Training of automatic preclassification during operation – the way to create an autonomous automatic analyzer of complex morphology

2021 ◽  
Vol 3 ◽  
Author(s):  
Keyword(s):  
Neural Networks ◽  
Local Adaptation ◽  
Combine Analysis ◽  
User Control ◽  
Complex Morphology ◽  
Automatic Analyzer ◽  
Automatic Adaptation ◽  
The Way

The development of a microscopy combine designed to automate the analysis of complex morphological objects is presented. Automatic tecniques of combine analysis form the results of the “preclassification” class with the control of automatic results by the user. The harvester has means of automatic adaptation to the current slide and to the population of objects of analysis presented in the streams of slides served by the team of combines of laboratories. Local adaptation optimizes the quality and speed of the slide scanning. Adaptation to the population of objects of analysis is carried out by training neural networks of combine analyzers using a common database of automatic preclassification adjustments by qualified laboratory users. Training is used to increase the accuracy of preclassification with the ultimate goal of creating a stand-alone analyzer without user control.

scholarly journals Image Analysis using Deep Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1385-1388
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Techniques ◽  
Massive Information ◽  
Comparative Examination ◽  
Hypothetical Structure ◽  
The Way

In late years, critical learning methodologies especially Convolutional Neural Networks have been utilized in different solicitations. CNN's have appeared to be a key capacity to ordinarily expel broad volumes of data from massive information. The uses of CNNs have inside and out ended up being useful especially in orchestrating ordinary pictures. Regardless, there have been essential obstacles in executing the CNNs in a restorative zone as a result of the nonattendance of genuine getting ready data. Consequently, general imaging benchmarks, for instance, Image Net have been conspicuously used in the restorative not too zone notwithstanding the way that they are perfect when appeared differently about the CNNs. In this paper, a comparative examination of LeNet, AlexNet, and GoogLeNet has been done. Starting there, the paper has proposed an improved hypothetical structure for requesting helpful life structures pictures using CNNs. In perspective on the proposed structure of the framework, the CNNs building are required to beat the previous three plans in requesting remedial pictures.

scholarly journals It’s only the beginning: Metadata Retention laws and the Internet of Things

2015 ◽  
Vol 3 (3) ◽  
pp. 47-57
Author(s):  
Clinton Fernandes ◽  
Vijay Sivaraman
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Apparent Discrepancy ◽  
Data Retention ◽  
User Control ◽  
The Law ◽  
Iot Devices ◽  
The Internet Of Things ◽  
The Way

This article examines the implications of selected aspects of the Telecommunications (Interception and Access) Amendment (Data Retention) Act 2015, which was passed by the Australian Parliament in March 2015. It shows how the new law has strengthened protections for privacy. However, focusing on the investigatory implications, it shows how the law provides a tactical advantage to investigators who pursue whistleblowers and investigative journalists. The article exposes an apparent discrepancy in the way ‘journalist’ is defined across different pieces of legislation. It argues that although legislators’ interest has been overwhelmingly focused on communications data, the explosion of data generated by the so-called Internet-of-Things (IoT) is as important or more. It shows how the sensors in selected IoT devices lead to a loss of user control and will enable non-stop, involuntary and ubiquitous monitoring of individuals. It suggests that the law will need to be amended further once legislators and investigators’ knowledge of the potential of IoT increases. 

Worshiping Computers

2019 ◽  
pp. 85-110
Author(s):  
Gary Smith ◽  
Jay Cordes
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Common Sense ◽  
Deep Neural Networks ◽  
Computer Software ◽  
Square Roots ◽  
The World ◽  
The Way

Computer software, particularly deep neural networks and Monte Carlo simulations, are extremely useful for the specific tasks that they have been designed to do, and they will get even better, much better. However, we should not assume that computers are smarter than us just because they can tell us the first 2000 digits of pi or show us a street map of every city in the world. One of the paradoxical things about computers is that they can excel at things that humans consider difficult (like calculating square roots) while failing at things that humans consider easy (like recognizing stop signs). They can’t pass simple tests like the Winograd Schema Challenge because they do not understand the world the way humans do. They have neither common sense nor wisdom. They are our tools, not our masters.

scholarly journals Using neural networks to mine text and predict metabolic traits for thousands of microbes

2020 ◽  
Author(s):  
Timothy J. Hackmann
Keyword(s):  
Neural Networks ◽  
Phylogenetic Trees ◽  
Chemical Compounds ◽  
Easy Access ◽  
Proof Of Concept ◽  
Metabolic Traits ◽  
Access Information ◽  
Written Descriptions ◽  
Extract Information ◽  
The Way

AbstractMicrobes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. Information is buried in text of books and journals, and investigators have no easy way to extract it out. Here we investigate if neural networks can extract out this information and predict metabolic traits. For proof of concept, we predicted two traits: whether microbes carry one type of metabolism (fermentation) or produce one metabolite (acetate). We collected written descriptions of 7,021 species of bacteria and archaea from Bergey’s Manual. We read the descriptions and manually identified (labeled) which species were fermentative or produced acetate. We then trained neural networks to predict these labels. In total, we identified 2,364 species as fermentative, and 1,009 species as also producing acetate. Neural networks could predict which species were fermentative with 97.3% accuracy. Accuracy was even higher (98.6%) when predicting species also producing acetate. We used these predictions to draw phylogenetic trees of species with these traits. The resulting trees were close to the actual trees (drawn using labels). Previous counts of fermentative species are 4-fold lower than our own. For acetate-producing species, they are 100-fold lower. This undercounting confirms past difficulty in extracting metabolic traits from text. Our approach with neural networks can extract information efficiently and accurately. It paves the way for putting more metabolic traits into databases, providing easy access of information by investigators.

