scholarly journals Transfer learning based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

2020 ◽  
Author(s):  
Mukul Singh ◽  
Shrey Bansal ◽  
Sakshi Ahuja ◽  
Rahul Kumar Dubey ◽  
Bijaya Ketan Panigrahi ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Ct Scan ◽  
Computerized Tomography ◽  
Principal Component ◽  
World Health ◽  
Support Vector ◽  
X Rays ◽  
Machine Model ◽  
Health Organization ◽  
Bagging Ensemble

Abstract The novel discovered disease coronavirus popularly known as COVID19 is a lung infection disease that causes adverse effects on the human respiratory system. It is caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). For COVID-19 detection, chest radiography, i.e., computerized tomography(CT) scan, X-rays, etc. are widely investigated. In the proposed work, a deep learning model, i.e., truncated VGG16(Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT Scan images. Further Principal Component Analysis (PCA) is used for feature selection. The final classification is performed using four different classifiers, namely deep convolutional neural network(CNN) , Extreme Learning Machine (ELM), Online sequential ELM, and Bagging Ensemble with support vector machine (SVM) . The best performing classifier Bagging Ensemble with SVM within 385 ms achieved an accuracy of 95.7%, precision of 95.8%, Area Under Curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work in comparison to the techniques available in the literature.

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

2021 ◽  
Author(s):  
Tim Brandes ◽  
Stefano Scarso ◽  
Christian Koch ◽  
Stephan Staudacher
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Principal Component ◽  
General Term ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Model ◽  
Precision Error ◽  
Operational Conditions ◽  
Wide Range

Abstract A numerical experiment of intentionally reduced complexity is used to demonstrate a method to classify flight missions in terms of the operational severity experienced by the engines. In this proof of concept, the general term of severity is limited to the erosion of the core flow compressor blade and vane leading edges. A Monte Carlo simulation of varying operational conditions generates a required database of 10000 flight missions. Each flight is sampled at a rate of 1 Hz. Eleven measurable or synthesizable physical parameters are deemed to be relevant for the problem. They are reduced to seven universal non-dimensional groups which are averaged for each flight. The application of principal component analysis allows a further reduction to three principal components. They are used to run a support-vector machine model in order to classify the flights. A linear kernel function is chosen for the support-vector machine due to its low computation time compared to other functions. The robustness of the classification approach against measurement precision error is evaluated. In addition, a minimum number of flights required for training and a sensible number of severity classes are documented. Furthermore, the importance to train the algorithms on a sufficiently wide range of operations is presented.

scholarly journals Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination

2020 ◽  
Vol 16 (1) ◽  
pp. 155014772090363 ◽  
Author(s):  
Ying Liu ◽  
Lihua Huang
Keyword(s):  
Risk Assessment ◽  
Support Vector Machine ◽  
Supply Chain ◽  
Credit Risk ◽  
Financial Analysis ◽  
Learning Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Machine Model ◽  
Supply Chain Finance

Recently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management. However, noise may increase the complexity of the algorithm building and destroy the performance of classifier. In our work, we propose an ensemble support vector machine model to solve the risk assessment of supply chain finance, combined with reducing noises method. The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. Then, we obtained the final classification results by combining finally individual prediction through AdaBoosting algorithm on the new sample set. Some experiments are applied on supply chain financial analysis of China’s listed companies. Results indicate that the credit assessment accuracy can be increased by applying this approach.

scholarly journals Sign Language Recognition using Principal Component Analysis and Support Vector Machine

2021 ◽  
Vol 4 (01) ◽  
pp. 49
Author(s):  
Astri Novianty ◽  
Fairuz Azmi
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Sign Language ◽  
Principal Component ◽  
Histogram Equalization ◽  
Component Analysis ◽  
World Health ◽  
Support Vector ◽  
Language Recognition ◽  
Sign Language Recognition

