Using an Artificial Neural Networks Experiment to Assess the Links among Financial Development and Growth in Agriculture

Financial development, productivity, and growth are interconnected, but the direction of causality remains unclear. The relevance of these linkages is likely different for developing and developed economies, yet comparative cross-country studies are scant. The paper analyses the relationship among credit access, output and productivity in the agricultural sector for a large set of countries, over the period 2000–2012, using an Artificial Neural Networks approach. Empirical findings show that these three variables influence each other reciprocally, although marked differences exist among groups of countries. The role of credit access is more prominent for the OECD countries and less important for countries with a lower level of economic de-elopement. Our analysis allows us to highlight the specific effects of credit in stimulating the development of the agricultural sector: in developing countries, credit access significantly affects production, whereas in developed countries, it also has an impact on productivity.

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Artificial Neural Networks in Pavement Engineering: A Recent Review

Proceedings of International Web Conference in Civil Engineering for a Sustainable Planet ◽

10.21467/proceedings.112.66 ◽

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.

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A Journal on Cervical Cancer Prediction Using Artificial Neural Networks

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.1124 ◽

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.

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MODEL JARINGAN SYARAF TIRUAN MEMPREDIKSI PRODUKSI EKSPOR BATU BARA MENURUT NEGARA TUJUAN UTAMA DALAM MENDORONG LAJU PERTUMBUHAN EKONOMI

KLIK - KUMPULAN JURNAL ILMU KOMPUTER ◽

10.20527/klik.v6i2.227 ◽

2019 ◽

Vol 6 (2) ◽

pp. 184

Author(s):

Rafiqa Dewi ◽

Sundari Retno Andani ◽

Solikhun Solikhun

Keyword(s):

Economic Growth ◽

Neural Networks ◽

Artificial Neural Networks ◽

Fossil Fuels ◽

Agricultural Sector ◽

General Definition ◽

Architectural Models ◽

Artificial Neural ◽

Input Variables ◽

The Government

Prediction is a process for estimating how many needs in the future. This study aims to predict the amount of coal exports according to the country the main goal in driving the pace of economic growth. The role of the agricultural sector in the national economy is very important and strategic. Coal is one of the fossil fuels. The general definition is a sedimentary rock that can burn, formed from organic deposits, mainly the remains of plants and formed through the process of pembatubaraan. The main elements consist of carbon, hydrogen and oxygen. Domestic production makes the government continue to implement coal export policies according to the state's main goal in driving the pace of economic growth in Indonesia. By using Artificial Neural Networks and backpropagation algorithms, architectural models will be sought to predict the amount of coal exports according to the state's main goal in driving the pace of economic growth to determine steps to assist the government in exporting coal based on the main destination country. This study uses 12 input variables with 1 target. Using 4 architectural models to test the data to be used for prediction, namely models 12-8-1, 12-16-1, 12-32-1 and 12-64-1. The best architectural model results obtained are 12-16-1 architectural models with 100% truth accuracy, the number of epoch 2602 and MSE is 0.0032. By using this model, predictions of coal exports are in accordance with the main destination countries with 100% accuracy.Keywords: Coal, Exports, predictions, backpropagation, Artificial Neural Networks Prediksi adalah proses untuk memperkirakan berapa banyak kebutuhan di masa depan. Studi ini bertujuan untuk memprediksi jumlah ekspor batubara menurut negara tujuan utama dalam mendorong laju pertumbuhan ekonomi. Peran sektor pertanian dalam ekonomi nasional sangat penting dan strategis. Batubara adalah salah satu bahan bakar fosil. Definisi umum adalah batuan sedimen yang dapat terbakar, terbentuk dari endapan organik, terutama sisa-sisa tanaman dan terbentuk melalui proses pembatubaraan. Unsur utama terdiri dari karbon, hidrogen, dan oksigen. Produksi dalam negeri membuat pemerintah terus menerapkan kebijakan ekspor batubara sesuai dengan tujuan utama negara dalam mendorong laju pertumbuhan ekonomi di Indonesia. Dengan menggunakan Jaringan Saraf Tiruan dan algoritma backpropagation, model arsitektur akan dicari untuk memprediksi jumlah ekspor batubara sesuai dengan tujuan utama negara dalam mendorong laju pertumbuhan ekonomi untuk menentukan langkah-langkah untuk membantu pemerintah dalam mengekspor batubara berdasarkan negara tujuan utama . Penelitian ini menggunakan 12 variabel input dengan 1 target. Menggunakan 4 model arsitektur untuk menguji data yang akan digunakan untuk prediksi, yaitu model 12-8-1, 12-16-1, 12-32-1 dan 12-64-1. Hasil model arsitektur terbaik yang diperoleh adalah model arsitektur 12-16-1 dengan akurasi 100%, jumlah zaman 2602 dan MSE adalah 0,0032. Dengan menggunakan model ini, prediksi ekspor batubara sesuai dengan negara tujuan utama dengan akurasi 100%.Kata kunci: Batubara, Ekspor, prediksi, backpropagation, Jaringan Syaraf Tiruan

