Prophecy of software maintenance effort with univariate and multivariate approach: By using support vector machine learning techniquewith radial Basis Kernel Function

2017 ◽  
Author(s):  
Dimple Chandra ◽  
Mehak Choudhary ◽  
Deepti Gupta
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Kernel Function ◽  
Software Maintenance ◽  
Support Vector ◽  
Multivariate Approach ◽  
Radial Basis

An Empirical Analysis of Evolved Radial Basis Function Networks and Support Vector Machines with Mixture of Kernels

2015 ◽  
Vol 24 (04) ◽  
pp. 1550013 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Pulak Sahoo ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Imbalanced Dataset ◽  
Radial Basis

Classification is one of the most fundamental and formidable tasks in many domains including biomedical. In biomedical domain, the distributions of data in most of the datasets into predefined number of classes is significantly different (i.e., the classes are distributed unevenly). Many mathematical, statistical, and machine learning approaches have been developed for classification of biomedical datasets with a varying degree of success. This paper attempts to analyze the empirical performance of two forefront machine learning algorithms particularly designed for classification problem by adding some novelty to address the problem of imbalanced dataset. The evolved radial basis function network with novel kernel and support vector machine with mixture of kernels are suitably designed for the purpose of classification of imbalanced dataset. The experimental outcome shows that both algorithms are promising compared to simple radial basis function neural networks and support vector machine, respectively. However, on an average, support vector machine with mixture kernels is better than evolved radial basis function neural networks.

scholarly journals Twin support vector machine using kernel function for colorectal cancer detection

2021 ◽  
Vol 10 (6) ◽  
pp. 3121-3126
Author(s):  
Zuherman Rustam ◽  
Fildzah Zhafarina ◽  
Jane Eva Aurelia ◽  
Yasirly Amalia
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Kernel Function ◽  
Cancer Detection ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Medical Field ◽  
Running Time ◽  
Polynomial Kernels

Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.

scholarly journals Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

2021 ◽  
Vol 50 (2) ◽  
pp. 319-331
Author(s):  
Wenlu Ma ◽  
Han Liu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Least Squares ◽  
Kernel Function ◽  
Large Scale ◽  
Kernel Learning ◽  
Selection Strategy ◽  
Support Vector ◽  
Power Load ◽  
Sparse Samples

Least squares support vector machine (LSSVM) is a machine learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, and it has good performance in the data processingof small samples. The LSSVM model lacks sparsity and is unable to handle large-scale data problem, this articleproposes an LSSVM method based on mixture kernel learning and sparse samples. This algorithm reduces theinitial training set to a sub-dataset using a sparse selection strategy. It converts the single kernel function in theLSSVM model into a mixed kernel function and optimizes its parameters. The reduced sub-dataset is used fortraining LSSVM. Finally, a group of datasets in the UCI Machine Learning Repository were used to verify theeffectiveness of the proposed algorithm, which is applied to real-world power load data to achieve better fittingand improve the prediction accuracy.

scholarly journals Penggunaan Fitur Saliency-SURF untuk Klasifikasi Citra Sel Darah Putih dengan Metode SVM

2021 ◽  
Vol 8 (4) ◽  
pp. 1998-2009
Author(s):  
Siska Devella ◽  
Yohannes Yohannes ◽  
Celvine Adi Putra
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis ◽  
Region Contrast

Sel darah putih merupakan sel pembentuk komponen darah yang berfungsi melawan berbagai penyakit dari dalam tubuh (sistem kekebalan tubuh). Sel darah putih dibagi menjadi lima jenis, yaitu basofil, eosinofil, neutrofil, limfosit, dan monosit. Pendeteksian jenis sel darah putih dilakukan di laboratorium yang memerlukan seorang spesialis serta usaha yang lebih, waktu, dan biaya. Solusi yang dapat dilakukan salah satunya adalah menggunakan machine learning seperti support vector machine (SVM) dengan ekstraksi fitur SURF. Penelitian ini menggunakan dataset citra sel darah putih yang sebelumnya dilakukan tahap pre-processing yang, terdiri dari crop, resize, dan saliency. Metode saliency mampu memberikan bagian yang bermakna pada sebuah citra. Metode ekstraksi fitur SURF mampu memberikan keypoint yang dapat digunakan SVM dalam mengenali jenis sel darah putih. Penggunaan region-contrast saliency dengan kernel radial basis function (RBF) mendapatkan hasil akurasi, presisi, dan recall yang baik di bandingkan dengan penggunaan kernel lain dalam penelitian ini. Berdasarkan hasil pengujian yang didapat pada penelitian ini, saliency dapat meningkatkan hasil akurasi, presisi, dan recall dari SVM untuk dataset citra sel darah putih dibandingkan dengan tanpa saliency.

scholarly journals Detection and Classification of COVID-19 Using Machine Learning

2021 ◽  
Author(s):  
Osama R. Shahin ◽  
Hamoud H. Alshammari ◽  
Ahmed I. Taloba ◽  
Rasha M. Abd El-Aziz
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Clinical Specimens ◽  
Cad System ◽  
Radial Basis ◽  
Pcr Test

Abstract As people all over the world are vulnerable to be affected by the COVID-19 virus, the automatic detection of such a virus is an important concern. The paper aims to detect and classify coronavirus using machine learning. To spot and identify coronavirus in CT-Lung screening and Computer-Aided diagnosis (CAD) system is projected to distinguish and classifies the COVID-19. By utilizing the clinical specimens obtained from the corona-infected patients with the help of some machine learning techniques like Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function. While some specialists believe that the RT-PCR test is the best option for diagnosing Covid-19 patients, others believe that CT scans of the lungs can be more accurate in diagnosing coronavirus infection, as well as being less expensive than the PCR test. The clinical specimens include serum specimens, respiratory secretions, and whole blood specimens. Overall, 15 factors are measured from these specimens as the result of the previous clinical examinations. The proposed CAD system consists of four phases starting with the CT lungs screening collection, followed by a pre-processing stage to enhance the appearance of the ground glass opacities (GGOs) nodules as they originally lock hazy with fainting contrast. A modified K-means algorithm will be used to detect and segment these regions. Finally, we will use the detected, infected areas that obtained in the detection phase with a scale of 50x50 and we will crop the solid false positives that seem to be GGOs as inputs and targets for the machine learning classifiers, here we will use a support vector machine (SVM) and Radial basis function (RBF). Moreover, a GUI application is developed which avoids the confusion of the doctors for getting the exact results by giving the 15 input factors obtained from the clinical specimens.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

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