scholarly journals Local Development and Gentrification Resulting from the Rehabilitation of Singular Buildings: Analysis of Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1500
Author(s):  
Juan Uribe-Toril ◽  
Alejandro C. Galindo ◽  
José A. Torres ◽  
Jaime De Pablo ◽  
José L. Ruiz-Real
Keyword(s):  
Neural Networks ◽  
Total Variation ◽  
Local Development ◽  
Mediterranean Coast ◽  
Economic Behavior ◽  
Self Organizing Maps ◽  
Occupancy Model ◽  
Commercial Property ◽  
Parallel Processes ◽  
Built Heritage

The recovery of a built heritage and specifically of singular buildings is a key aspect of local development. The aim of this study was to understand the influence of these regenerations on their environment by transforming adjacent businesses and initiating parallel processes of gentrification and local development. The renewed attraction of these new businesses to the area can result in increased employment and production. The methodology used was based on self-organizing maps of neural networks with matrix architecture and competitive learning. Through the analysis of neural networks, we were able to identify common relationships and behaviors in commercial properties which are adjacent to singular buildings and that share common patterns and characteristics or attributes. The singular buildings analyzed are located along the Spanish Mediterranean coast in the cities of Almería, Barcelona, and Valencia. The results obtained were based on the following hypotheses: occupancy model and the classification based on total occupancy, total variation in occupancy, and the most common types of usage of a given ground floor commercial property. Among the conclusions, we highlight the existence of commercial premises that display anti-cyclical economic behavior and the presence of commercial premises considered to be “unfortunate” or with low potential.

scholarly journals Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Main Idea ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
The Times ◽  
Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

A solution to the tag-assignment problem for neural networks

1989 ◽  
Vol 12 (3) ◽  
pp. 381-397 ◽  
Author(s):  
Gary W. Strong ◽  
Bruce A. Whitehead
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Assignment Problem ◽  
Model Object ◽  
Parallel Processes ◽  
Illusory Conjunctions ◽  
Feature Processing ◽  
Human Object ◽  
Processing Activity ◽  
Normal Perception

AbstractPurely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions (the incorrect combination of features into perceived objects in a stimulus array) have been demonstrated empirically (Treisman 1986; Treisman & Gelade 1980). Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that correct illusory conjunctions. Our approach addresses two questions: (i) How can objects be identified from simultaneously attended features in a parallel, distributed representation? (ii) How can the spatial selectional requirements of such an attentional process be met by a separation of pathways for spatial and nonspatial processing? Our analysis of these questions yields a neurally plausible simulation of tag assignment based on synchronizing feature processing activity in a spatial focus of attention.

scholarly journals Machine Learning–based Analysis of English Lateral Allophones

2019 ◽  
Vol 29 (2) ◽  
pp. 393-405 ◽  
Author(s):  
Magdalena Piotrowska ◽  
Gražina Korvel ◽  
Bożena Kostek ◽  
Tomasz Ciszewski ◽  
Andrzej Cżyzewski
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Nearest Neighbor ◽  
Native Speakers ◽  
Automatic Evaluation ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Self Organizing Maps ◽  
Automatic Methods ◽  
Audio Video

Abstract Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’ and phonology experts’ speech was selected for analyses. For the purpose of the present study, a sub-list of 103 words containing the English alveolar lateral phoneme /l/ was compiled. The list includes ‘dark’ (velarized) allophonic realizations (which occur before a consonant or at the end of the word before silence) and 52 ‘clear’ allophonic realizations (which occur before a vowel), as well as voicing variants. The recorded signals were segmented into allophones and parametrized using a set of descriptors, originating from the MPEG 7 standard, plus dedicated time-based parameters as well as modified MFCC features proposed by the authors. Classification methods such as ANNs, the kNN and the SOM were employed to automatically detect the two types of allophones. Various sets of features were tested to achieve the best performance of the automatic methods. In the final experiment, a selected set of features was used for automatic evaluation of the pronunciation of dark /l/ by non-native speakers.