scholarly journals Penerapan Algoritma Backpropagation Pada Game Pengenalan Nahwu Di Mi Darul Falah Jepara

2019 ◽  
Vol 5 (2) ◽  
pp. 103
Author(s):  
R Hadapiningradja Kusumodestoni ◽  
Adi Sucipto ◽  
Sela Nur Ismiati ◽  
M Novailul Abid
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Technological Development ◽  
Technological Advancement ◽  
Artificial Neural ◽  
Academic Year ◽  
Class Iv ◽  
The Way

Nahwu is a science that studies Arabic grammar. Nevertheless, the interest of students in learning nahwu is currently decreasing. It happens because the technological advancement vastly develops but the way of learning it is still conventional and tends to be boring. Based on the school data for the academic year of 2017/2018 at MI Darul Falah Sirahan Cluwak Pati, there were only 20 students able to undestand nahwu well out of 60 students who started learning nahwu in class IV. Technological development has brought many changes to things around us. One of the most developed at the meantime is game. Lately games have become something very fast developing. Using the game to be used as a secondary learning media for students in learning nahwu is considered quite effective. The method used in designing this game was Backpropagation meaning an algorithm based on artificial neural networks which is used to determine and take decision that is used to determine scores and levels in the Nahwu Introduction Game. The tools used in this game-making are construct 2, an HTML5-based game maker specifically for the 2d platform. The results of this study were an Android-based Nahwu Introduction Game.

scholarly journals Redes de neurônios

1992 ◽  
Vol 14 (14) ◽  
pp. 07
Author(s):  
Rita M. C. de Almeida
Keyword(s):  
Neural Networks ◽  
Spin Glass ◽  
Physical Problem ◽  
Information Storage ◽  
Hopfield Model ◽  
The Brain ◽  
Scientific Advances ◽  
The Way

In the last ten years many scientific advances regarding neurons and the way they are interconnected has mad o it possible to study the dynamics of storage and Processing of information in the brain. In particular, the physicist J. J. Hopfield proposed a formal minimalist model to these neural networks reducing the problem to a particular case of a well – defined physical problem – the spin glass. Although the problem í s well defined, its solution is far from being trivial.Here we introduce the problem, describe Hopfield model, with its achievements and limitations, and present our contribution to the description of information storage in neural networks.

scholarly journals ETALON IMAGES: UNDERSTANDING THE CONVOLUTION NEURAL NETWORKS

2018 ◽  
Vol XLII-2 ◽  
pp. 707-714 ◽  
Author(s):  
V. V. Molchanov ◽  
B. V. Vishnyakov ◽  
V. S. Gorbatsevich ◽  
Y. V. Vizilter
Keyword(s):  
Neural Networks ◽  
Input Image ◽  
Neural Net ◽  
Convolution Neural Networks ◽  
Affine Maps ◽  
Specific Input ◽  
Current Input ◽  
High Level ◽  
The Way

In this paper we propose a new technic called etalons, which allows us to interpret the way how convolution network makes its predictions. This mechanism is very similar to voting among different experts. Thereby CNN could be interpreted as a variety of experts, but it acts not like a sum or product of them, but rather represent a complicated hierarchy. We implement algorithm for etalon acquisition based on well-known properties of affine maps. We show that neural net has two high-level mechanisms of voting: first, based on attention to input image regions, specific to current input, and second, based on ignoring specific input regions. We also make an assumption that there is a connection between complexity of the underlying data manifold and the number of etalon images and their quality.

scholarly journals Bike Sharing Prediction using Deep Neural Networks

2017 ◽  
Vol 1 (3) ◽  
pp. 83 ◽  
Author(s):  
Chandrasegar Thirumalai ◽  
Ravisankar Koppuravuri
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Neural Nets ◽  
Machine Learning Techniques ◽  
Business Decisions ◽  
Learning Techniques ◽  
Bike Sharing ◽  
The Way

In this paper, we will use deep neural networks for predicting the bike sharing usage based on previous years usage data. We will use because deep neural nets for getting higher accuracy. Deep neural nets are quite different from other machine learning techniques; here we can add many numbers of hidden layers to improve the accuracy of our prediction and the model can be trained in the way we want such that we can achieve the results we want. Nowadays many AI experts will say that deep learning is the best AI technique available now and we can achieve some unbelievable results using this technique. Now we will use that technique to predict bike sharing usage of a rental company to make sure they can take good business decisions based on previous years data.

Research on the Application of Micro-Program Controller in Parallel Neural Networks

2011 ◽  
Vol 271-273 ◽  
pp. 1023-1028
Author(s):  
Xi Huang ◽  
Ping Wang ◽  
Zong Huang Weng ◽  
Xiao Zhang ◽  
Wei Han Zhong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Parallel Computing ◽  
Bp Neural Network ◽  
Network Control ◽  
Control Circuit ◽  
Bp Network ◽  
Hardware Circuit ◽  
Program Controller ◽  
The Way

In this paper a pipelined array of neurons based on the micro-program controller is proposed as the BP network control circuit implementations, without changing the hardware circuit under the premise of the way by increasing the instruction to meet the BP neural network parallel computing applications to enhance the flexibility of hardware.

Human-level blood cell counting on lens-free shadow images exploiting deep neural networks

The Analyst ◽  
2018 ◽  
Vol 143 (22) ◽  
pp. 5380-5387 ◽  
Author(s):  
DaeHan Ahn ◽  
JiYeong Lee ◽  
SangJun Moon ◽  
Taejoon Park
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Deep Neural Networks ◽  
Cell Counting ◽  
Blood Cell Counting ◽  
The Way

In-line holographic microscopes paved the way for realizing portable cell counting systems using deep neural networks.

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