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- 저자 : Farzan Majeed Noori (검색결과 2 건)
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Aaqib Majeed,Ahmad Zeeshan,Aqila Shaheen,Mohammed Sh. Alhodaly,Farzan Majeed Noori 한국자기학회 2022 Journal of Magnetics Vol.27 No.2
Power generators, Hall accelerators, and flight MHD all require high levels of Hall current. The influence of Hall current and viscous dissipation on time-independent hydro-magnetic mixed convective radiative flow across a porous heated surface has thus been investigated using numerical computing and mathematical modeling in the current study. The fluid is electrically conducted and varies exponentially. It is assumed that the wall temperature and elongation rate will vary with specific exponential shapes. A solid uniform magnetic field B0 is employed normally to the surface. The mathematical model of PDEs for incompressible flow is transformed into ODE by applying a numerical technique based on a finite-difference structure which includes a three-stage Lobatto IIIa scheme with the help of MATLAB. The obtained solution depends on the convergence constraints involving the radiation parameter R, magnetic parameter M, porosity parameter Ω, Hall parameter m, buoyancy parameter ε, temperature distribution parameter a, Eckert number Ec, Prandtl number Pr, and convective term bh. Graphs of the velocity and temperature profiles are explained via pertinent parameters. Skin friction factor, and Nusselt number are also evaluated and presented graphically and in tabular form. Results clarify that temperature profile reduces by increasing values of temperature distribution parameter whereas opposite behavior is noted for positive values of the buoyancy parameter.
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Naseer, Noman,Qureshi, Nauman Khalid,Noori, Farzan Majeed,Hong, Keum-Shik Hindawi Publishing Corporation 2016 Computational intelligence and neuroscience Vol.2016 No.-
<P>We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), <I>k</I>-nearest neighbour (<I>k</I>NN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the<I> p </I>values were statistically significant relative to all of the other classifiers (<I>p</I> < 0.005) using HbO signals. </P>
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