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Identification of scalar Jiles-Atherton hysteresis model parameters with a new particle behaviour based metaheuristic algorithm

100%

Friedl G. , Kuczmann M.

2015

tom R. 91, nr 12

87-90

This paper is about a possible determination process of the scalar Jiles-Atherton hysteresis model parameters. A newly developed particle behaviour based metaheuristic algorithm, the so-called Weighted Attraction Method is used to find an approximation to the model parameters. The paper shortly discuss phenomena of magnetic hysteresis, the equations of the Jiles-Atherton hysteresis model, and the applied optimization method, thereafter the identification process, and the effectiveness of the optimization process is presented.

W artykule opisano możliwy proces wyznaczania parametrów skalarnego modelu histerezy Jilesa-Athertona. Nowo opracowany model opiera się na algorytmie metaheurystycznym. Metoda Weighted Attraction Method jest używana do wyznaczenia przybliżonych parametrów modelu. W artykule krótko omówiono zjawisko histerezy magnetycznej, równania modelu histerezy Jilesa-Athertona i zastosowanej metody optymalizacji, a następnie zaprezentowano proces identyfikacji i skuteczności procesu optymalizacji.

Improving security performance of healthcare data in the Internet of medical things using a hybrid metaheuristic model

67%

Ashok K. , Gopikrishnan S.

International Journal of Applied Mathematics and Computer Science

2023

tom Vol. 33, no. 4

623--636

Internet of medical things (IoMT) network design integrates multiple healthcare devices to improve patient monitoring and real-time care operations. These networks use a wide range of devices to make critical patient care decisions. Thus, researchers have deployed multiple high-security frameworks with encryption, hashing, privacy preservation, attribute based access control, and more to secure these devices and networks. However, real-time monitoring security models are either complex or unreconfigurable. The existing models’ security depends on their internal configuration, which is rarely extensible for new attacks. This paper introduces a hybrid metaheuristic model to improve healthcare IoT security performance. The blockchain based model can be dynamically reconfigured by changing its encryption and hashing standards. The proposed model then continuously optimizes blockchain based IoMT deployment security and QoS performance using elephant herding optimization (EHO) and grey wolf optimization (GWO). Dual fitness functions improve security and QoS for multiple attack types in the proposed model. These fitness functions help reconfigure encryption and hashing parameters to improve performance under different attack configurations. The hybrid integration of EH and GW optimization models can tune blockchain based deployment for dynamic attack scenarios, making it scalable and useful for real-time scenarios. The model is tested under masquerading, Sybil, man-in-the-middle, and DDoS attacks and is compared with state-of-the-art models. The proposed model has 8.3% faster attack detection and mitigation, 5.9% better throughput, a 6.5% higher packet delivery ratio, and 10.3% better network consistency under attack scenarios. This performance enables real-time healthcare use cases for the proposed model.

Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare

59%

Qaisar S. M. , Khan S. I. , Dallet D. , Tadeusiewicz R. , Pławiak P.

tom Vol. 42, no. 2

681--694

The next generation healthcare systems will be based on the cloud connected wireless biomedical wearables. The key performance indicators of such systems are the compression, computational efficiency, transmission and power effectiveness with precision. The electrocardiogram (ECG) signals processing based novel technique is presented for the diagnosis of arrhythmia. It employs a novel mix of the Level-Crossing Sampling (LCS), Enhanced Activity Selection (EAS) based QRS complex selection, multirate processing, Wavelet Decomposition (WD), Metaheuristic Optimization (MO), and machine learning. The MIT-BIH dataset is used for experimentation. Dataset contains 5 classes namely, ‘‘Atrial premature contraction”, ‘‘premature ventricular contraction”, ‘‘right bundle branch block”, ‘‘left bundle branch block” and ‘‘normal sinus”. For each class, 450 cardiac pulses are collected from 3 different subjects. The performance of Marine Predators Algorithm (MPA) and Artificial Butterfly Optimization Algorithm (ABOA) is investigated for features selection. The selected features sets are passed to classifiers that use machine learning for an automated diagnosis. The performance is tested by using multiple evaluation metrics while following the 10-fold cross validation (10-CV). The LCS and EAS results in a 4.04-times diminishing in the average count of collected samples. The multirate processing lead to a more than 7-times computational effectiveness over the conventional fix-rate counter parts. The respective dimension reduction ratios and classification accuracies, for the MPA and ABOA algorithms, are 29.59-times & 22.19-times and 98.38% & 98.86%.