scholarly journals An ISaDE algorithm combined with support vector regression for estimating discharge coefficient of W-planform weirs

2021 ◽  
Author(s):  
Somayeh Emami ◽  
Javad Parsa ◽  
Hojjat Emami ◽  
Akram Abbaspour
Keyword(s):  
Flow Rate ◽  
High Performance ◽  
Discharge Coefficient ◽  
Support Vector ◽  
Hydraulic Parameters ◽  
Combined Effects ◽  
Hydraulic Parameter ◽  
Nonlinear Parameters ◽  
Proposed Model ◽  
Optimal Values

Abstract Various shapes of weirs, such as rectangular, trapezoidal, circular, and triangular plan forms, are used to adjust and measure the flow rate in irrigation networks. The discharge coefficient (Cd) of weirs, as the key hydraulic parameter, involves the combined effects of the geometric and hydraulic parameters. It is used to compute the flow rate over the weirs. For this purpose, a hybrid ISADE-SVR method is proposed as a hybrid model to estimate the Cd of sharp-crested W-planform weirs. ISaDE is a high-performance algorithm among other optimization algorithms in estimating the nonlinear parameters in different phenomena. ISaDE algorithm is used to improve the performance of SVR by finding optimal values for SVR's parameters. To test and validate the proposed model, the experimental dataset of Kumar et al. and Ghodsian were utilized. Six different input scenarios are presented to estimate the Cd. Based on the modeling results, the proposed hybrid method estimates the Cd in terms of the H/P, Lw/Wmc, and Lc/Wc. For the superior method, R2, RMSE, MAPE, and δ are obtained as 0.982, 0.006, 0.612, and 0.843, respectively. The amount of improvement in compared with GMDH, ANFIS and SVR is 3.6%, 1.2%, 1.5% in terms of R2.

scholarly journals A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yerui Fan ◽  
Chao Zhang ◽  
Yu Xue ◽  
Jianguo Wang ◽  
Fengshou Gu
Keyword(s):  
Support Vector Machine ◽  
High Performance ◽  
Particle Swarm ◽  
Rolling Bearing ◽  
Least Square ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Novel Model

In this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property. Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement. Finally, the proposed model is evaluated through experiments and comparative studies. The results prove its effectiveness in detecting and classifying bearing faults.

scholarly journals Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 293
Author(s):  
Sergio Cantillo-Luna ◽  
Ricardo Moreno-Chuquen ◽  
Harold R. Chamorro ◽  
Jose Miguel Riquelme-Dominguez ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Machine Learning ◽  
Electricity Markets ◽  
Electricity Market ◽  
High Performance ◽  
Support Vector ◽  
Average Prediction Error ◽  
Market Signals ◽  
Proposed Model ◽  
Power Load ◽  
Locational Marginal Price

Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

Optimum HPLC Conditions for Determination of Dibucaine HCL, Fluocortolone Pivalate and Fluocortolone Caproate by Using Experimental Design

2018 ◽  
Vol 15 (1) ◽  
pp. 32-38 ◽  
Author(s):  
Bürge Aşçı ◽  
Mesut Koç
Keyword(s):  
Experimental Design ◽  
Flow Rate ◽  
Mobile Phase ◽  
High Performance ◽  
Limit Of Detection ◽  
Pharmaceutical Preparations ◽  
Acid Mixture ◽  
Solution Stability ◽  
Uv Detector

Introduction:This paper presents the development and validation of a novel, fast, sensitive and accurate high performance liquid chromatography (HPLC) method for the simultaneous quantitative determination of dibucaine HCl, fluocortolone pivalate and fluocortolone caproate in pharmaceutical preparations.Experiment:Development of the chromatographic method was based on an experimental design approach. A five-level-three-factor central composite design requiring 20 experiments in this optimization study was performed in order to evaluate the effects of three independent variances including mobile phase ratio, flow rate and amount of acid in the mobile phase.Conclusion:The optimum composition for mobile phase was found as a methanol:water:acetic acid mixture at 71.6 : 26.4 : 2 (v/v/v) ratio and optimum separation was acquired by isocratic elution with a flow rate of 1.3 mL/min. The analytes were detected using a UV detector at 240 nm. The developed method was validated in terms of linearity, precision, accuracy, limit of detection/quantitation and solution stability and successfully applied to the determination of dibucaine HCl, fluocortolone pivalate and fluocortolone caproate in pharmaceutical topical formulations such as suppositories and ointments.

