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.

Evolutionary artificial immune system optimization

2010 ◽  
Author(s):  
André Ferry Barreira ◽  
Carlos Eduardo de Jesus Guimarães Oliveira ◽  
Otávio Noura Teixeira ◽  
Roberto Célio Limão de Oliveira
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
System Optimization ◽  
Artificial Immune

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 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.

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