scholarly journals An Immune Based Patient Anomaly Detection using RFID Technology

2013 ◽  
Vol 2 (1) ◽  
pp. 121-142 ◽  
Author(s):  
Sri Listia Rosa ◽  
Siti Mariyam Shamsuddin ◽  
Evizal Evizal
Keyword(s):  
Immune System ◽  
Monitoring System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Intelligent System ◽  
Patient Data ◽  
Rfid Technology ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Negative Selection Algorithm

Detecting of anomalies patients data are important to gives early alert to hospital, in this paper will explore on anomalies patient data detecting and processing using artificial computer intelligent system. Artificial Immune System (AIS) is an intelligent computational technique refers to human immunology system and has been used in many areas such as computer system, pattern recognition, stock market trading, etc. In this case, real value negative selection algorithm (RNSA) of artificial immune system used for detecting anomalies patient body parameters such as temperature. Patient data from monitoring system or database classified into real valued, real negative selection algorithm results is real values deduction by RNSA distance, the algorithm used is minimum distance and the value of detector generated for the algorithm. The real valued compared with the distance of data, if the distance is less than a RNSA detector distance then data classified into abnormal. To develop real time detecting and monitoring system, Radio Frequency Identification (RFID) technology has been used in this system. Keywords: AIS, RNSA, RFID, AbnormalDOI: 10.18495/comengapp.21.121142

scholarly journals DETECÇÃO DE FALHAS ESTRUTURAIS DE UM PRÉDIO DE DOIS ANDARES UTILIZANDO SISTEMA IMUNOLÓGICO ARTIFICIA

2019 ◽  
Vol 11 (4) ◽  
pp. 73-84
Author(s):  
Thiago Carreta Moro ◽  
Fábio Roberto Chavarette ◽  
Luiz Gustavo Pereira Roéfero ◽  
Roberto Outa
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Structural Integrity ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Intelligent Computing ◽  
Negative Selection Algorithm ◽  
Story Building

This work presents the innovative proposal for the development of SHMs with focus on physical cyber systems applied in a two-story building based on Intelligent Computing (CI) techniques, the negative selection algorithm from the Artificial Immune System, to perform the analysis and monitoring of structural integrity in a building.

scholarly journals A New Artificial Immune System Based on Continuous Learning for Pattern Recognition

2020 ◽  
Vol 27 (4) ◽  
pp. 34-44
Author(s):  
Simone F. Souza ◽  
Fernando Parra dos Anjos Lima ◽  
Fábio Roberto Chavarette
Keyword(s):  
Pattern Recognition ◽  
Immune System ◽  
Artificial Immune System ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Training Process ◽  
Continuous Training ◽  
Negative Selection Algorithm ◽  
Recognition Process

This paper presents a novel approach for pattern recognition based on continuous training inspired by the biological immune system operation. The main objective of this paper is to present a method capable of continually learn, i.e., being able to address new types of patterns without the need to restart the training process (artificial immune system with incremental learning). It is a useful method for solving problems involving a permanent knowledge extraction, e.g., 3D facial expression recognition, whose quality of the solutions is strongly dependent on a continuous training process. In this context, two artificial immune algorithms are employed: (1) the negative selection algorithm, which is responsible for the pattern recognition process and (2) the clonal selection algorithm, which is responsible for the learning process. The main application of this method is in assisting in decision-making on problems related to pattern recognition process. To evaluate and validate the efficiency of this method, the system has been tested on handwritten character recognition, which is a classic problem in the literature. The results show efficiency, accuracy and robustness of the proposed methodology.

scholarly journals MONITORAMENTO DE FALHAS EM SISTEMAS DINÂMICOS UTILIZANDO UM MÉTODO DE ANÁLISE DE DADOS BASEADO NO ALGORITMO DE SELEÇÃO NEGATIVA

2021 ◽  
Vol 13 (2) ◽  
pp. 78-87
Author(s):  
Driely Candido Santos ◽  
Mara Lúcia Martins Lopes ◽  
Fábio Roberto Chavarette ◽  
Bruno Ferreira Rossanês
Keyword(s):  
Negative Selection ◽  
Intelligent System ◽  
Artificial Immune Systems ◽  
Base Line ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Vibration Signals ◽  
Immune Systems ◽  
Negative Selection Algorithm

This work presents the application of a method for monitoring and diagnosing failures in mechanical structures based on the theory of vibration signals and on Artificial Immune Systems to assist in data processing. It uses the Negative Selection Algorithm as a tool to identify fault samples extracted from the laboratory simulated signals of a dynamic rotor. This methodology can help mechanical structure maintenance professionals, facilitating decision-making. The data set used in the processing of the intelligent system was generated through experiments. For normal (base-line) conditions, the signals of the rotor in free operation were used, that is, without the addition of unbalance mass, and for the fault conditions, unbalance masses were added to the system. The results are satisfactory, showing precision and robustness.

