scholarly journals Artificial Intelligence (AI) techniques to analyze the determinants attributes in housing prices

2016 ◽  
Vol 19 (58) ◽  
pp. 23 ◽  
Author(s):  
Julia M. Núñez Tabale ◽  
Francisco J. Rey Carmona ◽  
José Mª Caridad y Ocerin
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Explanatory Variable ◽  
Housing Prices ◽  
Good Alternative ◽  
Marginal Prices ◽  
Using Data ◽  
Econometric Approach ◽  
Hedonic Approach

The econometric approach to obtain the value of a property began with hedonic modelling, which were based on a set of property attributes, internal or external, associated to each particular dwelling. The final sale value can be estimated, and also the marginal prices of each exogenous explanatory variable. A good alternative to the hedonic approach is based on several Artificial Intelligence (AI) techniques, such as artificial neural networks (ANN), these tend to be more precise. Both methodologies are compared, and a case study is developed using data from Seville, the larger town in the South of Spain.

scholarly journals Technology analysis of artificial intelligence using Bayesian inference for neural networks

2018 ◽  
Vol 7 (2.3) ◽  
pp. 43
Author(s):  
Sunghae Jun
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Bayesian Inference ◽  
Industrial Revolution ◽  
Statistical Modelling ◽  
Fourth Industrial Revolution ◽  
Research Institutes ◽  
Patent Documents ◽  
The Internet Of Things

At present, artificial intelligence (AI) technology is receiving much attention and applied in each field of society. AI is one of the key technologies to lead the fourth industrial revolution along with the internet of things and big data. Therefore, many companies and research institutes are trying to systematically analyze AI technology in order to understand the AI itself correctly. In this paper, we also study on a method to analyze AI technology based on quantitative approach. We correct the patent documents related to AI technology, and analyze them using statistical modelling. We use Bayesian inference for neural networks to build our proposed method. To verify the validity of our research, we carry out a case study using the AI patent documents.

scholarly journals On-Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3479
Author(s):  
Maria Pia Del Rosso ◽  
Alessandro Sebastianelli ◽  
Dario Spiller ◽  
Pierre Philippe Mathieu ◽  
Silvia Liberata Ullo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Volcanic Eruption ◽  
Low Cost ◽  
Volcanic Eruptions ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Multispectral Imagery

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study, with a prototype of an on board Artificial Intelligence (AI) model, and realistic testing equipment and scenario are presented in this work. As a case study, the detection of volcanic eruptions has been investigated with the objective to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been designed and realized from scratch, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements. The CNNs are then tested with experimental hardware, by means of a drone with a paylod composed of a generic processing unit (Raspberry PI), an AI processing unit (Movidius stick) and a camera. The hardware employed to build the prototype is low-cost, easy to found and to use. Moreover, the dataset has been published on GitHub, made available to everyone. The results are promising and encouraging toward the employment of the proposed system in future missions, given that ESA has already moved the first steps of AI on board with the Phisat-1 satellite, launched on September 2020.

scholarly journals Proposta de modelo NEAT para jogar One Night Ultimate Werewolf

2021 ◽  
Author(s):  
Rafael Ferreira Costa ◽  
Alisson Steffens Henrique ◽  
Rodrigo Lyra ◽  
Anita Maria da Rocha Fernandes ◽  
Rudimar Luis Scaranto Dazzi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Incomplete Information ◽  
Board Games ◽  
Artificial Neural

The use of Artificial Intelligence approaches as NPCs in games is a very common practice, as they seek to convey the impression to players that these characters are somewhat autonomous. One of the approaches used is the technique called NEAT, which consists of making use of artificial neural networks together with genetic algorithms to manage the topology, connections, and weights of a network in an adaptive way. This work presents the proposal to create an NPC for games in a subcategory of board games, those based on bluff and incomplete information. The game used as a case study is One Night Ultimate Werewolf, a social deduction game, so that information is incomplete for players, and part of them must use the bluff in order to confuse other players. The objective is to evaluate the possibility of modeling the behaviors of this type of game for the application of NEAT.

