A Systemic Study of Pattern Recognition System Using Feedback Neural Networks

WSEAS TRANSACTIONS ON COMPUTERS ◽

10.37394/23205.2020.19.16 ◽

2020 ◽

Vol 19 ◽

Keyword(s):

Pattern Recognition ◽

Character Recognition ◽

Real Life ◽

Recognition System ◽

Person Identification ◽

Recognition Error ◽

Pattern Recognition System ◽

Feedback Neural Network ◽

Feedback Neural Networks

The evolution of artificial intelligence has led to the developments of various smart applications such as the pattern recognition models. Pattern recognition techniques has as widely applied in many real life applications such character recognition, speech recognition, and bio-metric authentication as well person identification. In this paper, we report on the detailed design of pattern recognition system using Hopfield Feedback Neural Network (HFNN) with the least possible recognition error. As a case study, we have applied the proposed HFNN model to recognize the decimal digits 0 - 9 where each image digit comprises a 12x10 pixels. The developed HFNN model has been efficiently used in recognizing the patterns with 20% random bit noise at maximum recognition accuracy. However, to assure the the least possible recognition error, we have trained our HFNN through the digit patterns’ perdition phase of 0% noisy patterns and the system was able to correctly predict all the patterns without any bit error. Finally, we have plotted all output patterns including the desired patterns, the training patterns, the 20% noisy patterns and recognized patterns, for comparison purposes and to gain more insights about the accuracy achieved by applying the proposed HFNN.

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Human Ear Pattern Recognition System

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.35 ◽

2017 ◽

Vol 7 (8) ◽

pp. 117 ◽

Cited By ~ 1

Author(s):

V. Jagan Naveen ◽

K. Krishna Kishore ◽

P. Rajesh Kumar

Keyword(s):

Pattern Recognition ◽

Expectation Maximization Algorithm ◽

Recognition System ◽

Modern World ◽

Person Identification ◽

Biometric Systems ◽

Different Types ◽

Recognition Systems ◽

Human Ear ◽

Pca Algorithm

In the modern world, human recognition systems play an important role to improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm. Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

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DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽

10.3390/s21020405 ◽

2021 ◽

Vol 21 (2) ◽

pp. 405

Author(s):

Marcos Lupión ◽

Javier Medina-Quero ◽

Juan F. Sanjuan ◽

Pilar M. Ortigosa

Keyword(s):

Real Time ◽

Activity Recognition ◽

Real Life ◽

Recognition System ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Heterogeneous Sensors ◽

On Line ◽

The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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