Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data ijscai
This document summarizes a research paper that proposes using a binary bat algorithm to classify breast cancer data. The researchers developed a hybrid model combining a binary bat algorithm and feedforward neural network. The binary bat algorithm was used to generate a activation function for training the neural network and minimize error. Testing of the model on three breast cancer datasets produced an accuracy of 92.61% for training data and 89.95% for testing data, showing potential for classifying breast cancer as malignant or benign.
Clustering and Classification of Cancer Data Using Soft Computing Technique IOSR Journals
Clustering and classification of cancer data has been used with success in field of medical side. In
this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of
the result. this paper address the problem of learning to classify the cancer data with two different method and
information derived from the training and testing .various soft computing based classification and show the
comparison of classification technique and classification of this health care data .this paper present the
accuracy of the result in cancer data.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
This document compares three classification methods - artificial neural networks, decision trees, and logistic regression - for predicting malignancy in thyroid tumor patients using a clinical dataset. It describes each method and applies them to a dataset of 259 thyroid tumor patients. The artificial neural network achieved 98% accuracy on the training set and 92% on the validation set. The decision tree method used 150 cases to build a model and achieved 86% accuracy. Logistic regression analysis resulted in 88% accuracy. The artificial neural network was able to accurately predict malignancy and identified important attributes like multiple nodules and family cancer history.
Comparison of Image Segmentation Algorithms for Brain Tumor DetectionIJMTST Journal
This paper deals with the implementation of Simple Algorithms for detection of size and shape of tumor in brain using MRI images. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor by applying Fuzzy C-Means, K-Means, Gaussian Kernel and Pillar K-means algorithms. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies FCM, Gaussian kernel and K-means clustering to the image later optimized by Pillar Algorithm. It designates the initial centroids’ positions by calculating the Euclidian distance metric between each data point and all previous centroids. Then it selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MRI image and its exact position and the shape is also determined. This paper evaluates the proposed approach for Brain tumor detection by comparing with K-means, Fuzzy C means, Gaussian Kernel and manually segmented algorithms. The experimental results clarify the effectiveness of proposed approach to improve the segmentation quality in aspects of precision and computational time.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
This paper presents a system for detecting and classifying brain tumors in MRI images. Features were extracted from 105 brain images, including mean, standard deviation, and derivative. Two classifiers, SVM and KNN, were tested on the features to classify images as normal or abnormal. The SVM classifier achieved 100% accuracy on the test set, demonstrating the system's ability to successfully separate the two classes.
Application of Hybrid Genetic Algorithm Using Artificial Neural Network in Da...IOSRjournaljce
The main purpose of data mining is to extract knowledge from large amount of data. Artificial Neural network (ANN) has already been applied in a variety of domains with remarkable success. This paper presents the application of hybrid model for stroke disease that integrates Genetic algorithm and back propagation algorithm. Selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to the area. In addition the hybrid model that leads to further improvised categorization, accuracy compared to the result produced by genetic algorithm alone. In this study, a new hybrid model of Neural Networks and Genetic Algorithm (GA) to initialize and optimize the connection weights of ANN so as to improve the performance of the ANN and the same has been applied in a medical problem of predicting stroke disease for verification of the results.
Classification of medical datasets using back propagation neural network powe...IJECEIAES
The classification is a one of the most indispensable domains in the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data ijscai
This document summarizes a research paper that proposes using a binary bat algorithm to classify breast cancer data. The researchers developed a hybrid model combining a binary bat algorithm and feedforward neural network. The binary bat algorithm was used to generate a activation function for training the neural network and minimize error. Testing of the model on three breast cancer datasets produced an accuracy of 92.61% for training data and 89.95% for testing data, showing potential for classifying breast cancer as malignant or benign.
Clustering and Classification of Cancer Data Using Soft Computing Technique IOSR Journals
Clustering and classification of cancer data has been used with success in field of medical side. In
this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of
the result. this paper address the problem of learning to classify the cancer data with two different method and
information derived from the training and testing .various soft computing based classification and show the
comparison of classification technique and classification of this health care data .this paper present the
accuracy of the result in cancer data.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
This document compares three classification methods - artificial neural networks, decision trees, and logistic regression - for predicting malignancy in thyroid tumor patients using a clinical dataset. It describes each method and applies them to a dataset of 259 thyroid tumor patients. The artificial neural network achieved 98% accuracy on the training set and 92% on the validation set. The decision tree method used 150 cases to build a model and achieved 86% accuracy. Logistic regression analysis resulted in 88% accuracy. The artificial neural network was able to accurately predict malignancy and identified important attributes like multiple nodules and family cancer history.
Comparison of Image Segmentation Algorithms for Brain Tumor DetectionIJMTST Journal
This paper deals with the implementation of Simple Algorithms for detection of size and shape of tumor in brain using MRI images. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor by applying Fuzzy C-Means, K-Means, Gaussian Kernel and Pillar K-means algorithms. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies FCM, Gaussian kernel and K-means clustering to the image later optimized by Pillar Algorithm. It designates the initial centroids’ positions by calculating the Euclidian distance metric between each data point and all previous centroids. Then it selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MRI image and its exact position and the shape is also determined. This paper evaluates the proposed approach for Brain tumor detection by comparing with K-means, Fuzzy C means, Gaussian Kernel and manually segmented algorithms. The experimental results clarify the effectiveness of proposed approach to improve the segmentation quality in aspects of precision and computational time.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
This paper presents a system for detecting and classifying brain tumors in MRI images. Features were extracted from 105 brain images, including mean, standard deviation, and derivative. Two classifiers, SVM and KNN, were tested on the features to classify images as normal or abnormal. The SVM classifier achieved 100% accuracy on the test set, demonstrating the system's ability to successfully separate the two classes.
Application of Hybrid Genetic Algorithm Using Artificial Neural Network in Da...IOSRjournaljce
The main purpose of data mining is to extract knowledge from large amount of data. Artificial Neural network (ANN) has already been applied in a variety of domains with remarkable success. This paper presents the application of hybrid model for stroke disease that integrates Genetic algorithm and back propagation algorithm. Selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to the area. In addition the hybrid model that leads to further improvised categorization, accuracy compared to the result produced by genetic algorithm alone. In this study, a new hybrid model of Neural Networks and Genetic Algorithm (GA) to initialize and optimize the connection weights of ANN so as to improve the performance of the ANN and the same has been applied in a medical problem of predicting stroke disease for verification of the results.
Classification of medical datasets using back propagation neural network powe...IJECEIAES
The classification is a one of the most indispensable domains in the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
Hybrid Approach for Brain Tumour Detection in Image Segmentationijtsrd
In this paper we have considered illustrating a few techniques. But the numbers of techniques are so large they cannot be all addressed. Image segmentation forms the basics of pattern recognition and scene analysis problems. The segmentation techniques are numerous in number but the choice of one technique over the other depends only on the application or requirements of the problem that is being considered. Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. The main idea of this is to present facts about brain tumour detection system and various data mining methods used in this system. This is focuses on scalable data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data power to perform complex transformations and analysis will be measured between the way of measuring the Furrier and Wavelet Transform distance. Sandeep | Jyoti Kataria "Hybrid Approach for Brain Tumour Detection in Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33409.pdf Paper Url: https://www.ijtsrd.com/medicine/other/33409/hybrid-approach-for-brain-tumour-detection-in-image-segmentation/sandeep
SVM-PSO based Feature Selection for Improving Medical Diagnosis Reliability u...cscpconf
Improving accuracy of supervised classification algorithms in biomedical applications,
especially CADx, is one of active area of research. This paper proposes construction of rotation
forest (RF) ensemble using 20 learners over two clinical datasets namely lymphography and
backache. We propose a new feature selection strategy based on support vector machines
optimized by particle swarm optimization for relevant and minimum feature subset for obtaining
higher accuracy of ensembles. We have quantitatively analyzed 20 base learners over two
datasets and carried out the experiments with 10 fold cross validation leave-one-out strategy
and the performance of 20 classifiers are evaluated using performance metrics namely accuracy
(acc), kappa value (K), root mean square error (RMSE) and area under receiver operating
characteristics curve (ROC). Base classifiers succeeded 79.96% & 81.71% average accuracies
for lymphography & backache datasets respectively. As for RF ensembles, they produced
average accuracies of 83.72% & 85.77% for respective diseases. The paper presents promising
results using RF ensembles and provides a new direction towards construction of reliable and robust medical diagnosis systems.
Comparative analysis of multimodal medical image fusion using pca and wavelet...IJLT EMAS
nowadays, there are a lot of medical images and their
numbers are increasing day by day. These medical images are
stored in large database. To minimize the redundancy and
optimize the storage capacity of images, medical image fusion is
used. The main aim of medical image fusion is to combine
complementary information from multiple imaging modalities
(Eg: CT, MRI, PET etc.) of the same scene. After performing
image fusion, the resultant image is more informative and
suitable for patient diagnosis. There are some fusion techniques
which are described in this paper to obtain fused image. This
paper presents two approaches to image fusion, namely Spatial
Fusion and Transform Fusion. This paper describes Techniques
such as Principal Component Analysis which is spatial domain
technique and Discrete Wavelet Transform, Stationary Wavelet
Transform which are Transform domain techniques.
Performance metrics are implemented to evaluate the
performance of image fusion algorithm. An experimental result
shows that image fusion method based on Stationary Wavelet
Transform is better than Principal Component Analysis and
Discrete Wavelet Transform.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
Hybrid Pixel-Based Method for Multimodal Medical Image Fusion Based on Integr...Dr.NAGARAJAN. S
Medical imaging plays a vital role in medical diagnosis and treatment. However, distinct imaging modality yields information only in limited domain. Studies are done for analysis information collected from distinct modalities of same patient. This led to the introduction of image fusion in the field of medicine and the progression of image fusion techniques. Image fusion is characterized as the amalgamation of significant data from numerous images and their incorporation into seldom images, generally a solitary one. This fused image will be more instructive and precise than the indi- vidual source images that have been utilized, and the resultant fused image comprises paramount information. The main objective of image fusion is to incorporate all the essential data from source images which would be pertinent and comprehensible for human and machine recognition. Image fusion is the strategy of combining images from distinct modalities into a single image [1]. The resultant image is utilized in variety of applications such as medical diagnosis, identification of tumor and surgery treatment [2]. Before fusing images from two distinct modalities, it is essential to preserve the features so that the fused image is free from inconsistencies or artifacts in the output.
An approach for breast cancer diagnosis classification using neural networkacijjournal
Artificial neural network has been widely used in various fields as an intelligent tool in recent years, such
as artificial intelligence, pattern recognition, medical diagnosis, machine learning and so on. The
classification of breast cancer is a medical application that poses a great challenge for researchers and
scientists. Recently, the neural network has become a popular tool in the classification of cancer datasets.
