Neural Networks Applications in Economics: a Statistical Point of View

1999 ◽  
pp. 357-375
Author(s):  
N. Carlo Lauro ◽  
Cristina Davino ◽  
Domenico Vistocco
Keyword(s):  
Neural Networks ◽  
Point Of View ◽  
Statistical Point

A Comparative Evaluation of Serum and Salivary Total Proteins and Immunoglobulins in Patients with Hepatitis A and Healthy Subjects

2018 ◽  
Vol 69 (5) ◽  
pp. 1125-1128
Author(s):  
Daniela G. Balan ◽  
Dan Piperea Sianu ◽  
Iulia I. Stanescu ◽  
Dorin Ionescu ◽  
Andra Elena Stroescu Balcangiu ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Comparative Evaluation ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Point Of View ◽  
Healthy Individuals ◽  
Total Proteins ◽  
Statistical Point ◽  
Immunological Tests ◽  
Acute Hepatitis A

Assessment of changes in total proteins level, serum and saliva IgG and IgA levels, serum IgM level, serum and saliva IgA/IgG ratio. The study was conducted on a group of 40 subjects, divided into 2 lots: the first lot consisting of 20 healthy individuals and the second consisting of 20 patients with hepatitis with hepatitis A virus (HAV). The levels of total proteins, serum and saliva IgG and IgA, serum IgM and serum and saliva IgA/IgG ratio have higher values in patients with hepatitis A, in comparison to healthy subjects, without necessarily exceeding the maximum admitted value. The results are significant from a statistical point of view. Due to the sensitivity and specificity of salivary anti-HAV IgM and IgG in patients with acute hepatitis A, compared with healthy subjects, there is a possibility of using salivary immunological tests instead of serum tests for the diagnosis and epidemiological study of HAV infection.

The Importance of Rainfall Distribution in Urban Drainage Operation

1992 ◽  
Vol 23 (2) ◽  
pp. 121-136 ◽  
Author(s):  
Fons Nelen ◽  
Annemarieke Mooijman ◽  
Per Jacobsen
Keyword(s):  
Real Time ◽  
Treatment Plant ◽  
Rain Gauge ◽  
Point Of View ◽  
Rain Event ◽  
Real Time Control ◽  
Rainfall Distribution ◽  
Statistical Point ◽  
Time Control ◽  
Spatially Distributed

A control simulation model, called LOCUS, is used to investigate the effects of spatially distributed rain and the possibilities to benefit from this phenomenon by means of real time control. The study is undertaken for a catchment in Copenhagen, where rainfall is measured with a network of 8 rain gauges. Simulation of a single rain event, which is assumed to be homogeneous, i.e. using one rain gauge for the whole catchment, leads to large over- and underestimates of the systems output variables. Therefore, when analyzing a single event the highest possible degree of rainfall information may be desired. Time-series simulations are performed for both an uncontrolled and a controlled system. It is shown that from a statistical point of view, rainfall distribution is NOT significant concerning the probability of occurrence of an overflow. The main contributing factor to the potential of real time control, concerning minimizing overflows, is to be found in the system itself, i.e. the distribution of available storage and discharge capacity. When other operational objectives are involved, e.g., to minimize peak flows to the treatment plant, rainfall distribution may be an important factor.

scholarly journals Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hamid Reza Marateb ◽  
Maja von Cube ◽  
Ramin Sami ◽  
Shaghayegh Haghjooy Javanmard ◽  
Marjan Mansourian ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Hospital Admission ◽  
Mortality Risk ◽  
Regression Models ◽  
Assessment Method ◽  
Point Of View ◽  
Statistical Point ◽  
Hazard Regression ◽  
Risk Assessment Method ◽  
Discharge From Hospital

Abstract Background Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. Methods We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. Results Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835–0.910]). Conclusions This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.

scholarly journals A Mathematical Study of Barcelona Metro Network

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 557
Author(s):  
Irene Mariñas-Collado ◽  
Elisa Frutos Bernal ◽  
Maria Teresa Santos Martin ◽  
Angel Martín del Rey ◽  
Roberto Casado Vara ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Transportation Network ◽  
Point Of View ◽  
Statistical Point ◽  
Transport Services ◽  
Urban Public Transport ◽  
The Common ◽  
Subway Stations ◽  
Metro Network

The knowledge of the topological structure and the automatic fare collection systems in urban public transport produce many data that need to be adequately analyzed, processed and presented. These data provide a powerful tool to improve the quality of transport services and plan ahead. This paper aims at studying, from a mathematical and statistical point of view, the Barcelona metro network; specifically: (1) the structural and robustness characteristics of the transportation network are computed and analyzed considering the complex network analysis; and (2) the common characteristics of the different subway stations of Barcelona, based on the passenger hourly entries, are identified through hierarchical clustering analysis. These results will be of great help in planning and restructuring transport to cope with the new social conditions, after the pandemic.

