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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

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Automotive Innovation 2022年第2期5卷 134-145页

作者： Huanyang Huang Jinhao Meng Yuhong Wang Lei Cai Jichang Peng Ji Wu Qian Xiao Tianqi Liu Remus Teodorescu College of Electrical Engineering Sichuan UniversityChengduChina Faculty of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security TechnologyXi’an University of TechnologyXi’anChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjingChina Department of Automation University of Science and Technology of ChinaHefeiChina Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjinChina Department of Energy Technology Aalborg University9220 AalborgDenmark

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management *** paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized *** Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the *** for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high *** the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing *** of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.

关键词： Li-ion battery State of health Gaussian process regression Kernel function Feature optimization

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Computational study of the role of counterions and solvent dielectric in determining the conductance of B-DNA

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Physical Review E 2023年第4期107卷 044404-044404页

作者： Yiren Wang Busra Demir Hashem Mohammad Ersin Emre Oren M. P. Anantram Deparment of Electrical and Computer Engineering University of Washington Seattle Washington 98105 USA Bionanodesign Laboratory Department of Biomedical Engineering and Department of Materials Science & Nanotechnology Engineering TOBB University of Economics and Technology Ankara 06510 Turkey Department of Electrical Engineering Kuwait University P.O. Box 5969 Safat 13060 Kuwait

DNA naturally exists in a solvent environment, comprising water and salt molecules such as sodium, potassium, magnesium, etc. Along with the sequence, the solvent conditions become a vital factor determining DNA structure and thus its conductance. Over the last two decades, researchers have measured DNA conductivity both in hydrated and almost dry (dehydrated) conditions. However, due to experimental limitations (the precise control of the environment), it is very difficult to analyze the conductance results in terms of individual contributions of the environment. Therefore, modeling studies can help us to gain a valuable understanding of various factors playing a role in charge transport phenomena. DNA naturally has negative charges located at the phosphate groups in the backbone, which provides both the connections between the base pairs and the structural support for the double helix. Positively charged ions such as the sodium ion (Na+), one of the most commonly used counterions, balance the negative charges at the backbone. This modeling study investigates the role of counterions both with and without the solvent (water) environment in charge transport through double-stranded DNA. Our computational experiments show that in dry DNA, the presence of counterions affects electron transmission at the lowest unoccupied molecular orbital energies. However, in solution, the counterions have a negligible role in transmission. Using the polarizable continuum model calculations, we demonstrate that the transmission is significantly higher at both the highest occupied and lowest unoccupied molecular orbital energies in a water environment as opposed to in a dry one. Moreover, calculations also show that the energy levels of neighboring bases are more closely aligned to ease electron flow in the solution.

关键词： electrical conductivity Electronic structure of atoms & molecules Quantum transport DNA Nucleic acids Density functional theory Methods in transport Nonequilibrium Green's function

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Fiber Optic Devices for High-Resolution Refractive Index Sensing

Fiber Optic Devices for High-Resolution Refractive Index Sen...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： S. Pevec M. Njegovec J. Javornik D. Donlagic Laboratory for Electro Optics and Sensor Systems Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

The paper describes a fiber optic Fabry-Perot (FP) refractive index (RI) sensors that provide performances required in gas sensing applications. Presented high-resolution FabryPerot Interferometer (FPI) sensors are based on open-path cells that can be integrated with the optical fiber in different ways. They can be fabricated as microcells on the fiber tip or along the optical fiber, or as external/extrinsic gas cells connected to the fiber. All presented configurations are based on low-finesse FPI as they allow for a simple and cost-efficient designs.

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An AOP-Based Security Verification Environment for KECCAK Hash Algorithm

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computers, Materials & Continua 2022年第11期73卷 4051-4066页

作者： Hassen Mestiri Imen Barraj Mohsen Machhout Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Higher Institute of Applied Sciences and Technology of Sousse University of SousseTunisia Electronics and Micro-Electronics Laboratory Faculty of Sciences of MonastirUniversity of MonastirTunisia Electrical Engineering Department Systems Integration&Emerging Energies(SI2E)National Engineers School of SfaxUniversity of SfaxTunisia Higher Institute of Computer Science and Multimedia of Gabes University of GabesTunisia

Robustness of the electronic cryptographic devices against fault injection attacks is a great concern to ensure *** to significant resource constraints,these devices are limited in their *** increasing complexity of cryptographic devices necessitates the development of a fast simulation environment capable of performing security tests against fault injection *** is a good choice for Electronic System Level(ESL)modeling since it enables models to run at a faster *** enable fault injection and detection inside a SystemC cryptographic model,however,the model’s source code must be *** altering the source code,Aspect-Oriented Programming(AOP)may be used to evaluate the robustness of cryptographic *** might replace conventional cryptanalysis methods in the real *** the ESL,we discuss a unique technique for simulating security fault attacks on cryptographic *** current study presents a fault injection/detection environment for assessing the KECCAK SystemC model’s resistance against fault injection *** approach of injecting faults into KECCAK SystemC model is accomplished via the use of weaving faults in AspectC++based on AOP programming *** confirm our technique by applying it to two scenarios using a SystemC KECCAK hash algorithm case study:The first concerns discuss the effect of the AOP on fault detection capabilities,while the second concerns discuss the effect of the AOP on simulation time and executable file *** simulation results demonstrate that this technique is fully capable of evaluating the fault injection resistance of a KECCAK *** demonstrate that AOP has a negligible effect on simulation time and executable file size.

