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1

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

Ashok Kanneboina, Gopikrishnan Sundaram

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 4

623--636

EN

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.

2

The application of different optimization techniques and Artificial Neural Networks (ANN) for coal-consumption forecasting: a case study

Seker Mustafa, Unal Kartal Neslihan, Karadirek Selin, Gulludag Cevdet Bertan

Gospodarka Surowcami Mineralnymi

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2022

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T. 38, z. 2

77--112

EN

The demand for energy on a global scale increases day by day. Unlike renewable energy sources, fossil fuels have limited reserves and meet most of the world’s energy needs despite their adverse environmental effects. This study presents a new forecast strategy, including an optimization-based S-curve approach for coal consumption in Turkey. For this approach, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) are among the meta-heuristic optimization techniques used to determine the optimum parameters of the S-curve. In addition, these algorithms and Artificial Neural Network (ANN) have also been used to estimate coal consumption. In evaluating coal consumption with ANN, energy and economic parameters such as installed capacity, gross generation, net electric consumption, import, export, and population energy are used for input parameters. In ANN modeling, the Feed Forward Multilayer Perceptron Network structure was used, and Levenberg-Marquardt Back Propagation has used to perform network training. S-curves have been calculated using optimization, and their performance in predicting coal consumption has been evaluated statistically. The findings reveal that the optimization-based S-curve approach gives higher accuracy than ANN in solving the presented problem. The statistical results calculated by the GWO have higher accuracy than the PSO, WOA, and GA with R2 = 0.9881, RE = 0.011, RMSE = 1.079, MAE = 1.3584, and STD = 1.5187. The novelty of this study, the presented methodology does not need more input parameters for analysis. Therefore, it can be easily used with high accuracy to estimate coal consumption within other countries with an increasing trend in coal consumption, such as Turkey.

PL

Zapotrzebowanie na energię w skali globalnej rośnie z dnia na dzień. W przeciwieństwie do odnawialnych źródeł energii, paliwa kopalne mają ograniczone rezerwy i zaspokajają większość światowego zapotrzebowania na energię pomimo ich niekorzystnego wpływu na środowisko. Niniejsze opracowanie przedstawia nową strategię prognozowania, w tym oparte na optymalizacji podejście oparte na krzywej S dla zużycia węgla w Turcji. W tym podejściu algorytmy optymalizacji genetycznej (GA) i optymalizacji roju cząstek (PSO), optymalizacja Gray Wolf (GWO) i algorytm optymalizacji wielorybów (WOA) należą do metaheurystycznych technik optymalizacji stosowanych do określenia optymalnych parametrów krzywej S. Ponadto algorytmy te oraz sztuczna sieć neuronowa (SSN) zostały również wykorzystane do oszacowania zużycia węgla. Przy ocenie zużycia węgla za pomocą SSN jako parametry wejściowe wykorzystuje się parametry energetyczne i ekonomiczne, takie jak moc zainstalowana, produkcja brutto, zużycie energii elektrycznej netto, import, eksport i energia ludności. W modelowaniu SSN wykorzystano strukturę Feed Forward Multilayer Perceptron Network, a do uczenia sieci wykorzystano propagację wsteczną Levenberg-Marquardt. Krzywe S zostały obliczone za pomocą optymalizacji, a ich skuteczność w przewidywaniu zużycia węgla została oceniona statystycznie. Wyniki pokazują, że podejście oparte na optymalizacji opartej na krzywej S zapewnia większą dokładność niż SSN w rozwiązaniu przedstawionego problemu. Wyniki statystyczne obliczone przez GWO mają wyższą dokładność niż PSO, WOA i GA z R2 = 0,9881, RE = 0,011, RMSE = 1,079, MAE = 1,3584 i STD = 1,5187. Nowość tego badania, prezentowana metodyka nie wymaga dodatkowych parametrów wejściowych do analizy. Dzięki temu może być z łatwością wykorzystany z dużą dokładnością do oszacowania zużycia węgla w innych krajach o tendencji wzrostowej zużycia węgla, takich jak Turcja.

3

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

Qaisar Saeed Mian, Khan Sibghatullah I., Dallet Dominique, Tadeusiewicz Ryszard, Pławiak Paweł

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

681--694

EN

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%.

4

Collectively intelligent prediction in evolutionary multi-agent system

Kijak J., Martyna P., Byrski A., Faber Ł., Piętak K., Kisiel-Dorohinicki M.

International Journal of Microelectronics and Computer Science

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2017

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Vol. 8, nr 3

85--96

EN

In the paper a summary of our previously realized and published work connected with constructing collective intelligent evolutionary multi-agent systems for time series prediction, based on multi-layered perceptrons is shown. Besides recalling our past papers, we describe the whole concept, present an implementation in a contemporary, componentoriented software framework AgE 3.0 and we conduct a number of experiments, finding different optimal parametrization for the considered instances of the problems (popular Mackey-Glass chaotic time series). The paper may be useful for a practitioner willing to use our meatheuristic algorithm (EMAS) along with the idea of collective agent-based system in order to realize prediction tasks.

5

Under Voltage Load Shedding Scheme Using Meta-heuristic Optimization Methods

Verayiah R., Mohamed A., Shareef H., Abidin I. Z.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 11

162--168

EN

Load shedding has been extensively studied because of multiple power system failure occurrences worldwide. Reliable techniques are required to provide rapid and precise load shedding to avert voltage collapse in power networks. Meta-heuristic optimization approaches are currently the widely developed methods because of their robustness and flexibility in dealing with complex and non-linear systems. These methods include genetic algorithm, fuzzy logic control, particle swarm optimization, artificial neural network, ant colony optimization, big-bang big-crunch optimization, and many others. This study provides an overview of all the meta-heuristic methods implemented for under voltage load shedding in power systems.

PL

Pozbywanie się obciążenia jest istotne z punktu widzenia możliwego zapadu systemu przesyłu energii. Do tego celu wykorzystuje się optymalizację meta-heurystyczną głównie dzięki odporności i szerokim możliwościom. W skład metody wchodzą: algorytm genetyczny, logika rozmyta, algorytmy mrówkowe, sieci neuronowe. W artykule dokonano przeglądu tych metod.

6

Identification of the initial rule-base of a multi-stroke fuzzy-based character recognition method with meta-heuristic techniques

Tormási A., Kóczy L. T.

Czasopismo Techniczne. Automatyka

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2013

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R. 110, z. 4-AC

103--115

EN

This paper summarizes the basic concept of the designed a fuzzy-based character recognition algorithm family and the results of the optimization of its rule-base with two various meta-heuristic methods, the Imperialist Competitive Algorithm and the bacterial evolutionary algorithm. The results are presented and compared with two other methods from literature after a short overview of the recognition algorithm.

PL

W niniejszym artykule podsumowano podstawową koncepcję projektowania rodziny algorytmów rozpoznawania pisma odręcznego opartej na logice rozmytej oraz wyniki optymalizacji bazy reguł, z wykorzystaniem dwóch różnych metod metaheurystycznych: algorytmu ewolucyjnego ICA (Imperialist Competitive Algorithm) oraz ewolucyjnego algorytmu bakteryjnego. Przedstawiono krótkie podsumowanie algorytmu rozpoznawania pisma, a także wyniki porównawcze z dwoma innych metodami dostępnymi w literaturze.