scholarly journals Conditions Of Polish Industry Innovativeness

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
Adam Mazurkiewicz ◽  
Rozalia Sitkowska ◽  
Magdalena Trzos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Developed Countries ◽  
Development Activity ◽  
Intensity Index ◽  
Artificial Neural ◽  
Polish Economy ◽  
High Level ◽  
Innovation Intensity ◽  
The Eu

One of the measures of Polish economy development, including its innovativeness and competitiveness is the level of gross domestic expenditures on research and development activity, so called GERD in relation to gross domestic product, i.e. GDP. Poland assigns for R&D one tenth of expenditures, which are assigned by countries with the highest index GERD/GDP: USA and Japan. Scientific and research-development units in our country are characterised by a relatively low level of investment expenditures. As a result, small rebuild scientific-research apparatus and high level of its wear occur. The level of expenditures on R&D per capita in Poland and small share of industry in financing research are reasons of still unsatisfactory pace of decreasing the distance between Poland and well - developed countries (including the EU countries). Lack of mechanisms encouraging industry to greater participation in expenditures on R&D destimulates innovativeness and competitiveness of economy. The paper presents indexes characterising the innovative potential of Polish economy against the background of selected countries. It analyses positive aspects and barriers of innovativeness growth. These problems make a subject of many years research and analyses carried out by authors presented among others in works [1], [2]. The paper described some trends and conditions occurring in Polish economy undergoing transformation. It presents an example of using a method of artificial neural networks in modelling innovativeness in industry on an example of the innovation intensity index. Research method proposed by authors, carried out with the use of a method of artificial neural networks confirm that positive trends concerning Polish industry innovativeness in the first years of the XXI century are maintained.

One-Day Wave Forecasts Based on Artificial Neural Networks

2006 ◽  
Vol 23 (11) ◽  
pp. 1593-1603 ◽  
Author(s):  
S. N. Londhe ◽  
Vijay Panchang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wave Model ◽  
Operational Forecasting ◽  
Artificial Neural ◽  
Wave Models ◽  
Using Data ◽  
Modeling Strategy ◽  
High Level ◽  
Real Time Simulations

Abstract Sophisticated wave models like the Wave Model (WAM) and Simulating Waves Nearshore (SWAN)/WAVEWATCH are used nowadays along with atmospheric models to produce forecasts of ocean wave conditions. These models are generally run operationally on large ocean-scale domains. In many coastal areas, on the other hand, operational forecasting is not performed for a variety of reasons, yet the need for wave forecasts remains. To address such cases, the production of forecasts through the use of artificial neural networks and buoy measurements is explored. A modeling strategy that predicts wave heights up to 24 h on the basis of judiciously selected measurements over the previous 7 days was examined. A detailed investigation of this strategy using data from six National Data Buoy Center (NDBC) buoys with diverse geographical and statistical properties demonstrates that 6-h forecasts can be obtained with a high level of fidelity, and forecasts up to 12 h showed a correlation of 67% or better relative to a full year of data. One limitation observed was the inability of the artificial neural network model to correctly predict the magnitude of the highest waves; although the occurrence of high waves was predicted, the peaks were underestimated. The inclusion of several years of data and the judicious selection of the training set, especially the inclusion of extreme events, were shown to be crucial for the model to recognize interannual variability and provide more reliable forecasts. Real-time simulations performed for April 2005 demonstrate the efficiency of this technology for operational forecasting.

scholarly journals A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4040
Author(s):  
Jamer Jiménez Mares ◽  
Loraine Navarro ◽  
Christian G. Quintero M. ◽  
Mauricio Pardo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Demand ◽  
Percentage Error ◽  
Artificial Neural ◽  
Short Term Forecasting ◽  
Dynamic Time ◽  
High Level ◽  
Energy Assets ◽  
Demand Changes

The electrical sector needs to study how energy demand changes to plan the maintenance and purchase of energy assets properly. Prediction studies for energy demand require a high level of reliability since a deviation in the forecasting demand could affect operation costs. This paper proposed a short-term forecasting energy demand methodology based on hierarchical clustering using Dynamic Time Warp as a similarity measure integrated with Artificial Neural Networks. Clustering was used to build the typical curve for each type of day, while Artificial Neural Networks handled the weather sensibility to correct a preliminary forecasting curve obtained in the clustering stage. A statistical analysis was carried out to identify those significant factors in the prediction model of energy demand. The performance of this proposed model was measured through the Mean Absolute Percentage Error (MAPE). The experimental results show that the three-stage methodology was able to improve the MAPE, reaching values as good as 2%.

scholarly journals Artificial Neural Networks in Pavement Engineering: A Recent Review

2021 ◽  
Author(s):  
Abhishek Kurian ◽  
Elvin Sunildutt
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Civil Engineering ◽  
Developed Countries ◽  
Pavement Management ◽  
Economic Management ◽  
Learning Techniques ◽  
Pavement Engineering ◽  
Artificial Neural

The application of Artificial Neural Networks (ANN) in civil engineering has increased drastically in the past few years. ANN tools are nowadays used commonly in developed countries over various fields of civil engineering like geotechnical, structural, traffic, pavement engineering etc. This paper deals with the review of recent advancements and utilization of ANNs in pavement engineering. The review will focus on pavement performance prediction, maintenance strategies, distress intensity detection through deep learning techniques, pavement condition index prediction etc. The use of ANNs in pavement management systems are expected to furnish a systematic schedule and economic management strategies in the field of pavement engineering. The use of ANNs combined with deep learning techniques help to address complex problems in pavement engineering and pave the way to a sustainable future.

scholarly journals A Journal on Cervical Cancer Prediction Using Artificial Neural Networks

