scholarly journals The Development of the Method of Radar Observation System Construction of the Airspace on the Basis of Genetic Algorithm

2021 ◽  
Vol 11 (8) ◽  
pp. 23-30
Author(s):  
Oleksandr Oleksenko ◽  
◽  
Hennadii Khudov ◽  
Kyrylo Petrenko ◽  
Yurii Horobets ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
False Alarm ◽  
Surveillance System ◽  
Incidence Matrix ◽  
Observation System ◽  
System Construction ◽  
Rational Structure ◽  
Probability Of False Alarm ◽  
Methodological Approaches

The methodological approaches to the use of genetic algorithm for the synthesis of the rational structure of the radar surveillance system are proposed in the paper. The structure of the radar surveillance system is presented in the form of an incidence matrix, which is used as a chromosome by the operators of the genetic algorithm. This matrix is used as a chromosome by the operators of the genetic algorithm. The elements of the incidence matrix that describe the relationships between the elements of the structure of the observation system are genes in the genetic algorithm. In each cycle of the genetic algorithm, a pair of chromosomes is paired, during which part of the genes are exchanged, which for the system under study means the appearance and disappearance of the corresponding connections between the elements. The calculation of the values of the efficiency of radar surveillance for each variant of the structure is proposed to be carried out using the ant colony optimization. The gain in the value of the conditional probability of correct detection with a fixed probability of false alarm is approximately 10% Keywords— genetic algorithm, artificial intelligence, optimization, route, radar surveillance system.

scholarly journals Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Optimization Technique ◽  
Primary User ◽  
Time Frame ◽  
Probability Of False Alarm ◽  
Sensing Time ◽  
Better Than

<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>

The Influence of False Alarms of Threats Technical Detection Means on the Security System Construction

2019 ◽  
Vol 7 (2) ◽  
pp. 22-28
Author(s):  
Александр Петренко ◽  
Aleksandr Petrenko ◽  
Александр Суворов ◽  
Aleksandr Suvorov ◽  
Евгения Плотникова ◽  
...  
Keyword(s):  
Mathematical Model ◽  
False Alarm ◽  
False Alarms ◽  
System Construction ◽  
Security Assurance ◽  
Probability Of False Alarm ◽  
Negative Factor ◽  
Timely Response ◽  
The Impact ◽  
Negative Factors

Some theoretical aspects for determining the required number of mobile groups involved in objects security assurance from the impact of various negative factors have been considered. A mathematical model, that allow substantiate the required number of groups for timely response to negative factors has been developed. The total number of response teams has been determined and corrected with account for probability of false alarm related to several technical detection means. The influence of placement for technical detection means, and response teams, as well as of negative factors’ characteristics on the catch line tailoring has been shown. The negative factor’s catch line sizes in dependence to the detection means’ concrete location have been calculated. A certainty increase for operation of technical detection means is carried out by determining the regularity of triggering moments, taking into account the appearance of false alarms. An inspection for received signals’ certainty is carried out by guard personnel from response teams. The developed model will allow increase the security system’s response time to the negative factor. The specified models will allow develop the software for modeling of real situations.

scholarly journals Security of Things Intrusion Detection System for Smart Healthcare

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1375
Author(s):  
Celestine Iwendi ◽  
Joseph Henry Anajemba ◽  
Cresantus Biamba ◽  
Desire Ngabo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Web Security ◽  
Intrusion Detection Systems ◽  
High Detection Rate ◽  
Detection Systems ◽  
Smart Healthcare

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%.

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Artificial Intelligence-based Surveillance System for Railway Crossing Traffic

2020 ◽  
pp. 1-1
Author(s):  
Pavel Sikora ◽  
Lukas Malina ◽  
Martin Kiac ◽  
Zdenek Martinasek ◽  
Kamil Riha ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Surveillance System ◽  
Railway Crossing

scholarly journals Optimization of spectrum sensing in cognitive radio using genetic algorithm

2012 ◽  
Vol 25 (3) ◽  
pp. 235-243 ◽  
Author(s):  
Rashmi Deka ◽  
Soma Chakraborty ◽  
Sekhar Roy
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Rapid Development ◽  
Probability Of Detection ◽  
Radio System ◽  
Probability Of False Alarm ◽  
Space Allocation ◽  
Wireless Environment ◽  
Better Than

Spectrum availability is becoming scarce due to the rise of number of users and rapid development in wireless environment. Cognitive radio (CR) is an intelligent radio system which uses its in-built technology to use the vacant spectrum holes for the use of another service provider. In this paper, genetic algorithm (GA) is used for the best possible space allocation to cognitive radio in the spectrum available. For spectrum reuse, two criteria have to be fulfilled - 1) probability of detection has to be maximized, and 2) probability of false alarm should be minimized. It is found that with the help of genetic algorithm the optimized result is better than without using genetic algorithm. It is necessary that the secondary user should vacate the spectrum in use when licensed users are demanding and detecting the primary users accurately by the cognitive radio. Here, bit error rate (BER) is minimized for better spectrum sensing purpose using GA.

scholarly journals Structural Combinatorial of Network Information System of Systems based on Evolutionary Optimization Method.pdf

2020 ◽  
Author(s):  
Tingting Zhang ◽  
Yushi Lan ◽  
Aiguo Song ◽  
Kun Liu ◽  
Nan Wang
Keyword(s):  
Genetic Algorithm ◽  
Information System ◽  
System Of Systems ◽  
Information Architecture ◽  
Information Network ◽  
Network System ◽  
System Construction ◽  
Network Information ◽  
Evolution Characteristics ◽  
Evolutionary Simulation

<p>The network information system is a military information network system with evolution characteristics. Evolution is a process of replacement between disorder and order, chaos and equilibrium. Given that the concept of evolution originates from biological systems, in this article, the evolution of network information architecture is analyzed by genetic algorithms, and the network information architecture is represented by chromosomes. Besides, the genetic algorithm is also applied to find the optimal chromosome in the architecture space. The evolutionary simulation is used to predict the optimal scheme of the network information architecture and provide a reference for system construction.</p><br>

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