Emotion Classification with Reduced Feature Set SGDClassifier, Random Forest and Performance Tuning

2020 ◽  
pp. 95-108
Author(s):  
Kaushika Pal ◽  
Biraj V. Patel
Keyword(s):  
Random Forest ◽  
Performance Tuning ◽  
Emotion Classification ◽  
And Performance

SQL*Net Diagnostics and Performance Tuning

2001 ◽  
pp. 475-487
Author(s):  
Dmitry Petrov ◽  
Serg Shestakov
Keyword(s):  
Performance Tuning ◽  
And Performance

scholarly journals Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Peter Appiahene ◽  
Yaw Marfo Missah ◽  
Ussiph Najim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Banking Sector ◽  
Banking Industry ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
And Performance

The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

scholarly journals Accelerated FDPS: Algorithms to use accelerators with FDPS

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masaki Iwasawa ◽  
Daisuke Namekata ◽  
Keigo Nitadori ◽  
Kentaro Nomura ◽  
Long Wang ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
General Purpose ◽  
Performance Model ◽  
Performance Tuning ◽  
Data Types ◽  
Interaction Function ◽  
Current Implementation ◽  
And Performance ◽  
Graphics Processing

Abstract We describe algorithms implemented in FDPS (Framework for Developing Particle Simulators) to make efficient use of accelerator hardware such as GPGPUs (general-purpose computing on graphics processing units). We have developed FDPS to make it possible for researchers to develop their own high-performance parallel particle-based simulation programs without spending large amounts of time on parallelization and performance tuning. FDPS provides a high-performance implementation of parallel algorithms for particle-based simulations in a “generic” form, so that researchers can define their own particle data structure and interparticle interaction functions. FDPS compiled with user-supplied data types and interaction functions provides all the necessary functions for parallelization, and researchers can thus write their programs as though they are writing simple non-parallel code. It has previously been possible to use accelerators with FDPS by writing an interaction function that uses the accelerator. However, the efficiency was limited by the latency and bandwidth of communication between the CPU and the accelerator, and also by the mismatch between the available degree of parallelism of the interaction function and that of the hardware parallelism. We have modified the interface of the user-provided interaction functions so that accelerators are more efficiently used. We also implemented new techniques which reduce the amount of work on the CPU side and the amount of communication between CPU and accelerators. We have measured the performance of N-body simulations on a system with an NVIDIA Volta GPGPU using FDPS and the achieved performance is around 27% of the theoretical peak limit. We have constructed a detailed performance model, and found that the current implementation can achieve good performance on systems with much smaller memory and communication bandwidth. Thus, our implementation will be applicable to future generations of accelerator system.

Design of a Robust H∞ PID Control for Industrial Manipulators

1999 ◽  
Vol 122 (4) ◽  
pp. 803-812 ◽  
Author(s):  
Jonghoon Park ◽  
Wankyun Chung
Keyword(s):  
Pid Control ◽  
Degrees Of Freedom ◽  
Performance Tuning ◽  
Motion Controller ◽  
Six Degrees Of Freedom ◽  
Stability Robustness ◽  
Industrial Manipulators ◽  
And Performance ◽  
The Stability ◽  
L2 Gain

Industrial manipulators are under various limitations against high quality motion control; for example, both frictional and dynamic disturbances should be dealt with a simple PID control structure. A robust linear PID motion controller, called the reference error feedback (REF), is proposed, which solves the nonlinear L2-gain attenuation control problem for robotic manipulators. The stability, robustness, and performance tuning of the proposed controller are analyzed. Making use of the fact that the single parameter of the induced L2-gain γ controls the performance with stability attained, we propose a simple and stable method of performance tuning called “the square law.” The analytical results are verified through experiments of a six-degrees-of-freedom industrial manipulator. [S0022-0434(00)00104-0]

scholarly journals Special Issue on Automatic Application Tuning for HPC Architectures

2014 ◽  
Vol 22 (4) ◽  
pp. 259-260 ◽  
Author(s):  
Siegfried Benkner ◽  
Franz Franchetti ◽  
Hans Michael Gerndt ◽  
Jeffrey K. Hollingsworth
Keyword(s):  
High Performance ◽  
Research Field ◽  
Performance Tuning ◽  
Full Potential ◽  
Research Groups ◽  
Special Issue ◽  
The World ◽  
And Performance ◽  
Application Tuning ◽  
Performance Computing

High Performance Computing architectures have become incredibly complex and exploiting their full potential is becoming more and more challenging. As a consequence, automatic performance tuning (autotuning) of HPC applications is of growing interest and many research groups around the world are currently involved. Autotuning is still a rapidly evolving research field with many different approaches being taken. This special issue features selected papers presented at the Dagstuhl seminar on “Automatic Application Tuning for HPC Architectures” in October 2013, which brought together researchers from the areas of autotuning and performance analysis in order to exchange ideas and steer future collaborations.

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