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Kanokwan Sitthithakerngkiet

Researcher at King Mongkut's University of Technology North Bangkok

Publications -  92
Citations -  568

Kanokwan Sitthithakerngkiet is an academic researcher from King Mongkut's University of Technology North Bangkok. The author has contributed to research in topics: Computer science & Hilbert space. The author has an hindex of 10, co-authored 57 publications receiving 283 citations. Previous affiliations of Kanokwan Sitthithakerngkiet include King Mongkut's University of Technology Thonburi & Naresuan University.

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Stability Results for Implicit Fractional Pantograph Differential Equations via ϕ-Hilfer Fractional Derivative with a Nonlocal Riemann-Liouville Fractional Integral Condition

TL;DR: In this article, a class of implicit pantograph fractional differential equation with more general Riemann-Liouville fractional integral condition is presented, where a certain class of generalized fractional derivative is used to set the problem.
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A hybrid viscosity algorithm via modify the hybrid steepest descent method for solving the split variational inclusion in image reconstruction and fixed point problems

TL;DR: A new viscosity approximation method is introduced by modify the hybrid steepest descent method for finding a common solution of split variational inclusion problem and fixed point problem of a countable family of nonexpansive mappings and it is proved that the sequences generated by the proposed iterative method converge strongly to acommon solution.
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A Modified Fletcher–Reeves Conjugate Gradient Method for Monotone Nonlinear Equations with Some Applications

TL;DR: In this article, a modification of the Fletcher-Reeves (FR) conjugate gradient projection method for constrained monotone nonlinear equations is presented, which possesses sufficient descent property and its global convergence was proved using some appropriate assumptions.
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ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors

TL;DR: A deep neural network composed of convolutional layers and residual networks was developed to address the problems pertaining to complex HAR and showed that deep residual networks are more durable and superior at activity recognition than the existing models.
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Nonlinear Caputo Fractional Derivative with Nonlocal Riemann-Liouville Fractional Integral Condition Via Fixed Point Theorems

TL;DR: In this paper, an interesting Caputo fractional derivative and Riemann-Liouville integral boundary value problem (BVP) was investigated and the existence of solutions to this problem was studied.