scholarly journals Penentuan Kualitas Buah Jeruk (Citrus Sinensis L) Menggunakan Teknologi Laser Photo-Acoustics (LPAS) Dengan Metode Support Vector Machine (SVM)

2019 ◽  
Vol 4 (2) ◽  
pp. 377-386
Author(s):  
Dian Sri Bintang Hasiholan Manihuruk ◽  
Darwin Darwin ◽  
Agus Arip Munawar
Keyword(s):  
Support Vector Machine ◽  
Vitamin C ◽  
Citrus Sinensis ◽  
Sugar Content ◽  
Model Performance ◽  
Support Vector ◽  
Citrus Fruits ◽  
Laser Technology ◽  
Sugar Levels

Abstrak.  Penelitian ini bertujuan untuk mengetahui kandungan vitamin C dan kadar gula pada buah jeruk dengan menggunakan teknologi Laser Photo Acoustics. Penelitian ini menggunakan sampel buah jeruk manis sebanyak 20 buah dengan empat indeks yaitu indeks pertama tidak matang (TM), setengah matang (SM), matang (M) dan matang sekali (MS).  Hasil penelitian menunjukan bahwa panjang gelombang yang diperoleh dalam menduga kadar vitamin C dan kadar gula dikisaran 4418 cm-1 - 4595 cm-1. Selanjutnya prediksi kadar gula terbaik mendapatkan (R2) sebesar 0.769, (r) sebesar 0.877, RMSEC 0.803 sedangkan RPD 2.09 yang tergolong Good Model Performance. Sedangkan untuk vitamin C mendaptkan koefisien determinasi (R2) 0.6182, koefisien kolerasi (r) sebesar 0.7862 dengan RMSEC 0.0231 sedangkan rasio RPD sebesar 1.61 yang merupakan prediksi yang masih kasar. Berdasarkan hasil penelitian yang dilakukan untuk aplikasi teknologi laser photo acoustics dapat disimpulkan bahwa teknologi laser dapat mendeteksi kandungan vitamin C dan Kadar gula pada jeruk.Determination of the Quality of Oranges (Citrus sinensis L.) Using Laser Photo Acoustics (LPAS) Technology with the Support Vector Machine (SVM)Abstract. This research aims to determine the content of vitamin C and sugar levels in citrus fruits using Laser Photo Acoustics technology. This study used a sample of 20 sweet oranges with four indices, the first index was not mature (TM), half cooked (SM), mature (M) and very mature (MS).The results showed that the wavelength obtained in estimating vitamin C levels and sugar levels in the range 4418 cm-1 - 4595 cm-1. Furthermore, the best sugar content prediction gets (R2) of 0.769, (r) of 0.877, RMSEC 0.803 while RPD 2.09 is classified as Good Model Performance. Whereas for vitamin C the determination coefficient (R2) 0.6182, the correlation coefficient (r) is 0.7862 with RMSEC 0.0231 while the RPD ratio is 1.61 which is a rough prediction. Based on the results of the research carried out for the application of photo acoustics laser technology it can be concluded that laser technology can detect the content of vitamin C and sugar content in oranges.

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.

scholarly journals Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Akash Saxena ◽  
Shalini Shekhawat
Keyword(s):  
Support Vector Machine ◽  
Air Quality ◽  
Industrial Development ◽  
Learning Algorithm ◽  
Ambient Air ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Mathematical Framework ◽  
Primary Concern

With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air.

A Novel Image Segmentation Method for Cardiac MRI Using Support Vector Machine Algorithm Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (12) ◽  
pp. 3174-3180
Author(s):  
Guanghui Wang ◽  
Lihong Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Heart Disease ◽  
Cardiac Mri ◽  
Support Vector ◽  
Swarm Optimization ◽  
Running Time ◽  
Segmentation Accuracy

At present, heart disease not only has a significant impact on the quality of human life but also poses a greater impact on people’s health. Therefore, it is very important to be able to diagnose heart disease as early as possible and give corresponding treatment. Heart image segmentation is the primary operation of intelligent heart disease diagnosis. The quality of segmentation directly determines the effect of intelligent diagnosis. Because the running time of image segmentation is often longer, coupled with the characteristics of cardiac MR imaging technology and the structural characteristics of the cardiac target itself, the rapid segmentation of cardiac MRI images still has challenges. Aiming at the long running time of traditional methods and low segmentation accuracy, a medical image segmentation (MIS) method based on particle swarm optimization (PSO) optimized support vector machine (SVM) is proposed, referred to as PSO-SVM. First, the current iteration number and population number in PSO are added to the control strategy of inertial weight λ to improve the performance of PSO inertial weight λ. Find the optimal penalty coefficient C and γ in the gaussian kernel function by PSO. Then use the SVM method to establish the best classification model and test the data. Compared with traditional methods, this method not only shortens the running time, but also improves the segmentation accuracy. At the same time, comparing the influence of traditional inertial weights on segmentation results, the improved method reduces the average convergence algebra and shortens the optimization time.

