scholarly journals National projects and government programmes: functional algorithm for evaluating and modelling using the Data Science methodology

2020 ◽  
Vol 183 (5-6) ◽  
pp. 51-59
Author(s):  
Olim Astanakulov ◽  
Keyword(s):  
Neural Network ◽  
Performance Indicators ◽  
Data Science ◽  
Mean Square ◽  
High Quality ◽  
Functional Algorithm ◽  
Legislative Framework ◽  
The Government ◽  
Science Methodology ◽  
Selection Of

Programme and target planning procedures in Russia have a lot of shortcomings, related to the selection of priority goals, establishment of criteria for evaluating the effectiveness of target programmes, as well as achievement of goals, development of a system of performance indicators, and so on. In addition, the problem of the lack of a high-quality theoretical and legislative framework for the transition to budget expenditures planning in accordance with the principles of result-oriented budgeting remains urgent. The purpose of this paper is to develop a functional fuzzy computing algorithm for modelling the evaluation of government programmes using neural networks. As a part of this work, we obtained stable results in the form of creating a neural network that can analyze government projects using a multi-criteria method, taking into account the root-mean-square error, with an accuracy of up to 95%. The analysis criteria cover all effective areas for predicting the correct use of the government projects by implementing them in the government systems.

scholarly journals Face recognition using enhancement discrete wavelet transform based on MATLAB

2021 ◽  
Vol 23 (2) ◽  
pp. 1128
Author(s):  
Asma Abdulelah Abdulrahman ◽  
Fouad Shaker Tahir
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
High Quality

<p>In this work, it was proposed to compress the color image after de-noise by proposing a coding for the discrete transport of new wavelets called discrete chebysheve wavelet transduction (DCHWT) and linking it to a neural network that relies on the convolutional neural network to compress the color image. The aim of this work is to find an effective method for face recognition, which is to raise the noise and compress the image in convolutional neural networks to remove the noise that caused the image while it was being transmitted in the communication network. The work results of the algorithm were calculated by calculating the peak signal to noise ratio (PSNR), mean square error (MSE), compression ratio (CR) and bit-per-pixel (BPP) of the compressed image after a color image (256×256) was entered to demonstrate the quality and efficiency of the proposed algorithm in this work. The result obtained by using a convolutional neural network with new wavelets is to provide a better CR with the ratio of PSNR to be a high value that increases the high-quality ratio of the compressed image to be ready for face recognition.</p>

scholarly journals Real-Time Forecasting of Snowfall Using a Neural Network

2007 ◽  
Vol 22 (3) ◽  
pp. 676-684 ◽  
Author(s):  
Paul J. Roebber ◽  
Melissa R. Butt ◽  
Sarah J. Reinke ◽  
Thomas J. Grafenauer
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Real Time ◽  
Inverse Relationship ◽  
Mean Square ◽  
Snow Removal ◽  
The Neural Network ◽  
Verification Methods ◽  
Network Method ◽  
Selection Of

Abstract A set of 53 snowfall reports was collected in real time from the 2004/05 and 2005/06 cold seasons (November–March). Three snowfall-amount forecast methods were tested: neural network, surface-temperature-based 676-USDT table, and climatological snow ratio. Standard verification methods (mean, median, bias, and root-mean-square error) and a new method that places the forecasts in the context of municipal snow removal, and introduces the concept of forecast credibility, are used. Results suggest that the neural network method performs best for individual events, owing in part to the inverse relationship between melted liquid equivalent and snow ratio; hence, the ongoing difficulty of producing accurate forecasts of melted equivalent precipitation (a problem in all seasons) is compensated for rather than amplified when converting to snowfall amounts. This analysis should be extended to a larger selection of reports, which is anticipated in conjunction with efforts currently ongoing at the National Oceanic and Atmospheric Administration’s Hydrometeorological Prediction Center.

scholarly journals Can the NIST Adsorption Database Be Used to Highlight Potential Materials for Gas Separation?