La inteligencia artificial y su aplicación al Protocolo de Estambul

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Omar Mireles Loera
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
The Body ◽  
Hopfield Network ◽  
Post Processing ◽  
Photographic Material ◽  
Istanbul Protocol

This work proposes the post-processing of photographic material accompanied by artificial intelligence algorithms as an alternative to strengthen the photography opinion within the Istanbul Protocol. The methods presented throughout this paper were applied to a judicialized case study in which the existence of injuries to the body anatomy of a defendant could be demonstrated five years after the fact reported as torture.Keywords: Neural Networks, Chronochromodiagnostic, Hopfield Network

scholarly journals Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve

2020 ◽  
Vol 17 (10) ◽  
pp. 3437 ◽  
Author(s):  
Murat Simsek ◽  
Burak Kantarci
Keyword(s):  
Artificial Intelligence ◽  
Worst Case Analysis ◽  
Assessment Centers ◽  
Mobility Patterns ◽  
Worst Case ◽  
Mobile Crowdsensing ◽  
Using Data ◽  
Effective Assessment ◽  
The City

The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9–30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.

scholarly journals Development of an Optimised Dataset for Training a Deep Neural Network

2021 ◽  
Author(s):  
Callum Newman ◽  
Jon Petzing ◽  
Yee Mey Goh ◽  
Laura Justham
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Light Intensity ◽  
Test Performance ◽  
Deep Neural Network ◽  
Training Dataset ◽  
Multiple Factors

Artificial intelligence in computer vision has focused on improving test performance using techniques and architectures related to deep neural networks. However, improvements can also be achieved by carefully selecting the training dataset images. Environmental factors, such as light intensity, affect the image’s appearance and by choosing optimal factor levels the neural network’s performance can improve. However, little research into processes which help identify optimal levels is available. This research presents a case study which uses a process for developing an optimised dataset for training an object detection neural network. Images are gathered under controlled conditions using multiple factors to construct various training datasets. Each dataset is used to train the same neural network and the test performance compared to identify the optimal factors. The opportunity to use synthetic images is introduced, which has many advantages including creating images when real-world images are unavailable, and more easily controlled factors.

Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering

2006 ◽  
pp. 166-187 ◽  
Author(s):  
Juan R. Rabunal ◽  
Juan Puertas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Computation ◽  
Real Time ◽  
Hybrid System ◽  
Civil Engineering ◽  
Alarm System ◽  
Artificial Neural

This chapter proposes an application of two techniques of artificial intelligence in a civil engineering area: the artificial neural networks (ANN) and the evolutionary computation (EC). In this chapter, it is shown how these two techniques can work together in order to solve a problem in hydrology. This problem consists on modeling the effect of rain on the runoff flow in a typical urban basin. The ultimate goal is to design a real-time alarm system for floods or subsidence warning in various types of urban basins. A case study is included as an example.

scholarly journals Artificial Intelligence: Learning and Limitations

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Life ◽  
New Technology ◽  
Development History ◽  
Adversarial Examples ◽  
Real Value ◽  
Artificial Neural

Artificial Intelligence, IA, is a new technology with enormous potential to change the world forever as we know it. It finds applications in many fields of human activity, including services, industry, education, social networks, transportation, among others. However, there is little discussion about the accuracy and reliability of such technology, which has been used in situations where human life depends on its decision-making process, which is the result of its training, one of the stages of development. It is known that the learning process of an Artificial Intelligence, which can use the Artificial Neural Networks technology, presents an error of the predicted value in relation to the real value, which can compromise its application, being more critical in situations where the user's security is a major issue. In this article, we discuss the main technologies used in AI, their development history, considerations about Artificial Neural Networks and the failures arising from the training and hardware processes used. Three types of errors are discussed: The Adversarial Examples, the Soft Errors and the Errors due the lack of Appropriate Training. A case study associated with the third type of error is discussed and actions based on Design of Experiments are proposed. The objective is to change the way the AI models are trained, to add some rare conditions, and to improve their ability to forecast with greater accuracy in any situation

Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering

2011 ◽  
pp. 1084-1101
Author(s):  
Juan R. Rabunal ◽  
Jerónimo Puertas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Computation ◽  
Real Time ◽  
Hybrid System ◽  
Civil Engineering ◽  
Alarm System ◽  
Artificial Neural

This chapter proposes an application of two techniques of artificial intelligence in a civil engineering area: the artificial neural networks (ANN) and the evolutionary computation (EC). In this chapter, it is shown how these two techniques can work together in order to solve a problem in hydrology. This problem consists on modeling the effect of rain on the runoff flow in a typical urban basin. The ultimate goal is to design a real-time alarm system for floods or subsidence warning in various types of urban basins. A case study is included as an example.

Deep Learning

2021 ◽  
pp. 171-196
Author(s):  
Semra Erpolat Taşabat ◽  
Olgun Aydin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Smart Cities ◽  
Key Technology ◽  
Smart Systems ◽  
Advantages And Disadvantages

Deep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities, security, automated machines. In this chapter, brief information about DL theory is given, advantages and disadvantages of deep learning are discussed, most used types of DNN are mentioned, popular DL architectures and frameworks are glanced and aimed to build smart systems for the finance and real estate domains. Finally, a case study about image recognition using transfer learning is developed.

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