scholarly journals Relating Blindsight and AI: A Review

2021 ◽  
pp. 1-15
Author(s):  
Joshua Bensemann ◽  
Qiming Bao ◽  
Gaël Gendron ◽  
Tim Hartill ◽  
Michael Witbrock
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Models ◽  
Visual Experience ◽  
Biological Research ◽  
Future Research ◽  
Network Architectures ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Artificial Neural

Processes occurring in brains, a.k.a. biological neural networks, can and have been modeled within artificial neural network architectures. Due to this, we have conducted a review of research on the phenomenon of blindsight in an attempt to generate ideas for artificial intelligence models. Blindsight can be considered as a diminished form of visual experience. If we assume that artificial networks have no form of visual experience, then deficits caused by blindsight give us insights into the processes occurring within visual experience that we can incorporate into artificial neural networks. This paper has been structured into three parts. Section 2 is a review of blindsight research, looking specifically at the errors occurring during this condition compared to normal vision. Section 3 identifies overall patterns from Sec. 2 to generate insights for computational models of vision. Section 4 demonstrates the utility of examining biological research to inform artificial intelligence research by examining computational models of visual attention relevant to one of the insights generated in Sec. 3. The research covered in Sec. 4 shows that incorporating one of our insights into computational vision does benefit those models. Future research will be required to determine whether our other insights are as valuable.

Forecasting Stock Returns with Artificial Neural Networks

2011 ◽  
pp. 47-79 ◽  
Author(s):  
Suraphan Thawornwong ◽  
David Enke
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Stock Returns ◽  
Future Research ◽  
Network Architectures ◽  
Network Applications ◽  
Artificial Neural ◽  
Neural Network Applications

During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In fact, a great deal of attention has been placed in the area of stock return forecasting. This is due to the fact that once artificial neural network applications are successful, monetary rewards will be substantial. Many studies have reported promising results in successfully applying various types of artificial neural network architectures for predicting stock returns. This chapter reviews and discusses various neural network research methodologies used in 45 journal articles that attempted to forecast stock returns. Modeling techniques and suggestions from the literature are also compiled and addressed. The results show that artificial neural networks are an emerging and promising computational technology that will continue to be a challenging tool for future research.

scholarly journals Artificial Intelligence Applications and Future Research Directions

2019 ◽  
Vol 8 (10S) ◽  
pp. 35-40
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Current Knowledge ◽  
Extensive Study ◽  
Performance Comparison ◽  
Future Research ◽  
Neural Network Models ◽  
Feed Forward ◽  
Artificial Neural

This is an extensive study of Artificial Intelligence applications. It offers artificial neural networks (ANN) taxonomy and supplies investigators with current knowledge and raising needs in ANN based research applications and concentration for investigators. In addition, this study offers an ANN application contributions, challenges, performance comparison and evaluation. This study is demonstrated various ANN applications in diverse disciplines comprise science, computing, medicine, environmental, engineering, climate, technology, mining, arts, nanotechnology, business and so on. Based on this review, it is identified that neural network models like Feedback propagation and Feed forward artificial neural networks performs effectually in human problems based application. Henceforth, feed forward and feed backward propagation ANN focuses on research sourced on data analysis parameters such as accuracy, fault tolerance, latency, volume, convergence, scalability and performance. However, this study suggests that indeed of utilizing single method, future investigation concentrates on merging ANN models into cloud and dentistry based network wide application.

scholarly journals Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC

2020 ◽  
Vol 73 ◽  
pp. 01033
Author(s):  
Jaromír Vrbka ◽  
Marek Vochozka
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Seasonal Fluctuations ◽  
Republic Of China ◽  
The World Bank ◽  
Intelligence Models ◽  
The Usa ◽  
Artificial Intelligence Models ◽  
Artificial Neural

The paper’s objective is to propose a particular methodology to be used to regard seasonal fluctuations on balancing time series while using artificial neural networks based on the example of imports from the People's Republic of China (PRC) to the USA (US). The difficulty of forecasting the volume of foreign trade is usually given by the limitations of many conventional forecasting models. For the improvement of forecasting it is necessary to propose an approach that would hybridize econometric models and artificial intelligence models. Data for an analysis to be conducted are available on the World Bank website, etc. Information on US imports from the PRC will be used. Each forecast is given by a certain degree of probability which it will be fulfilled with. Although it appeared before the experiment that there was no reason to include the categorical variable to reflect seasonal fluctuations of the USA imports from the PRC, the assumption was not correct. An additional variable, in the form of monthly value measurements, brought greater order and accuracy to the balanced time series.

scholarly journals Artificial Intelligence Models and Techniques Applied to COVID-19: A Review

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2901
Author(s):  
Lilia Muñoz ◽  
Vladimir Villarreal ◽  
Mel Nielsen ◽  
Yen Caballero ◽  
Inés Sittón-Candanedo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computational Models ◽  
Population Data ◽  
Planning And Design ◽  
Predictive Algorithms ◽  
Rapid Spread ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Decision Making Processes ◽  
User Friendly