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

scholarly journals Development and validation of RP-HPLC method for estimation of brexpiprazole in its bulk and tablet dosage form using Quality by Design approach

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Amol S. Jagdale ◽  
Nilesh S. Pendbhaje ◽  
Rupali V. Nirmal ◽  
Poonam M. Bachhav ◽  
Dayandeo B. Sumbre
Keyword(s):  
Flow Rate ◽  
High Performance ◽  
Reversed Phase ◽  
Hplc Method ◽  
Control Analysis ◽  
Uv Detector ◽  
Mobile Phase Flow Rate ◽  
Rp Hplc ◽  
Quality By Design Approach ◽  
Bulk Drug

Abstract Background A new, sensitive, suitable, clear, accurate, and robust reversed-phase high-performance liquid chromatography (RP-HPLC) method for the determination of brexpiprazole in bulk drug and tablet formulation was developed and validated in this research. Surface methodology was used to optimize the data, with a three-level Box-Behnken design. Methanol concentration in the mobile phase, flow rate, and pH were chosen as the three variables. The separation was performed using an HPLC method with a UV detector and Openlab EZchrom program, as well as a Water spherisorb C18 column (100 mm × 4.6; 5m). Acetonitrile was pumped at a flow rate of 1.0 mL/min with a 10 mM phosphate buffer balanced to a pH of 2.50.05 by diluted OPA (65:35% v/v) and detected at 216 nm. Result The developed RP-HPLC method yielded a suitable retention time for brexpiprazole of 4.22 min, which was optimized using the Design Expert-12 software. The linearity of the established method was verified with a correlation coefficient (r2) of 0.999 over the concentration range of 5.05–75.75 g/mL. For API and formulation, the percent assay was 99.46% and 100.91%, respectively. The percentage RSD for the method’s precision was found to be less than 2.0%. The percentage recoveries were discovered to be between 99.38 and 101.07%. 0.64 μg/mL and 1.95 μg/mL were found to be the LOD and LOQ, respectively. Conclusion The developed and validated RP-HPLC system takes less time and can be used in the industry for routine quality control/analysis of bulk drug and marketed brexpiprazole products. Graphical abstract

BioSignal modelling for prediction of cardiac diseases using intra group selection method

2021 ◽  
Vol 15 (1) ◽  
pp. 151-160
Author(s):  
Hemant P. Kasturiwale ◽  
Sujata N. Kale
Keyword(s):  
Machine Learning ◽  
Nervous System ◽  
Random Forest ◽  
Normal Sinus Rhythm ◽  
Heart Defects ◽  
Support Vector ◽  
Autonomous Nervous System ◽  
Cardiac Diseases ◽  
Vast Number ◽  
Proposed Model

The Autonomous Nervous System (ANS) controls the nervous system and Heart Rate Variability (HRV) can be used as a diagnostic tool to diagnose heart defects. HRV can be classified into linear and nonlinear HRV indices which are used mostly to measure the efficiency of the model. For prediction of cardiac diseases, the selection and extraction features of machine learning model are effective. The available model used till date is based on HRV indices to predict the cardiac diseases accurately. The model could hardly throw light on specifics of indices, selection process and stability of the model. The proposed model is developed considering all facet electrocardiogram amplitude (ECG), frequency components, sampling frequency, extraction methods and acquisition techniques. The machine learning based model and its performance shall be tested using the standard BioSignal method, both on the data available and on the data obtained by the author. This is unique model developed by considering the vast number of mixtures sets and more than four complex cardiac classes. The statistical analysis is performed on a variety of databases such as MIT/BIH Normal Sinus Rhythm (NSR), MIT/BIH Arrhythmia (AR) and MIT/BIH Atrial Fibrillation (AF) and Peripheral Pule Analyser using feature compatibility techniques. The classifiers are trained for prediction with approximately 40000 sets of parameters. The proposed model reaches an average accuracy of 97.87 percent and is sensitive and précised. The best features are chosen from the different HRV features that will be used for classification. The present model was checked under all possible subject scenarios, such as the raw database and the non-ECG signal. In this sense, robustness is defined not only by the specificity parameter, but also by other measuring output parameters. Support Vector Machine (SVM), K-nearest Neighbour (KNN), Ensemble Adaboost (EAB) with Random Forest (RF) are tested in a 5% higher precision band and a lower band configuration. The Random Forest has produced better results, and its robustness has been established.

scholarly journals Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 651
Author(s):  
Shengyi Zhao ◽  
Yun Peng ◽  
Jizhan Liu ◽  
Shuo Wu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Model Comparison ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Identification Accuracy ◽  
Main Research ◽  
Proposed Model ◽  
Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

4-Nitrophenol in 4-nitrophenyl phosphate, a substrate for alkaline phosphatase, as measured by paired-ion high-performance liquid chromatography.

1977 ◽  
Vol 23 (12) ◽  
pp. 2288-2291 ◽  
Author(s):  
P H Culbreth ◽  
I W Duncan ◽  
C A Burtis
Keyword(s):  
Alkaline Phosphatase ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Flow Rate ◽  
Mobile Phase ◽  
High Performance ◽  
Reversed Phase ◽  
Nitrophenyl Phosphate ◽  
Phase Column ◽  
Reversed Phase Column

Abstract We used paired-ion high-performance liquid chromatography to determine the 4-nitrophenol content of 4-nitrophenyl phosphate, a substrate for alkaline phosphatase analysis. This was done on a reversed-phase column with a mobile phase of methanol/water, 45/55 by vol, containing 3 ml of tetrabutylammonium phosphate reagent per 200 ml of solvent. At a flow rate of 1 ml/min, 4-nitrophenol was eluted at 9 min and monitored at 404 nm; 4-nitrophenyl phosphate was eluted at 5 min and could be monitored at 311 nm. Samples of 4-nitrophenyl phosphate obtained from several sources contained 0.3 to 7.8 mole of 4-nitrophenol per mole of 4-nitrophenyl phosphate.

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