2021 ◽  
Vol 12 (2) ◽  
pp. 1085-1091
Author(s):  
K.Deepa , Et. al.
Keyword(s):  
Neural Networks ◽  
Cervical Cancer ◽  
Artificial Neural Networks ◽  
Death Rate ◽  
Developed Countries ◽  
Medical Procedures ◽  
Clinical Imaging ◽  
Cancer Prediction ◽  
Artificial Neural ◽  
Cervical Cells

Artificial neural networks (ANN) assume a significant part in numerous clinical imaging applications. Cervical cancer ranks the 4th dangerous women cancers in less developed countries due to insufficient trained staffs and medical procedures. The location of cervical malignancy cells utilizes ANN for characterizing the typical and unusual cells in the cervix wall of the uterus. Cervical malignancy location is exceptionally difficult on the grounds that this disease happens with no manifestations.  The order between the typical,unusual and malignant cells produces exact outcomes than other manual screening techniques.The ANN utilizes a few models for a simple and precise identification of cervical cells. The main aim of artificial neural networks is to supply right information at a right time. Hence we implement artificial neural techniques with collected data Analysis,to improve the life of an individual and to decrease the death rate of the society respectively.   

Artificial neural networks for prediction of local thermal insulation of clothing protecting against cold

2018 ◽  
Vol 30 (1) ◽  
pp. 82-100
Author(s):  
Anna Katarzyna Dabrowska
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thermal Insulation ◽  
Thermal Manikin ◽  
Content Type ◽  
Artificial Neural ◽  
Local Thermal Insulation ◽  
High Level ◽  
Hands And Feet ◽  
The Impact

Purpose The purpose of this paper is to develop artificial neural networks (ANNs) allowing us to simulate the local thermal insulation of clothing protecting against cold on a basis of the characteristics of materials and design solutions used. Design/methodology/approach For this purpose, laboratory tests of thermal insulation of clothing protecting against cold as well as thermal resistance of textile systems used in the clothing were performed. These tests were conducted with a use of thermal manikin and so-called skin model, respectively. On a basis of results gathered, 12 ANNs were developed that correspond to each thermal manikin’s segment besides hands and feet which are not covered by protective clothing. Findings In order to obtain high level of simulations, optimization measures for the developed ANNs were introduced. Finally, conducted validation indicated a very high correlation (above 0.95) between theoretical and experimental results, as well as a low error of the simulations (max 8 percent). Originality/value The literature reports addressing the problem of modeling thermal insulation of clothing focus mainly on the impact of the degree of fit and the velocity of air movement on thermal insulation properties, whereas reports dedicated to modeling the impact of the construction of clothing protecting against cold as well as of diverse material systems used within one design of clothing on its thermal insulation are scarce.

scholarly journals Correlation analysis and prediction of personality traits using graphic data collections

2019 ◽  
Vol 72 ◽  
pp. 01012
Author(s):  
Vitaly Fralenko ◽  
Vyacheslav Khachumov ◽  
Mikhail Khachumov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Personality Traits ◽  
Internet Users ◽  
Graphic Information ◽  
Series Of Experiments ◽  
Artificial Neural ◽  
High Level ◽  
Data Collections

The questions of building mechanisms for identifying patterns and building modern tools for analyzing data from social networks are considered. It is proposed to apply modern methods of web pages’ automatic analysis, testing hypotheses about the presence of correlation links, automatic classification of graphic information using the apparatus of artificial neural networks. The presence of correlation between personality traits of the "Big Five" is investigated. Strong fluctuations in the values of personality traits were revealed depending on various types for groups of people. The problem of predicting the personality traits of the Internet user by the images posted by him is investigated, artificial neural networks are used as a tool. Two series of experiments were carried out, in the first series, a convolutional neural network, trained on the images and results of the NEO-FFI questionnaire, was used to predict personality traits. The sequential use of convolution and subsampling in the convolution network leads to the so-called increase in the level of features: if the first layer extracts local features from the image, then subsequent layers extract common features that are called high-order features. In the second series of experiments, this type of artificial neural network was used to extract high-level features, which were then used to train a direct distribution network that performs forecasting. Thus, the more layers are used, the more features associated with personality traits are extracted from the images. For processing arrays of graphic information, the “Microsoft Cognitive Toolkit library” and the Nvidia Geforce GTX 1080 Ti graphics accelerator were used. The results of the experiments revealed those personality traits that are most correlated with the images posted by Internet users.

The effectiveness of artificial neural networks applied to analytical procedures using high level data: a simulation analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stewart Li ◽  
Richard Fisher ◽  
Michael Falta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Neural Network Approach ◽  
Content Type ◽  
Level Data ◽  
Artificial Neural ◽  
Analytical Procedures ◽  
High Level

Purpose Auditors are required to perform analytical procedures during the planning and concluding phases of the audit. Such procedures typically use data aggregated at a high level. The authors investigate whether artificial neural networks, a more sophisticated technique for analytical review than typically used by auditors, may be effective when using high level data. Design/methodology/approach Data from companies operating in the dairy industry were used to train an artificial neural network. Data with and without material seeded errors were used to test alternative techniques. Findings Results suggest that the artificial neural network approach was not significantly more effective (taking into account both Type I and II errors) than traditional ratio and regression analysis, and none of the three approaches provided more overall effectiveness than a purely random procedure. However, the artificial neural network approach did yield considerably fewer Type II errors than the other methods, which suggests artificial neural networks could be a candidate to improve the performance of analytical procedures in circumstances where Type II error rates are the primary concern of the auditor. Originality/value The authors extend the work of Coakley and Brown (1983) by investigating the application of artificial neural networks as an analytical procedure using aggregated data. Furthermore, the authors examine multiple companies from one industry and supplement financial information with both exogenous industry and macro-economic data.

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