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Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI

Computers, Materials & Continua 2022年第1期70卷 535-555页

作者： Arshia Arif M.Jawad Khan Kashif Javed hasan sajid Saddaf Rubab Noman Naseer Talha Irfan Khan National University of Sciences and Technology(NUST) IslamabadPakistan Intelligent Robotics Lab National Center of Artificial IntelligenceNational University of Sciences and Technology(NUST)IslamabadPakistan Department of Mechatronics Engineering Air UniversityIslamabadPakistan Institute of Space Technology IslamabadPakistan

For BCI systems,it is important to have an accurate and less complex architecture to control a device with enhanced *** this paper,a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface(BCI).An integrated classifier has been developed for achieving better classification accuracy using two *** integrated EEG-fNIRS-based vector-phase analysis(VPA)has been *** open-source dataset collected at the TechnischeUniversit鋞Berlin,including simultaneous electroencephalography(EEG)and functional near-infrared spectroscopy(fNIRS)signals of 26 healthy participants during n-back tests,has been used for this *** and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both *** resting state threshold circle,VPA has been used to detect a hemodynamic response in fNIRS signals,whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each *** threshold circles are drawn in the vector plane,where each circle is drawn after task completion in each trial of EEG ***,both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for *** of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy,that is 91.35%,as compared to other classifiers such as support vector machine(SVM),convolutional neural networks(CNN),deep neural networks(DNN)and VPA(with dual-threshold circles)with classification accuracies 82%,89%,87%and 86%*** of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.

关键词： EEG-fNIRS hybrid BCI vector-phase analysis hemodynamic response detection

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Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM

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Computers, Materials & Continua 2021年第10期69卷 191-203页

作者： Taher M.Ghazal Marrium Anam Mohammad Kamrul hasan Muzammil Hussain Muhammad sajid Farooq Hafiz Muhammad Ammar Ali Munir Ahmad Tariq Rahim Soomro Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797UAE Department of Computer Science Government College UniversityFaisalabad38000Pakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan CCSIS Institute of Business ManagementKarachi75190SindhPakistan

Hepatitis C is a contagious blood-borne infection,and it is mostly asymptomatic during the initial ***,it is difficult to diagnose and treat patients in the early stages of *** disease’s progression to its last stages makes diagnosis and treatment more *** this study,an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis *** dataset used for our Hep-Pred model is based on a literature study,and includes the records of 1385 patients infected with the hepatitis C *** in this dataset received treatment dosages for the hepatitis C virus for about 18 months.A former study divided the disease into four main *** stages have proven helpful for doctors to analyze the liver’s *** traditional way to check the staging is the biopsy,which is a painful and time-consuming *** article aims to provide an effective and efficient approach to predict hepatitis C *** this purpose,the proposed technique uses a fine Gaussian SVM learning algorithm,providing 97.9%accurate results.

关键词： Hepatitis C artificial intelligence Hep-Pred support vector machine machine learning hepatitis staging

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Analysis the Influenced of Plain and Twill Structures on Woven Fabric Properties

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Journal of Textile Science and Technology 2020年第3期6卷 107-113页

作者： Monour Khan hasan Mahmud Tirtha Sarathi Das Muhammad Abdullah Tushar Rahman Khan Rokibul hasan Shohan sajid Al Rafi Md Solaiman Department of Fabric Manufacturing Engineering Textile Engineering College Noakhali Affiliated by Bangladesh University of Textiles Dhaka Bangladesh Department of Wet Processing Engineering Textile Engineering College Noakhali Affiliated by Bangladesh University of Textiles Dhaka Bangladesh

Fabric outer appearance is influenced by various fabric structures. In this research work, the physical properties of plain and twill were analyzed. Physical, mechanical and other aesthetic properties are also tested and the finding results are also compared between two samples. After testing the samples it is found that rubbing fastness remains unchanged for plain and twill fabric. Abrasion resistance, pilling, crease recovery and cover factor were evaluated for twill and plain structures using ISO 12945-2, ISO 12945-1 and ISO 2313 methods respectively. Abrasion resistance and pills property are not good for twill fabric in comparison with plain fabric but crease recovery is better of twill fabric.

关键词： Abrasion Pilling Crease Recovery Color Fastness Cover Factor EPI & PPI

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