Structural failures diagnosis using a hybrid artificial intelligence method / Diagnóstico de falhas estruturais utilizando um método híbrido de inteligência artificial

2021 ◽  
Vol 7 (7) ◽  
pp. 66873-66893
Author(s):  
Simone Silva Frutuoso de Souza ◽  
Mailon Bruno Pedri de Campos ◽  
Fábio Roberto Chavarette ◽  
Fernando Parra dos Anjos Lima
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Artificial Immune ◽  
Aircraft Structure ◽  
Inspection Process ◽  
Artificial Immune System Algorithm ◽  
Structural Failures ◽  
Aluminum Beam ◽  
Hybrid Artificial Intelligence

This paper presents a Wavelet-artificial immune system algorithm to diagnose failures in aeronautical structures. Basically, after obtaining the vibration signals in the structure, is used the wavelet module for transformed the signals into the wavelet domain. Afterward, a negative selection artificial immune system realizes the diagnosis, identifying and classifying the failures. The main application of this methodology is the auxiliary structures inspection process in order to identify and characterize the flaws, as well as perform the decisions aiming at avoiding accidents or disasters. In order to evaluate this methodology, we carried out the modeling and simulation of signals from a numerical model of an aluminum beam, representing an aircraft structure such as a wing. The results demonstrate the robustness and accuracy methodology.

Performance of Negative Selection Algorithms in Patient Detection and Classification

2018 ◽  
pp. 78-102
Author(s):  
Orhan Bölükbaş ◽  
Harun Uğuz
Keyword(s):  
Immune System ◽  
Error Detection ◽  
Classification Accuracy ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Detection Accuracy ◽  
Data Sets ◽  
Selection Algorithm ◽  
Artificial Immune

Artificial immune systems inspired by the natural immune system are used in problems such as classification, optimization, anomaly detection, and error detection. In these problems, clonal selection algorithm, artificial immune network algorithm, and negative selection algorithm are generally used. This chapter aims to solve the problem of correct identification and classification of patients using negative selection (NS) and variable detector negative selection (V-DET NS) algorithms. The authors examine the performance of NSA and V-DET NSA algorithms using three sets of medical data sets from Parkinson, carotid artery doppler, and epilepsy patients. According to the obtained results, NSA achieved 92.45%, 91.46%, and 92.21% detection accuracy and 92.46%, 93.40%, and 90.57% classification accuracy. V-DET NSA achieved 94.34%, 94.52%, and 91.51% classification accuracy and 94.23%, 94.40%, and 89.29% detection accuracy. As can be seen from these values, V-Det NSA yielded a better result. Artificial immune system emerges as an effective and promising system in terms of problem-solving performance.

scholarly journals Pengenalan Pola Angka Menggunakan Pendekatan Optimisasi Sistem Kekebalan Buatan (Artificial Immune System)

2021 ◽  
Vol 5 (3) ◽  
pp. 856
Author(s):  
Anggari Ayu Prahartiningsyah ◽  
Tri Basuki Kurniawan
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
System Optimization ◽  
Recognition System ◽  
Optimization Approach ◽  
Artificial Immune ◽  
Technical Problems ◽  
Negative Selection Algorithm ◽  
Number Pattern ◽  
The Difference

The general election in Indonesia itself still experiences technical and non-technical problems where the technical problems occur in the recapitulation of votes from sheet C1 which are still incorrectly inputted and done manually. The problem occurred with the difference in the uploaded C1 data and the data in the KPU Situng and the C1 sheet uploaded was blurry, unclear, sheet C1 which was crossed out or folded in the KPU Situng. The purpose of this research is to reduce errors in data input and change the work that is done manually to the system, create a number pattern recognition system using an Artificial Immune System optimization approach, test and analyze the work of the system by taking into account the level of accuracy, preciseness and speed in recognize number patterns. The system created to applies an artificial immune system optimization approach with the Artificial Immune System using the Randomized Real-Valued Negative Selection Algorithm algorithm.

A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set

2010 ◽  
Vol 121-122 ◽  
pp. 486-489
Author(s):  
Wen Chen ◽  
Tao Li ◽  
Xiao Jie Liu ◽  
Yuan Quan Shi
Keyword(s):  
Negative Selection ◽  
The Self ◽  
Hierarchical Level ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Cluster Radius ◽  
Negative Selection Algorithm ◽  
Value Range ◽  
Level Cluster

In this article, we proposed a negative selection algorithm which based on hierarchical level cluster of self dataset CB-RNSA. First the self data set is clustered by different cluster radius, and then the self data are substituted by cluster centers to compare with candidate detectors to reduce the number of distance counting. In the detector creating process, the value of each detector property was restricted to a given value range so as to decrease the redundancy of detectors. The stimulation result shows that CB-RNSA is an effective algorithm for the creation of artificial immune detectors.

Sign in / Sign up

Export Citation Format

Share Document