Classification is one of the most active research and application areas of neural networks. Major
disadvantages of artificial neural network (ANN) classifier are due to its sluggish convergence and always
being trapped at the local minima. To overcome this problem, differential evolution algorithm (DE) has
been used to determine optimal value or near optimal value for ANN parameters. DE has been applied
successfully to improve ANN learning from previous studies. However, there are still some issues on DE
approach such as longer training time and lower classification accuracy. To overcome these problems,
island based model has been proposed in this system. The aim of our study is to propose an approach for
breast cancer distinguishing between different classes of breast cancer. This approach is based on the
Wisconsin Diagnostic and Prognostic Breast Cancer and the classification of different types of breast
cancer datasets. The proposed system implements the island-based training method to be better accuracy
and less training time by using and analysing between two different migration topologies
Classification of MR medical images Based Rough-Fuzzy KMeansIOSRJM
The document summarizes a proposed algorithm for classifying MR medical images using Rough-Fuzzy K-Means (FRKM). It begins with an introduction to the challenges of medical image classification and a literature review of previous techniques. It then provides background on rough set theory, fuzzy set theory, and K-means clustering. The proposed FRKM algorithm is described as using rough set theory for feature selection and dimensionality reduction, followed by a K-means clustering with probabilities assigned based on rough set approximations to classify ambiguous areas. Experimental results show the FRKM approach achieves 94.4% accuracy, higher than other techniques.
IRJET - Clustering Algorithm for Brain Image SegmentationIRJET Journal
The document presents a clustering algorithm for brain image segmentation using fuzzy c-means clustering. It aims to optimize the segmentation process and achieve higher accuracy rates when segmenting human MRI brain images. The fuzzy c-means algorithm is combined with rough set theory for segmentation. The algorithm segments images into homogeneous regions where adjacent regions are heterogeneous. This approach is evaluated on a set of brain images and demonstrates effectiveness as well as a comparison to other related algorithms. The goal of the algorithm is to simplify images and extract useful information for detecting brain tumors.
This summary provides the key details from the document in 3 sentences:
The document discusses optimizing the parameters of an artificial neural network (ANN) model for predicting schizophrenia. It explores different numbers of hidden layers, epochs, and fold units to determine the configuration that results in the highest training accuracy and lowest error. The optimal parameters were found to be 8 hidden layers, 2 fold units, and up to 1000 epochs, achieving a peak training accuracy of 71.34% and prediction accuracy of 74.53%.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
Analysis On Classification Techniques In Mammographic Mass Data SetIJERA Editor
Data mining, the extraction of hidden information from large databases, is to predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data-Mining classification techniques deals with determining to which group each data instances are associated with. It can deal with a wide variety of data so that large amount of data can be involved in processing. This paper deals with analysis on various data mining classification techniques such as Decision Tree Induction, Naïve Bayes , k-Nearest Neighbour (KNN) classifiers in mammographic mass dataset.
Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensem...IJECEIAES
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.
This document discusses a thesis that uses machine learning algorithms to diagnose mental illness using MRI brain scans. Specifically, it analyzes schizophrenia, bipolar disorder, and healthy control subject data from multiple imaging modalities. It trains and tests eight machine learning classifiers - support vector machines, k-nearest neighbors, logistic regression, naive Bayes, and random forests - on the raw imaging data as well as data transformed through dimensionality reduction techniques. The results aim to demonstrate the efficacy of these algorithms at classifying subjects based on their brain scans and diagnosing their mental condition.
Image Fusion and Image Quality Assessment of Fused ImagesCSCJournals
Accurate diagnosis of tumor extent is important in radiotherapy. This paper presents the use of image fusion of PET and MRI image. Multi-sensor image fusion is the process of combining information from two or more images into a single image. The resulting image contains more information as compared to individual images. PET delivers high-resolution molecular imaging with a resolution down to 2.5 mm full width at half maximum (FWHM), which allows us to observe the brain\'s molecular changes using the specific reporter genes and probes. On the other hand, the 7.0 T-MRI, with sub-millimeter resolution images of the cortical areas down to 250 m, allows us to visualize the fine details of the brainstem areas as well as the many cortical and sub-cortical areas. The PET-MRI fusion imaging system provides complete information on neurological diseases as well as cognitive neurosciences. The paper presents PCA based image fusion and also focuses on image fusion algorithm based on wavelet transform to improve resolution of the images in which two images to be fused are firstly decomposed into sub-images with different frequency and then the information fusion is performed and finally these sub-images are reconstructed into result image with plentiful information. . We also propose image fusion in Radon space. This paper presents assessment of image fusion by measuring the quantity of enhanced information in fused images. We use entropy, mean, standard deviation and Fusion Mutual Information, cross correlation , Mutual Information Root Mean Square Error, Universal Image Quality Index and Relative shift in mean to compare fused image quality. Comparative evaluation of fused images is a critical step to evaluate the relative performance of different image fusion algorithms. In this paper, we also propose image quality metric based on the human vision system (HVS).
Techniques of Brain Cancer Detection from MRI using Machine LearningIRJET Journal
The document discusses techniques for detecting brain cancer from MRI scans using machine learning. It first provides background on brain tumors and MRI. It then outlines the cancer detection process, including pre-processing the MRI data, segmenting the images, extracting features, and classifying tumors using techniques like CNNs, SVMs, MLP, and Naive Bayes. The document reviews related work applying these techniques and compares their results, finding accuracy can be improved with larger, higher resolution datasets.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
This document discusses using artificial neural networks for satellite image classification. It begins with an introduction to remote sensing and the importance of image classification. It then discusses how artificial neural networks can be used for image classification, specifically using the backpropagation algorithm. The backpropagation algorithm allows training of a neural network to classify images into different land use and land cover classes. An example is provided showing an original satellite image and the classified image output from the neural network. The conclusion states that artificial neural networks are well-suited for analyzing remote sensing data and developing image classification algorithms due to their ability to extract information from incomplete inputs.
Opposition Based Firefly Algorithm Optimized Feature Subset Selection Approac...mlaij
Recently huge amount of data is available in the field of medicine that helps the doctors in diagnosing diseases when analysed. Data mining techniques can be applied to these medical data to extract knowledge so that disease prediction becomes accurate and easier. In this work, cardiotocogram (CTG) data is analysed using Support Vector Machine (SVM) for predicting fetal risk. Opposition based firefly algorithm (OBFA) is proposed to extract the relevant features that maximise the classification performance of SVM. The obtained results show that opposition based firefly algorithm outperforms the standard firefly algorithm (FA).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
This summary provides the high-level information from the document in 3 sentences:
The document proposes a Particle Swarm Optimization (PSO) based ensemble classification model to improve classification of high-dimensional biomedical datasets. It develops an optimized PSO technique to select optimal features and initialize weights for base classifiers in the ensemble model. Experimental results on microarray datasets show the proposed model achieves higher accuracy, true positive rate, and lower error rate compared to traditional feature selection based classification models.
Hybrid Approach for Brain Tumour Detection in Image Segmentationijtsrd
In this paper we have considered illustrating a few techniques. But the numbers of techniques are so large they cannot be all addressed. Image segmentation forms the basics of pattern recognition and scene analysis problems. The segmentation techniques are numerous in number but the choice of one technique over the other depends only on the application or requirements of the problem that is being considered. Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. The main idea of this is to present facts about brain tumour detection system and various data mining methods used in this system. This is focuses on scalable data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data power to perform complex transformations and analysis will be measured between the way of measuring the Furrier and Wavelet Transform distance. Sandeep | Jyoti Kataria "Hybrid Approach for Brain Tumour Detection in Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33409.pdf Paper Url: https://www.ijtsrd.com/medicine/other/33409/hybrid-approach-for-brain-tumour-detection-in-image-segmentation/sandeep
SVM-PSO based Feature Selection for Improving Medical Diagnosis Reliability u...cscpconf
Improving accuracy of supervised classification algorithms in biomedical applications,
especially CADx, is one of active area of research. This paper proposes construction of rotation
forest (RF) ensemble using 20 learners over two clinical datasets namely lymphography and
backache. We propose a new feature selection strategy based on support vector machines
optimized by particle swarm optimization for relevant and minimum feature subset for obtaining
higher accuracy of ensembles. We have quantitatively analyzed 20 base learners over two
datasets and carried out the experiments with 10 fold cross validation leave-one-out strategy
and the performance of 20 classifiers are evaluated using performance metrics namely accuracy
(acc), kappa value (K), root mean square error (RMSE) and area under receiver operating
characteristics curve (ROC). Base classifiers succeeded 79.96% & 81.71% average accuracies
for lymphography & backache datasets respectively. As for RF ensembles, they produced
average accuracies of 83.72% & 85.77% for respective diseases. The paper presents promising
results using RF ensembles and provides a new direction towards construction of reliable and robust medical diagnosis systems.
Comparative analysis of multimodal medical image fusion using pca and wavelet...IJLT EMAS
nowadays, there are a lot of medical images and their
numbers are increasing day by day. These medical images are
stored in large database. To minimize the redundancy and
optimize the storage capacity of images, medical image fusion is
used. The main aim of medical image fusion is to combine
complementary information from multiple imaging modalities
(Eg: CT, MRI, PET etc.) of the same scene. After performing
image fusion, the resultant image is more informative and
suitable for patient diagnosis. There are some fusion techniques
which are described in this paper to obtain fused image. This
paper presents two approaches to image fusion, namely Spatial
Fusion and Transform Fusion. This paper describes Techniques
such as Principal Component Analysis which is spatial domain
technique and Discrete Wavelet Transform, Stationary Wavelet
Transform which are Transform domain techniques.
Performance metrics are implemented to evaluate the
performance of image fusion algorithm. An experimental result
shows that image fusion method based on Stationary Wavelet
Transform is better than Principal Component Analysis and
Discrete Wavelet Transform.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
Hybrid Pixel-Based Method for Multimodal Medical Image Fusion Based on Integr...Dr.NAGARAJAN. S
Medical imaging plays a vital role in medical diagnosis and treatment. However, distinct imaging modality yields information only in limited domain. Studies are done for analysis information collected from distinct modalities of same patient. This led to the introduction of image fusion in the field of medicine and the progression of image fusion techniques. Image fusion is characterized as the amalgamation of significant data from numerous images and their incorporation into seldom images, generally a solitary one. This fused image will be more instructive and precise than the indi- vidual source images that have been utilized, and the resultant fused image comprises paramount information. The main objective of image fusion is to incorporate all the essential data from source images which would be pertinent and comprehensible for human and machine recognition. Image fusion is the strategy of combining images from distinct modalities into a single image [1]. The resultant image is utilized in variety of applications such as medical diagnosis, identification of tumor and surgery treatment [2]. Before fusing images from two distinct modalities, it is essential to preserve the features so that the fused image is free from inconsistencies or artifacts in the output.
