RC6 cryptography algorithm to secure AODV by encrypting data packets and detecting and removing malicious nodes launching black hole attacks. Simulation results show that after applying RC6, the packet delivery ratio and throughput of AODV increase while delay decreases, improving the security and performance of the network under attack.
F011123134
The document describes a proposed modification to the conventional Booth multiplier that aims to increase its speed by applying concepts from Vedic mathematics. Specifically, it utilizes the Urdhva Tiryakbhyam formula to generate all partial products concurrently rather than sequentially. The proposed 8x8 bit multiplier was coded in VHDL, simulated, and found to have a path delay 44.35% lower than a conventional Booth multiplier, demonstrating its potential for higher speed.
E011122530
This document discusses image deblurring techniques. It begins by introducing image restoration and focusing on image deblurring. It then discusses challenges with image deblurring being an ill-posed problem. It reviews existing approaches to screen image deconvolution including estimating point spread functions and iteratively estimating blur kernels and sharp images. The document also discusses handling spatially variant blur and summarizes the relationship between the proposed method and previous work for different blur types. It proposes using color filters in the aperture to exploit parallax cues for segmentation and blur estimation. Finally, it proposes moving the image sensor circularly during exposure to prevent high frequency attenuation from motion blur.
D011121524
This document describes modeling an adaptive controller for an aircraft roll control system using PID, fuzzy-PID, and genetic algorithm. It begins by introducing the aircraft roll control system and motivation for developing an adaptive controller to minimize errors from noisy analog sensor signals. It then provides the mathematical model of aircraft roll dynamics and describes modeling the real-time flight control system in MATLAB/Simulink. The document evaluates PID, fuzzy-PID, and PID-GA (genetic algorithm) controllers for aircraft roll control and finds that the PID-GA controller delivers the best performance.
More from IOSR Journals (20)
Recently uploaded
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
* Registration is currently open *
Call for Research Papers!!!
Free – Extended Paper will be published as free of cost.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)
July 13 ~ 14, 2024, Virtual Conference
Webpage URL: https://aiad2024.org/index
Submission Deadline: June 22, 2024
Submission System URL:
https://aiad2024.org/submission/index.php
Contact Us:
Here's where you can reach us : aiad@aiad2024.org (or) aiadconference@yahoo.com
WikiCFP URL: http://wikicfp.com/cfp/servlet/event.showcfp?eventid=180509©ownerid=171656
#artificialintelligence #softcomputing #machinelearning #technology #datascience #python #deeplearning #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #engineering #robot #datascientist #software #automation #analytics #ml #pythonprogramming #programmer #digitaltransformation #developer #promptengineering #generativeai #genai #chatgpt #artificial #intelligence #datamining #networkscommunications #robotics #callforsubmission #submissionsopen #deadline #opencall #virtual #conference
Generative AI Use cases applications solutions and implementation.pdf
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Null Bangalore | Pentesters Approach to AWS IAM
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Blood finder application project report (1).pdf
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
原版一模一样【微信：741003700 】【(爱大毕业证书)爱荷华大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Mechatronics material . Mechanical engineering
Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems. Mechatronics is an essential foundation for the expected growth in automation and manufacturing.
Mechatronics deals with robotics, control systems, and electro-mechanical systems.
Mechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering PROJECT REPORT ON SUMMER VOCATIONAL TRAINING
AT MBB AIRPORT
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Pressure Relief valve used in flow line to release the over pressure at our d...
Pressure Relief valve used in flow line to release the over pressure at our desired pressure
AI-Based Home Security System : Home security
Home security is of paramount importance in today's world, where we rely more on technology, home
security is crucial. Using technology to make homes safer and easier to control from anywhere is
important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost,
AI based model home security system. The system has a user-friendly interface, allowing users to start
model training and face detection with simple keyboard commands. Our goal is to introduce an innovative
home security system using facial recognition technology. Unlike traditional systems, this system trains
and saves images of friends and family members. The system scans this folder to recognize familiar faces
and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert,
ensuring a proactive response to potential security threats.
Height and depth gauge linear metrology.pdf
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
SCALING OF MOS CIRCUITS m .pptx
this ppt explains about scaling parameters of the mosfet it is basically vlsi subject
Recently uploaded (20)
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Generative AI Use cases applications solutions and implementation.pdf
Generative AI Use cases applications solutions and implementation.pdf
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
2. protection of river banks and bed erosion protection works.ppt
2. protection of river banks and bed erosion protection works.ppt
Mechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering on AAI Summer Training Report-003.pdf
SENTIMENT ANALYSIS ON PPT AND Project template_.pptx
SENTIMENT ANALYSIS ON PPT AND Project template_.pptx
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Pressure Relief valve used in flow line to release the over pressure at our d...
Pressure Relief valve used in flow line to release the over pressure at our d...
