scholarly journals Neural networks and perspectives of their use in forensic medicine

2017 ◽  
pp. 88-91
Author(s):  
Natalia Kozan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Processing ◽  
Forensic Medicine ◽  
Computer Processing ◽  
Modern Computer ◽  
Processing Data ◽  
Artificial Neural ◽  
Forensic Investigations

This paper presents the trends and tendencies of modern computer processing data obtained during forensic investigations. Examined the system of artificial neural networks, principles and characteristics of their work. Prospects using artificial neural networks when dermatoglyphics data processing research.

Neuromorphic Functional Modules of Spiking Neural Network

2021 ◽  
Vol 23 (6) ◽  
pp. 317-326
Author(s):  
E.A. Ryndin ◽  
◽  
N.V. Andreeva ◽  
V.V. Luchinin ◽  
K.S. Goncharov ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Plasticity ◽  
Data Processing ◽  
Circuit Design ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Wide Range ◽  
Artificial Neural

In the current era, design and development of artificial neural networks exploiting the architecture of the human brain have evolved rapidly. Artificial neural networks effectively solve a wide range of common for artificial intelligence tasks involving data classification and recognition, prediction, forecasting and adaptive control of object behavior. Biologically inspired underlying principles of ANN operation have certain advantages over the conventional von Neumann architecture including unsupervised learning, architectural flexibility and adaptability to environmental change and high performance under significantly reduced power consumption due to heavy parallel and asynchronous data processing. In this paper, we present the circuit design of main functional blocks (neurons and synapses) intended for hardware implementation of a perceptron-based feedforward spiking neural network. As the third generation of artificial neural networks, spiking neural networks perform data processing utilizing spikes, which are discrete events (or functions) that take place at points in time. Neurons in spiking neural networks initiate precisely timing spikes and communicate with each other via spikes transmitted through synaptic connections or synapses with adaptable scalable weight. One of the prospective approach to emulate the synaptic behavior in hardware implemented spiking neural networks is to use non-volatile memory devices with analog conduction modulation (or memristive structures). Here we propose a circuit design for functional analogues of memristive structure to mimic a synaptic plasticity, pre- and postsynaptic neurons which could be used for developing circuit design of spiking neural network architectures with different training algorithms including spike-timing dependent plasticity learning rule. Two different circuits of electronic synapse were developed. The first one is an analog synapse with photoresistive optocoupler used to ensure the tunable conductivity for synaptic plasticity emulation. While the second one is a digital synapse, in which the synaptic weight is stored in a digital code with its direct conversion into conductivity (without digital-to-analog converter andphotoresistive optocoupler). The results of the prototyping of developed circuits for electronic analogues of synapses, pre- and postsynaptic neurons and the study of transient processes are presented. The developed approach could provide a basis for ASIC design of spiking neural networks based on CMOS (complementary metal oxide semiconductor) design technology.

Analysis of ethanol–glucose mixtures by two microbial sensors: application of chemometrics and artificial neural networks for data processing

2001 ◽  
Vol 16 (9-12) ◽  
pp. 1001-1007 ◽  
Author(s):  
Alexei V Lobanov ◽  
Ivan A Borisov ◽  
Sherald H Gordon ◽  
Richard V Greene ◽  
Timothy D Leathers ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Processing ◽  
Microbial Sensors ◽  
Artificial Neural

Enhanced interpretation of magnetic survey data from archaeological sites using artificial neural networks

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. H45-H53 ◽  
Author(s):  
David. J. Bescoby ◽  
Gavin C. Cawley ◽  
P. Neil Chroston
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Processing ◽  
Survey Data ◽  
Source Parameters ◽  
Magnetic Data ◽  
Burial Depth ◽  
Training Procedure ◽  
Wide Range ◽  
Artificial Neural

The use of magnetic surveys for archaeological prospecting is a well-established and versatile technique, and a wide range of data processing routines are often applied to further enhance acquired data or derive source parameters. Of particular interest in this respect is the application of artificial neural networks (ANNs) to predict source parameters such as the burial depths of detected features of interest. Within this study, ANNs based upon a multilayer perceptron architecture are used to perform the nonlinear mapping between buried wall features detected within the magnetic data and their corresponding burial depth for surveys in the ancient city of Butrint in southern Albania, achieving a greater level of information from the survey data. Suitable network training examples and test data were generated using forward models based upon ground-truth observations. The training procedure adopts a supervised learning routine that is optimized using a conjugate gradient method, while the learning algorithm also prunes network elements to prevent overregularization by reducing model complexity. Data processing was further enhanced by introducing rotational invariance using Zernike moments and by utilizing the combined output of a number, or committee, of networks. When applied to a section of survey data from Butrint, the ANN routine successfully predicted the burial depth of a number of detected wall features, with an rms error on the order of [Formula: see text], and provided a coherent map of the buried building foundations. The neural network approach offered advantages in terms of efficiency and flexibility over more conventional data-inversion techniques within the context of the study, giving fast solutions for large, complex data sets while having high noise tolerance.