An approach for breast cancer diagnosis classification using neural networkacijjournal
Artificial neural network has been widely used in various fields as an intelligent tool in recent years, such
as artificial intelligence, pattern recognition, medical diagnosis, machine learning and so on. The
classification of breast cancer is a medical application that poses a great challenge for researchers and
scientists. Recently, the neural network has become a popular tool in the classification of cancer datasets.
Classification is one of the most active research and application areas of neural networks. Major
disadvantages of artificial neural network (ANN) classifier are due to its sluggish convergence and always
being trapped at the local minima. To overcome this problem, differential evolution algorithm (DE) has
been used to determine optimal value or near optimal value for ANN parameters. DE has been applied
successfully to improve ANN learning from previous studies. However, there are still some issues on DE
approach such as longer training time and lower classification accuracy. To overcome these problems,
island based model has been proposed in this system. The aim of our study is to propose an approach for
breast cancer distinguishing between different classes of breast cancer. This approach is based on the
Wisconsin Diagnostic and Prognostic Breast Cancer and the classification of different types of breast
cancer datasets. The proposed system implements the island-based training method to be better accuracy
and less training time by using and analysing between two different migration topologies
Classification of MR medical images Based Rough-Fuzzy KMeansIOSRJM
The document summarizes a proposed algorithm for classifying MR medical images using Rough-Fuzzy K-Means (FRKM). It begins with an introduction to the challenges of medical image classification and a literature review of previous techniques. It then provides background on rough set theory, fuzzy set theory, and K-means clustering. The proposed FRKM algorithm is described as using rough set theory for feature selection and dimensionality reduction, followed by a K-means clustering with probabilities assigned based on rough set approximations to classify ambiguous areas. Experimental results show the FRKM approach achieves 94.4% accuracy, higher than other techniques.
IRJET - Clustering Algorithm for Brain Image SegmentationIRJET Journal
The document presents a clustering algorithm for brain image segmentation using fuzzy c-means clustering. It aims to optimize the segmentation process and achieve higher accuracy rates when segmenting human MRI brain images. The fuzzy c-means algorithm is combined with rough set theory for segmentation. The algorithm segments images into homogeneous regions where adjacent regions are heterogeneous. This approach is evaluated on a set of brain images and demonstrates effectiveness as well as a comparison to other related algorithms. The goal of the algorithm is to simplify images and extract useful information for detecting brain tumors.
This summary provides the key details from the document in 3 sentences:
The document discusses optimizing the parameters of an artificial neural network (ANN) model for predicting schizophrenia. It explores different numbers of hidden layers, epochs, and fold units to determine the configuration that results in the highest training accuracy and lowest error. The optimal parameters were found to be 8 hidden layers, 2 fold units, and up to 1000 epochs, achieving a peak training accuracy of 71.34% and prediction accuracy of 74.53%.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
Analysis On Classification Techniques In Mammographic Mass Data SetIJERA Editor
Data mining, the extraction of hidden information from large databases, is to predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data-Mining classification techniques deals with determining to which group each data instances are associated with. It can deal with a wide variety of data so that large amount of data can be involved in processing. This paper deals with analysis on various data mining classification techniques such as Decision Tree Induction, Naïve Bayes , k-Nearest Neighbour (KNN) classifiers in mammographic mass dataset.
Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensem...IJECEIAES
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.
This document discusses a thesis that uses machine learning algorithms to diagnose mental illness using MRI brain scans. Specifically, it analyzes schizophrenia, bipolar disorder, and healthy control subject data from multiple imaging modalities. It trains and tests eight machine learning classifiers - support vector machines, k-nearest neighbors, logistic regression, naive Bayes, and random forests - on the raw imaging data as well as data transformed through dimensionality reduction techniques. The results aim to demonstrate the efficacy of these algorithms at classifying subjects based on their brain scans and diagnosing their mental condition.
Image Fusion and Image Quality Assessment of Fused ImagesCSCJournals
Accurate diagnosis of tumor extent is important in radiotherapy. This paper presents the use of image fusion of PET and MRI image. Multi-sensor image fusion is the process of combining information from two or more images into a single image. The resulting image contains more information as compared to individual images. PET delivers high-resolution molecular imaging with a resolution down to 2.5 mm full width at half maximum (FWHM), which allows us to observe the brain\'s molecular changes using the specific reporter genes and probes. On the other hand, the 7.0 T-MRI, with sub-millimeter resolution images of the cortical areas down to 250 m, allows us to visualize the fine details of the brainstem areas as well as the many cortical and sub-cortical areas. The PET-MRI fusion imaging system provides complete information on neurological diseases as well as cognitive neurosciences. The paper presents PCA based image fusion and also focuses on image fusion algorithm based on wavelet transform to improve resolution of the images in which two images to be fused are firstly decomposed into sub-images with different frequency and then the information fusion is performed and finally these sub-images are reconstructed into result image with plentiful information. . We also propose image fusion in Radon space. This paper presents assessment of image fusion by measuring the quantity of enhanced information in fused images. We use entropy, mean, standard deviation and Fusion Mutual Information, cross correlation , Mutual Information Root Mean Square Error, Universal Image Quality Index and Relative shift in mean to compare fused image quality. Comparative evaluation of fused images is a critical step to evaluate the relative performance of different image fusion algorithms. In this paper, we also propose image quality metric based on the human vision system (HVS).
Techniques of Brain Cancer Detection from MRI using Machine LearningIRJET Journal
The document discusses techniques for detecting brain cancer from MRI scans using machine learning. It first provides background on brain tumors and MRI. It then outlines the cancer detection process, including pre-processing the MRI data, segmenting the images, extracting features, and classifying tumors using techniques like CNNs, SVMs, MLP, and Naive Bayes. The document reviews related work applying these techniques and compares their results, finding accuracy can be improved with larger, higher resolution datasets.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
This document discusses using artificial neural networks for satellite image classification. It begins with an introduction to remote sensing and the importance of image classification. It then discusses how artificial neural networks can be used for image classification, specifically using the backpropagation algorithm. The backpropagation algorithm allows training of a neural network to classify images into different land use and land cover classes. An example is provided showing an original satellite image and the classified image output from the neural network. The conclusion states that artificial neural networks are well-suited for analyzing remote sensing data and developing image classification algorithms due to their ability to extract information from incomplete inputs.
Opposition Based Firefly Algorithm Optimized Feature Subset Selection Approac...mlaij
Recently huge amount of data is available in the field of medicine that helps the doctors in diagnosing diseases when analysed. Data mining techniques can be applied to these medical data to extract knowledge so that disease prediction becomes accurate and easier. In this work, cardiotocogram (CTG) data is analysed using Support Vector Machine (SVM) for predicting fetal risk. Opposition based firefly algorithm (OBFA) is proposed to extract the relevant features that maximise the classification performance of SVM. The obtained results show that opposition based firefly algorithm outperforms the standard firefly algorithm (FA).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
This summary provides the high-level information from the document in 3 sentences:
The document proposes a Particle Swarm Optimization (PSO) based ensemble classification model to improve classification of high-dimensional biomedical datasets. It develops an optimized PSO technique to select optimal features and initialize weights for base classifiers in the ensemble model. Experimental results on microarray datasets show the proposed model achieves higher accuracy, true positive rate, and lower error rate compared to traditional feature selection based classification models.
IRJET - Detection of Heamorrhage in Brain using Deep LearningIRJET Journal
This document presents a method for detecting hemorrhage in brain CT scans using deep learning. It begins with an introduction to brain hemorrhage and the need for automated detection. Previous related work using various segmentation and classification methods is summarized. Deep learning is identified as a promising technique due to its ability to extract complex features from images. The proposed method uses a convolutional neural network model with several convolutional, max pooling, dropout and dense layers to classify brain CT scans as either normal or hemorrhagic. The model is trained on 180 images and tested on 20 images, achieving an accuracy of 94.4% at predicting hemorrhage. The method provides a fast and automated way to detect hemorrhage in brain CT scans to help
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
Iganfis Data Mining Approach for Forecasting Cancer Threatsijsrd.com
This document proposes a new approach called IGANFIS that combines information gain (IG) and adaptive neuro fuzzy inference system (ANFIS) to classify cancer threats from medical data. IG is used to select the most important cancer features from the data to reduce dimensionality before being input to ANFIS. ANFIS then trains on the selected features to build a fuzzy inference system for cancer diagnosis and prediction. The approach is tested on breast cancer datasets and achieves higher classification accuracy compared to other methods.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
Survey of various methods used for integrating machine learning into brain tu...Drjabez
This document surveys various machine learning methods used for integrating machine learning into brain tumor detection and classification from MRI images. It discusses preprocessing techniques like median filtering, Gaussian high pass filtering, and morphology dilation to enhance images. Segmentation techniques covered include thresholding, edge detection, region-based, watershed, Berkeley wavelet transform, K-means clustering, and neural networks. Feature extraction calculates correlation, skewness. Classification algorithms discussed are multi-layer perceptron, naive Bayes, and support vector machines. The document provides an overview of key steps and methods for machine learning-based brain tumor detection and segmentation from MRI images.
An Introduction To Artificial Intelligence And Its Applications In Biomedical...Jill Brown
1) The document discusses the use of artificial intelligence techniques in biomedical engineering and medicine. It focuses on using AI to analyze medical signals and images to assist clinicians.
2) Key applications discussed include using neural networks and expert knowledge as intelligent agents to aid diagnosis, as well as using AI systems to automatically realign MRI images and identify corresponding slices from different scans.
3) The document also outlines the major components of AI, including problem solving, knowledge representation, and perception, and how AI can be applied through intelligent agents, a task manager, and a communication system to integrate different types of medical data and decision-making approaches.
IRJET - Heart Health Classification and Prediction using Machine LearningIRJET Journal
1) The document discusses using machine learning models to classify heart health based on medical records and heart sounds.
2) Support vector machines, logistic regression, random forest, and ensemble learning algorithms were used to predict heart disease risk based on medical records.
3) Convolutional neural networks were used to develop models for heart sound segmentation and classification. The models classify heart sounds as normal, murmur, or extrasystole based on the timing of the S1 and S2 heart sounds.
Hybrid bat-ant colony optimization algorithm for rule-based feature selection...IJECEIAES
Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.
A Novel Efficient Medical Image Segmentation Methodologyaciijournal
Image segmentation plays a crucial role in many medical applications. The threshold based medical image
segmentation approach is the most common and effective method for medical image segmentation, but it
has some shortcomings such as high complexity, poor real time capability and premature convergence, etc.
