scholarly journals Stock price trend prediction method based on support vector machines with Fisher score

2020 ◽  
Author(s):  
N. Nurrimah ◽  
Z. Rustam
Keyword(s):  
Support Vector Machines ◽  
Stock Price ◽  
Prediction Method ◽  
Support Vector ◽  
Trend Prediction ◽  
Fisher Score ◽  
Price Trend ◽  
Vector Machines

scholarly journals Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Seizi Someya ◽  
Masanori Kakuta ◽  
Mizuki Morita ◽  
Kazuya Sumikoshi ◽  
Wei Cao ◽  
...  
Keyword(s):  
Amino Acid ◽  
Support Vector Machines ◽  
Binding Proteins ◽  
Prediction Method ◽  
Amino Acid Sequences ◽  
Support Vector ◽  
Carbohydrate Binding ◽  
Genome Database ◽  
Vector Machines ◽  
Carbohydrate Binding Proteins

Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs). We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC) curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

scholarly journals Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3598
Author(s):  
Jose R. Huerta-Rosales ◽  
David Granados-Lieberman ◽  
Arturo Garcia-Perez ◽  
David Camarena-Martinez ◽  
Juan P. Amezquita-Sanchez ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Electrical Network ◽  
Support Vector ◽  
Fisher Score ◽  
Vibration Signals ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Field Programmable ◽  
Statistical Time ◽  
High Level

One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.

Sign in / Sign up

Export Citation Format

Share Document