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USING ARTIFICIAL NEURAL NETWORKS TO ACCELERATE TRANSPORT SOLVES

EPJ Web of Conferences ◽

10.1051/epjconf/202124703027 ◽

2021 ◽

Vol 247 ◽

pp. 03027

Author(s):

Mauricio E. Tano ◽

Jean C. Ragusa

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Gaussian Elimination ◽

Computing Time ◽

Computational Cost ◽

Large Set ◽

Time Step ◽

Local Systems ◽

Artificial Neural ◽

Transport Problems

Discontinuous Finite Element Methods (DFEM) have been widely used for solving SN radiation transport problems in participative and non-participative media. In this method, small matrix-vector systems are assembled and solved for each cell, angle, energy group, and time step while sweeping through the computational mesh. In practice, these systems are generally solved directly using Gaussian elimination, as computational acceleration for solving this small systems are often inadequate. Nonetheless, the computational cost of assembling and solving these local systems, repeated for each cell in the phase-space, can amount to a large fraction of the total computing time. In this paper, a Machine Learning algorithm is designed to accelerate the solution of local systems. This one is based on Artificial Neural Networks (ANNs). Its key idea is training an ANN with a large set of solutions to random one-cell transport problems and, then, replacing the assembling and solution of the local systems by the feedforward evaluation of the trained ANN. It is observed that the optimized ANNs are able to reduce the compute times by a factor of ~ 3:6 per source iteration, while introducing mean absolute errors between 0:5 – 2% in transport solutions.

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Conditions Of Polish Industry Innovativeness

International Business & Economics Research Journal (IBER) ◽

10.19030/iber.v1i3.3907 ◽

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.

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Prediction prices of basrah light oil using artificial neural networks

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i3.pp2682-2689 ◽

2020 ◽

Vol 10 (3) ◽

pp. 2682

Author(s):

Maysaa Abd Ulkareem Naser

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Economic Activity ◽

Global Economy ◽

Time Series Data ◽

Supply And Demand ◽

Oil Prices ◽

Developed Countries ◽

Series Data ◽

Artificial Neural

The global economy is assured to be very sensitive to the volatility of the oil market. The beneficial from oil prices collapse are both consumers and developed countries. Iraq economy is a one-sided economy which is completely depends on oil revenue to charge the economic activity. Hence, the current decline in oil prices will produce serious concerns. Some factors stopped most investment projects, rationalize the recurrent outflow, and decrease the development of economic activity. The study of forecast oil prices is considered among the most complex studies because of the different dynamic variables that affects the strategic goods. Moreover, the laws of economics controlling the prices of oil such as the supply and demand law. Some other variables that control the oil prices are the political conditions when these conditions contribute to the world production. The subject of forecasting has been extremely developing during recent years and some modern methods have been appeared in this regards, for example, Artificial Neural Networks. In this study, an artificial neural network (FFNN) is adopted to extract the complex relationships among divergent parameters that have the abilities to predict oil prices serving as an inputs to the network data collected in this research represent monthly time series data are Oil prices series in (US dollars) over a period of 11 years (2008–2018) in Iraq

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Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems

Environment and Planning A Economy and Space ◽

10.1068/a33210 ◽

2001 ◽

Vol 33 (8) ◽

pp. 1445-1462 ◽

Cited By ~ 92

Author(s):

Xia Li ◽

Anthony Gar-On Yeh

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Cellular Automata ◽

Critical Issue ◽

Urban Systems ◽

Large Set ◽

Determine Parameter ◽

Ca Model ◽

Artificial Neural ◽

Parameter Values

This paper presents a new cellular automata (CA) model which uses artificial neural networks for both calibration and simulation. A critical issue for urban CA simulation is how to determine parameter values and define model structures. The simulation of real cities involves the use of many variables and parameters. The calibration of CA models is very difficult when there is a large set of parameters. In the proposed model, most of the parameter values for CA simulation are automatically determined by the training of artificial neural networks. The parameter values from the training are then imported into the CA model which is also based on the algorithm of neural networks. With the use of neural networks, users do not need to provide detailed transition rules which are difficult to define. The study shows that the model has better accuracy than traditional CA models in the simulation of nonlinear complex urban systems.

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