To address above issues, an improved evolution strategies is proposed to use for medical image
segmentation, there are 2 populations concurrently during evolution, one focuses on local search in order
to search solutions near optimal solution, and the other population that implemented based on chaotic
theory focuses on global search so as to keep the variety of individuals and jump out from the local
maximum to overcome the problem of premature convergence. The encoding scheme, fitness function, and
evolution operators are also designed. The experimental results validated the effectiveness and efficiency of
the proposed approach.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Computer Aided System for Detection and Classification of Breast CancerIJITCA Journal
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
A review on detecting brain tumors using deep learning and magnetic resonanc...IJECEIAES
Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.
Predicting disease at an early stage becomes critical, and the most difficult challenge is to predict it correctly along with the sickness. The prediction happens based on the symptoms of an individual. The model presented can work like a digital doctor for disease prediction, which helps to timely diagnose the disease and can be efficient for the person to take immediate measures. The model is much more accurate in the prediction of potential ailments. The work was tested with four machine learning algorithms and got the best accuracy with Random Forest.
Comparative study of artificial neural network based classification for liver...Alexander Decker
This document presents a comparative study of different artificial neural network (ANN) classification models for predicting liver disease in patients. It evaluates ANN models like backpropagation, radial basis function, self-organizing map, and support vector machine on liver patient data. The support vector machine model achieved the highest accuracy at 99.76% for men data and 97.7% for women data, indicating it may be effective as a predictive tool for liver patients.
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...AI Publications
Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as a neural network. Use neural network in forecasts time series can be a good solution, but the problem is network architecture and the training method in the right direction. One of the choices that might be using a genetic algorithm. A genetic algorithm is a search algorithm stochastic resonance based on how it works by the mechanisms of natural selection and genetic variation that aims to find a solution to a problem. This algorithm can be used as teaching methods in train models are sent back propagation neural network. The application genetic algorithm and neural network for divination time series aim to get the weight optimum. From the training and testing on the data index share price euro 50 obtained by the RMSE testing 27.8744 and 39.2852 RMSE training. The weight or parameters that produced by has reached an optimum level in second-generation 1000 with the best fitness and the average 0.027771 the fitness of 0.0027847.Model is good to be used to give a prediction that is quite accurate information that is shown by the close target with the output.
Similar to A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA (20)
Design and Implementation of Smart Cooking Based on Amazon EchoIJSCAI Journal
Smart cooking based on Amazon Echo uses the internet of things and cloud computing to assist in cooking
food. People may speak to Amazon Echo during the cooking in order to get the information and situation of
the cooking. Amazon Echo recognizes what people say, then transfers the information to the cloud services,
and speaks to people the results that cloud services make by querying the embedded cooking knowledge and
achieving the information of intelligent kitchen devices online. An intelligent food thermometer and its mobile
application are well-designed and implemented to monitor the temperature of cooking food
Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysi...IJSCAI Journal
The importance of economic freedom has often been stressed by supporters of liberalism, but can its actual
effect be observed in a data driven, objective way? To analyze this relation the Economic Freedom of the
World (EFW) index and the Human Development Index (HDI) were examined with modern machine learning algorithms and a wide-ranging approach. Considering the EFW index’s preference of a liberalistic
oriented economic policy, an objective recommendation for creating an economic policy that improves
people’s everyday lives might be derived by the analysis results. It was found that these more advanced
algorithms achieve a considerably stronger correlation between both indices than pure statistical means
yet leave a small room for interpretation towards a counter-liberalistic implementation of demand-driven
economic policy.
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Minin...IJSCAI Journal
In the proposed paper are discussed results of an industry project concerning energy management in
building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a
logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural
network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets
placed into a building and having defined priorities. The priority rules are grouped into two level: the first
level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm,
together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting
management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three
for three outlets and able to control load matching with defined thresholds. The goal of the paper is to
provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the
electrical network in a building. Finally in the paper are analyzed the correlation between global active
power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction
and the correlation analyses provide information about load balancing, possible electrical faults and energy
cost optimization.
Nov 2018 Table of contents; current issue -International Journal on Soft Comp...IJSCAI Journal
The document discusses using machine learning algorithms to analyze the relationship between economic freedom and quality of life. It examines the Economic Freedom of the World index and the Human Development Index with modern machine learning methods. The analysis finds that these advanced algorithms achieve a stronger correlation between the indices than statistical means alone. However, there is still some room for non-liberal interpretations of how to design economic policies to improve people's lives. The goal is to objectively evaluate the impact of economic freedom through a data-driven approach.
6th international conference on artificial intelligence and applications (aia...IJSCAI Journal
6th International Conference on Artificial Intelligence and Applications (AIAP-2019) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Generating images from a text description is as challenging as it is interesting. The Adversarial network
performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of
Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With
generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS
step-by-step in this paper that could be easily understood. This paper presents visual comparative study of
other models attempting to generate image conditioned on the text description. One sentence can be related
to many images. And to achieve this multi-modal characteristic, conditioning augmentation is also
performed. The performance of Stack-GAN is better in generating images from captions due to its unique
architecture. As it consists of two GANS instead of one, it first draws a rough sketch and then corrects the
defects yielding a high-resolution image.
Temporally Extended Actions For Reinforcement Learning Based Schedulers IJSCAI Journal
Temporally extended actions have been proved to enhance the performance of reinforcement learning
agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of
action in Markov decision processes. In this work we try to adapt options framework to model an operating
system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting
for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and
subsequent context switch leads to considerable overhead. In this work we try to utilize the historical
performances of a scheduler and try to reduce the number of redundant context switches. We propose a
machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better
performing timeslice. We measure the importance of states, in option framework, by evaluating the impact
of their absence and propose an algorithm to identify such checkpoint states. We present empirical
evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice
parameter" show efficient throughput time.
Knowledgebase Systems in Neuro Science - A StudyIJSCAI Journal
The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
when compared to international standards. The average Indian spending on healthcare is much below the
global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global
market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the
world, because it has a large pool of low-cost scientifically trained technical personal and is one of the
favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological
Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the
world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to
gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the
Soft Computing techniques in treating neural patient’s problems used throughout the world
An Iranian Cash Recognition Assistance System For Visually Impaireds IJSCAI Journal
In economical societies of today, using cash is an inseparable aspect of human’s life. People use cash for
marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and
the necessity of knowing the monetary value of it caused one of the most challenging problems for visually
impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a
picture taken from a banknote using some image processing and machine vision techniques. While the
developed approach is very fast, it can recognize the value of the banknote by an average accuracy rate of
about 97% and can overcome different challenges like rotation, scaling, collision, illumination changes,
perspective, and some others.
An Experimental Study of Feature Extraction Techniques in Opinion MiningIJSCAI Journal
This document summarizes an experimental study on different feature extraction techniques for opinion mining and sentiment analysis. The study used a freely available dataset containing 200 reviews from TripAdvisor. Various feature extraction methods were applied, including part-of-speech tags, entities, phrases, document-level features, and term weighting. The results found that term frequency-inverse document frequency (tf-idf) weighting at the single word and multi-word levels achieved the highest accuracy of 98.36% and 98.26% respectively. Overall accuracy across the different feature sets ranged from 95.91% to 98.36%. The study aims to evaluate different feature extraction methods for sentiment classification tasks in opinion mining.
Monte-Carlo Tree Search For The "Mr Jack" Board Game IJSCAI Journal
Recently the use of the Monte-Carlo Tree Search algorithm, and in particular its most famous
implementation, the Upper Confidence Tree can be seen has a key moment for artificial intelligence in
games. This family of algorithms provides huge improvements in numerous games, such as Go, Havannah,
Hex or Amazon. In this paper we study the use of this algorithm on the game of Mr Jack and in particular
how to deal with a specific decision-making process.Mr Jack is a 2-player game, from the family of board
games. We will present the difficulties of designing an artificial intelligence for this kind of games, and we
show that Monte-Carlo Tree Search is robust enough to be competitive in this game with a smart approach.
Unsupervised learning models of invariant features in images: Recent developm...IJSCAI Journal
Object detection and recognition are important problems in computer vision and pattern recognition
domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on
computer based systems has proved to be a non-trivial task. In particular, despite significant research
efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition
systems in real time remain elusive. Here we present a survey of one particular approach that has proved
very promising for invariant feature recognition and which is a key initial stage of multi-stage network
architecture methods for the high level task of object recognition.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
A Study on Graph Storage Database of NOSQLIJSCAI Journal
This document summarizes a research paper on graph storage databases in NoSQL. It discusses big data and the need for alternative databases to handle large, diverse datasets. It defines the key aspects of big data including volume, velocity, variety and complexity. It also describes different types of NoSQL databases, focusing on the basic structure of graph databases. Graph databases use nodes and relationships to model connected data. The document compares several graph database systems and discusses advantages like performance and flexibility as well as disadvantages like complexity. It outlines several applications of graph databases in areas like social networks and logistics.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Estimation Of The Parameters Of Solar Cells From Current-Voltage Characterist...IJSCAI Journal
This paper presents a method for calculating the light generated current, the series resistance, shun
resistance and the two components of the reverse saturation current usually encountered in the double
diode representation of
the solar cell from the experimental values of the current
-
voltage characteristics
of the cell using genetic algorithm. The theory is able to regenerate the above mentioned parameters to
very good accuracy when applied to cell data that was generated from
pre
-
defined parameters. The
method is applied to various types of space quality solar cells and sub cells. All parameters except the
light generated current are seen to be nearly the same in the case of a cell whose characteristics under
illumination and i
n dark were analyzed. The light generated current is nearly equal to the short
-
circuit
current in all cases. The parameters obtained by this method and another method are nearly equal
wherever applicable. The parameters are also shown to represent the cur
rent
-
voltage characteristics
well
Implementation of Folksonomy Based Tag Cloud Model for Information Retrieval ...IJSCAI Journal
In the magnitude of internet one need to devote extra time to investigate an
ticipated resource, especially
when one need to search information from documents. For the higher range internet there is serious need
to demand the essentiality to discover the reserved resources. One of the solutions for information retrieval
from docume
nt repository is to attach tags to documents. Numerous online social bookmarking services
permit users to attach tags with resources which are eventually meta
-
data, frequently stated as folksonomy.
