The future of neural networks

2004 ◽  
Author(s):  
J G Taylor
Keyword(s):  
Neural Networks ◽  
The Future

scholarly journals Predicting the future direction of cell movement with convolutional neural networks

2018 ◽  
Author(s):  
Shori Nishimoto ◽  
Yuta Tokuoka ◽  
Takahiro G Yamada ◽  
Noriko F Hiroi ◽  
Akira Funahashi
Keyword(s):  
Neural Networks ◽  
Cell Fate ◽  
Convolutional Neural Networks ◽  
Cell Shape ◽  
Cell Movement ◽  
Image Features ◽  
Morphological Features ◽  
Current Cell ◽  
The Future ◽  
Future Direction

SummaryImage-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell fate. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future cell fate from current cell shape, and can be used to automatically identify those morphological features that influence future cell fate.

Applications of Artificial Neural Networks in Economics and Finance

2018 ◽  
pp. 6631-6641
Author(s):  
Iva Mihaylova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Trends ◽  
Dependence Analysis ◽  
Future Trends ◽  
Powerful Technique ◽  
Analytic Tool ◽  
The Future ◽  
Multivariate Dependence ◽  
Artificial Neural

Artificial neural Networks (ANNs) are a powerful technique for multivariate dependence analysis. Originally inspired by neuroscience, ANNs are becoming an increasingly attractive analytic tool for applications in the area of economics and finance due to the flexible solutions they offer. The purpose of this article is to present such important applications with an emphasis on recent research trends. The contributions are grouped as follows: ANNs (1) for prediction, (2) for classification and (3) for modelling. The chapter concludes with the future trends in the ANNs research in economics and finance.

scholarly journals Development of a software product for forecasting the entrance of applicants to higher educational institutions

2018 ◽  
Vol 6 (3) ◽  
pp. 55-69 ◽  
Author(s):  
Viacheslav Osadchyi ◽  
Vladyslav Kruglyk ◽  
Dmitriy Bukreyev
Keyword(s):  
Higher Education ◽  
Neural Networks ◽  
Higher Education Institutions ◽  
Software Tool ◽  
Educational Institutions ◽  
Higher Educational Institutions ◽  
Advantages And Disadvantages ◽  
Software Product ◽  
The Future ◽  
Higher Educational

The article highlights the problems of forecasting the entrance of university entrants into higher education institutions in connection with the constant fluctuations of the labor market and socio-demographic processes, which completely violate the results of the predictions of classical statistical methods, therefore the author studies the necessity of developing a software tool for forecasting the entrance of entrants to higher education institutions , which will operate on the basis of the neural network and will be able to adapt to the conditions of constant chaotic oscillations. The author emphasizes that neural networks are a modern and leading area of research and program development, and proves that the use of neural networks in the prediction of educational processes will allow obtaining results with a much higher level of accuracy and less time. The article contains analysis of theoretical information about neural networks and analysis of existing algorithms of neural networks operation. The author mentions the advantages and disadvantages of each algorithm, provides a comparative analysis and concludes that it is expedient to use each of the methods in a software tool for forecasting the entrance of entrants to higher education institutions. In the course of the work, the author carried out software modelling of the various methods of teaching neural networks, conducted testing, received and disclosed the results of each method, carried out an analysis of their actual effectiveness in predicting small and large volumes of information with different inputs and made the conclusion that the expediency of their use in the future software. The mathematical features of the construction of neural networks, their training and further use are revealed, the basic requirements for the future of the software product, namely the method of work, input data, the method of displaying the results and the layout of the future software, are revealed. The main blocks of the software for forecasting the entrance of entrants to higher education institutions are shown. It was concluded that it is expedient to use neural networks and work on a software tool for forecasting the entrance of entrants to higher educational institutions has been started, vectors of further researches and developments have been selected.

