Smart Supervision of Cardiomyopathy Based on Fuzzy Harris Hawks Optimizer and Wearable Sensing Data Optimization: A New Model

Ding, Weiping; Abdel‐Basset, Mohamed; Eldrandaly, Khalid A.; Abdel-Fatah, Laila; Albuquerque, Victor Hugo C. de

doi:10.1109/tcyb.2020.3000440

Cited by 31 publications

(15 citation statements)

References 38 publications

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“…Ding et al [17] presented the Fuzzy Harris hawks optimizer (FHHO) for 3D monitoring of cardiomyopathy patients. In addition, the authors proposed the Wearable Sensing Data Optimization (WSDO) algorithm for precise and consistent cardiomyopathy sensing data manipulation.…”

Section: B the Literature Of 3d Coverage Optimizationmentioning

confidence: 99%

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Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

et al. 2021

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The next generation 6G communication network is typically characterized by the full connectivity and coverage of Users Equipment (UEs). This leads to the need for moving beyond the traditional twodimensional (2D) coverage service to the three-dimensional (3D) full-service one. The 6G 3D architecture leverages different types of non-terrestrial or aerial nodes that can act as mobile Base Stations (BSs) such as Unmanned Aerial Vehicles (UAVs), Low Altitude Platforms (LAPs), High-Altitude Platform Stations (HAPSs), or even Low Earth Orbit (LEO) satellites. Moreover, aided technologies have been added to the 6G architecture to dynamically increase its coverage efficiency such as the Reconfigurable Intelligent Surfaces (RIS). In this paper, an enhanced Computational Intelligence (CI) algorithm is introduced for optimizing the coverage of UAV-BSs with respect to their location from RIS in the 3D space of 6G architecture. The regarded problem is formulated as a constrained 3D coverage optimization problem. In order to increase the convergence of the proposed algorithm, it is hybridized with a crossover operator. For the validation of the proposed method, it is tested on different scenarios with large-scale coordinates and compared with many recent and hybrid CI algorithms, as Slime Mould Algorithm (SMA), Lévy Flight Distribution (LFD), hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and hybrid Grey Wolf Optimizer and Cuckoo Search (GWOCS). The experiment and the statistical analysis show the significant efficiency of the proposed algorithm in achieving complete coverage with a lower number of UAV-BSs and without constraints violation.INDEX TERMS 6G technology, computational intelligence, non-terrestrial base stations, reconfigurable intelligent surfaces, three-dimensional coverage optimization problem.

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Section: B the Literature Of 3d Coverage Optimizationmentioning

confidence: 99%

“…where L is the volume of the area to be covered, and X is the theoretically expected coverage ratio. Thence, the corresponding number of UAV-BSs that attains X coverage percentage can be computed as [17]:…”

Section: Theoretical 3d Coverage Percentagementioning

confidence: 99%

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

et al. 2021

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“…As aforementioned in these examples, the use of signal-based methods, such as electronic sensors and ML techniques has proved to be a reliable procedure in identifying biomarkers, aiding the diagnosis of neurodegenerative disease patients, and demonstrating the applicability of this methodology for diagnosing PD subjects [ 3 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis

Júnior

Delmiro

Magaia

et al. 2020

Sensors

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In this paper, we propose a pen device capable of detecting specific features from dynamic handwriting tests for aiding on automatic Parkinson’s disease identification. The method used in this work uses machine learning to compare the raw signals from different sensors in the device coupled to a pen and extract relevant information such as tremors and hand acceleration to diagnose the patient clinically. Additionally, the datasets composed of raw signals from healthy and Parkinson’s disease patients acquired here are made available to further contribute to research related to this topic.

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“…Digital Object Identifier AIAD tools such as information fusion-based online heart monitoring systems [3], smart health systems for ambulatory maternal and fetal monitoring [4] and smart supervision of cardiomyopathy [5], the ambiguity of which depends in part on the quality of the measured data. Unfortunately, artifacts can interfere with FHR recording due to the gravida's heartbeat, and suffer from frequent invalid or missing samples due to fetal or maternal movements, sensor malfunctions, or misplaced electrodes.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Missing Samples in Antepartum and Intrapartum FHR Measurements Via Mini-Batch-Based Minimized Sparse Dictionary Learning

Zhang

Zhao

Deng

et al. 2022

IEEE J. Biomed. Health Inform.

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Smart Supervision of Cardiomyopathy Based on Fuzzy Harris Hawks Optimizer and Wearable Sensing Data Optimization: A New Model

Cited by 31 publications

References 38 publications

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis

Reconstruction of Missing Samples in Antepartum and Intrapartum FHR Measurements Via Mini-Batch-Based Minimized Sparse Dictionary Learning

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