Using Self Organizing Maps for Banking Oversight

2016 ◽  
pp. 1306-1332 ◽  
Author(s):  
Felix Lopez-Iturriaga ◽  
Iván Pastor-Sanz
Keyword(s):  
Neural Networks ◽  
Financial Stability ◽  
Bankruptcy Prediction ◽  
Self Organizing Maps ◽  
Policy Makers ◽  
Savings Banks ◽  
Visual Tool ◽  
The Government ◽  
Over Time ◽  
Self Organizing

This chapter combines two methods based on neural networks (trait recognition and self-organizing maps) to develop a model of bankruptcy prediction. The authors apply the method to the Spanish savings banks, most of them rescued by the Government between 2008 and 2013 in a costly massive process. First, the authors detect the combinations of variables (performance, asset structure, and capitalization) that best describe the profile of the rescued savings banks. Then, the authors use these combinations on a yearly basis to generate bi-dimensional maps in which banks are placed according to their risk and similarities. This method provides a visual tool that can improve the oversight of policy makers on the whole financial system and enable time pertinent answers to some threatens to the country financial stability. The maps are useful means to detect and understand how the financial threats emerge over time too.

scholarly journals Complementary currency systems as a source of endogenous development of localities

2012 ◽  
Vol 50 (No. 10) ◽  
pp. 477-484 ◽  
Author(s):  
L. Zagata
Keyword(s):  
Local Development ◽  
Local Level ◽  
Social Institution ◽  
Economic Behavior ◽  
Point Of View ◽  
Social Benefits ◽  
Endogenous Development ◽  
Local Society ◽  
The World ◽  
Complementary Currency

The paper tackles the issue of regional and social development. From a sociological point of view, it focuses on the phenomenon of complementary currency systems. The analysis shows that money, as a social institution, has got certain features, which have an impact on economic behavior of people. Establishing a currency on the local level, which would circulate as a complement of the national currency, brings certain social benefits to local society. Nowadays, there are many complementary currency systems all over the world, including Europe. The paper attempts to answer the question, how they can contribute to the local development.

scholarly journals Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?

2020 ◽  
Vol 17 (23) ◽  
pp. 8921
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
Neural Networks ◽  
Intensive Care Unit ◽  
Influenza Vaccine ◽  
Human Development Index ◽  
Safety Data ◽  
Health And Safety ◽  
Protective Equipment ◽  
Self Organizing Maps ◽  
Federal Funds ◽  
The Relationship

Infinite factors can influence the spread of COVID-19. Evaluating factors related to the spread of the disease is essential to point out measures that take effect. In this study, the influence of 14 variables was assessed together by Artificial Neural Networks (ANN) of the type Self-Organizing Maps (SOM), to verify the relationship between numbers of cases and deaths from COVID-19 in Brazilian states for 110 days. The SOM analysis showed that the variables that presented a more significant relationship with the numbers of cases and deaths by COVID-19 were influenza vaccine applied, Intensive Care Unit (ICU), ventilators, physicians, nurses, and the Human Development Index (HDI). In general, Brazilian states with the highest rates of influenza vaccine applied, ICU beds, ventilators, physicians, and nurses, per 100,000 inhabitants, had the lowest number of cases and deaths from COVID-19, while the states with the lowest rates were most affected by the disease. According to the SOM analysis, other variables such as Personal Protective Equipment (PPE), tests, drugs, and Federal funds, did not have as significant effect as expected.

Local high-resolution passive seismic tomography and Kohonen neural networks — Application at the Rio-Antirio Strait, central Greece

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. B93-B106 ◽  
Author(s):  
G-Akis Tselentis ◽  
Anna Serpetsidaki ◽  
Nikolaos Martakis ◽  
Efthimios Sokos ◽  
Paraskevas Paraskevopoulos ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Seismic Tomography ◽  
Least Squares Method ◽  
Velocity Model ◽  
Nonlinear Inversion ◽  
Self Organizing Maps ◽  
Central Greece ◽  
Passive Seismic ◽  
First Time

A high-resolution passive seismic investigation was performed in a [Formula: see text] area around the Rio-Antirio Strait in central Greece using natural microearthquakes recorded during three months by a dense, temporary seismic network consisting of 70 three-component surface stations. This work was part of the investigation for a planned underwater rail tunnel, and it gives us the opportunity to investigate the potential of this methodology. First, 150 well-located earthquake events were selected to compute a minimum (1D) velocity model for the region. Next, the 1D model served as the initial model for nonlinear inversion for a 3D P- and S- velocity crustal structure by iteratively solving the coupled hypocenter-velocity problem using a least-squares method. The retrieved [Formula: see text] and [Formula: see text] images were used as an input to Kohonen self-organizing maps (SOMs) to identify, systematically and objectively, the prominent lithologies in the region. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. This analysis revealed the existence of five major clusters, one of which may be related to the existence of an evaporite body not shown in the conventional seismic tomography velocity volumes. The survey results provide, for the first time, a 3D model of the subsurface in and around the Rio-Antirio Strait. It is the first time that passive seismic tomography is used together with SOM methodologies at this scale, thus revealing the method’s potential.

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