In current paper, authors implemented this model for infor
mation retrieval by utilizing these tags, after
retrieving by using delicious API and synthesize tag cloud in an Indian University to search and retrieve
information from document repository
Study of Distance Measurement Techniques in Context to Prediction Model of We...IJSCAI Journal
Internet is the boon in modern era as every organization uses it for dissemination of information and ecommerce
related applications. Sometimes people of organization feel delay while accessing internet in
spite of proper bandwidth. Prediction model of web caching and prefetching is an ideal solution of this
delay problem. Prediction model analysing history of internet user from server raw log files and determine
future sequence of web objects and placed all web objects to nearer to the user so access latency could be
reduced to some extent and problem of delay is to be solved. To determine sequence of future web objects,
it is necessary to determine proximity of one web object with other by identifying proper distance metric
technique related to web caching and prefetching. This paper studies different distance metric techniques
and concludes that bio informatics based distance metric techniques are ideal in context to Web Caching
and Web Prefetching
Design of Dual Axis Solar Tracker System Based on Fuzzy Inference SystemsIJSCAI Journal
Electric power is a basic need in today’s life. Due to the extensive usage of power, there is a need to look
for an alternate clean energy source. Recently many researchers have focused on the solar energy as a
reliable alternative power source. Photovoltaic panels are used to collect sun radiation and convert it into
electrical energy. Most of the photovoltaic panels are deployed in a fixed position, they are inefficient as
they are fixed only at a specific angle. The efficiency of photovoltaic systems can be considerably increased
with an ability to change the panels angel according to the sun position. The main goal of such systems is
to make the sun radiation perpendicular to the photovoltaic panels as much as possible all the day times.
This paper presents a dual axis design for a fuzzy inference approach-based solar tracking system. The
system is modeled using Mamdani fuzzy logic model and the different combinations of ANFIS modeling.
Models are compared in terms of the correlation between the actual testing data output and their
corresponding forecasted output. The Mean Absolute Percent Error and Mean Percentage Error are used
to measure the models error size. In order to measure the effectiveness of the proposed models, we
compare the output power produced by a fixed photovoltaic panels with the output which would be
produced if the dual-axis panels are used. Results show that dual-axis solar tracker system will produce
22% more power than a fixed panels system.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
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"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
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inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
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Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
QA or the Highway - Component Testing: Bridging the gap between frontend appl...zjhamm304
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Discover the Unseen: Tailored Recommendation of Unwatched ContentScyllaDB
The session shares how JioCinema approaches ""watch discounting."" This capability ensures that if a user watched a certain amount of a show/movie, the platform no longer recommends that particular content to the user. Flawless operation of this feature promotes the discover of new content, improving the overall user experience.
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"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
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LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
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This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
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AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
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Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
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How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
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Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
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A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA
1. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
DOI :10.5121/ijscai.2016.5301 1
A BINARY BAT INSPIRED ALGORITHM FOR
THE CLASSIFICATION OF BREAST CANCER
DATA
Doreswamy1
and Umme Salma M2
1
Department of Computer Science, Mangalore University, Mangalagangothri, Mangalore,
India, 574199
2
Department of Computer Science, Mangalore University, Mangalagangothri, Mangalore,
India, 574199
ABSTRACT
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
KEYWORDS
Data mining, Classification, Binary Bat, FNN, Breast Cancer
1. INTRODUCTION
Medical data mining is a sub-branch of data mining which deals with extraction, transformation,
analysis, interpretation and visualization of medical data stored on a computer. Analysis of
medical data is interesting and equally challenging. In medical data mining, classification and
prediction of data is not just a matter of accuracy but the matter of life and death. One wrong
decision can have a disastrous effect on the life of patients and their families. Thus medical data
mining is considered as the decision making frame work which provides assistance for the experts
to properly classify and predict the data in a quick time. Classification techniques can be broadly
divided into two categories, traditional classification techniques and modern classification
techniques.Traditional classification problems are based on the design of classifiers working on
the type of structural parameters chosen. If it is a fuzzy classifier then the rules, antecedent,
consequent etc acts as the structural parameters, in case of K-Nearest Neighbor (KNN) classifier
it is the distance metric and in Artificial Neural Network (ANN) the number of hidden layers,
weights and biases serve as the structural parameters. Tuning of these parameters is a chaotic
task. The modern classification techniques are the combination of advanced classification
techniques such as SVM and ANN with the nature inspired algorithms which are meta-heuristic
2. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
2
in nature. Meta heuristic algorithms help us in designing non-parametric classifiers which
directly classify the data based upon the updation of optimum decision function also called as
cost function or on the basis of rules and conditions. These algorithms provide an optimal
solution even in a complex search space. Apart from this, they are capable of escaping from the
problem of local minima or maxima [30]. These two characteristics of meta-heuristic algorithms
make them capable of producing highly accurate and robust solutions in the shortest time. The
selection of nature-inspired algorithm depends upon the problem statement and the solution
required for solving it. One such meta-heuristic, nature-inspired algorithm is Bat algorithm [31]
which has diverse applications. It can be applied for accomplishment of classification task [16],
for optimization problems (both single objective optimization and multi objective optimization)
[32], for data prediction and so on. Bat algorithm mimics the way the Bat searches for its prey
based upon echolocation technique. Using echolocation the Bat changes its direction and speed
based upon the sound that strikes back after reaching the target. It updates its velocity randomly
to reach its prey in the shortest span. Earlier studies reveal that Bat algorithm outperforms Particle
Swarm Optimization (PSO) and Genetic Algorithm (GA) in providing solution to the
unconstrained optimization problems [9]. In this paper the authors are intended to test the
performance of Bat algorithm for the classification of breast cancer data into benign and
malignant classes. The breast cancer is chosen as a classification problem because it is one of the
famous cancers, killing one among every four women [1]. In 2013-14, approximately 64,640
United States women were diagnosed with breast cancer [23]and immediate efforts were made to
reduce the death rate by providing proper awareness, analysis and treatment. But, in the
developing countries like India, the count is increasing with an alarming rate [2]. A better way to
treat a disease is to find its patterns in its early stage.Thus, the authors have specifically chosen
the breast cancer data so that through the data mining a betteranalysis can be provided which can
help the doctors in decision making. The paper is categorized in the following way, after the
introduction part the second section is motivation and related work, followed by preliminary
view, proposed model, results and discussions and finally ends with conclusion and future work.
2. MOTIVATION AND RELATED WORK
Nature is both an inspiration and a motivation. Researchers and computer scientists too are
inspired by nature and have found solutions for various problems by observing mother nature.
The best example is ANN which is built based upon the design and functionality of human brain.
Apart from ANN, many nature-inspired algorithms like Particle Swarm Optimization (PSO)
algorithm, Bee Colony Optimization (BCO) algorithm, Ant Colony Optimization (ACO)
algorithm etc were designed mainly to solve the optimization problems, but now, they are
extended to find solutions for diverse problems. One such algorithm is Bat algorithm which has
diverse applications [33]. In [6] a simple Bat algorithm was used to solve constrained based
optimization problem. A novel hybrid algorithm was designed for global numerical optimization
by using Bat algorithm and Harmonic Search (HS) [27] . Another approach used Bat algorithm
for solving optimization problems in engineering field [34]. For the first time Bat algorithm was
used to select features from various digital data sets [18]. In [22] Optimum-path forest technique
and Bat algorithm were combined to select the features using wrapper approach. Gradually from
optimization problem and feature selection the researchers focus shifted towards classification
and clustering problem. Micro array data was classified using meta-heuristic Bat algorithm [16].
Bat algorithm was also used for clustering by combining traditional K-means clustering with
simple Bat algorithm [25].
3. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
3
3. PRELIMINARIES
3.1 Feed-forward neural network
The simplest and the most famous ANN is a feed forward neural network (FNN). In FNN the
information flows in unidirectional way i.e. moving forward from an input layer to the output
layer. Figure 1 shows a typical feed forward neural network with one hidden layer and one
output layer.
Figure 1: A typical feed forward neural network
Consider an input I which contains several data elements, a simple FNN is designed to generate
an output O, satisfying a threshold Θ with the help of weighted summation of inputs and biases.
A learning function also called as activation function is used to train the network. The default
activation function is sigmoid; however hyperbolic tangent function, Gaussian function and many
others can also be used to activate the neural network. The input fed to the FNN is represented by
Equation 1.
}...XX,X,{X=I 321 n
(1)
Where I indicate set of inputs and Xi represents the data samples where i is an integer always i>
0. Hidden layer always uses weighted summation of inputs (∑Wi Xi ) and bias (Bi) to carry out
learning.
σ, the functionality of the hidden layer is given by the Equation 2.
j
J
jj bxw += ∑1
σ
(2)
4. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
The output of the feed forward neural network is given by the Equation (3)
j
n
j
ji
nh
i
i xwwY
1
1
,,
1
2
)(
+= ∑∑ ==
σθ
Here y(θ) represent the output following a desired threshold, h indicates the number of neurons in
the hidden layer and n indicates the number of input fed to the network. The activation function
used is sigmoid and is represented by the Equation
x
e
xSigmoid −
+
=
1
1
)(
Figure 2: A typical sigmoid function
3.2 Bat algorithm
After Odontocetes, Bats are the mammals which possess a very strong sound propagation
technique called Bio−Sonar. Bio−sonar also called as echolocation is used to identify their prey
and/or to identify the obstacles while traveling. Echolocation can be de
where the animals use their sound for ranging. The delay in time between the emission of sound
from the animals and the echoes reached back after striking the obstacles gives us the actual range
of the target from its position. Bats are
change their direction based upon the distance of availability of the prey. This peculiarity of the
Bats has inspired the researchers and they came up with the concept of Digital Bats. Xin
Yang for the first time proposed a novel Bat algorithm which was used to solve many
optimization problems [31]. Later, many variations of Bat algorithms were put forth by many
researchers. A Binary Bat Algorithm
logic Bat algorithm [12] etc. Even though there are many variants of Bat algorithm designed to
solve various applications the basic working principle remains the same.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
The output of the feed forward neural network is given by the Equation (3)
ijij bb ,
1
, +
+
) represent the output following a desired threshold, h indicates the number of neurons in
the hidden layer and n indicates the number of input fed to the network. The activation function
used is sigmoid and is represented by the Equation 4 and is as shown in Figure 2.
x
Figure 2: A typical sigmoid function
After Odontocetes, Bats are the mammals which possess a very strong sound propagation
−Sonar. Bio−sonar also called as echolocation is used to identify their prey
and/or to identify the obstacles while traveling. Echolocation can be defined as phenomenon
where the animals use their sound for ranging. The delay in time between the emission of sound
from the animals and the echoes reached back after striking the obstacles gives us the actual range
of the target from its position. Bats are very clever mammals, they adjust their velocity and
change their direction based upon the distance of availability of the prey. This peculiarity of the
Bats has inspired the researchers and they came up with the concept of Digital Bats. Xin
he first time proposed a novel Bat algorithm which was used to solve many
. Later, many variations of Bat algorithms were put forth by many
researchers. A Binary Bat Algorithm [15], Multi Objective Bat Algorithm (MOBA)
etc. Even though there are many variants of Bat algorithm designed to
solve various applications the basic working principle remains the same.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
4
(3)
) represent the output following a desired threshold, h indicates the number of neurons in
the hidden layer and n indicates the number of input fed to the network. The activation function
(4)
After Odontocetes, Bats are the mammals which possess a very strong sound propagation
−Sonar. Bio−sonar also called as echolocation is used to identify their prey
fined as phenomenon
where the animals use their sound for ranging. The delay in time between the emission of sound
from the animals and the echoes reached back after striking the obstacles gives us the actual range
very clever mammals, they adjust their velocity and
change their direction based upon the distance of availability of the prey. This peculiarity of the
Bats has inspired the researchers and they came up with the concept of Digital Bats. Xin-She
he first time proposed a novel Bat algorithm which was used to solve many
. Later, many variations of Bat algorithms were put forth by many
, Multi Objective Bat Algorithm (MOBA) [32], Fuzzy
etc. Even though there are many variants of Bat algorithm designed to
5. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
5
It is known that through echolocation the Bat keeps on updating its velocity, position and
frequency to catch its prey as soon as possible. The same principle is mimicked by the
researchers. A digital Bat is initialized with a fixed population of size n, a fixed frequency (Fmin),
initial loudness (A0), pulse emission rate (r) and a wavelength (λ). It is also defined by velocity
vector (or velocity matrix) Vi, position vector (or position matrix) Xi and frequency vector (or
frequency matrix ) Fi which are updated on demand.