关键词： Aspect-Oriented Programming SystemC AspectC++ cryptographic circuits KECCAK fault detection fault attacks

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Emotional Stimulus Classification from Brain electrical Activity using Multivariate Empirical Mode Decomposition

Emotional Stimulus Classification from Brain Electrical Acti...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Merve Dogruyol Basar Adil Deniz Duru Aydin Akan Dept. of Biomedical Engineering Faculty of Engineering Istanbul University-Cerrahpasa Istanbul Turkey Neuroscience in Sports Laboratory Faculty of Sport Science Marmara University Istanbul Turkey Dept. of Electrical and Electronics Eng Faculty of Engineering Izmir University of Economics Izmir Turkey

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Emotions play a crucial role in shaping various aspects of our daily lives, influencing our psychology, perspectives, feelings, and behaviors. Investigating the relationship between visual stimuli and emotions has become a prominent focus in neurophysiological studies. This study offers an overview of emotional responses elicited by different types of arousal-inducing pictures, specifically utilizing the Nencki Affective Picture System (NAPS). The chosen pictures aim to evoke three basic affects: positive, neutral, and negative emotions. Visual stimuli are presented, and emotional data are captured through multichannel Electroencephalogram (EEG) recordings. Additionally, we employ a MEMD-based iterative feature extraction method to decompose the raw signals into sets of oscillations, referred to as intrinsic mode functions (IMFs). Eight reduced IMFs for each visual stimulus are subjected to statistical analysis to assess the emotional state and bolster the understanding of emotional stimulation. The experimental findings indicate that visual stimuli amplify the emotional experience triggered by affective pictures. The results from the collected emotional EEG data demonstrate interdependence between the IMFs and emotional pictures. Furthermore, brain topographs support the statistical analysis by revealing that brain activation is more neuroactive for neutral-based visual stimuli compared to other types of visual stimuli.

关键词： Visualization Brain Empirical mode decomposition Electric variables measurement Feature extraction Electroencephalography Recording

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks

Swallowing Assessment using High-Resolution Cervical Auscult...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ayman Anwar Yassin Khalifa Erin Lucatorto James L. Coyle Ervin Sejdic Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto Toronto Canada Center for Research Computing and Information Technology Analytics University of Pittsburgh Pittsburgh PA USA Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Giza Egypt Department of Communication Science and Disorders School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto and North York General Hospital Toronto Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient’s neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics; specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers’ ability to generalize on unseen data.

关键词： Accuracy Sensitivity Recurrent neural networks Convolution Kinematics Transformers Feature extraction Solids Data models Signal resolution

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A Multimode Null Reconfigurable Antenna for Grating Lobe Suppression

A Multimode Null Reconfigurable Antenna for Grating Lobe Sup...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Shi-Tong Wang Lei Zhu Department of Electrical and Computer Engineering Faculty of Science and Technology State Key Laboratory of Internet of Things for Smart City University of Macau Macau China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

This paper introduces a novel multimode null reconfigurable antenna (NRA) and its attractive application in grating lobe suppression. The proposed NRA generates a pattern null by combining far fields of TM 01 and TM 02 modes of a rectangular patch antenna. The theoretical predictions of null angles versus feeding locations are presented based on electric field distributions under the patch. The proposed NRA is realized with a switchable feeding network. Simulated and measured results are presented demonstrating that the pattern null can be switched from -64° to 54° at 2.45 GHz. In final, the proposed NRA is utilized as antenna elements of a sparse array with the element spacing of one wavelength. Simulated array patterns strongly verify the grating lobe suppression capacity of the proposed NRA array compared to the conventional patch array.

关键词： Microwave measurement Microwave technology Wavelength measurement Patch antennas Millimeter wave technology Millimeter wave measurements Switches

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Compact Null Steering Antenna with Hemispherical Null Coverage Under Feed Position Controlling

Compact Null Steering Antenna with Hemispherical Null Covera...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Shi-Tong Wang Lei Zhu Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9798331520717

ISBN: (纸本)9798331520724

This paper proposes an effective strategy to implement pattern null steering by means of controlling the feed position. To demonstrate this strategy, a circular patch antenna with several shorting pins is developed. Under the simultaneous excitation of TM 11 and TM 02 modes, a pattern null is generated and can be reconfigured on the upper hemisphere by switching the feed position. Based on the theoretical analysis of null formation, the mapping relationship between null direction and feed position is revealed. According to the presented mapping relationship, the required feed position for a randomly given null direction can be conveniently determined. The measurement verification is presented. Moreover, a potential application of the proposed antenna in reducing the grating lobes is discussed in the last part of this paper.

关键词： Radio frequency Antenna measurements Patch antennas Conferences Switches Antenna feeds Pins Feeds Gratings

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Classification of Time Series Using FCM-Based Forecasting Models

Classification of Time Series Using FCM-Based Forecasting Mo...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Miłosz Wrzesień Mariusz Wrzesień Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Kraków Poland Faculty of Applied Informatics University of Information Technology and Management Rzeszów Poland

In this study, a method of time series classification is considered. Classification is performed using forecasting models. It is assumed that processed time series are of different natures, i.e., they belong to different classes. Each class has its forecasting model. Thus, an unknown time series is presented to the models to evaluate forecasting errors. The classified time series is assigned to the class with the winning forecasting model. In the study, Fuzzy Cognitive Maps are used to build forecasting models. Prior to forecasting, the processed raw time series are preprocessed. Six different error functions having the most significant influence on classification are used. The error functions come from root mean square error and mean percentage error.

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