SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop

The present study investigated the application of support vector machine algorithms for predicting hydraulic parameters of a vertical drop equipped with horizontal screens. The study incorporated varying sizes of a rectangular channel. Horizontal screens, in addition to being able to dissipate the destructive energy of the flow, cause turbulence. The turbulence in turn supplies oxygen to the system through the promotion of air–water mixing. To achieve the objectives of the present study, 164 experiments were analyzed under the same experimental conditions using a support vector machine. The approach utilized dimensionless terms that included scenario 1: the relative energy consumption and scenario 2: the relative pool depth. The performance of the models was evaluated with statistical criteria (RMSE, R2 and KGE) and the best model was introduced for each of the parameters. RMSE is the root mean square error, R2 is the correlation coefficient and KGE is the Kling–Gupta criterion. The results of the support vector machine showed that for the first scenario, the third combination with R2 = 0.991, RMSE = 0.00565 and KGE = 0.998 for the training mode and R2 = 0.991, RMSE = 0.00489 and KGE = 0.991 for the testing mode were optimal. For the second scenario, the third combination with R2 = 0.988, RMSE = 0.0395 and KGE = 0.998 for the training mode and R2 = 0.988, RMSE = 0.0389 and KGE = 0.993 for the testing mode were selected. Finally, a sensitivity analysis was performed that showed that the yc/H and D/H parameters are the most effective parameters for predicting relative energy dissipation and relative pool depth, respectively.

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Study of the performance of support vector machine for predicting vertical drop hydraulic parameters in the presence of dual horizontal screens

Water Science & Technology Water Supply ◽

10.2166/ws.2020.279 ◽

2020 ◽

Author(s):

Rasoul Daneshfaraz ◽

Mohammad Bagherzadeh ◽

Reza Esmaeeli ◽

Reza Norouzi ◽

John Abraham

Keyword(s):

Support Vector Machine ◽

Laboratory Data ◽

Evaluation Criteria ◽

Energy Parameter ◽

Relative Length ◽

Relative Energy ◽

Support Vector ◽

Relative Depth ◽

Hydraulic Parameters ◽

Testing Stage

Abstract In the present study, the performance of the support vector machine for estimating vertical drop hydraulic parameters in the presence of dual horizontal screens has been investigated. For this purpose, 120 different laboratory data were used to estimate three parameters of the drop: the relative length, the downstream relative depth, and the residual relative energy in the support vector machine. For each parameter, 12 models were analyzed by using a support vector machine. The performance of the models was evaluated with statistical criteria (R2, DC, and RMSE) and the best model was introduced for each of the parameters. The evaluation criteria for the relative length of the vertical drop equipped with dual horizontal screens for the testing stage are R2 = 0.992, DC = 0.981 and RMSE = 0.050. Also, the values of the downstream relative depth evaluation indicators for the testing stage are R2 = 0.9866, DC = 0.980 and, RMSE = 0.0064. For the residual relative energy parameter, the values of the residual relative energy evaluation indicators are R2 = 0.9949, DC = 0.9853 and RMSE = 0.0056. The results showed the capacity for this approach to predict the hydraulic performance of these systems with accuracy.

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Stroke Prediction using Machine Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35303 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 1518-1523

Author(s):

R. Bhavana

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Actual State ◽

Support Vector ◽

Clinical Report ◽

Way Of Life ◽

Current Time ◽

Characterization Result ◽

The Third ◽

The World

Stroke is a drawn out inability sickness caused everywhere on the world and it is the third driving reason for demise. Early forecast of stroke gives more important to the current time. Stroke happens fundamentally due to individuals' way of life in the advanced time changing elements, for example, high glucose, coronary illness, weight, diabetes. In this examination, we analyze the Support vector machine, Decision tree, Random forest and XG Boost.we have utilized four AI calculations to recognize the sort of stroke that can happen or happened to structure an individual's actual state and clinical report information. We have gathered a decent number of sections from the clinics and use them to take care of our concern. The characterization result shows that the outcome is good and can be utilized continuously clinical report. We accept that AI calculations can help better comprehension of illnesses and can be a decent medical care buddy.

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Cerebral infarction classification using multiple support vector machine with information gain feature selection

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i4.1997 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1578-1584

Author(s):

Zuherman Rustam ◽

Arfiani Arfiani ◽

Jacub Pandelaki

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Cerebral Infarction ◽

Information Gain ◽

The Elderly ◽

Common Type ◽

Support Vector ◽

Classification Result ◽

The Third ◽

Multiple Support

Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.

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Predicting the relative energy dissipation of hydraulic jump in rough and smooth bed compound channels using SVM

Water Science & Technology Water Supply ◽

10.2166/ws.2018.162 ◽

2018 ◽

Vol 19 (4) ◽

pp. 1110-1119

Author(s):

Seyed Mahdi Saghebian

Keyword(s):

Energy Dissipation ◽

Hydraulic Jump ◽

Dominant Role ◽

Rectangular Channel ◽

Relative Energy ◽

Support Vector ◽

Channel Models ◽

Rough Bed ◽

Different Shapes ◽

Rough Beds

Abstract Channels with different shapes and bed conditions are used as useful appurtenances to dissipate the extra energy of a hydraulic jump. Accurate prediction of hydraulic jump energy dissipation is important in design of hydraulic structures. In the current study, hydraulic jump energy dissipation was assessed in channels with different shapes and bed conditions (i.e. smooth and rough beds) using the support vector machine (SVM) as an intelligence approach. Five series of experimental datasets were applied to develop the models. The results showed that the SVM model is successful in estimating the relative energy dissipation. For the smooth bed, it was observed that the sloping channel models with steps performed more successfully than rectangular and trapezoidal channels and the step height is an effective variable in the estimation process. For the rough bed, the trapezoidal channel models were more accurate than the rectangular channel. It was found that rough element geometry is effective in estimation of the energy dissipation. The result showed that the models of rough channels led to better predictions. The sensitivity analysis results revealed that Froude number had the more dominant role in the modeling. Comparison among SVM and two other intelligence approaches showed that SVM is more successful in the prediction process.

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Support vector machine? – not only for MRI-images

10.1055/s-0039-3403050 ◽

2020 ◽

Author(s):

V Vasilevska ◽

K Schlaaf ◽

H Dobrowolny ◽

G Meyer-Lotz ◽

HG Bernstein ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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