scholarly journals PREDIKSI PENGADAAN DAN PENGELOLAAN INVENTORI JARINGAN SYARAF TIRUAN ALGORITMA BACKPROPAGATION PADA PERUM BULOG

KOMPUTEK ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Ethan Mahesa Murty
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Food Availability ◽  
Public Company ◽  
The Future ◽  
Rice Consumption ◽  
Food Stability ◽  
Artificial Neural ◽  
Prediction Techniques

Perum Bulog is a state-owned public company in food logistics field. Perum Bulog has a duty to stabilize food availability in Indonesia. The most consumed food by Indonesians is rice. It is estimated that the total national rice consumption reaches 30.25 million tons of rice. In this way, Perum Bulog must be able to meet their rice stock to maintain national food stability. However, in fact, in 2019 as many as 20 thousand tons of domestic rice had gone bad and caused the company to lose up to 167 billion. Thus, it is important to make predictions to determine the amount of rice stock in the future. One of the prediction techniques that can be used is prediction using Artificial Neural Networks. This study aims to determine the future rice stock of Perum Bulog using Artificial Neural Networks. Perum Bulog merupakan perusahaan umum milik negara yang bergerak di  bidang logistik pangan.  Perum Bulog memiliki tugas untuk menstabilkan ketersediaan pangan di Indonesia. makanan pokok yang paling sering dikonsumsi masyarakat Indonesia adalah beras. Diperkirakan jumlah konsumsi beras nasional mencapai 30,25 juta ton beras. Dengan begitu Perum Bulog harus dapat memenuhi stok beras mereka untuk menjaga kestabilan pangan nasional. Namun, nyatanya dilapan pada tahun 2019  sebanyak 20 ribu ton beras dalam negeri mengalami pembusukan dan membuat perusahaan rugi hingga 167 miliar. Dengan begitu pentingnya melakukan prediksi  untuk mengetahui jumlah stok beras dimasa depan. Salah satu teknik prediksi yang dapa digunakan adalah prediksi menggunakan jaringan syaraf tiruan. Penelitian ini bertujuan untuk mengetahui stok beras masa depan Perum Bulog menggunakan jaringan syaraf tiruan.

scholarly journals Machine learning forecasting of CR and PRC balance of trade

2020 ◽  
Vol 73 ◽  
pp. 01004
Author(s):  
Tomàš Brabenec ◽  
Petr Šuleř
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Data Set ◽  
Republic Of China ◽  
The People ◽  
The Future ◽  
Balance Of Trade ◽  
Political Economic ◽  
Social Importance

International trade is an important factor of economic growth. While foreign trade has existed throughout the history, its political, economic and social importance has grown significantly in the last centuries. The objective of the contribution is to use machine learning forecasting for predicting the balance of trade of the Czech Republic (CR) and the People´s Republic of China (PRC) through analysing and machine learning forecasting of the CR import from the PRC and the CR export to the PRC. The data set includes monthly trade balance intervals from January 2000 to June 2019. The contribution investigates and subsequently smooths two time series: the CR import from the PRC; the CR export to the PRC. The balance of trade of both countries in the entire monitored period is negative from the perspective of the CR. A total of 10,000 neural networks are generated. 5 neural structures with the best characteristics are retained. Neural networks are able to capture both the trend of the entire time series and its seasonal fluctuations, but it is necessary to work with time series lag. The CR import from the PRC is growing and it is expected to grow in the future. The CR export to the PRC is growing and it is expected to grow in the future, but its increase in absolute values will be slower than the increase of the CR import from the PRC.

