Neural networks for processing data structures

1998 ◽  
pp. 121-144 ◽  
Author(s):  
Alessandro Sperduti
Keyword(s):  
Neural Networks ◽  
Data Structures ◽  
Processing Data

Exploring the Unknown Nature of Data

2010 ◽  
pp. 1-27
Author(s):  
Rui Xu ◽  
Donald C. Wunsch II
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Data Structures ◽  
Clustering Algorithms ◽  
Ground Truth ◽  
Human Beings ◽  
Data Set ◽  
Learning Capabilities ◽  
Good Learning ◽  
Hidden Data

To classify objects based on their features and characteristics is one of the most important and primitive activities of human beings. The task becomes even more challenging when there is no ground truth available. Cluster analysis allows new opportunities in exploring the unknown nature of data through its aim to separate a finite data set, with little or no prior information, into a finite and discrete set of “natural,” hidden data structures. Here, the authors introduce and discuss clustering algorithms that are related to machine learning and computational intelligence, particularly those based on neural networks. Neural networks are well known for their good learning capabilities, adaptation, ease of implementation, parallelization, speed, and flexibility, and they have demonstrated many successful applications in cluster analysis. The applications of cluster analysis in real world problems are also illustrated. Portions of the chapter are taken from Xu and Wunsch (2008).

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.

Recent Advances in Artificial Immune Systems

2017 ◽  
pp. 92-114 ◽  
Author(s):  
Florin Popentiu Vladicescu ◽  
Grigore Albeanu
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Data Structures ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Computing Methods ◽  
Artificial Immune ◽  
Immune Systems ◽  
Methods And Techniques ◽  
Soft Computing Methods

The designers of Artificial Immune Systems (AIS) had been inspired from the properties of natural immune systems: self-organization, adaptation and diversity, learning by continual exposure, knowledge extraction and generalization, clonal selection, networking and meta-dynamics, knowledge of self and non-self, etc. The aim of this chapter, along its sections, is to describe the principles of artificial immune systems, the most representational data structures (for the representation of antibodies and antigens), suitable metrics (which quantifies the interactions between components of the AIS) and their properties, AIS specific algorithms and their characteristics, some hybrid computational schemes (based on various soft computing methods and techniques like artificial neural networks, fuzzy and intuitionistic-fuzzy systems, evolutionary computation, and genetic algorithms), both standard and extended AIS models/architectures, and AIS applications, in the end.

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.

scholarly journals Hash Tables for a Digital Lexicon

2021 ◽  
Vol 28 (2) ◽  
pp. 25-38
Author(s):  
Fábio Carlos Moreno ◽  
Cinthyan Sachs C. de Barbosa ◽  
Edio Roberto Manfio
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Data Structures ◽  
Response Speed ◽  
Hash Tables ◽  
Processing Data ◽  
Natural Language Interface ◽  
Performance Results ◽  
And Storage

This paper deals with the construction of digital lexicons within the scope of Natural Language Processing. Data Structures called Hash Tables have demonstrated to generate good results for Natural Language Interface for Databases and have data dispersion, response speed and programming simplicity as main features. The storage of the desired information is done by associating a key through the hashing functions that is responsible for distributing the information in this table. The objective of this paper is to present the tool called Visual TaHs that uses a sparse table to a real lexicon (Lexicon of Herbs), improving performance results of several implemented hash functions. Such structure has achieved satisfactory results in terms of speed and storage when compared to conventional databases and can work in various media, such as desktop, Web and mobile.

Application of Convolution with Periodic Boundary Condition for Processing Data from Cylindrical Electrode Arrays

2021 ◽  
Vol 27 (3) ◽  
pp. 125-131
Author(s):  
V. V. Sapunov ◽  
◽  
S. A. Botman ◽  
G. V. Kamyshov ◽  
N. N. Shusharina ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Input Data ◽  
Periodic Boundary ◽  
Electrode Arrays ◽  
Cylindrical Electrode ◽  
Processing Data ◽  
New Type ◽  
Cyclic Shifts ◽  
And Training

In this paper, modification of convolutional neural networks for purposes of processing electromyographic data obtained from cylindrical arrays of electrodes was proposed. Taking into account the spatial symmetry of the array, convolution operation was redefined using periodic boundary conditions, which allowed to construct a neural network that is invariant to rotations of electrodes array around its axis. Applicability of the proposed approach was evaluated by constructing a neural network containing a new type of convolutional layer and training it on the open UC2018 DualMyo dataset in order to classify gestures basing on data from a single myobracelet. The network based on the new type of convolution performed better compared to common convolutions when trained on data without augmentation, which indicates that such a network is invari­able to cyclic shifts in the input data. Neural networks with modified convolutional layers and common convolutional layers achieved f-1 scores of 0.96 and 0.65 respectively with no augmentation for input data and f-1 scores of 0.98 and 0.96 in case when train-time augmentation was applied. Test data was augmented in both cases. Potentially, proposed convolution can be applied in processing any data with the same connectivity in such a way that allows to adapt time-tested architectural solutions for networks by replacing common convolutions with modified ones.

DGPS Correction Prediction Using Artificial Neural Networks

2007 ◽  
Vol 60 (2) ◽  
pp. 291-301 ◽  
Author(s):  
M. Mohasseb ◽  
A. El-Rabbany ◽  
O. Abd El-Alim ◽  
R. Rashad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Structures ◽  
Model Building ◽  
Data Sets ◽  
Training Algorithms ◽  
Differential Gps ◽  
The Neural Network ◽  
Artificial Neural

This paper focuses on modelling and predicting differential GPS corrections transmitted by marine radio-beacon systems using artificial neural networks. Various neural network structures with various training algorithms were examined, including Linear, Radial Biases, and Feedforward. Matlab Neural Network toolbox is used for this purpose. Data sets used in building the model are the transmitted pseudorange corrections and broadcast navigation message. Model design is passed through several stages, namely data collection, preprocessing, model building, and finally model validation. It is found that feedforward neural network with automated regularization is the most suitable for our data. In training the neural network, different approaches are used to take advantage of the pseudorange corrections history while taking into account the required time for prediction and storage limitations. Three data structures are considered in training the neural network, namely all round, compound, and average. Of the various data structures examined, it is found that the average data structure is the most suitable. It is shown that the developed model is capable of predicting the differential correction with an accuracy level comparable to that of beacon-transmitted real-time DGPS correction.

scholarly journals Multi-Sensor Mobile Platform for the Recognition of Activities of Daily Living and their Environments based on Artificial Neural Networks

2018 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno Pombo ◽  
Nuno M. Garcia ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Neural Networks ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Global Positioning System ◽  
Mobile Application ◽  
Daily Living ◽  
Deep Neural Networks ◽  
Gps Receiver ◽  
Processing Data ◽  
Global Positioning

The recognition of Activities of Daily Living (ADL) and their environments based on sensors available in off-the-shelf mobile devices is an emerging topic. These devices are capable to acquire and process the sensors' data for the correct recognition of the ADL and their environments, providing a fast and reliable feedback to the user. However, the methods implemented in a mobile application for this purpose should be adapted to the low resources of these devices. This paper focuses on the demonstration of a mobile application that implements a framework, that forks their implementation in several modules, including data acquisition, data processing, data fusion and classification methods based on the sensors? data acquired from the accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver. The framework presented is a function of the number of sensors available in the mobile devices and implements the classification with Deep Neural Networks (DNN) that reports an accuracy between 58.02% and 89.15%.

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