Neural Network Studies

1993 ◽  
Author(s):  
Gregg Wilensky ◽  
Narbik Manukian ◽  
Joseph Neuhaus ◽  
Natalie Rivetti
Keyword(s):  
Neural Network ◽  
Network Studies

Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture

1998 ◽  
Vol 38 (4) ◽  
pp. 651-659 ◽  
Author(s):  
Vasyl V. Kovalishyn ◽  
Igor V. Tetko ◽  
Alexander I. Luik ◽  
Vladyslav V. Kholodovych ◽  
Alessandro E. P. Villa ◽  
...  
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Cascade Correlation ◽  
Correlation Learning ◽  
Network Studies

Neural Network-based Evaluation of the Effect of the Motivation of Hospital Employees on Patients’ Satisfaction

2010 ◽  
Vol 5 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Payam Hanafizadeh ◽  
Neda Rastkhiz Paydar ◽  
Neda Aliabadi
Keyword(s):  
Neural Network ◽  
Organizational Performance ◽  
Back Propagation ◽  
Employee Motivation ◽  
Back Propagation Neural Network ◽  
Distributed Data ◽  
Hospital Employees ◽  
The Neural Network ◽  
Using Data ◽  
Network Studies

This article evaluates the effect of the motivation of employees on organizational performance using a neural network. Studies show that employee motivation influences organizational performance, particularly in organizations providing services. Methods based on statistical computations like regression and correlation analysis were used to measure the mutual effects of these factors. As these statistical methods necessitate the fulfillment of certain requirements like normally distributed data and because they are not able to express non-linear relations and hidden complicated patterns, a back propagation neural network has been used. The neural network was trained by using data from 300 questionnaires answered by hospital employees and 1933 patients hospitalized in a private hospital in Tehran over three successive months.

Neural network studies 2. Use of a neural net to estimate oxidation energies for substituted dihydropyridines and related heterocycles

Tetrahedron ◽  
1992 ◽  
Vol 48 (17) ◽  
pp. 3463-3472 ◽  
Author(s):  
Marcus E. Brewster ◽  
Ming-Ju Huang ◽  
Alan Harget ◽  
Nicholas Bodor
Keyword(s):  
Neural Network ◽  
Neural Net ◽  
Network Studies

Neural Network Studies. 2. Variable Selection

1996 ◽  
Vol 36 (4) ◽  
pp. 794-803 ◽  
Author(s):  
Igor V. Tetko ◽  
Alessandro E. P. Villa ◽  
David J. Livingstone
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Studies

scholarly journals 3D Micro Scaffolds for Tailor-Made Three-Dimensional Neural Network Studies

2018 ◽  
Vol 114 (3) ◽  
pp. 672a-673a
Author(s):  
Cornelius Fendler ◽  
Christian Denker ◽  
Gabriele Loers ◽  
Jann I. Harberts ◽  
Robert Zierold ◽  
...  
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Network Studies

scholarly journals Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Changfan Zhang ◽  
Xiang Cheng ◽  
Jianhua Liu ◽  
Jing He ◽  
Guangwei Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Single Layer ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Sparse Autoencoder ◽  
Single Layer Network ◽  
Network Studies ◽  
Fine Tune

The model is difficult to establish because the principle of the locomotive adhesion process is complex. This paper presents a data-driven adhesion status fault diagnosis method based on deep learning theory. The adhesion coefficient and creep speed of a locomotive constitute the characteristic vector. The sparse autoencoder unsupervised learning network studies the input vector, and the single-layer network is superimposed to form a deep neural network. Finally, a small amount of labeled data is used to fine-tune training the entire deep neural network, and the locomotive adhesion state fault diagnosis model is established. Experimental results show that the proposed method can achieve a 99.3% locomotive adhesion state diagnosis accuracy and satisfy actual engineering monitoring requirements.

Piano playing teaching system based on artificial intelligence – design and research

2020 ◽  
pp. 1-9
Author(s):  
Meichen Liu ◽  
Jieru Huang
Keyword(s):  
Neural Network ◽  
Teaching Model ◽  
Multimedia Software ◽  
Teaching System ◽  
Piano Teaching ◽  
Teachers And Students ◽  
Piano Education ◽  
Computer Teaching ◽  
Network Studies ◽  
Piano Playing

In recent years, with the rise of piano teaching, many people began to learn to play the piano. However, the expensive piano teaching cost and its unique teaching model that teachers and students are one to one have caused the shortage of piano education resources, and people learn piano playing has become a luxury activity. The use of computer multimedia software for piano teaching has become a feasible way to alleviate this contradiction. This paper proposes the design of an intelligent piano playing teaching system based on neural network, studies the realization method of the piano teaching system, presents a method of evaluating piano playing by using neural network model for the difficulties in computer piano teaching, that is, computer teaching is one-way knowledge transfer without interaction. In addition, this paper simulates the teacher to guide the students to carry on the playing practice, which is of great significance to the teaching of the piano.

Neural Network-Based Evaluation of the Effect of the Motivation of Hospital Employees on Patients’ Satisfaction

2012 ◽  
pp. 125-143
Author(s):  
Payam Hanafizadeh ◽  
Neda Rastkhiz Paydar ◽  
Neda Aliabadi
Keyword(s):  
Neural Network ◽  
Organizational Performance ◽  
Back Propagation ◽  
Employee Motivation ◽  
Back Propagation Neural Network ◽  
Distributed Data ◽  
Hospital Employees ◽  
The Neural Network ◽  
Using Data ◽  
Network Studies

This article evaluates the effect of the motivation of employees on organizational performance using a neural network. Studies show that employee motivation influences organizational performance, particularly in organizations providing services. Methods based on statistical computations like regression and correlation analysis were used to measure the mutual effects of these factors. As these statistical methods necessitate the fulfillment of certain requirements like normally distributed data and because they are not able to express non-linear relations and hidden complicated patterns, a back propagation neural network has been used. The neural network was trained by using data from 300 questionnaires answered by hospital employees and 1933 patients hospitalized in a private hospital in Tehran over three successive months.

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