The Future of Memristors: Materials Engineering and Neural Networks

2020 ◽  
pp. 2006773
Author(s):  
Kaixuan Sun ◽  
Jingsheng Chen ◽  
Xiaobing Yan
Keyword(s):  
Neural Networks ◽  
Materials Engineering ◽  
The Future

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 055-055
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Bayesian Inference ◽  
Data Assimilation ◽  
Image Enhancement ◽  
Privacy Preserving ◽  
Similarity Learning ◽  
Cardiac Image ◽  
The Future

Albers DJ, Levine ME, Stuart A, Mamykina L, Gluckman B, Hripcsak G. Mechanistic machine learning: how data assimilation leverages physiological knowledge using bayesian inference to forecast the future, infer the present, and phenotype. J Am Med Inform Assoc 2018;25(10):1392-401 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6188514/ Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook SA, de Marvao A, Dawes T, O'Regan DP, Kainz B, Glocker B, Rueckert D. Anatomically Constrained Neural Networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans Med Imaging 2018;37(2):384-95 https://spiral.imperial.ac.uk:8443/handle/10044/1/50440 Lee J, Sun J, Wang F, Wang S, Jun CH, Jiang X. Privacy-preserving patient similarity learning in a federated environment: development and analysis. JMIR Med Inform 2018;6(2):e20 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5924379/

Homo Ludens, Machine Ludens: The Way to Ideal Neural Network and Background of Posthumanism

2021 ◽  
Vol 13 (1-1) ◽  
pp. 151-165
Author(s):  
Maria Ivanchenko ◽  
◽  
Pavel Arkhipov ◽  
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Homo Sapiens ◽  
Philosophical Concept ◽  
Critical Methodology ◽  
Strong Artificial Intelligence ◽  
The Future ◽  
History Of ◽  
The Creation

The article consists of an introduction, a main part with three sections and a conclusion. The purpose of the study is to disclose the content of the concepts of “A Man Playing”, “A Machine Playing”, “Posthumanism” and “Essentiocognitivism”; review current advances in artificial intelligence and neural networks. The article focuses on the philosophy of posthumanism in the context of its application in machine learning, as well as a new philosophical concept called “essentiocognitivism” in its relation to artificial intelligence. The object of the study is the philosophical concept of essentiosocognitivism. The subject of the article is the consideration of certain aspects of this concept related to artificial intelligence as a “playing machine” and the positioning of a human being in the world of posthumanism. In the course of the work, critical methodology was used, on the basis of which the strengths and weaknesses of artificial neural networks were highlighted, the current state of the most famous playing neural networks, such as OpenAI and Alpha series from DeepMind, was analyzed, and the upcoming development of AI is considered in the context of a technological singularity. A philosophical comprehension has been made of certain aspects of essentiocognitivism, which play an important role in the history of the development of posthumanism. It is noted that the future of neural networks is largely determined by the gaming industry and moves towards the creation of a strong artificial intelligence, like the Playing Machine. Scientific novelty consists in examining a fundamentally new concept in the history of philosophy and substantiating the place and role of AI in the evolution of intelligent man. In the course of work, it was revealed that AI and, in particular, promising neural networks allow us to predict the probable future of mankind. As a basic thesis, we use the position derived from biological sciences that the evolution of the species Homo sapiens is not over, and will continue in a technological manner. As a result of the study, a working concept of essentiocognitivism was introduced, and the conclusion was made that trans- and posthumanism can solve many global problems of mankind. It is emphasized that the future lies in the creation of a strong AI.

Applications of Artificial Neural Networks in Economics and Finance

2019 ◽  
pp. 997-1008 ◽  
Author(s):  
Iva Mihaylova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Trends ◽  
Dependence Analysis ◽  
Future Trends ◽  
Powerful Technique ◽  
Analytic Tool ◽  
The Future ◽  
Multivariate Dependence ◽  
Artificial Neural

Artificial neural networks (ANNs) are a powerful technique for multivariate dependence analysis. Originally inspired by neuroscience, ANNs are becoming an increasingly attractive analytic tool for applications in the area of economics and finance due to the flexible solutions they offer. The purpose of this chapter is to present such important applications with an emphasis on recent research trends. The contributions are grouped as follows: ANNs (1) for prediction, (2) for classification, and (3) for modelling. The chapter concludes with the future trends in the ANNs research in economics and finance.

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