The rapid spread of SARS-CoV-2 and the consequent global COVID-19 pandemic has prompted the public administrations of different countries to establish health procedures and protocols based on information generated through predictive techniques and models, which, in turn, are based on technology such as artificial intelligence (AI) and machine learning (ML). This article presents some AI tools and computational models used to collaborate in the control and detection of COVID-19 cases. In addition, the main features of the Epidempredict project regarding COVID-19 in Panama are presented. This initiative consists of the planning and design of a digital platform, with cloud-based technology, to manage the ingestion, analysis, visualization and exportation of data regarding the evolution of COVID-19 in Panama. The methodology for the design of predictive algorithms is based on a hybrid model that combines the dynamics associated with population data of an SIR model of differential equations and extrapolation with recurrent neural networks. The technological solution developed suggests that adjustments can be made to the rules implemented in the expert processes that are considered. Furthermore, the resulting information is displayed and explored through user-friendly dashboards, contributing to more meaningful decision-making processes.

Artificial Neural Networks

2018 ◽  
pp. 120-131 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Future Research ◽  
Network Architectures ◽  
Advantages And Disadvantages ◽  
History Of ◽  
Future Research Directions ◽  
Artificial Neural

This chapter examines the history of artificial neural networks research through the present day. The components of artificial neural network architectures and both unsupervised and supervised learning methods are discussed. Although a step-by-step tutorial of how to develop artificial neural networks is not included, additional reading suggestions covering artificial neural network development are provided. The advantages and disadvantages of artificial neural networks for research and real-world applications are presented as well as potential solutions to many of the disadvantages. Future research directions for the field of artificial neural networks are presented.

scholarly journals A comparison of activation functions in multilayer neural network for predicting the production and consumption of electricity power

2021 ◽  
Vol 11 (1) ◽  
pp. 163
Author(s):  
Abdulwahed Salam ◽  
Abdelaaziz El Hibaoui ◽  
Abdulgabbar Saif
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Activation Function ◽  
Activation Functions ◽  
Paper Cover ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Electricity Power ◽  
Multilayer Neural Networks ◽  
Power Utilities

Predicting electricity power is an important task, which helps power utilities in improving their systems’ performance in terms of effectiveness, productivity, management and control. Several researches had introduced this task using three main models: engineering, statistical and artificial intelligence. Based on the experiments, which used artificial intelligence models, multilayer neural networks model has proven its success in predicting many evaluation datasets. However, the performance of this model depends mainly on the type of activation function. Therefore, this paper introduces an experimental study for investigating the performance of the multilayer neural networks model with respect to different activation functions and different depths of hidden layers. The experiments in this paper cover the comparison among eleven activation functions using four benchmark electricity datasets. The activation functions under examination are sigmoid, hyperbolic tangent, SoftSign, SoftPlus, ReLU, Leak ReLU, Gaussian, ELU, SELU, Swish and Adjust-Swish. Experimental results show that ReLU and Leak ReLU activation functions outperform their counterparts in all datasets.

Artificial Neural Networks

2019 ◽  
pp. 40-53 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Future Research ◽  
Network Architectures ◽  
Advantages And Disadvantages ◽  
History Of ◽  
Future Research Directions ◽  
Artificial Neural

This chapter examines the history of artificial neural networks research through the present day. The components of artificial neural network architectures and both unsupervised and supervised learning methods are discussed. Although a step-by-step tutorial of how to develop artificial neural networks is not included, additional reading suggestions covering artificial neural network development are provided. The advantages and disadvantages of artificial neural networks for research and real-world applications are presented as well as potential solutions to many of the disadvantages. Future research directions for the field of artificial neural networks are presented.

Artificial Neural Networks Used for Development Prediction of State-of-the-Art Surface Engineering Areas

2015 ◽  
Vol 220-221 ◽  
pp. 785-789
Author(s):  
Anna Danuta Dobrzańska-Danikiewicz ◽  
Jacek Trzaska ◽  
Agnieszka Sękala ◽  
Adam Jagiełło
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Models ◽  
Surface Engineering ◽  
Materials Science ◽  
Added Value ◽  
Future Research ◽  
Processing Technologies ◽  
Future Events ◽  
Artificial Neural

The paper presents new, possible applications of artificial neural networks in the field of materials science and material engineering in relation to other artificial intelligence methods known and applied in this area. The most recent simulation experiments, the exemplary results of which are presented in this paper, point out that the scope of the existing applications of artificial neural networks can be extended to encompass new areas related to prediction of development of materials treatment and processing technologies. The goal of such research is to focus, intentionally, the areas of future research and investments on the most promising areas likely to yield the highest added value in the future together with mitigating a risk relating to such a process. The computational models created were used for creating multi-variant probabilistic scenarios of future events based on heuristic independent variables acquired in the process of multi-stage expert surveys. Dependencies were determined, in particular, between the probability of occurrence of alternative macro-scenarios of future events and the development of the relevant thematic areas of M1–M7 and P1–P7.

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