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Gas Separation ◽  
High Throughput ◽  
Performance Indicators ◽  
Scientific Literature ◽  
Gas Storage ◽  
High Quality ◽  
Binary Separation ◽  
Relevant Case ◽  
Adsorbent Materials ◽  
Selection Of

<div>Scientific literature is replete with descriptions of novel adsorbent materials, making the selection of such adsorbents for gas storage and separation a trudging task, and often resulting in overlooked materials. Here, we use a high throughput methodology o process a dataset of 28 000 adsorption isotherms from the NIST adsorption database (ISODB) and generate key performance indicators applicable to ambient temperature binary separation on 1500 materials in the collection, with 30 adsorbed guests. The procedure is validated against high-quality laboratory isotherms to confirm the accuracy of the derived indicators. The results are then collated in a powerful online dashboard, which can be used to explore the binary correlations. Finally, we use this toolchain to scrutinize several challenging and industrially relevant case studies and highlight somematerials which may be promising for further analysis.</div>

Can the NIST Adsorption Database Be Used to Highlight Potential Materials for Gas Separation?

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Gas Separation ◽  
High Throughput ◽  
Performance Indicators ◽  
Scientific Literature ◽  
Gas Storage ◽  
High Quality ◽  
Binary Separation ◽  
Relevant Case ◽  
Adsorbent Materials ◽  
Selection Of

<div>Scientific literature is replete with descriptions of novel adsorbent materials, making the selection of such adsorbents for gas storage and separation a trudging task, and often resulting in overlooked materials. Here, we use a high throughput methodology o process a dataset of 28 000 adsorption isotherms from the NIST adsorption database (ISODB) and generate key performance indicators applicable to ambient temperature binary separation on 1500 materials in the collection, with 30 adsorbed guests. The procedure is validated against high-quality laboratory isotherms to confirm the accuracy of the derived indicators. The results are then collated in a powerful online dashboard, which can be used to explore the binary correlations. Finally, we use this toolchain to scrutinize several challenging and industrially relevant case studies and highlight somematerials which may be promising for further analysis.</div>

scholarly journals Can the NIST Adsorption Database Be Used to Highlight Potential Materials for Gas Separation?

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Gas Separation ◽  
High Throughput ◽  
Performance Indicators ◽  
Scientific Literature ◽  
Gas Storage ◽  
High Quality ◽  
Binary Separation ◽  
Relevant Case ◽  
Adsorbent Materials ◽  
Selection Of

<div>Scientific literature is replete with descriptions of novel adsorbent materials, making the selection of such adsorbents for gas storage and separation a trudging task, and often resulting in overlooked materials. Here, we use a high throughput methodology o process a dataset of 28 000 adsorption isotherms from the NIST adsorption database (ISODB) and generate key performance indicators applicable to ambient temperature binary separation on 1500 materials in the collection, with 30 adsorbed guests. The procedure is validated against high-quality laboratory isotherms to confirm the accuracy of the derived indicators. The results are then collated in a powerful online dashboard, which can be used to explore the binary correlations. Finally, we use this toolchain to scrutinize several challenging and industrially relevant case studies and highlight somematerials which may be promising for further analysis.</div>

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

CRITERIONS FOR THE EFFECTIVENESS OF SOCIALLY IMPORTANT INVESTMENT PROJECTS AND THEIR FORMULAS ADOPTED IN RUSSIAN PRACTICE

2020 ◽  
Vol 2 (8) ◽  
pp. 58-64
Author(s):  
A. F. AGEEVA ◽  
Keyword(s):  
Performance Indicators ◽  
Payback Period ◽  
Internal Rate Of Return ◽  
Investment Projects ◽  
Regulatory Documents ◽  
Project Analysis ◽  
Analytical Work ◽  
Methodological Approaches ◽  
Further Development ◽  
Selection Of

The article analyzes domestic guidelines for assessing the effectiveness of investment projects reflected in the regulatory documentation, both current and invalid. Considered are methodological approaches to calculating key performance indicators of investment projects - net discounted income, internal rate of return, discounted payback period and profitability index. The results of the analysis and recommendations for the further development of national regulatory documents for project analysis and methodological approaches to assessing the effectiveness of socially significant investment projects are presented. The results of the analytical work presented in the article are planned to be used to create a methodology for the selection of socially significant projects for the provision of state support.

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