Deep neural network based on generalized neo-fuzzy neurons and its learning based on backpropagation

2021 ◽  
Vol 26 (jai2021.26(1)) ◽  
pp. 32-41
Author(s):  
Bodyanskiy Y ◽  
◽  
Antonenko T ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Process ◽  
Activation Function ◽  
Point Of View ◽  
Basic Unit ◽  
Theoretical Point ◽  
Activation Functions ◽  
Approximation Properties ◽  
Network Training

Modern approaches in deep neural networks have a number of issues related to the learning process and computational costs. This article considers the architecture grounded on an alternative approach to the basic unit of the neural network. This approach achieves optimization in the calculations and gives rise to an alternative way to solve the problems of the vanishing and exploding gradient. The main issue of the article is the usage of the deep stacked neo-fuzzy system, which uses a generalized neo-fuzzy neuron to optimize the learning process. This approach is non-standard from a theoretical point of view, so the paper presents the necessary mathematical calculations and describes all the intricacies of using this architecture from a practical point of view. From a theoretical point, the network learning process is fully disclosed. Derived all necessary calculations for the use of the backpropagation algorithm for network training. A feature of the network is the rapid calculation of the derivative for the activation functions of neurons. This is achieved through the use of fuzzy membership functions. The paper shows that the derivative of such function is a constant, and this is a reason for the statement of increasing in the optimization rate in comparison with neural networks which use neurons with more common activation functions (ReLU, sigmoid). The paper highlights the main points that can be improved in further theoretical developments on this topic. In general, these issues are related to the calculation of the activation function. The proposed methods cope with these points and allow approximation using the network, but the authors already have theoretical justifications for improving the speed and approximation properties of the network. The results of the comparison of the proposed network with standard neural network architectures are shown

Recurrent Neural Networks with Small Weights Implement Definite Memory Machines

2003 ◽  
Vol 15 (8) ◽  
pp. 1897-1929 ◽  
Author(s):  
Barbara Hammer ◽  
Peter Tiňo
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Experimental Studies ◽  
Activation Function ◽  
Transition Function ◽  
Recurrent Network ◽  
Point Of View ◽  
Recurrent Networks ◽  
Contraction Parameter ◽  
Arbitrary Precision

Recent experimental studies indicate that recurrent neural networks initialized with “small” weights are inherently biased toward definite memory machines (Tiňno, Čerňanský, & Beňušková, 2002a, 2002b). This article establishes a theoretical counterpart: transition function of recurrent network with small weights and squashing activation function is a contraction. We prove that recurrent networks with contractive transition function can be approximated arbitrarily well on input sequences of unbounded length by a definite memory machine. Conversely, every definite memory machine can be simulated by a recurrent network with contractive transition function. Hence, initialization with small weights induces an architectural bias into learning with recurrent neural networks. This bias might have benefits from the point of view of statistical learning theory: it emphasizes one possible region of the weight space where generalization ability can be formally proved. It is well known that standard recurrent neural networks are not distribution independent learnable in the probably approximately correct (PAC) sense if arbitrary precision and inputs are considered. We prove that recurrent networks with contractive transition function with a fixed contraction parameter fulfill the so-called distribution independent uniform convergence of empirical distances property and hence, unlike general recurrent networks, are distribution independent PAC learnable.

A statistical approach to micro-plastic strain in metals

1966 ◽  
Vol 1 (5) ◽  
pp. 415-421 ◽  
Author(s):  
A Esin ◽  
W J D Jones
Keyword(s):  
Plastic Strain ◽  
Elastic Limit ◽  
Statistical Approach ◽  
Physical Reality ◽  
General Concept ◽  
Distribution Functions ◽  
Point Of View ◽  
Plastic Strains ◽  
Statistical Point ◽  
Suggested Approach

The paper presents an outline of a theory of micro-inhomogeneity of stresses and strains resulting from the micro-structural properties of engineering materials. The problem is approached from a statistical point of view and it is experimentally shown that the degree of micro-inhomogeneity can be defined by normal distribution functions. Using the experimental results a general concept is postulated which takes into account the physical reality as completely as is practicable. It is shown that the suggested approach can be used to take into account the micro-plastic strains which exist while the material is nominally within the elastic limit.

Sign in / Sign up

Export Citation Format

Share Document