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Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

Computers, Materials & Continua 2021年第5期67卷 2353-2371页

作者： Sally M.Elghamrawy Aboul Ella Hassnien vaclav snasel MISR Higher Institute for Engineering and Technology MansouraEgypt Faculty of Computers and Information Cairo UniversityCairoEgypt VSB-Technical University of Ostrava Czech Republic

Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected *** study aimed to develop a COVID-19 diagnosis and prediction(AIMDP)model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography(CT)*** proposed system uses convolutional neural networks(CNNs)as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its *** employed the whale optimization algorithm(WOA)to select the most relevant patient signs.A set of experiments validated AIMDP *** demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic(AUC-ROC)curve,positive predictive value(PPV),negative predictive rate(NPR)and negative predictive value(NPV).AIMDP was applied to a dataset of hundreds of real data and CT images,and it was found to achieve 96%AUC for diagnosing COVID-19 and 98%for overall *** results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.

关键词： Convolutional neural networks coronavirus disease 2019(COVID-19) CT chest scan imaging deep learning technique feature selection whale optimization algorithm

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Enhancements of SDR-Based FPGA System for V2X-VLC Communications

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Computers, Materials & Continua 2021年第9期68卷 3629-3652页

作者： Lukas Danys Radek Martinek Rene Jaros Jan Baros Petr Simonik vaclav snasel Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer ScienceVSB–Technical University of OstravaOstrava-Poruba70800Czechia Department of Electronics Faculty of Electrical Engineering and Computer ScienceVSB–Technical University of OstravaOstrava-Poruba70800Czechia Department of Computer Science Faculty of Electrical Engineering and Computer ScienceVSB–Technical University of OstravaOstrava-Poruba70800Czechia

This pilot study focuses on a real measurements and enhancements of a software defined radio-based system for vehicle-to everything visible light communication(SDR-V2X-VLC).The presented system is based on a novel adaptive optimization of the feed-forward software defined equalization(FFSDE)methods of the least mean squares(LMS),normalized LMS(NLMS)and QR decomposition-based recursive least squares(QR-RLS)*** parameters of adaptive equalizations are adjusted in real-time to reach the best possible *** were carried out on a conventional LED Octavia III taillight drafted directly from production line and universal software radio peripherals(USRP)from National *** transmitting/receiving elements used multistate quadrature amplitude modulation(M-QAM)implemented in LabVIEW programming *** results were verified based on bit error ratio(BER),error vector magnitude(EVM)and modulation error ratio(MER).Experimental results of the pilot study unambiguously confirmed the effectiveness of the proposed solution(longer effective communication range,higher immunity to interference,deployment of higher state QAM modulation formats,higher transmission speeds etc.),as the adaptive equalization significantly improved BER,MER and EVM *** best results were achieved using the QR-RLS *** results measured on deployed QR-RLS algorithm had significantly better Eb/N0(improved by approx.20 dB)and BER values(difference by up to two orders of magnitude).

关键词： 5G feed-forward software defined equalization multistate quadrature amplitude modulation software defined radio visible light communication

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WEIGHTED PSEUDO ALMOST-PERIODIC SOLUTIONS OF SHUNTING INHIBITORY CELLULAR NEURAL NETWORKS WITH MIXED DELAYS

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Acta Mathematica Scientia 2016年第6期36卷 1662-1682页

作者： Mohammed Salah M'HAMDI Chaouki AOUITI Abderrahmane TOUATI Adel M. ALIMI vaclav snasel Department of Mathematics University of Carthage Faculty of Sciences of Bizerta BP W 7021 Zarzouna Bizerta Tunisia REGIM-lab: Research Groups on Intelligent Machines University of Sfax National Engineering School of Sfax (ENIS) BP 1173 Sfax 3038 Tunisia VSB Technical University of Ostrava Czech Republic

In this paper, we prove the existence and the global exponential stability of the unique weighted pseudo almost-periodic solution of shunting inhibitory cellular neural networks with mixed time-varying delays comprising different discrete and distributed time delays. Some sufficient conditions are given for the existence and the global exponential stability of the weighted pseudo almost-periodic solution by employing fixed point theorem and differential inequality techniques. The results of this paper complement the previously known ones. Finally, an illustrative example is given to demonstrate the effectiveness of our results.

关键词： weighted pseudo almost-periodic solution shunting inhibitory cellular neuralnetworks mixed delays global exponential stability

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