Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification

Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data.L2regularization has been commonly used. If the training dataset contains many noise variables,L1regularization SVM will provide a better performance. However, bothL1andL2are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweightedp-norm regularization support vector machine for 0 <p≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that apvalue of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using apvalue less thanL1norm. Moreover, we observe that the proposedLppenalty is more robust to noise variables than theL1andL2penalties.

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Detection of Breast Cancer Using Machine Learning Support Vector Machine Algorithm

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2019.7747 ◽

2019 ◽

Vol 16 (2) ◽

pp. 441-444

Author(s):

D. V. Soundari ◽

R. Padmapriya ◽

C. Thirumariselvi ◽

N. Nanthini ◽

K. Priyadharsini

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Support Vector Machine ◽

Support Vector ◽

Learning Support ◽

Support Vector Machine Algorithm ◽

Breast Cancer Data ◽

Data Set ◽

Cancer Data ◽

Hormone Imbalance

A woman majorly suffers due to breast cancer which is due to hormone imbalance. It leads to huge death in recent years. Early detection of the breast cancer is more important to prevent human lives. Image Processing plays an important to classify and detect the same. So this paper proposes machine learning based cancer classification using support vector machine with Wisconsin breast cancer data set.

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Breast Cancer Prediction using SVM with PCA Feature Selection Method

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952277 ◽

2019 ◽

pp. 969-978

Author(s):

Akshya Yadav ◽

Imlikumla Jamir ◽

Raj Rajeshwari Jain ◽

Mayank Sohani

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Selection ◽

Learning Algorithm ◽

Feature Selection Method ◽

Selection Method ◽

Training Dataset ◽

Support Vector ◽

Improved Accuracy

Cancer has been characterized as one of the leading diseases that cause death in humans. Breast cancer, being a subtype of cancer, causes death in one out of every eight women worldwide. The solution to counter this is by conducting early and accurate diagnosis for faster treatment. To achieve such accuracy in a short span of time proves difficult with existing techniques. Also, the medical tests conducted in hospitals for detecting cancer is expensive and is difficult for any common man to afford. To counter these problems, in this paper, we use the concept of applying Support Vector machine a Machine Learning algorithm to predict whether a person is prone to breast cancer. We evaluate the performance of this algorithm by calculating its accuracy and apply a min-max scaling method so as to counter and overcome the problem of overfitting and outliers. After scaling of the dataset, we apply a feature selection method called Principle component analysis to improve the algorithms accuracy by decreasing the number of parameters. The final algorithm has improved accuracy with the absence of overfitting and outliers, thus this algorithm can be used to develop and build systems that can be deployed in clinics, hospitals and medical centers for early and quick diagnosis of breast cancer. The training dataset is from the University of Wisconsin (UCI) Machine Learning Repository which is used to evaluate the performance of the Support vector machine by calculating its accuracy.

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Breast Cancer Prediction Using Machine Learning Algorithm with Big Data Concept

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset1207232 ◽

2020 ◽

pp. 123-127

Author(s):

R. Nirmalan ◽

M. Javith Hussain Khan ◽

V. Sounder ◽

A. Manikkaraja

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Support Vector Machine ◽

Big Data ◽

Learning Algorithm ◽

Support Vector ◽

Data Set ◽

Cancer Prediction ◽

Modern Computer ◽

Huge Data

The evolution in modern computer technology produce an huge amount of data by the way of using updated technology world with the lot and lot of inventions. The algorithms which we used in machine-learning traditionally might not support the concept of big data. Here we have discussed and implemented the solution for the problem, while predicting breast cancer using big data. DNA methylation (DM) as well gene expression (GE) are the two types of data used for the prediction of breast cancer. The main objective is to classify individual data set in the separate manner. To achieve this main objective, we have used a platform Apache Spark. Here,we have applied three types of algorithms used for classification, they are decision tree, random forest algorithm, support vector machine algorithm which will be mentioned as SVM .These three types of algorithm used for producing models used for breast cancer prediction. Analyze have done for finding which algorithm will produce the better result with good accuracy and less error rate. Additionally, the platforms like Weka and Spark are compared, to find which will have the better performance while dealing with the huge data. The obtained outcome have proved that the Support Vector Machine classifier which is scalable might given the better performance than all other classifiers and it have achieved the lowest error range with the highest accuracy using GE data set