The World Health Organization (WHO) estimates that over five percent of the world's population are hearing-impaired. One of the communication problems that often arise between deaf or speech impaired with normal people is the low level of knowledge and understanding of the deaf or speech impaired's normal sign language in their daily communication. To overcome this problem, we build a sign language recognition system, especially for the Indonesian language. The sign language system for Bahasa Indonesia, called Bisindo, is unique from the others. Our work utilizes two image processing algorithms for the pre-processing, namely the grayscale conversion and the histogram equalization. Subsequently, the principal component analysis (PCA) is employed for dimensional reduction and feature extraction. Finally, the support vector machine (SVM) is applied as the classifier. Results indicate that the use of the histogram equalization significantly enhances the accuracy of the recognition. Comprehensive experiments by applying different random seeds for testing data confirm that our method achieves 76.8% accuracy. Accordingly, a more robust method is still open to enhance the accuracy in sign language recognition.

scholarly journals Sentimen Analisis Terkait Lockdown pada Sosial Media Twitter

2020 ◽  
Vol 6 (2) ◽  
pp. 223-229
Author(s):  
Muhammad Dwison Alizah ◽  
Arifin Nugroho ◽  
Ummu Radiyah ◽  
Windu Gata
Keyword(s):  
Support Vector Machine ◽  
World Health Organization ◽  
Predictive Modeling ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
World Health ◽  
Support Vector ◽  
The World ◽  
Negative Impacts ◽  
Health Organization

Abstract:  Covid-19 has been set as a Pandemic by the World Health Organization (WHO). The very large impact and the infection that is fast enough are the reasons for making Covid-19 as a pandemic and efforts to overcome. One anticipation that can be done is to do lockdown. Making the decision to carry out a lockdown is intended to reduce the spread that occurs. Lockdown is certainly not a 100% good solution for all of individual. There are individual who agree that the lockdown will be implemented, also there are those who think that the lockdown is better not to be carried out considering the negative impacts that can occur. Therefore in this study will be presented the predictive modeling for sentiment analysis related to "lockdown" specially on social media Twitter. The method used to labeled was using Vader then the tweets are extracted using TF-IDF, and modeling is made for the prediction of sentiment using Naïve Bayes and Support Vector Machine. The results obtained from the two algorithms are more than 80%. Keywords: Covid-19, lockdown, TF-IDF, Naïve Bayes, Support Vector Machine Abstrak: Covid-19 telah ditetapkan sebagia Pandemi oleh World Health Organization (WHO). Dampak yang sangat besar dan penyebaran yang cukup cepat menjadi alsan untuk menjadikan Covid-19 sebagai Pandemi dan perlu dilakukan upaya penanggulangan. Salah satu upaya yang bisa dilakukan adalah dengan melakukan lockdown. Pengambilan keputusan untuk melakukan lockdown diperuntukan guna mengurangi penyebaran yang terjadi. Lockdown tentunya bukanlah solusi yang 100% baik bagi segala pihak. Terdapat pihak - pihak yang menyetujui akan dilaksanakannya lockdown, ada pula yang beranggapan bahwa lockdown lebih baik tidak dilaksanakan dengan pertimbangan dampak negatif yang bisa terjadi. Oleh karena itu, pada penelitian ini akan disampaikan mengenai pembuatan pemodelan prediksi terkait analisa sentimen terkait “Lockdown” yang dikhususkan pada media sosial Twitter. Metode yang digunakan adalah dengan melakukan labeling menggunakan Vader dan selanjutnya tweet tersebut dilakukan ekstraksi menggunakan TF-IDF, dan dibuatkan pemodelan untuk prediksi sentimen menggunakan Naïve Bayes dan Support Vector Machine. Hasil evaluasi yang didapat dari kedua algoritma tersebut ialah mencapai lebih dari 80%. Kata kunci: Covid-19, lockdown, TF-IDF, Naïve Bayes, Support Vector Machine Abstract:  Covid-19 has been set as a Pandemic by the World Health Organization (WHO). The very large impact and the infection that is fast enough are the reasons for making Covid-19 as a pandemic and efforts to overcome. One anticipation that can be done is to do lockdown. Making the decision to carry out a lockdown is intended to reduce the spread that occurs. Lockdown is certainly not a 100% good solution for all of individual. There are individual who agree that the lockdown will be implemented, also there are those who think that the lockdown is better not to be carried out considering the negative impacts that can occur. Therefore in this study will be presented the predictive modeling for sentiment analysis related to "lockdown" specially on social media Twitter. The method used to labeled was using Vader then the tweets are extracted using TF-IDF, and modeling is made for the prediction of sentiment using Naïve Bayes and Support Vector Machine. The results obtained from the two algorithms are more than 80%. Keywords:Covid-19, lockdown, TF-IDF, Naïve Bayes, Support Vector Machine Abstrak: Covid-19 telah ditetapkan sebagia Pandemi oleh World Health Organization (WHO). Dampak yang sangat besar dan penyebaran yang cukup cepat menjadi alsan untuk menjadikan Covid-19 sebagai Pandemi dan perlu dilakukan upaya penanggulangan. Salah satu upaya yang bisa dilakukan adalah dengan melakukan lockdown. Pengambilan keputusan untuk melakukan lockdown diperuntukan guna mengurangi penyebaran yang terjadi. Lockdown tentunya bukanlah solusi yang 100% baik bagi segala pihak. Terdapat pihak - pihak yang menyetujui akan dilaksanakannya lockdown, ada pula yang beranggapan bahwa lockdown lebih baik tidak dilaksanakan dengan pertimbangan dampak negatif yang bisa terjadi. Oleh karena itu, pada penelitian ini akan disampaikan mengenai pembuatan pemodelan prediksi terkait analisa sentimen terkait “Lockdown” yang dikhususkan pada media sosial Twitter. Metode yang digunakan adalah dengan melakukan labeling menggunakan Vader dan selanjutnya tweet tersebut dilakukan ekstraksi menggunakan TF-IDF, dan dibuatkan pemodelan untuk prediksi sentimen menggunakan Naïve Bayes dan Support Vector Machine. Hasil evaluasi yang didapat dari kedua algoritma tersebut ialah mencapai lebih dari 80%. Kata kunci: Covid-19, lockdown, TF-IDF, Naïve Bayes, Support Vector Machine