Before dynamic updation the authors have some of the predefined assumptions.
1. The pulse rate r is inversely proportional to the distance of the Bat from its target (prey)
i.e. pulse rate increases as the Bat reaches near to its target.
2. The loudness A is directly proportional to the distance of the Bat from its target i.e. as the
distance between the Bat and target decreases the loudness also decreases.
3. It is also assumed that the loudness value decreases from a large positive value A0 to a
fixed minimum Amin.
4. The initial frequency F0 is assigned to each Bat randomly which lies in the range of [Fmin
, Fmax].
The Fmin and Fmax are fixed based on the domain size of the given problem. The velocity and
position vectors are iteratively updated as follows;
iiii FGbesttXtVtV ))()()1( −+=+ (5)
)1()()1( ++=+ tVtXtX iii (6)
βmin)max(min FFFFi −+= (7)
In the Equations 7, i is a positive integer ranging from 1 to n indicating the ith
Bat. t indicates the
iteration. Gbestis the best solution obtained so far. β is a randomly generated number which is in
the range of [0, 1].
From Equations5-6, it can be noticed that difference in frequencies leads the Bats to have
different tendency over obtaining the best solutions.
The best solution is given by the formula
t
oldnew AXX ε+= (8)
In Equation 8At
represents the average loudness of all Bats at the iteration t and ε is the random
number ranging between [−1, 1].
In order to find the best solution the Bat algorithm explores the dimension space by using updated
values of pulse rate and loudness as follows.
)]exp(1)[0()1( trtr ii λ−==+ (9)
6. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
6
)()1( tAtA ii α=+ (10)
In Equations9-10,γ and α are constants and ri(0) is the final value of ri. It is to be noted that when
rireaches ri(0) the value of Ai becomes zero which means the Bat has reached its target.
The pseudo code for Bat algorithm is given in Algorithm 1.
The entire procedure of working of Bat algorithm is represented using flow chart in Figure 3.
In this paper the authors have chosen a strong variant of bat algorithm called binary bat algorithm
(BBA) to build a hybrid model for the classification of breast cancer data using simple feed
forward neural network.
Even though there are many Meta-heuristic algorithms available, we choose BBA because of the
following reasons;
1. Better Convergence: Compared to other algorithms Bat and its variants have better
convergence rate.
2. Auto zooming: Auto zooming is the ability of the Bat to reach the region of promising
solution at a sooner rate.
3. Auto switching: Zooming is always accompanied by auto switching where the digital
Bats switch from explorative moves to local intensive exploitation of search space. The
more efficient auto switching, better is the convergence rate.
Algorithm 1: Bat Algorithm [15]
1. Initialize Bat population: Xi (i = 1, 2, ..., n)
2. Define frequency Fiand velocity Vi
3. Initialize pulse rates riand the loudness Ai
4. whilet< Maximumiterations do
5. update frequency and velocity
6. Calculate transfer function values using Equation (4)
7. Update Vi, Xi, and Fi using Equations 5 to 7
8. if (rand >ri ) then
9. Select the global best solution (Gbest) among the
available best solutionsand with the available
Gbest dimensions modify the dimensions of
Xirandomly.
10. end
11. Generate new solution randomly Equation (8)
12. if ((rand <Ai) and (F (Xi )<F(Gbest)) then
13. Accept the new solutions Increase ri and reduce
Aiusing Equations (9 to 10)
14. end
15. Find the current Gbest and Rank the Bat
16. end
7. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
4. Parameter control: Parameter
instead they keep on varying with iteration count. In Bat algorithm
controlled parameters which vary in each iteration. But, usually in other meta
algorithms the parameters are fixed. Parameter control aids in auto switching.
5. Frequency tuning: Echolocation behavior of Bats is mimicked by frequency tuning. The
frequency tuning property can also be found in PSO, Cuckoo Search,
etc. Thus frequency tuning c
key features of other swarm intelligence based algorithms.
Apart from these advantages it has been proved by preliminary theoretical analysis that Bat
algorithms under right conditions assured g
The complete working details of the BBA are provided in the next sub section.
Figure 3: Flow chart representing general procedure of Bat algorithm
3.3 Binary bat algorithm
BBA is conceptually similar to the general Bat algorithm,
General Bat executes in continuous search space, whereas, BBA executes in binary space. Since
the binary space is restricted to 0’s and 1’s the change of velocity and position cannot be
performed using Equations5-6 thus, a mechanism
is required. In order to update the position of a binary Bat, mapping the velocity values to the
probability values is required. This can be done by deploying a transfer function. Care must
taken to select a transfer function that is bound in the interval of [0,1] and the return value of
transfer function must be directly proportional to the change in the velocity. Keeping this points
in mind the authors have chosen V
which is given by Equation 11
given by Figure 5.
A typical V-shaped transfer function looks like Figure 4
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
Parameter control: Parameter control is a mechanism where the parameters are not static;
instead they keep on varying with iteration count. In Bat algorithm A and
controlled parameters which vary in each iteration. But, usually in other meta
eters are fixed. Parameter control aids in auto switching.
Frequency tuning: Echolocation behavior of Bats is mimicked by frequency tuning. The
frequency tuning property can also be found in PSO, Cuckoo Search, Harmony
etc. Thus frequency tuning can be exploited to provide some functionality similar to the
key features of other swarm intelligence based algorithms.
Apart from these advantages it has been proved by preliminary theoretical analysis that Bat
algorithms under right conditions assured global convergence [9].
The complete working details of the BBA are provided in the next sub section.
3: Flow chart representing general procedure of Bat algorithm
BBA is conceptually similar to the general Bat algorithm, the difference lies in the search space.
General Bat executes in continuous search space, whereas, BBA executes in binary space. Since
the binary space is restricted to 0’s and 1’s the change of velocity and position cannot be
thus, a mechanism to use velocities for changing agent’s position
is required. In order to update the position of a binary Bat, mapping the velocity values to the
probability values is required. This can be done by deploying a transfer function. Care must
taken to select a transfer function that is bound in the interval of [0,1] and the return value of
transfer function must be directly proportional to the change in the velocity. Keeping this points
in mind the authors have chosen V-shaped transfer function called hyperbolic tangent function
and its mapping from continuous domain to binary domain is
shaped transfer function looks like Figure 4
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
7
control is a mechanism where the parameters are not static;
and r are the
controlled parameters which vary in each iteration. But, usually in other meta-heuristic
eters are fixed. Parameter control aids in auto switching.
Frequency tuning: Echolocation behavior of Bats is mimicked by frequency tuning. The
Harmony Search
an be exploited to provide some functionality similar to the
Apart from these advantages it has been proved by preliminary theoretical analysis that Bat
the difference lies in the search space.
General Bat executes in continuous search space, whereas, BBA executes in binary space. Since
the binary space is restricted to 0’s and 1’s the change of velocity and position cannot be
agent’s position
is required. In order to update the position of a binary Bat, mapping the velocity values to the
probability values is required. This can be done by deploying a transfer function. Care must be
taken to select a transfer function that is bound in the interval of [0,1] and the return value of
transfer function must be directly proportional to the change in the velocity. Keeping this points
ion called hyperbolic tangent function
and its mapping from continuous domain to binary domain is
8. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
8
Figure 4: A typical V-shaped transfer function
Figure 5: Mapping from continuous domain to binary domain using transfer function[15].
))(tanh())(( tvtvV k
i
k
i = (11)
Using tanh() transfer function the probability based change in position of an agent (Binary Bat) is
given by Equation 12.
)(
)()1(
)))1(((
'
telseX
tXtX
tVVrandif
k
i
k
i
k
i
k
i
=+
+<
(12)
Where Vi
k
(t) and Xi
k
(t) are the velocity and position of ith
agent in kth
dimension at the iteration t.
Similarly Xi
k
(t)ʹ is the complement of Xi
k
(t).
9. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
9
The algorithm for Binary Bat is given by Algorithm 2.
Algorithm 2: Binary Bat Algorithm [15]
1. Initialize Bat population: Xi (i = 1, 2, ..., n) rand(0 or 1) and Vi= 0
2. Define pulse frequency Fi
3. Initialize pulse rates ri and the loudness Ai
4. whilet < Maximum iterations do
5. update velocities and adjust frequencies
6. Using Equation (11) Calculate transfer function value
7. Using Equation (12) update Xi
8. if (rand >ri ) then
9. Select the global best solution (Gbest) among the
available best solutions and with the available Gbest
dimensions modify the dimensions of Xi randomly
10. end
11. Generate new solution randomly
12. if ((rand <Ai )and (F(Xi )<F(Gbest)) then
13. Accept the new solutions Increase riand reduce Ai
14. end
15. Find the current Gbest and Rank the Bat
16. end
Since the BBA is similar to general Bat algorithm we have continued with the same flowchart and
no separate flow chart is given for BBA.
4. PROPOSED WORK
Our problem statement is to classify the given breast cancer data into its two constituent classes
(Benign and malignant) using the proposed hybrid model which is a fusion of Bat algorithm and
FNN. Here the authors have used Bat algorithm to train the FNN. The authors follow an
incremental training approach where the network is trained for a fixed number of iterations and
then tested for its performance. The Binary Bat Algorithm is used to minimize the classification
error calculated for randomly generated combination of biases and weights. The main aim of the
proposed model is to improve the rate of accuracy.
