Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k -NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

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Severity Classification of Multiple Sclerosis Disease: A Rough Set-Based Method

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1049.0789s19 ◽

2019 ◽

Vol 8 (9S) ◽

pp. 307-314

Keyword(s):

Multiple Sclerosis ◽

Random Forest ◽

Rough Set ◽

Weighted Average ◽

Support Vector ◽

Neurologic Disorder ◽

Severity Classification ◽

Average Accuracy ◽

Multiple Sclerosis Disease

Multiple sclerosis (MS) is among the world’s most common neurologic disorder. Severity classification of MS disease is necessary for treatment and medication dosage decisions and to understand the disease progression. To the best of authors’ knowledge, this is the first study for the severity classification of MS disease. In this study, Rough set (RS) approach is applied to discern the three classes (mild, moderate, and severe) of the severity of MS disease. Furthermore, the performance of the RS approach is compared with Machine learning (ML) classifiers namely, random forest, K-nearest neighbour, and support vector machine. The performance is evaluated on the dataset acquired from Multiple sclerosis outcome assessments consortium (MSOAC), Arizona, US. The weighted average accuracy, precision, recall, and specificity values for the RS approach are found to be 84.04%, 76.99%, 76.75%, and 83.84% respectively. However, among the ML classifiers, the performance of random forest classifier is found best for which the weighted average accuracy, precision, recall, and specificity values are 62.19 %, 52.65 %, 56.84 %, and 59.87 % respectively. The RS approach is found much superior to ML classifiers and may be used for MS disease severity classification. This study may be helpful for the clinicians to assess the severity of the MS patients and to take medication and dosage decisions.

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Data mining methods for prediction of air pollution

International Journal of Applied Mathematics and Computer Science ◽

10.1515/amcs-2016-0033 ◽

2016 ◽

Vol 26 (2) ◽

pp. 467-478 ◽

Cited By ~ 22

Author(s):

Krzysztof Siwek ◽

Stanisław Osowski

Keyword(s):

Data Mining ◽

Air Pollution ◽

Linear Method ◽

Point Of View ◽

Atmospheric Pollutants ◽

Support Vector ◽

Forecasting Accuracy ◽

Prognostic System ◽

Prognostic Features ◽

Selection Of

Abstract The paper discusses methods of data mining for prediction of air pollution. Two tasks in such a problem are important: generation and selection of the prognostic features, and the final prognostic system of the pollution for the next day. An advanced set of features, created on the basis of the atmospheric parameters, is proposed. This set is subject to analysis and selection of the most important features from the prediction point of view. Two methods of feature selection are compared. One applies a genetic algorithm (a global approach), and the other-a linear method of stepwise fit (a locally optimized approach). On the basis of such analysis, two sets of the most predictive features are selected. These sets take part in prediction of the atmospheric pollutants PM10, SO2, NO2 and O3. Two approaches to prediction are compared. In the first one, the features selected are directly applied to the random forest (RF), which forms an ensemble of decision trees. In the second case, intermediate predictors built on the basis of neural networks (the multilayer perceptron, the radial basis function and the support vector machine) are used. They create an ensemble integrated into the final prognosis. The paper shows that preselection of the most important features, cooperating with an ensemble of predictors, allows increasing the forecasting accuracy of atmospheric pollution in a significant way.

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Optimal Selection of Support Vector Regression Parameters and Molecular Descriptors for Retention Indices Prediction

Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-14932-0_11 ◽

2010 ◽

pp. 83-90 ◽

Cited By ~ 1

Author(s):

Jun Zhang ◽

Bing Wang ◽

Xiang Zhang

Keyword(s):

Support Vector Regression ◽

Molecular Descriptors ◽

Retention Indices ◽

Support Vector ◽

Optimal Selection ◽

Regression Parameters ◽

Selection Of

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Application and Research of Data Mining Technology in Communication Network Environment

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.3814 ◽

2014 ◽

Vol 989-994 ◽

pp. 3814-3817

Author(s):

Zhi Zhang

Keyword(s):

Data Mining ◽

Communication Network ◽

Communication Networks ◽

Least Square ◽

Support Vector ◽

Data Mining Algorithms ◽

Training Samples ◽

Fuzzy C Means Clustering ◽

Membership Matrix ◽

Mining Algorithms

The abnormal data of communication networks are complex and diverse, it is difficult to recognize and mine the abnormal data accurately and effectively with traditional methods. In order to improve the recognition accuracy of communication network, a data mining algorithms based on the method of communication network abnormal data recognition is presented. Firstly, the communication network data samples are analyzed for fuzzy c-means clustering in order to obtain the degree of membership matrix. Secondly, the training samples of communication network abnormal data mining are selected according to the membership. At last, the training samples are put into the least square support vector machine learning, which establish the model of abnormal data identification in communication network. The performance of the algorithm was tested by simulation tests, and the results show that, the abnormal data recognition efficiency and accuracy in this paper was improved much more than the traditional identification methods.

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The economics of selection of mail orders Drs. Zahavi and Levin are the masterminds behind the development of AMOS, a customized predictive modeling system for the Franklin Mint in Philadelphia, and GainSmarts, a general purpose data mining system that is the two-time winner of the KDD-CUP competition for the best data mining tools (1997 and 1998) sponsored by the American Association for Artificial Intelligence.

Journal of Interactive Marketing ◽

10.1002/dir.1016.abs ◽

2001 ◽

Vol 15 (3) ◽

pp. 53

Author(s):

Nissan Levin ◽

Jacob Zahavi

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Predictive Modeling ◽

American Association ◽

General Purpose ◽

Mining System ◽

Data Mining System ◽

Mining Tools ◽

Selection Of

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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Optimal selection of sensor network and backed-off operating point based on economics

2013 European Control Conference (ECC) ◽

10.23919/ecc.2013.6669516 ◽

2013 ◽

Author(s):

Nabil Magbool Jan ◽

Sigurd Skogestad ◽

Sridharakumar Narasimhan

Keyword(s):

Sensor Network ◽

Operating Point ◽

Optimal Selection ◽

Selection Of

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The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v7i1.25 ◽

2018 ◽

Vol 7 (1) ◽

pp. 72

Author(s):

Khalid AA Abakar ◽

Chongwen Yu

Keyword(s):

Data Mining ◽

Kernel Functions ◽

Support Vector ◽

Ann Model ◽

Data Mining Techniques ◽

Yarn Quality ◽

Yarn Properties ◽

Svm Model ◽

Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

Unes journal of Information System ◽

10.31933/ujis.3.1.001-007.2018 ◽

2018 ◽

Vol 3 (1) ◽

pp. 001

Author(s):

Zulhendra Zulhendra ◽

Gunadi Widi Nurcahyo ◽

Julius Santony

Keyword(s):

Data Mining ◽

Data Analysis ◽

Clustering Algorithm ◽

Customer Complaints ◽

Using Data ◽

Clustering Data ◽

Service Data ◽

Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

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