scholarly journals Classification of movements based on wearable device data in biometric authentication

2021 ◽  
Vol 2094 (3) ◽  
pp. 032017
Author(s):  
A V Grecheneva ◽  
N V Dorofeev
Keyword(s):  
Neural Network ◽  
Mobile Device ◽  
Single Step ◽  
Mobile Phone Data ◽  
Accelerometer Data ◽  
Biometric Authentication ◽  
Intelligent Algorithms ◽  
Human Movements ◽  
Neural Network Algorithm

Abstract The paper proposes a neural network algorithm for classifying human movements according to the accelerometer data, which is located in a mobile device. Intelligent algorithms for classifying movement types (single step, walking, walking on stairs, running) are considered on 9 types of different movements that a person performs in everyday life. The developed algorithm is proposed to be used in biometric authentication systems based on mobile phone data.

scholarly journals Requirements for the Procedure for Assessing the Operator’s Person When Processing Accelerometer Data

2021 ◽  
Vol 2096 (1) ◽  
pp. 012118
Author(s):  
A V Grecheneva ◽  
N V Dorofeev ◽  
M S Goryachev
Keyword(s):  
Neural Network ◽  
Portable Device ◽  
Accelerometer Data ◽  
Biometric Authentication ◽  
Intelligent Processing ◽  
Informative Parameters ◽  
Gait Parameters ◽  
Gait Biometrics ◽  
Neural Network Algorithm ◽  
Single Sensor

Abstract The article considers the possibility of biometric authentication based on gait parameters, which are obtained after intelligent processing of the accelerometer data of a wearable device. The article discusses the main trends and trends in the field of biometric authentication, as well as authentication by gait parameters. The developed neural network algorithm and informative parameters are described in the authentication procedure based on the data of a single sensor of a portable device. The practical verification of the proposed approach is carried out on 32 subjects of different physiology. The results of the study show the possibility of distinguishing their own movements in 100% of cases, and the distinction of the subjects is more than 90%. Also, the final part of the article provides the requirements for the authentication procedure when processing accelerometric data of gait biometrics, the level of trust of the developed algorithm is determined.

scholarly journals SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 1-5
Author(s):  
D. Akbari ◽  
M. Moradizadeh ◽  
M. Akbari
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Washington Dc ◽  
Hierarchical Segmentation ◽  
Vector Machines ◽  
Neural Network Algorithm ◽  
New Framework

<p><strong>Abstract.</strong> This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.</p>

Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method

2021 ◽  
Author(s):  
Farrel Athaillah Putra ◽  
Dwi Anggun Cahyati Jamil ◽  
Briliantino Abhista Prabandanu ◽  
Suhaili Faruq ◽  
Firsta Adi Pradana ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Method ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Research on Sentiment Classification Algorithms on Online Review

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Ruixia Yan ◽  
Zhijie Xia ◽  
Yanxi Xie ◽  
Xiaoli Wang ◽  
Zukang Song
Keyword(s):  
Neural Network ◽  
Sentiment Classification ◽  
Online Review ◽  
Support Vector ◽  
Classification Algorithms ◽  
Evaluation Indexes ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Reinforcement Learning Models

The product online review text contains a large number of opinions and emotions. In order to identify the public’s emotional and tendentious information, we present reinforcement learning models in which sentiment classification algorithms of product online review corpus are discussed in this paper. In order to explore the classification effect of different sentiment classification algorithms, we conducted a research on Naive Bayesian algorithm, support vector machine algorithm, and neural network algorithm and carried out some comparison using a concrete example. The evaluation indexes and the three algorithms are compared in different lengths of sentence and word vector dimensions. The results present that neural network algorithm is effective in the sentiment classification of product online review corpus.

scholarly journals Deep Extreme Learning Machine and Its Application in EEG Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shifei Ding ◽  
Nan Zhang ◽  
Xinzheng Xu ◽  
Lili Guo ◽  
Jian Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Eeg Classification ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Learning Machine

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.

scholarly journals Literature Study of Convolutional Neural Network Algorithm for Batik Classification

2021 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Nardianti Dewi Girsang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Algorithm Analysis ◽  
Image Size ◽  
Literature Study ◽  
Aesthetic Value ◽  
Network Algorithm ◽  
Neural Network Algorithm

Batik is a hereditary cultural heritage that has high aesthetic value and deep philosophy. Currently, Indonesian batik has various types of different motifs and patterns, which are spread in Indonesia with their names and meanings. Batik classification uses Convolutional Neural Network as a pattern recognition method, especially batik image classification. The method used is a literature study, looking at studies from several journals regarding the Convolutional Neural Network Algorithm in Classification and providing conclusions about the usefulness of the algorithm. Analysis This literature study analyzes each journal from previous research related to the Convolutional Neural Network Algorithm in classifying Batik. The results of the analysis, conducted a discussion to better know the characteristics and application of Convolutional Neural Network in the classification of Batik. After discussing, this analysis ends with conclusions about the Convolutional Neural Network algorithm in classifying Batik. Based on previous studies, it can be seen that the convolution neural network can work well for image classification with large datasets. By evaluating the method that has been described by considering the architecture and the level of accuracy, namely getting an accuracy level of 100% with an image size of 128 x 128 and regarding the classification of batik, it shows that image size, image quality, image patterns affect the batik classification process.

Automatic Detection and Classification of Diabetes Using Artificial Intelligence

2021 ◽  
Vol 8 (1) ◽  
pp. 01-05
Author(s):  
V. Nithyalakshmi ◽  
Dr.R. Sivakumar ◽  
Dr.A. Sivaramakrishnan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Diabetes Diagnosis ◽  
Artificial Intelligence Technique ◽  
Classification Rate ◽  
Neural Network Algorithm ◽  
Health Complications

Diabetes is characterized as a chronic disease that may cause many health complications. Artificial intelligence techniques are adopted diagnose diabetes more accurately. This paper presents an artificial intelligence technique for diabetes diagnosis. Efficacy of the technique is evaluated using diabetes database. Experimental results show that the back propagation neural network algorithm yields the highest classification rate compared to k-nearest neighbourhood classifier. Additionally, the back propagation neural network provides error with the highest area under curve of 90 %.

Implementation of Mail Classification Using Neural Networks of the Second Type Spline Weight Functions

2014 ◽  
Vol 513-517 ◽  
pp. 687-690 ◽  
Author(s):  
Dai Yuan Zhang ◽  
Lei Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recall Rate ◽  
High Accuracy ◽  
Weight Functions ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Sorting Process ◽  
E Mail

How to effectively filter out spam is a topic worthy of further study for the growing proliferation of spam. The main purpose of this paper is to apply a new neural network algorithm to the classification of spam. In this paper, we introduce a second type of spline weight function neural network algorithm, as well as e-mail feature extraction and vectorization, and then introduced the mail sorting process. Experiments show that it can get a relatively high accuracy and recall rate on the spam classification. Therefore, with this new algorithm, we can achieve better classification results.

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