ARIMA and Neural Networks: An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A.

2009 ◽  
Author(s):  
Eleftherios Giovanis
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Unemployment Rate ◽  
The Real

scholarly journals DeepFogSim: A Toolbox for Execution and Performance Evaluation of the Inference Phase of Conditional Deep Neural Networks with Early Exits Atop Distributed Fog Platforms

2021 ◽  
Vol 11 (1) ◽  
pp. 377
Author(s):  
Michele Scarpiniti ◽  
Enzo Baccarelli ◽  
Alireza Momenzadeh ◽  
Sima Sarv Ahrabi
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Future Internet ◽  
Operating Conditions ◽  
Graphic User Interface ◽  
Energy Aware ◽  
Internet Applications ◽  
The Real ◽  
Delay Performance ◽  
Hard Constraints

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox.

The Spatiotemporal Evolution of Urban Impervious Surface for Chengdu, China

2021 ◽  
Vol 87 (7) ◽  
pp. 491-502
Author(s):  
Mujie Li ◽  
Zezhong Zheng ◽  
Mingcang Zhu ◽  
Yue He ◽  
Jun Xia ◽  
...  
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Urban Area ◽  
Landsat Imagery ◽  
Impervious Surface ◽  
Kappa Coefficient ◽  
Spatiotemporal Evolution ◽  
Urban Impervious Surface ◽  
The Past ◽  
Secondary Industry

The spatiotemporal evolution of an impervious surface (IS) is significant for urban planning. In this paper, the IS was extracted and its spatiotemporal evolution for the Chengdu urban area was analyzed based on Landsat imagery. Our experimental results indicated that convolutional neural networks achieved the better performance with an overall accuracy of 98.32%, Kappa coefficient of 0.98, and Macro F1 of 98.28%, and the farmland was replaced by IS from 2001 to 2017, and the IS area (ISA) increased by 51.24 km2; that is, the growth rate was up to 13.8% in sixteen years. According to the landscape metrics, the IS expanded and agglomerated into large patches from small fragmented ones. In addition, the gross domestic product change of the secondary industry was similar to the change of ISA between 2001 and 2017. Thus, the spatiotemporal evolution of IS was associated with the economic development of the Chengdu urban area in the past sixteen years.

Hindutva Philosophy Reinforcement by the rss/bjp against Minorities and the Economic Performance of Narendra Modi’s Government in India

2021 ◽  
Vol 28 (2) ◽  
pp. 351-366
Author(s):  
Rajeev Ranjan Kumar ◽  
Muhammad Rizwan
Keyword(s):  
Growth Rate ◽  
Unemployment Rate ◽  
Economic Performance ◽  
Prime Minister ◽  
Economic Decline ◽  
Religious Minorities ◽  
Lower Caste ◽  
The Core ◽  
The People ◽  
The Government

Abstract Indian Prime Minister Narendra Modi is a controversial figure and has polarised public debate for over a decade. He is criticised for the decline in growth rate and increase in unemployment rate. It has been five years since the Modi-led Bhartiya Janata Party (bjp) came to power, so analysing the economic performance and extremist religious behaviour of the Modi-led bjp/rss (Rastriya Sevak Sangh) is interesting. This article discusses the non-conventional views on the economic performance of the government in India, and the ideology of Hindutva and hatred towards religious minorities. This deep-rooted hatred of religious minorities and the lower caste is the core philosophy of Hindutva and is followed by the bjp and rss. Under the shadow of the rss, the Modi government has focused on Hindutva rather than the economy and the people, which has been the most important factor in the economic decline of India.

scholarly journals Effect of Unemployment Rate on Growth Rate of Gross Domestic Product of Jordan

2019 ◽  
Vol 10 (3) ◽  
pp. 217 ◽  
Author(s):  
Khaled Abdalla Moh'd AL-Tamimi
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Labor Force ◽  
Unemployment Rate ◽  
Ordinary Least Squares ◽  
Total Labor Force ◽  
Independent Variable ◽  
Total Labor ◽  
Level Of Significance ◽  
Yearly Data