The classification of breast cancer data is carried out in two simple steps:
• Step 1: Representation strategy
• Step 2: Defining fitness function and learning function
4.1 Representation strategy
Binary representation, matrix representation and vector representation are the three widely used
methods for representing (encoding)weights and biases in a neural network [36] each having
their own set of advantages and disadvantages [35]. The choice of the method depends upon the
10. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
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application. For our binary classification problem the authors have chosen matrix representation
method to train feed forward neural network as it is more suitable for training neural network
because of its easy decoding phase.
A simple illustrative form of matrix encoding strategy is represented diagrammatically in Figure
6 and correspondingly the dimensions are represented in Equation 13.
Figure 6: Structure of Feed forward Neural Network
],,,[ 2211 BWBWi = (13)
Here W1 indicates the weight matrix at hidden layer B1 indicates the bias matrixat hidden layer,
W2 indicates the weight matrix at output layer and B2 indicates the bias matrix at output layer i.e,
=
14
13
12
1
w
w
w
W [ ]453525
'
2 wwwW =
[ ]3211 θθθ=B and [ ]42 θ=B
4.2 Defining fitness function and learning function
A learning function is a function used to make the neural network learn. Whereas, the fitness
function is a function whose main aim is to minimize the error rate of the output generated from
the proposed model as much as possible. So that, obtained result is near to the required result.
For a typical feed forward neural network containing n input nodes h hidden and one output node
the learning function is calculated as in Equation 14.
)))(exp(1(
1
)(
1
j
n
i
ij
j
bW
sf
−−+
=
∑=
(14)
11. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
11
Where j=1,2,….h
Sj is the output calculated for every hidden node in each iteration. Wij represents the connected
weight from the jth
node of the hidden layer and ith
node of the input layer and bj is the
corresponding bias [35].
The final output is the sum of output derived from all hidden nodes and is given by Equation15.
jj
h
k
kjk bSfWO −= ∑=
)(.
1
(15)
Where j=1,2,…m
Where Wkj represents the connection of weights from the kth
output node and jth
input node.
jk bOif >=)(
=
=
0
1
class
else
class
16)
2
1
)( k
i
m
i
k
k dOF i
−= ∑=
(17)
∑=
=
c
k
k
c
F
F
1
(18)
Finally the fitness function for the proposed method is given by the equation 18. Here c is the
number of training samples used and d is the desired output of the ith
input unit with reference to
the kth
training sample [13].
5. EXPERIMENTATION AND EVALUATION
The proposed model has been implemented on matlab 2014a platform and tested on a bench mark
dataset called Wisconsin Breast Cancer Diagnostic (WBCD) [28]. The Binary Bat Algorithm is
used to deploy a fitness function for error minimization and to generate weights and biases
required for learning. The feed-forward neural network contains 15 input nodes, 15 hidden nodes
and 1 output node. Hyperbolic tangent function is chosen as the learning function to train the
network. The network is trained up to 100 iterations to produce the classification rate.
5.1 Data set used
The bench mark data set has been used to check out the performance of the proposed model on
the classification of breast cancer data. The breast cancer data set used is Wisconsin Breast
Cancer data Diagnostic (WBCD). The dataset is collected from UCI repository [ 26] and it
12. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
12
contains 569 rows and 32 columns (1 class attribute and 31 independent attributes), the same
dataset can also be found in [17].
5.2 Parameter setup
A neural network based classification model goes through three phases viz., training, validation
and testing. Training phase is a phase where the data is trained so that the network can learn about
the patterns which can help in classification.Validation phase is a phase where we check the
model with different parameters and come up with the finest set of parameters required for proper
classification.Testing is a phase where the unknown data is given to the network to check its
ability, how better does it classifythe unknown data based upon the previously learnt
knowledge.Since learning plays a vital role in building a neural network based classification
model, first and foremostwe check the performance of various learning functions on validating
data. The training function which provides highest accuracy will be the chosen function to carry
out the entire experimentation.A set of four different V-shaped transfer functions are analyzed by
executing them in 10 independent trials, for 100 iterations (which is the usual standard) andtheir
corresponding accuracies are tabulated in Table 1.
Table 1 Impact of various V-shaped transfer functions on the proposed model
Function Formula Maximum
accuracy in %
Maximumtime
taken in secs
F1: hyperbolic
tangent function
|tanh x | 89.95 101.57
F2: erf function
erf
π
2x
85.23 744.30
F3: arctan 2
π
∗ atan
π
2x
83.83 114.99
F4: inverse of
square root of x2
x
1 + √x
73.28 359.36
Figure 1 to 4 shows the MSE transfer curve of the four different transfer functions with
maximum accuracy as given in Table 1.
13. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
Figure 1: MSE transfer curve for F1 V
Figure 2: MSE
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
: MSE transfer curve for F1 V-shaped transfer functions.
: MSE transfer curve for F2 V-shaped transfer functions.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
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14. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
Figure 3:MSE transfer curve for F3 V
Figure 4: MSE transfer curve for F4 V
Figure 1 to 4 and Table 1 clearly suggest that
parts.
The V-shaped transfer functions are much better in updating the position than the S
functions because, in V-shaped transfer functions the search agents are assigned the values either
0 or 1.
V-shaped transfer functions tend more often to form the c
(12). This mechanism promotes and guarantees changing the position of search agents
proportional to their velocities. This is the main reason for the superiority of v
functions.
However, it is also important to analyze the effect of change in number of iterations in a neural
network. Thus we check the performance of tanh() on different iterations.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
:MSE transfer curve for F3 V-shaped transfer functions.
: MSE transfer curve for F4 V-shaped transfer functions.
clearly suggest that tanh() function performs better than its counter
shaped transfer functions are much better in updating the position than the S
shaped transfer functions the search agents are assigned the values either
shaped transfer functions tend more often to form the complement of variables using Equation
). This mechanism promotes and guarantees changing the position of search agents
proportional to their velocities. This is the main reason for the superiority of v
ant to analyze the effect of change in number of iterations in a neural
network. Thus we check the performance of tanh() on different iterations.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
14
than its counter
shaped transfer functions are much better in updating the position than the S-shaped
shaped transfer functions the search agents are assigned the values either
omplement of variables using Equation
). This mechanism promotes and guarantees changing the position of search agents
proportional to their velocities. This is the main reason for the superiority of v-shaped test
ant to analyze the effect of change in number of iterations in a neural
15. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
15
The Table 2 provides the details about the change in accuracy of the model with change in
number of iterations. Here the minimum, maximum and mean accuracy of the tanh() for different
iterations is given.
Table 2: Impact of number of iterations on the proposed model.
Sl.
No
No .of
Iterations
Maximum
accuracy in %
Mean
accuracy in %
Minimum
accuracy in %
1. 10 48.33 37.59 40.97
2. 30 85.41 83.04 65.71
3. 50 92.64 87.88 84.2
4. 75 78.73 68.10 50
5. 100 89.95 76.71 67.3
6. 150 72.23 69.66 65.47
7. 200 74.34 72.2 68.26
After deciding to go with which transfer function and fixing up the iterations, we move on to
check the impact of other parameters.The hidden nodes play a major role in deciding the
complexity of the ANN structure. More the number of hidden nodes more complex will be the
structure and lesser the number of hidden nodes, simple will be the structure. Our concern is to
keep the structure as simple as possible and at the same time not compromising with the
accuracy.Thus, knowing about an optimal number of hidden nodes that can serve our purpose is
must.
As the maximum number of inputs taken in our model is 15, we restrict the maximum number of
hidden nodes to 15.Since there is a thumb rule that the hidden nodes can't exceed the number of
input nodes[8].
Table 3 provides the impact of various number of hidden nodes on the accuracy of the model.
Table 3 clearly specify that 15 number of hidden nodes are more suitable for carrying out the
classification task.
Table 3: Impact of number of hidden nodes on the proposed model.
Sl.
No
No .of
Hidden nodes
Maximum
accuracy in %
Mean
accuracy in %
Minimum
accuracy in %
1. 3 72.4 61.86 50.48
2. 5 75.21 66.06 55.70
3. 10 83.83 68.96 54.30
4. 15 92.61 88.7 84.40
In nature inspired algorithms the role of Number of Particles (NoP) chosen and how many times
these particles aretrained has a major impact on deciding the accuracy of the model. Table 4
provides the information regarding the change in performance of the model withchange in NoP
values.
16. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
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Table4: Impact of number of particles (NoP) on the proposed model
Sl.No NoP
count
Maximum
accuracy in %
Mean
accuracy in %
Minimum
accuracy in %
1. 10 56.23 41.62 24.90
2. 20 64.85 54.70 33.74
3. 30 92.61 88.70 84.4
4. 40 80.31 62.38 47.75
5. 50 82.77 60.93 47.95
6. 60 68.80 56.01 37.00
From Table 4 it is clear that NoP at 30 produces good classification results.Similar to NoP, the
NoV parameter was checked with different values and its details are tabulated in Table 5.
Table5: Impact of number of dimensions (NoV) on the proposed model
Sl.No NoV
count
Maximum
accuracy in %
Mean
accuracy in %
Minimum
accuracy in %
1. 25 75.75 60.68 45.51
2. 50 80.47 61.09 33.74
3. 75 81.63 62.29 36.55
4. 100 82.95 57.29 40.77
5. 125 86.46 59.76 42.16
6. 150 92.61 88.7 84.4
7. 175 87.22 65.35 75.21
8. 200 77.85 62.05 49.05
Finally the pulse rate (r) and loudness (A) are confirmed by checking different values and the
corresponding accuracies are tabulated in Table 6 loudness values are checked from 0.99 to
0.1pulse rate is checked from 0.2 to 1.
Table6: Impact of A and r on the proposed model
Sl.
No
A and r
values
Maximum
accuracy in %
Mean accuracy
in %
Minimum
accuracy in %
1. 0.9 and 0.2 84.60 69.18 46
2. 0.7 and 0.4 84.69 70.51 54.1
3. 0.5 and 0.6 92.61 88.70 84.4
4. 0.3 and 0.8 81.01 67.50 60
5. 0.1 and1.0 65.90 46.50 34.6
The A=0.5 and r=0.6 produces good classification results when compared to other values.Apart
from these, the two constants Qmin and Qmax are assigned values of 1 and 5 respectively. These
values are fixed by referring the literature [13-14].
The final set of parameters for BBA algorithm confirmed after an intense preliminary study,
careful examination and repeated experimentation are tabulated in Table 7.
17. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
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Table7:Finalizedset of parameters for BBA
Parameter Value
Maximum Iterations 50
Number of Particles (NoP) 30
Number of Dimensions (NoV) 150
Loudness (A) 0.5
Pulse Rate (r) 0.6
Minimum frequency (Qmin) 1
Maximum frequency (Qmax) 5
Initial Frequency (for each particle) 0
Initial Velocity (for each particle) 0
Initial Position (for each particle) 0
5.3 Results
In order to get the better results the algorithm was executed 10 times for the given dataset under
10 fold cross validation scheme and the highest accuracy was selected as the best accuracy of the
model.The results include Confusion matrices for training and testing phase. Receiver Operating
Characteristic (ROC) curve for the testing data and MSE performance plot for testing data.