1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf
1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf
A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin & Eosin Images of Colon Cancer
- 1. IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 11, Issue 5 (May. - Jun. 2013), PP 79-83 www.iosrjournals.org www.iosrjournals.org 79 | Page A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin & Eosin Images of Colon Cancer K. Satish Reddy1 , Shaik Basha2 , B. Prashanth3 , K. Sridhar Reddy4 1,2,3,4 Department of ECE, Vardhaman College of Engineering, Hyderabad 501218 , India. Abstract: Color Image segmentation plays a crucial role in many medical imaging applications. In this paper we present a novel algorithm for computer aided molecular modeling in MATLAB R2012, a real time simulation environment. This manuscript presents a novel algorithm for color image segmentation of Haematoxilin and Eosin images of colon cancer. With help of this algorithm we can classify the differences between benign and malignant tumor cells. Keywords: - Color segmentation, Hierarchical Clustering, Real Time Simulation Environment, Molecular Modeling. I. INTRODUCTION In this manuscript we compare the existing k-means clustering based Computer Aided Molecular Modeling approach of H & E (Haematoxilin & Eosin) images of Colon Cancer as illustrated in [1] with the current hierarchical clustering based approach. Ideally image segmentation is defined as the separation of an image into non-overlapping, constituent regions which are homogeneous with respect to some characteristics such as intensity or texture [2]. In present research work, we developed computer image-processing techniques for classifying colon cancer and non-malignant tissue in digital histological images acquired from tissue sections captured using scanning electronic microscope (SEM) acquired with high resolution charged coupled device (CCD) camera from regions of interest (ROI) marked by pathologists on colon tissue sections [5]. II. DESIGN OF ALGORITHM The different steps involved in the design of algorithm are explained as follows: a. The input color image will be coarsely represented using 25 bins. b. Coarse representation uses the spatial information from a Histogram based windowing process. c. Hierarchical clustering is used to cluster the coarse image data. d. Parameters Used Input Color Image Number of bins for coarse representation Window size for histogram processing Number of classes Output segmented image The coding part is illustrated as shown in the below code snippet, this coding part is done in MATLAB R2012 - a real time simulation environment [6]. %Clear Memory & Command Window clc; clear all; close all; %Parameters for the Segmentation nBins=5; winSize=7; nClass=6; %Read Input Image x = imread('DSS.PNG'); figure;imshow(x);title('Input Image'); %Segmentation outImg = colImgSeg(x, nBins,winSize,nClass); %Displaying Output
- 2. A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin www.iosrjournals.org 80 | Page figure;imshow(outImg);title('Image labled by cluster index'); %colormap('default'); %Image segmentation using Hierarchical clustering algorithm xr = x(:, :, 1); xg = x(:, :, 2); xb = x(:, :, 3); figure , imshow(xr, [ ]);title('objects in cluster1'); figure ,imshow(xg, [ ]);title('objects in cluster2'); figure ,imshow(xb, [ ]);title('objects in cluster3'); se = strel('disk', 3); f = imopen(xg, se); %figure ,imshow(f, [ ]); xgd = im2double(f); w = fspecial('average'); xgd2 = imfilter(xgd, w, 'replicate'); %figure ,imshow(xgd2, [ ]); wg = fspecial('gaussian',[9 9], 1.8); xgd3 = imfilter(xgd2, wg,'conv', 'replicate'); %figure ,imshow(xgd3, [ ]); wm = fspecial('average',[69 69]); xgb = imfilter(xgd3,wm, 'replicate'); %figure ,imshow(xgb, [ ]); d = 255*xgd3-255*xgb; %figure ,imshow(d, [ ]); dmat = mat2gray(d); isc = im2uint8(dmat); %figure ,imshow(isc, [ ]); ginmax = max(max(isc)); [m, n] = size(isc); giimax = max(isc); for i = 1:m for j = 1:n g = double(isc(i,j))+128-ginmax; if g<0 ih(i,j) = 0; elseif g>255 ih(i,j) = 255; else ih(i,j) = g; end end end figure ,imshow(ih, [ ]); title('blue nuclei'); Modeling steps involved are from reading the image to segmentation of the nuclei into a separate image [3]. Further there are also intermediate steps that are involved between reading the image and segmenting the nuclei into a separate image in MATLAB real-time simulation environment [4].
- 3. A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin www.iosrjournals.org 81 | Page Flow chart is designed as follows
- 4. A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin www.iosrjournals.org 82 | Page III. RESULTS AND DISCUSSIONS The resulting output flow diagram as shown in Figure 1 is derived from the algorithm based on the above steps. Figure 1: Output Results IV. CONCLUSIONS We have proposed a novel algorithm for color segmentation based on hierarchical clustering. Our algorithm improves the color segmentation accuracy more significantly than previous approaches. One distinctive advantage of our approach is that our algorithm also provides an explicit feature for ROI which can be used for classification and recognition of benign and malignant tissues.
- 5. A Hierarchical Clustering Algorithm Based Computer Aided Molecular Modeling with Haematoxylin www.iosrjournals.org 83 | Page REFERENCES [1] Venkata Subbaiah Kotakadi, Gaddam Susmila Aparna, Prashanth B. U. V. and Sai D. V. R. Gopal. Article: Computer Aided Molecular modeling approach of H & E (Haemotoxylin & Eosin) images of Colon Cancer. International Journal of Computer Applications 44(9):5-8, April 2012. Published by Foundation of Computer Science, New York, USA. [2] Otsu Nobuyuki, “A Threshold Selection Method from gray level Histograms”, vol. SMC-9, no.1, January 1979. [3] G.Deng, L. W. Cahill and G.R. Tobin, “The Study of Logarithmic Image Processing Model and Its Application to Image Enhancement”. [4] Joongho Chang, Gunhee Han, Hose M. Valverde, Norman C. Grisworld, J. Francisco Duque-Carillo, Edgar anchez-Sinencio,( 1997) “Cork Quality Classification System using a Unified Image Processing and Fuzzy-Neural Network Methodology”, vol. 8, no. 4, July. [5] Crabb D.P., Edgar D.F., Fitzke F.W., Mcnaught A.I. & Wynn H.P. (1995). New Approach to Estimating Variability in Visual-Field Data Using An Image-Processing Technique. British Journal of Ophthalmology 79:213-217. [6] R.C Gonzalez and R.E Woods , “ Digital Image Processing using MATLAB”, Addison Wesley/ Pearson education,2nd Education, 2002.