scholarly journals Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 43-51
Author(s):  
Hendrik Setiawan ◽  
Ema Utami ◽  
Sudarmawan Sudarmawan
Keyword(s):  
Support Vector Machine ◽  
World Health ◽  
Support Vector ◽  
Online Lectures ◽  
Svm Algorithm ◽  
The World ◽  
The Government ◽  
Bayes Algorithm ◽  
Health Organization ◽  
Performance Results

The World Health Organization (WHO) COVID-19 is an infectious disease caused by the Coronavirus which originally came from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that occurred in many countries around the world. This disease has caused the government to give a regional lockdown status to give students the status of "at home" for students to enforce online or online lectures, this has caused various sentiments given by students in responding to online lectures via social media twitter. For sentiment analysis, the researcher applies the nave Bayes algorithm and support vector machine (SVM) with the performance results obtained on the Bayes algorithm with an accuracy of 81.20%, time 9.00 seconds, recall 79.60% and precision 79.40% while for the SVM algorithm get an accuracy value of 85%, time 31.60 seconds, recall 84% and precision 83.60%, the performance results are obtained in the 1st iteration for nave Bayes and the 423th iteration for the SVM algorithm  

scholarly journals Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 19-25
Author(s):  
Frizka Fitriana ◽  
Ema Utami ◽  
Hanif Al Fatta
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
World Health ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Bayes Algorithm ◽  
Health Organization ◽  
The Right ◽  
The Impact