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Breast Cancer Detection with Revamped Dataset Using Machine Learning Techniques

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3892 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2996-3009

Author(s):

Sundarambal Balaraman ◽

Ramesh Ramamoorthy ◽

Raja Krishnamoorthi

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Logistic Regression ◽

Learning Algorithm ◽

Machine Learning Techniques ◽

Support Vector ◽

Data Set ◽

Cancer Data ◽

Learning Techniques ◽

Incidence And Mortality

Machine learning is a current topic of interest in research and industry, with the implementation of novel strategies all the time. The main purpose of this research activity is to determine the efficiency of machine learning techniques in the detection research of breast cancer. The incidence and mortality of breast cancer in women are increasing day by day. Worldwide, researchers have worked hard to help clinicians provide the best model for detecting diagnosis and breast cancer. In this work, learning UCI machine Wisconsin breast cancer data from a set of databases, model, and analyze the performance of existing work use, compared to the same data set. The dataset is analyzed, and the revamped dataset is constructed by eliminating redundant features and appending new features essential for prediction. Logistic regression, K nearest neighbors (KNN), support vector machine (SVM), decision trees, random forest, XGBoost, using a machine learning algorithm, such as re-organized data set of artificial neural network AdaBoost, 8 one of prediction build the model application (ANN). Standard to analyze the accuracy rate. In the experiment, these classifications have been shown to work for breast cancer with >97% accuracy. Logistic regression, XGBoost and Adaboost, stand on top with 99.28 percent accuracy. The experiment also, the balanced data set of removal outliers and balance, shows that have a significant impact on the model’s prediction performance.

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Misalignment Detection of a Rotating Machine Shaft Using a Support Vector Machine Learning Algorithm

International Journal of Precision Engineering and Manufacturing ◽

10.1007/s12541-020-00462-1 ◽

2021 ◽

Vol 22 (3) ◽

pp. 409-416

Author(s):

Yong Eun Lee ◽

Bok-Kyung Kim ◽

Jun-Hee Bae ◽

Kyung Chun Kim

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Algorithm ◽

Support Vector ◽

Machine Learning Algorithm ◽

Rotating Machine

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Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e9689.069520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 560-565

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Algorithm ◽

Early Stage ◽

Region Of Interest ◽

Input Image ◽

Disease Classification ◽

Support Vector ◽

Leaf Disease ◽

Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

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Predicting Student’s Performance Using Machine Learning Algorithm

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1209 ◽

2021 ◽

pp. 53-58

Author(s):

Sheela Rani P ◽

Dhivya S ◽

Dharshini Priya M ◽

Dharmila Chowdary A

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Prediction Model ◽

Naive Bayes ◽

Learning Algorithm ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

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Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c5808.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 2758-2762

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Storage ◽

Clinical Data ◽

Disease Risk ◽

Learning Algorithm ◽

Information Storage ◽

Support Vector ◽

Machine Learning Algorithm ◽

Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

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Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

10.31219/osf.io/scz4r ◽

2020 ◽

Author(s):

Castro Mayleen Dorcas Bondoc ◽

Tumibay Gilbert Malawit

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Process ◽

Learning Algorithm ◽

Research Work ◽

Supervised Machine Learning ◽

Support Vector ◽

Video Tutorials ◽

Learning Management ◽

Face To Face Instruction

Today many schools, universities and institutions recognize the necessity and importance of using Learning Management Systems (LMS) as part of their educational services. This research work has applied LMS in the teaching and learning process of Bulacan State University (BulSU) Graduate School (GS) Program that enhances the face-to-face instruction with online components. The researchers uses an LMS that provides educators a platform that can motivate and engage students to new educational environment through manage online classes. The LMS allows educators to distribute information, manage learning materials, assignments, quizzes, and communications. Aside from the basic functions of the LMS, the researchers uses Machine Learning (ML) Algorithms applying Support Vector Machine (SVM) that will classify and identify the best related videos per topic. SVM is a supervised machine learning algorithm that analyzes data for classification and regression analysis by Maity [1]. The results of this study showed that integration of video tutorials in LMS can significantly contribute knowledge and skills in the learning process of the students.

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