Comprehensive Evaluation on Power Quality of Tidal Current Convertors

2019 ◽  
Vol 53 (3) ◽  
pp. 46-53
Author(s):  
Caixia Xue ◽  
Xiang-nan Wang ◽  
Ning Jia ◽  
Yuan-fei Zhang ◽  
Hai-nan Xia
Keyword(s):  
Power Generation ◽  
Support Vector Machine ◽  
Power Quality ◽  
Tidal Current ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Support Vector ◽  
Operational Data ◽  
Testing And Evaluation

AbstractWith the continuous development of testing and evaluation of tidal current convertors, power quality assessment is becoming more and more critical. According to the characteristics of Chinese tidal current power generation and power quality standards, this paper proposes a comprehensive evaluation method of power quality based on K-means clustering and a support vector machine. The fundamental purpose of the method is to automatically select the weights of various indicators in the comprehensive assessment of power quality, by which the influence of subjective factors can be eliminated. In order to achieve the above purpose, K-means clustering is used for automatically classifying the operational data into five different categories. Then, a support vector machine is used to study and estimate the relationship of the operational data and categories. Using the method proposed in the paper, the analysis of operational data of a tidal current power generation shows that calculation results can objectively reflect the power quality of the device, and the influence of subjective factors is eliminated. The method can provide a reference for the testing and evaluation of a large amount of tidal current convertors in the future.

scholarly journals Rice Plant Disease Detection using Twin Support Vector Machine (TSVM)

2019 ◽  
Vol 7 ◽  
pp. 61-69
Author(s):  
Bikash Chawal ◽  
Sanjeev Prasad Panday
Keyword(s):  
Support Vector Machine ◽  
Binary Data ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Prototype System ◽  
Crop Disease ◽  
Processing Techniques ◽  
Disease Epidemics

Crop disease epidemics can cause severe losses and affect agricultural products and food security especially in south Asian countries and Nepal where rice is enjoyed as a staple throughout the year. To achieve automatic diagnosis of crop disease the proposed system aims to develop a prototype system for detection of the paddy disease. Image recognition of the disease would be conducted based on Image Processing techniques to enhance the quality of the image and Twin Support Vector Machine (TSVM) technique to classify the paddy disease. The methodology involves image acquisition, pre-processing, analysis and classification of the paddy disease. All the paddy sample images will be passed through the RGB calculation before it proceeds to the binary conversion. If the sample is in the range of normal paddy RGB, then it is automatically classify as normal. Then, all the segmented paddy disease sample will be converted into the binary data in data base before proceed through the TSVM for training and testing. The proposed system is targeted to achieve better recognition results.

scholarly journals CVPDr DNA FRAGMENT AFFECT DIFFERENCES IN RESISTANT TO CITRUS VEIN PHLOEM DEGENERATION (CVPD) DISEASE, NUTRIENT DEFICIENCIES AND QUALITY OF FRUITS

2017 ◽  
Vol 5 (1) ◽  
pp. 60
Author(s):  
I Gusti Ayu Diah Yuniti ◽  
I Gede Putu Wirawan ◽  
I Nyoman Wijaya ◽  
Made Sritamin
Keyword(s):  
Vitamin C ◽  
Water Content ◽  
Major Obstacle ◽  
Nutrient Deficiencies ◽  
Citrus Fruits ◽  
Sour Taste ◽  
Citrus Plant ◽  
Dna Fragment ◽  
The Difference

Citrus Vein Phloem Degeneration (CVPD) disease is a major obstacle in the effort to develop and increase the production of citrus fruits in Bali. The study on the polymorphism of CVPDr DNA fragment shows that the CVPDr DNA fragment is resistant factor againt CVPD disease. This study try to elaborate the difference in resistance led to differences in plant nutrients deficiencies in the citrus plant with CVPD disease. . Besides, there are also difference in the quality of fruit due to CVPD disease attacks such as water content, vitamin C content and antioxidants in citrus fruits, color, flavor, taste and texture and fruit into small, hard and sour taste.

scholarly journals Hierarchical attention networks for information extraction from cancer pathology reports

2017 ◽  
Vol 25 (3) ◽  
pp. 321-330 ◽  
Author(s):  
Shang Gao ◽  
Michael T Young ◽  
John X Qiu ◽  
Hong-Jun Yoon ◽  
James B Christian ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Information Extraction ◽  
Model Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Attention Networks ◽  
Cancer Pathology ◽  
Extreme Gradient Boosting ◽  
Pathology Reports

Abstract Objective We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. Materials and Methods Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classification, matched to G1–G4. Model performance metrics were compared to conventional machine learning (ML) approaches including naive Bayes, logistic regression, support vector machine, random forest, and extreme gradient boosting, and other DL models, including a recurrent neural network (RNN), a recurrent neural network with attention (RNN w/A), and a convolutional neural network. Results Our results demonstrate that for both information tasks, HAN performed significantly better compared to the conventional ML and DL techniques. In particular, across the 2 tasks, the mean micro and macroF-scores for the HAN with pretraining were (0.852,0.708), compared to naive Bayes (0.518, 0.213), logistic regression (0.682, 0.453), support vector machine (0.634, 0.434), random forest (0.698, 0.508), extreme gradient boosting (0.696, 0.522), RNN (0.505, 0.301), RNN w/A (0.637, 0.471), and convolutional neural network (0.714, 0.460). Conclusions HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports.

VITAMIN C CONTENT AND JUICE QUALITY OF EXPOSED AND SHADED CITRUS FRUITS

2006 ◽  
Vol 13 (6) ◽  
pp. 456-460 ◽  
Author(s):  
J. R. WINSTON ◽  
ESTON V. MILLER
Keyword(s):  
Vitamin C ◽  
Juice Quality ◽  
Citrus Fruits
Sign in / Sign up

Export Citation Format

Share Document