scholarly journals Prediction of sweet corn yield depending on cultivation technology parameters by using linear regression and artificial neural network methods

2018 ◽  
Vol 26 (1) ◽  
pp. 11-15 ◽  
Author(s):  
P. V. Lykhovyd
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Data Processing ◽  
Field Experiments ◽  
Sweet Corn ◽  
Corn Yield ◽  
Artificial Neural

Artificial neural networks and linear regression are widely used in particularly all branches of science for modeling and prediction. Linear regression is an old data processing tool, and artificial neural networks are a comparatively new one. The goal of the study was to determine whether artificial neural networks are more accurate than linear regression in sweet corn yield prediction. In the study we used a dataset obtained from field experiments on the technological improvement of sweet corn cultivation. The field experiments were conducted during the period from 2014 to 2016 on dark-chestnut soil under drip irrigated conditions in the Steppe Zone of Ukraine. We studied the impact of the moldboard plowing depths, mineral fertilizer application rates and plant densities on the crop yield. A significant impact of all the studied factors on the sweet corn productivity was proved by using the analysis of variance. The highest yield of sweet corn ears without husks (10.93 t ha–1) was under the moldboard plowing at the depth of 20–22 cm, mineral fertilizers application rate of N120P120, plant density of 65,000 plants ha–1. Data processing by using the linear regression and artificial neural network methods showed that the latter is a great deal better than linear regression in sweet corn yield prediction. Higher accuracy of the artificial neural network prediction was proved by the higher value of the coefficient of determination (R2) – 0.978, in comparison to 0.897 for the linear regression prediction model. We conclude that artificial neural networks are a much better data processing tool, especially, in the life sciences and for prediction of the non-linear natural processes and phenomena. The main disadvantage of the neural network models is their “black box” nature. However, linear regression will not lose its popularity among scientists in the nearest future. Linear regression is a much simpler data analysis tool, it is easier to perform the prediction, but it still provides a sufficiently high level of accuracy.

scholarly journals Jaringan Syaraf Tiruan Untuk Memprediksi Nilai Siswa SMA Menggunakan Backpropagation

2021 ◽  
Vol 4 (2) ◽  
pp. 1-7
Author(s):  
Evan Hikler Damanik ◽  
Eka Irawan ◽  
Fitri Rizki
Keyword(s):  
High School ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rapid Development ◽  
Processing Data ◽  
The Future ◽  
Artificial Neural ◽  
Exam Scores ◽  
Student Grades

A student's mastery of a subject greatly influences the marking given by the teacher / teacher concerned. The need for instructors or teachers to monitor every value of students who are taught science in their respective fields. With the rapid development of technology, it is very helpful for teachers in knowing or predicting the value that students will get related. This study aims to apply the performance of backpropagation artificial neural networks in predicting the value of students of SMA N 1 Sidamanik with various models and minimizing their errors. In this study the authors used data on student grades from SMA N 1 Sidamanik. In processing data values, the authors use artificial neural networks with backpropagation algorithms as logical steps to predict student National Exam Scores in SMA N 1 Sidamanik. The main problem in this study is the decline in student grades in some subjects, in the future students will experience difficulties in reaching the desired university or high school.

The Advent of Artificial Intelligence in Diabetic Foot Medicine: A New Horizon, a New Order, or a False Dawn?

2021 ◽  
pp. 153473462110418
Author(s):  
Theodore Howard ◽  
Raju Ahluwalia ◽  
Nikolas Papanas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diabetic Foot ◽  
Delayed Diagnosis ◽  
Computer Processing ◽  
New Order ◽  
Foot Care ◽  
Artificial Neural

In a world where automation is becoming increasingly common, easier collection of mass of data and powerful computer processing has meant a transformation in the field of artificial intelligence (AI). The diabetic foot is a multifactorial problem; its issues render it suitable for analysis, interrogation, and development of AI. The latter has the potential to deliver many solutions to issues of delayed diagnosis, compliance, and defining preventative treatments. We describe the use of AI and the development of artificial neural networks that may supplement the failed networks in the diabetic foot. The potential of this technology, current developing applications, and their limitations for diabetic foot care are suggested.

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