From Table 8 and 9, the confusion matrices for the WBCD datasets for training and testing phase
is given which provide us the information regarding the number of true positives (TP), true
negatives (TN) false positives (FP) and false negatives (FN) obtained for the WBCD dataset.
Table8: Confusion matrix for WBCD dataset used for training
WBCD Dataset Malignant Benign
Malignant 200 33
Benign 9 327
Table 9: Confusion matrix for WBCD dataset used for testing
WBCD Dataset Malignant Benign
Malignant 195 40
Benign 17 317
The Receiver Operating Characteristic (ROC) curve obtained for the WBCD dataset during
testing is as shown in Figure 5.It provides the details of the area covered by the proper
classification.
18. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
Figure
The Area Under Curve (AUC)covered by WB
performance plot provides the information regarding the minimum error of the model.The
performance plot for WBCD is as shown in
be 0.34.
Figure
The evaluation measures such as precision, recall, accuracy, Matthews coefficient etc are
calculated using true positives (TPs), true negatives (TNs), false positives (FPs) and false
negatives of the testing confusion matrix is given in
Table 10: Various measures deduced from Confusion matrix for
Measure
Sensitivity (TPR)/Recall
Specificity (TNR)
Precision (PPV)
Negative Predictive Value (NPV)
Accuracy
Matthews Correlation Coefficient
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
Figure 5: ROC curve for WBCD dataset.
The Area Under Curve (AUC)covered by WBCD dataset is 0.9000 i.e. 90% of the total area.
performance plot provides the information regarding the minimum error of the model.The
performance plot for WBCD is as shown in Figure 6.The MSE of the proposed model is found to
Figure 6: MSE curve for WBCD dataset.
The evaluation measures such as precision, recall, accuracy, Matthews coefficient etc are
calculated using true positives (TPs), true negatives (TNs), false positives (FPs) and false
testing confusion matrix is given in Table 10.
Table 10: Various measures deduced from Confusion matrix for WBCD data set.
Formula Value
Sensitivity (TPR)/Recall TPR = TP / (TP + FN) 0.9198
SPC = TN / (FP + TN) 0.8880
PPV = TP / (TP + FP) 0.8298
Negative Predictive Value (NPV) NPV = TN / (TN + FN) 0.9491
ACC = (TP + TN) / (P + N) 0.8991
Matthews Correlation Coefficient F1 = 2TP / (2TP + FP + FN) 0.7932
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
18
% of the total area. The
performance plot provides the information regarding the minimum error of the model.The
The MSE of the proposed model is found to
The evaluation measures such as precision, recall, accuracy, Matthews coefficient etc are
calculated using true positives (TPs), true negatives (TNs), false positives (FPs) and false
set.
Value
0.9198
0.8880
0.8298
0.9491
0.8991
0.7932
19. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
19
5.4 Comparative analysis
We compared our results with nearly 9 other classification techniques which included both nature
inspired algorithms and regular techniques. The accuracy values (in %) of various
classification techniques and that of proposed model are mentioned in Table 7.
Table 11: Comparing accuracy of various classification techniques
Algorithm/Techniques Accuracy in %
ACO [4] 47.45
BBO [14] 91.1
KNN [29] 80.03
MNN [10] 92.1
NaiveBayes [29] 91.63
PSO [11] 91.16
Random Forest [24] 89.12
ES [19] 91.81
Proposed Method 92.61
The classification accuracy obtained for WBCD dataset is 92.61% for training and 89.91% for
testing which is higher than other techniques however, MNN give a tough competition to the
proposed model. Since the advantages of BBA are superior to PSO in many aspects,we claim that
the proposed model is better than all other compared techniques.
1. CONCLUSION AND FUTURE WORK
From the available results and comparative analysis we can strongly conclude that the proposed
model - BBA inspired Feed-forward neural network performs very well in classifying the data
into benign and malignant classes and giving us the maximum accuracy of 92.61% for training
WBCD data and 89.951% accuracy for testing. Even though the accuracy of PSO and GSA is
higher than BBA, the time taken and MSE obtained is less. Thus the time efficiency and error
minimization makes the proposed model more suitable than other algorithms in solving binary
classification problem.
But it is to be noted that, both the dataset and the structure of the FNN is kept simple. In future
we are interested in carrying out the classification task on huge dataset and complex network
structure.
ACKNOWLEDGEMENT
Wisconsin Hospital and its team: A Wisconsin Original Breast Cancer dataset was obtained from
theUniversity of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. A hearty thanks to
the institutesand personnel, my teachers and my fellow researchers for their timely support.
Funding: Maulana Azad National Fellowship for Minority Students, (Grant/Award Number: ‘F1-
17/2013-14/MANF-2013-14-MUS-KAR-24350’).
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AUTHORS
Doreswamy received B.Sc degree in Computer Science and M.Sc Degree in
Computer Science from University of Mysore in 1993 and 1995 respectively. Ph.D
degree in Computer Science from Mangalore University in the year 2007. After
completion of his Post-Graduation Degre
Lecturer in Computer Science at St. Joseph’s College, Bangalore from 1996
1999.Then he has elevated to the position Reader in Computer Science at Mangalore
University in year 2003. He was the Chairman of the Departme
Studies and research in computer science from 2003
served at varies capacities in Mangalore University at present he is the Chairman of Board of Studies and
Professor in Computer Science of Mangalore Universi
Mining and Knowledge Discovery, Artificial Intelligence and Expert Systems, Bioinformatics, Molecular
modelling and simulation, Computational Intelligence, Nanotechnology, Image Processing and Pattern
recognition. He has been granted a Major Research project entitled “Scientific Knowledge Discovery
Systems (SKDS) for Advanced Engineering Materials Design Applications” from the funding agency
University Grant Commission, New Delhi, India. He has been published
reviewed Papers at national/International Journal and Conferences. He received SHIKSHA RATTAN
PURASKAR for his outstanding achievements in the year 2009 and RASTRIYA VIDYA SARASWATHI
AWARD for outstanding achievement in chosen fi
your short resume.
Umme Salma M received BSc and MSc (Computer Science) degree from Kuvempu
University. She has secured 1st
rank in MSc.Cs in 2009 and is a gold medalist. She is an
awardee of Maulan Azad National Fellowship. Currently pursuing her PhD in Mangalore
university and her research topic is Exploration of advanced datamining techniques for
the classification of breast cancer data.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
Wisconsin original benchmark data from uci repository
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original).Accessed online on 10
“A novel hybrid bat algorithm with harmony search for global numerical
Journal of Applied Mathematics, 2013.
Breast Cancer Diagnostic (2015)
ttps://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic). Accessed online on10
Wu, X., Kumar V., Quinlan J.R., Motoda H., & Zhou Z.H. (2008) “Top 10 algorithms in data mining
Knowledge and information systems, 14(1), pp1-37.
inspired metaheuristic algorithms”, Luniver press.
A new metaheuristic bat-inspired algorithm”, In Nature-inspired cooperative strategies for
65–74.
Bat algorithm for multi-objective optimization”, International Journal of Bio
Bat algorithm: literature review and applications”, International Journal of Bio
141–149.
Hossein Gandomi, A. (2012) “Bat algorithm: a novel approach for global engineerin
Engineering Computations, 29(5), pp464–483.
R., Zhang, J., Lok, T.-M., & Lyu, M. R. (2007) “A hybrid particle swarm optimization
propagation algorithm for feedforward neural network training”, Applied Mathematics and Computa
An online gradient method with momentum for two-layer feed- forward neural networks
Applied Mathematics and Computation, 212(2), pp488–498.
received B.Sc degree in Computer Science and M.Sc Degree in
Computer Science from University of Mysore in 1993 and 1995 respectively. Ph.D
degree in Computer Science from Mangalore University in the year 2007. After
Graduation Degree, he subsequently joined and served as
Lecturer in Computer Science at St. Joseph’s College, Bangalore from 1996-
1999.Then he has elevated to the position Reader in Computer Science at Mangalore
University in year 2003. He was the Chairman of the Department of Post-Graduate
Studies and research in computer science from 2003-2005 and from 2009-2008 and
served at varies capacities in Mangalore University at present he is the Chairman of Board of Studies and
Professor in Computer Science of Mangalore University. His areas of Research interests include Data
Mining and Knowledge Discovery, Artificial Intelligence and Expert Systems, Bioinformatics, Molecular
modelling and simulation, Computational Intelligence, Nanotechnology, Image Processing and Pattern
ition. He has been granted a Major Research project entitled “Scientific Knowledge Discovery
Systems (SKDS) for Advanced Engineering Materials Design Applications” from the funding agency
University Grant Commission, New Delhi, India. He has been published about 30 contributed peer
reviewed Papers at national/International Journal and Conferences. He received SHIKSHA RATTAN
PURASKAR for his outstanding achievements in the year 2009 and RASTRIYA VIDYA SARASWATHI
AWARD for outstanding achievement in chosen field of activity in the year 2010.This space is for writing
received BSc and MSc (Computer Science) degree from Kuvempu
rank in MSc.Cs in 2009 and is a gold medalist. She is an
awardee of Maulan Azad National Fellowship. Currently pursuing her PhD in Mangalore
university and her research topic is Exploration of advanced datamining techniques for
t cancer data.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
21
a from uci repository”,
nline on 10-05-2014.
hybrid bat algorithm with harmony search for global numerical
Breast Cancer Diagnostic (2015)
ttps://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic). Accessed online on10-02-2015.
Top 10 algorithms in data mining”,
inspired cooperative strategies for
International Journal of Bio-Inspired
International Journal of Bio-
Bat algorithm: a novel approach for global engineering
A hybrid particle swarm optimization–back-
Applied Mathematics and Computation, 185(2),
forward neural networks”,
served at varies capacities in Mangalore University at present he is the Chairman of Board of Studies and
ty. His areas of Research interests include Data
Mining and Knowledge Discovery, Artificial Intelligence and Expert Systems, Bioinformatics, Molecular
modelling and simulation, Computational Intelligence, Nanotechnology, Image Processing and Pattern
ition. He has been granted a Major Research project entitled “Scientific Knowledge Discovery
Systems (SKDS) for Advanced Engineering Materials Design Applications” from the funding agency
about 30 contributed peer
reviewed Papers at national/International Journal and Conferences. He received SHIKSHA RATTAN
PURASKAR for his outstanding achievements in the year 2009 and RASTRIYA VIDYA SARASWATHI
eld of activity in the year 2010.This space is for writing