This study explains the effect of unemployment rate on growth rate of GDP of Jordan by depending on yearly data for the period (2009 – 2016) as unemployment rate is independent variable, and growth rate of GDP (Avariable of economic growth) as a dependent variable. This study focuses on explaining the literature both in theoretical and empirical ways of the effect of unemployment rate on growth rate of GDP, and analyzing the effect of unemployment rate on growth rate of GDP of Jordan by depending on yearly data for the period (2009 – 2016) by using the technique of ordinary least squares in version of E-views. This paper found that there are insignificant impacts of unemployment percentage to total labor force, unemployment of males percentage to male labor force, unemployment of females percentage to female labor force on growth rate of GDP of Jordan by relying on yearly data for the period 2009 to 2016 at level of significance 5%. This paper recommends testing the impacts of other obstacles in Jordan on growth rate on GDP, in order to know the variables that effect growth rate of GDP in Jordan.

scholarly journals Economic Growth and Unemployment Nexus in USA

2019 ◽  
Vol 8 (4) ◽  
pp. 1317-1325
Keyword(s):  
Growth Rate ◽  
Error Correction ◽  
Unemployment Rate ◽  
High Speed ◽  
Empirical Relationship ◽  
Var Model ◽  
Output Gap ◽  
Causality Test ◽  
Vector Error Correction ◽  
Vector Error

Empirical relationship between unemployment and growth is not pronounced as we investigate the economic scenario of the nations. Author attempted to relate US unemployment rate to the growth during 1948-2016 by using bivariate and log regression models, Bai-Perron Model, Granger Causality test, Johansen cointegration test, vector auto regression and vector error correction models. Even, author also verified relationship between unemployment gap, output gap and growth in USA during the same period. Data on US unemployment rate, GDP and growth rate have been taken from Bureau of US census during 1948-2016. Data on US natural rate of unemployment was taken from Fed Bank of St.Louis from 1949 to 2016.The paper concludes that US unemployment rate is increasing at the rate of 0.507 per cent per annum and it has upward structural break in 1971.The nexus follows the Okun’s law in USA. US unemployment is negatively related with growth rate during 1948-2016.Their relationships are causal and cointegrated. VAR model is stable and stationary. Residual test showed non-normality and autocorrelations.Moreover, author showed negative relation between growth and unemployment gap in USA during 1949-2016.They have no causality and cointegration. Their VAR model is stable and stationary. The residual test proved non-normality and auto-correlation problems. Perceptible output gap influences unemployment gap negatively during 1949-2016 .It has significant bi-directional causality and one cointegrating equation. In Vector error correction model, error corrections are significant with high speed having stability, autocorrelation and non-normality. The rate of decline in unemployment rate due to increased growth rate in USA during 1948-2016 was marginal.

scholarly journals A Simulation of the Broiler Growth Rate Using Artificial Neural Networks

2015 ◽  
Vol 104 (1) ◽  
pp. 81-90
Author(s):  
Raquel Salazar ◽  
Fernando Rojano ◽  
Abraham Rojano
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Artificial Neural Networks ◽  
Artificial Neural

PREDIKSI PERTUMBUHAN PENDUDUK PADA PROVINSI SUMATERA BARAT DENGAN MENGGUNAKAN METODE BACKPROPAGATION

2020 ◽  
Vol 7 (4) ◽  
pp. 216
Author(s):  
Hijrah Yanti Sitanggang ◽  
Vera Irma Delianti
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Artificial Neural Networks ◽  
Population Growth ◽  
Population Growth Rate ◽  
Backpropagation Neural Networks ◽  
West Sumatra ◽  
Artificial Neural ◽  
The City

The problem of population is one of the problems in the Province of West Sumatra, especially in the City of Padang, Kota Bukitinggi, and the City of Payakumbuh which has a very fast population growth rate, this occurs due to several factors such as births, deaths, residents who come, and residents who leave. The highest population growth occurred in Padang City in 2018 amounting to 939,112 residents and the smallest population growth occurred in the City of Bukitinggi in 2014 amounting to 120,491 residents. The purpose of this study is to predict population growth that will occur in 2019 in the cities of Padang, Bukittinggi and Payakumbuh. The method used in this research is descriptive correlational by applying backpropagation neural networks. The application used is Matlab. Based on the problems and methods obtained, the predicted results in 2019 in Padang City amounted to 124,7150, Bukittinggi numbered 126,8040 and Payakumbuh totaled 128.7830.  Keywords: Artificial Neural Networks, Backpropagation, Matlab.

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