Drought Modelling Based on Artificial Intelligence and Neural Network Algorithms: A Case Study in Queensland, Australia

Author(s):  
Kavina Dayal ◽  
Ravinesh Deo ◽  
Armando A. Apan
2020 ◽  
pp. 1-12
Author(s):  
Yingli Duan

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school’s teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them.


Author(s):  
Vladimir Sergeevich Smolin

The commercially successful application of neural network algorithms in artificial intelligence (AI) systems and devices after 2010 has significantly accelerated the process of achieving new successes in solving “intellectual problems. Further development of work on AI will affect not only the technological order, but also social relations in human society, and it is necessary to think about the possible consequences of such an influence right now.


Author(s):  
S.V. Volodenkov ◽  
S.N. Fedorchenko

The work aimed to study the peculiarities of the subjectness of the phenomenon of digital communication in the context of intensive digitalization of key spheres of life of modern society, as well as to identify the prospects and threats of introducing self-learning neural network algorithms and artificial intelligence technologies into communication processes unfolding in the social and political sphere. One of the study's key objectives was to identify scenarios of possible social changes in the context of society digitalization and the traditional social practices transformation in terms of the emergence of new digital subjects of mass public communication that form the pseudo structure of digital interaction between people. As a methodological optics, the work used the method of discourse analysis of scientific research devoted to the implementation and application of artificial intelligence technologies and self-learning neural networks in the processes of socio-political digitalization, as well as the method of critical analysis of current communication practice in the socio-political sphere. At the same time, when analyzing the current practice of digitalization in foreign countries, the case study method was used. In turn, to determine the scenarios for the transformation of traditional social space and social practices, the method of scenario techniques and scenario forecasting was applied. As a research result, it was concluded that the introduction of technological solutions based on artificial intelligence algorithms and self-learning neural networks into contemporary socio-political communication processes creates the potential for the problem of identifying the subjects of communicative acts in the socio-political sphere of the contemporary society life. Based on the results of the study, it is shown that artificial intelligence and self-learning neural network algorithms are increasingly being implemented in the current practice of contemporary digital communications, forming a high potential for information and communication impact on the mass consciousness of technological solutions that no longer require self-control from human operators. The work also concludes that in the current practice of social interactions in the digital space, a person faces a new phenomenon – interfaceization, within which self-communication stimulates the universalization and standardization of digital behavior, creating, disseminating, strengthening, and imposing special digital rituals. The article proves that digital rituals blur the line between digital avatars' activity based on artificial intelligence and the activity of real people, resulting in the potential for a person to lose their own subjectness in the digital universe.


Author(s):  
Muhammad Noor Hasan Siregar

This study aims to maximize the activation function used in backpropogation networks in finding the best architectural model. The case study used is the sale of state retail sukuk based on professional groups. The combination of activation functions used for training and testing is tansig-tansig, tansig-purelin and tansig logsig. The architectural model used is the architectural model 6-2-1 and 6-5-1. The evaluation parameters used are epoch, MSE training, MSE testing and accuracy level of truth. Data processing is assisted by using Matlab software. The results showed that the tansig-logsig activation function had more stable results than tansig-tansig and tansig-purelin.


2021 ◽  
Author(s):  
Callum Newman ◽  
Jon Petzing ◽  
Yee Mey Goh ◽  
Laura Justham

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.


2018 ◽  
Vol 7 (4.10) ◽  
pp. 169
Author(s):  
Jitendra Kumar Jaiswal ◽  
Raja Das

The involvement of big populace in the quantitative trading has been increased remarkably since the wired and wireless systems have become quite ubiquitous in the fields of finance and economics. Statistical, mathematical and technical analysis in parallel with machine learning and artificial intelligence are frequently being applied to perceive prices moving pattern and forecasting. However stock price do not follow any deterministic regulatory function, factor or circumstances rather than many considerations such as economy and finance, political environments, demand and supply, buying and selling tendency, trading and investment, etc. Historical data assist remarkably for prices forecasting as an important option for mathematicians and researchers. In this paper, we have followed backpropagation and radial basis function neural network for predicting future prices by modifying these techniques as per requirements. We have also performed a comparative analysis of the two ANN techniques for existing and our modified models.  


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