The corona virus outbreak, commonly referred to as COVID-19, has been officially designated a global pandemic by the World Health Organization (WHO). To minimize the impact caused by the virus, one of the right steps is to develop a vaccine, however, with the vaccination for the Indonesian people, it is controversial so that it invites many people to give an opinion assessment, but the limited space makes it difficult for the public to express their opinion, because Therefore, people choose social media as a place to channel public opinion. Support vector machine algorithm has better performance in terms of accuracy, precision and recall with values ​​of 90.47%, 90.23%, 90.78% with performance values ​​on the Bayes algorithm, namely 88.64%, 87.32%, 88, 13%, with a difference of 1.83% accuracy, 2.91% precision and 2.65% recall, while for time the Naive Bayes algorithm has a better performance level with a value of 8.1 seconds and the Support vector machine algorithm gets a time speed of 11 seconds with a difference of 2, 9 seconds. With the results of sentiment analysis neutral 8.76%, negative 42.92% and positive 48.32% for Bayes and neutral 10.56%, negative 41.28% and positive 48.16% for SVM.

KLASIFIKASI TUMOR JINAK DAN TUMOR GANAS PADA CITRA MAMMOGRAM MENGGUNAKAN GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN SUPPORT VECTOR MACHINE (SVM)

2021 ◽  
Vol 26 (2) ◽  
pp. 176-186
Author(s):  
Lulu Mawaddah Wisudawati
Keyword(s):  
Support Vector Machine ◽  
World Health Organization ◽  
Computer Aided Diagnosis ◽  
World Health ◽  
Support Vector ◽  
Gray Level ◽  
Computer Aided ◽  
Health Organization ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Kanker payudara merupakan penyebab utama kematian pada wanita. Data Global Cancer Observatory 2018 dari World Health Organization (WHO, 2020) menunjukkan kasus kanker yang paling banyak terjadi di Indonesia adalah kanker payudara, yakni 58.256 kasus atau 16.7% dari total 348.809 kasus kanker. Mamografi merupakan teknik yang paling umum digunakan dalam mendeteksi tumor payudara menggunakan sistem sinar-X dosis rendah. Ada beberapa tipe abnormalitas dalam citra mammogram, yaitu mikrokalsifikasi dan massa. Penelitian ini bertujuan untuk meningkatkan performa sistem Computer-Aided Diagnosis (CAD) dalam mengklasifikasi tumor jinak dan tumor ganas dengan mengembangkan metode ekstraksi fitur menggunakan Gray Level Co-Occurrence Matrix (GLCM) dan metode klasifikasi menggunakan Support Vector Machine (SVM). Uji coba dilakukan dengan menggunakan database DDSM dengan 256 citra abnormal (95 tumor jinak dan 161 tumor ganas) menghasilkan nilai akurasi sebesar 83.59% dengan nilai sensitivitas dan spesifisitas 87.58% dan 76.84%. Selain itu, didapatkan nilai AUC sebesar 0.98%. Metode tersebut menunjukkan bahwa sistem memberikan hasil performa yang baik dalam mengklasifikasi tumor jinak dan tumor ganas.

scholarly journals Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1531
Author(s):  
Shanshan Huang ◽  
Yikun Yang ◽  
Xin Jin ◽  
Ya Zhang ◽  
Qian Jiang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Image Fusion ◽  
Principal Component ◽  
Sliding Window ◽  
Component Analysis ◽  
Image Features ◽  
Support Vector ◽  
Source Image ◽  
Machine Model

Multi-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into image fusion because of the prominent advantages. In this work, a new multi-sensor image fusion method based on the support vector machine and principal component analysis is proposed. First, the key features of the source images are extracted by combining the sliding window technique and five effective evaluation indicators. Second, a trained support vector machine model is used to extract the focus region and the non-focus region of the source images according to the extracted image features, the fusion decision is therefore obtained for each source image. Then, the consistency verification operation is used to absorb a single singular point in the decisions of the trained classifier. Finally, a novel method based on principal component analysis and the multi-scale sliding window is proposed to handle the disputed areas in the fusion decision pair. Experiments are performed to verify the performance of the new combined method.

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