Computer Simulating Effluent Quality of Vertical Tube Biological Reactor Using Support Vector Machine

2011 ◽  
Vol 219-220 ◽  
pp. 322-326
Author(s):  
Jing Zhang ◽  
Ji Ti Zhou ◽  
Yu Ming Li ◽  
Hua Yang
Keyword(s):  
Support Vector Machine ◽  
Control Strategies ◽  
Predictive Ability ◽  
Vertical Tube ◽  
Support Vector ◽  
Effluent Quality ◽  
Promising Tool ◽  
Treatment Processes ◽  
Advanced Control

Effluent quality of vertical tube biological reactor (VTBR) was simulated by support vector machine. AverageR2of the training set and the test set were used to balance the fitness and the predictive ability of SVR models, and evaluate the general performance of SVR models. Following a special grid search procedure, optimal parameters were determined for these SVR models. Results indicted that these SVR models have good fitness and predictive ability. The developed model may be integrated into an advanced control system for the VTBR using different control strategies with further work. Furthermore, SVR was found to be a useful and promising tool that is worth consideration in the prediction of effluent of wastewater treatment processes.

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 Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jhansi Rani Kaka ◽  
K. Satya Prasad
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Differential Evolution ◽  
Optimization Method ◽  
Support Vector ◽  
Local Optima ◽  
Whale Optimization ◽  
Multiclass Support Vector Machine ◽  
Poor Convergence

Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.

scholarly journals Studi Komparatif Metode Ekstraksi Fitur pada Analisis Sentimen Maskapai Penerbangan Menggunakan Support Vector Machine dan Maximum Entropy

2019 ◽  
Vol 3 (3) ◽  
pp. 402-407 ◽  
Author(s):  
Mona Cindo ◽  
Dian Palupi Rini ◽  
Ermatita
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Support Vector ◽  
Machine Method ◽  
N Gram ◽  
Almost All

Almost all companies use social media to improve their product services and provide after-sales services that allow their customers to review the quality of their products. By using Twitter social media to be an important source for tracking sentiment analysis. Sentiment analysis is one of the most popular studies today, using sentiment analysis companies can analyze customer satisfaction to improve their services. This study aims to analyze airline sentiments with five different features such as pragmatic, lexical n-gram, POS, sentiment, and LDA using the Support Vector Machine and Maximum Entropy methods. The best results can be obtained using the Maximum Entropy method using all feature extraction with an accuracy of 92.7% and in the Support Vector Machine method, the accuracy obtained is 89.2%.

scholarly journals Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine

2019 ◽  
Vol 16 (21) ◽  
pp. 4269 ◽  
Author(s):  
Haewon Byeon
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Predictive Power ◽  
Gaussian Function ◽  
The Elderly ◽  
Support Vector ◽  
Factors Associated

Background and Objectives: This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swallowing quality of elderly people’s lives in the future. Methods and Material: This study sampled 142 elderly people equal to or older than 65 years old who were using a senior welfare center. The swallowing problem associated quality of life was defined by the swallowing quality-of-life (SWAL-QOL). In order to verify the predictive power of the model, this study compared the predictive power of the Gaussian function with that of a linear algorithm, polynomial algorithm, and a sigmoid algorithm. Results: A total of 33.9% of the subjects decreased in swallowing quality-of-life. The swallowing quality-of-life prediction model for the elderly, based on the SVM, showed both preventive factors and risk factors. Risk factors were denture use, experience of using aspiration in the past one month, being economically inactive, having a mean monthly household income <2 million KRW, being an elementary school graduate or below, female, 75 years old or older, living alone, requiring time for finishing one meal on average ≤15 min or ≥40 min, having depression, stress, and cognitive impairment. Conclusions: It is necessary to monitor the high-risk group constantly in order to maintain the swallowing quality-of-life in the elderly based on the prevention and risk factors associated with the swallowing quality-of-life derived from this prediction model.

Sign in / Sign up

Export Citation Format

Share Document