scholarly journals Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N

2014 ◽  
pp. 17-32 ◽  
Author(s):  
Yang Shen ◽  
Ad Bax
Keyword(s):  
Neural Network ◽  
Chemical Shift ◽  
Structural Information ◽  
Nmr Chemical Shift ◽  
The Neural Network ◽  
Network Program

Qualitative Applications of the NMR Spectra of Alkylpyridines: Part I. Chemical Shifts of the Ring Protons

1968 ◽  
Vol 22 (4) ◽  
pp. 325-329 ◽  
Author(s):  
F. R. McDonald ◽  
A. W. Decora ◽  
G. L. Cook
Keyword(s):  
Chemical Shift ◽  
Nmr Spectra ◽  
Pyridine Ring ◽  
Structural Information ◽  
Chemical Shifts ◽  
Shale Oil ◽  
Nmr Chemical Shift ◽  
Chemical Shift Data ◽  
Spectroscopic Identification ◽  
Shift Data

Spectroscopic identification of pyridine compounds isolated from complex substances such as shale oil is greatly aided by NMR chemical-shift data on the pyridine-ring protons. Chemical shifts of the ring protons in CCl4 and C6H6 solution and the differential shift of the protons in these two solvents are reported. A paramagnetic shift is observed in the directional character of the proton alpha to the nitrogen in the pyridine ring. These data are used to determine structural information from the spectrum of a mixture of pyridine homologs.

scholarly journals Passenger Capacity of Underground Metro by the Use of Neural Network Program (NNP)

2019 ◽  
Vol 8 (10) ◽  
pp. 2469-2473
Keyword(s):  
Neural Network ◽  
The Other ◽  
Rolling Stock ◽  
Input And Output ◽  
The Neural Network ◽  
The Future ◽  
Network Program ◽  
Economic Future ◽  
Inputs And Outputs

The present paper deals with studying on (GCUM) by the use of (NNP), consequently, the future passenger fluctuations can be well predicted and it will be helpful to make wise decisions for realizing the most safety and economic future operation.To attain this goal, a methodology was proposed to collect the necessary data and analyze them. These data were applied as the inputs into the Neural Network Program (NNP) for the two (GCUM) lines ‘1’ & ‘2’ to have two models as inputs and outputs, one for the 1st line and the other for the 2nd one, taking only into consideration, the input, and output variables which gave tolerances less 19% than that were obtained by applying excel program. Thus, it is easily to predict the future capacity for any predicted year, and the corresponding headway as well as to prepare an estimated schedule complies with the required future Rolling Stock (RS).

scholarly journals Causative effects of motivation to transfer learning among relational dyads: the test of a model

2020 ◽  
Vol 29 (3) ◽  
pp. 297-314
Author(s):  
Brian Matthews ◽  
Jamie Daigle ◽  
Joy Cooper
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Employee Motivation ◽  
General Belief ◽  
Content Type ◽  
Positive Effects ◽  
The Neural Network ◽  
Network Program ◽  
And Performance ◽  
Motivation To Transfer

PurposeThe purpose of this study is to validate multiplicative cycle that exists between the job readiness and satisfaction model explored by Matthews et al. (2018), the satisfaction and performance paradigmatic nuances analyzed by Judge et al. (2001) and Gu and Chi (2009), in addition to the expectancy model theorized by Vroom (1964). The motivation to transfer learning serves as a conveyable variable transmitted within a learning continuum that sustains cyclical outputs.Design/methodology/approachAn archetype to explore the connection between the three hypothesized theories is created through a neural network program. Exploring this connection develops deeper understandings of the derivatives of employee motivation as it pertains to its effect on readiness, satisfaction, performance and achievement dyads. A detailed analysis of the literature leads to the hypothesis that the motivation to transfer learning creates a multiplicative effect among hypothesized relationships.FindingsThe neural network program scaffolds the proposed general belief that positive effects of transfer motives cause a cyclical effect that continues to perpetuate among hypothesized dyads. Conversely, if this motivation decreases or ceases among one or more dyads, the cyclical effect will retract and, eventually stop.Originality/valueBased on the neurologic outcome, one central theme emerged: managers must offer opportunities to acquire knowledge through assistive mechanisms (i.e. training) by providing external stability through controlled channels that activates the motivation to transfer learning into new opportunities. The transference of this knowledge produces reconstructive growth opportunities through continuous learning thus increasing performance.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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