JAEM 2023, Vol 13, No 1JAEM 2023, Vol 13, No 1 koleksiyonunu içerir.https://hdl.handle.net/11729/51952024-03-28T14:22:06Z2024-03-28T14:22:06ZSD-prime cordial labeling of subdivision K₄−snake and related graphsPrajapati, Udayan M.Vasantlal, Vantiya Anitkumarhttps://hdl.handle.net/11729/52302023-01-05T16:25:11Z2023-01-01T00:00:00ZSD-prime cordial labeling of subdivision K₄−snake and related graphs
Prajapati, Udayan M.; Vasantlal, Vantiya Anitkumar
Let f : V (G) → {1, 2, . . . , |V (G)|} be a bijection, and let us denote S = f(u)+f(v) and D = |f(u)−f(v)| for every edge uv in E(G). Let fʹ be the induced edge labeling, induced by the vertex labeling f, defined as fʹ: E(G) → {0, 1} such that for any edge uv in E(G), fʹ(uv) = 1 if gcd(S, D) = 1, and fʹ(uv) = 0 otherwise. Let e(fʹ) (0) and e(fʹ) (1) be the number of edges labeled with 0 and 1 respectively. f is SD-prime cordial labeling if |e(fʹ) (0) − e(fʹ) (1)| ≤ 1 and G is SD-prime cordial graph if it admits SD-prime cordial labeling. In this paper, we have discussed the SD-prime cordial labeling of subdivision of K4−snake S(K₄Sn), subdivision of double K₄−snake S(D(K₄Sn)), subdivision of alternate K₄−snake S(A(K₄Sn)) of type 1, 2 and 3, and subdivision of double alternate K₄− snake S(DA(K₄Sn)) of type 1, 2 and 3.
2023-01-01T00:00:00ZPredicting the ocean currents using deep learningBayındır, Cihanhttps://hdl.handle.net/11729/52292023-01-05T15:21:47Z2023-01-01T00:00:00ZPredicting the ocean currents using deep learning
Bayındır, Cihan
In this paper, we analyze the predictability of the ocean currents using deep learning. More specifically, we apply the Long Short Term Memory (LSTM) deep learning network to a data set collected by the National Oceanic and Atmospheric Administration (NOAA) in Massachusetts Bay between November 2002-February 2003. We show that the current speed in two horizontal directions, namely u and v, can be predicted using the LSTM. We discuss the effect of training data set on the prediction error and on the spectral properties of predictions. Depending on the temporal or the spatial resolution of the data, the prediction times and distances can vary, and in some cases, they can be very beneficial for the prediction of the ocean current parameters. Our results can find many important applications including but are not limited to predicting the statistics and characteristics of tidal energy variation, controlling the current induced vibrations of marine structures and estimation of the wave blocking point by the chaotic oceanic current and circulation.
2023-01-01T00:00:00ZApplying VIM to conformable partial differential equationsHarir, AtimadMelliani, SaidChadli, Lalla Saadiahttps://hdl.handle.net/11729/52282023-01-05T14:52:18Z2023-01-01T00:00:00ZApplying VIM to conformable partial differential equations
Harir, Atimad; Melliani, Said; Chadli, Lalla Saadia
In this paper, we used new conformable variational iteration method, by the conformable derivative, for solving fractional heat-like and wave-like equations. This method is simple and very effective in the solution procedures of the fractional partial differential equations that have complicated solutions with classical fractional derivative definitions like Caputo, Riemann-Liouville and etc. The results show that conformable variational iteration method is usable and convenient for the solution of fractional partial differential equations. Obtained results are compared to the exact solutions and their graphics are plotted to demonstrate efficiency and accuracy of the method.
2023-01-01T00:00:00ZA technique for solving system of generalized Emden-Fowler equation using Legendre waveletBarnwal, Amit K.Sriwastav, Nikhilhttps://hdl.handle.net/11729/52272023-01-05T14:38:37Z2023-01-01T00:00:00ZA technique for solving system of generalized Emden-Fowler equation using Legendre wavelet
Barnwal, Amit K.; Sriwastav, Nikhil
This article is concerned with the development of an efficient numerical algorithm for the solution of a system of generalized nonlinear Emden-Fowler equation. The proposed algorithm is based on the Legendre wavelet operational matrix of integration technique. This method decreases the storage and computational complexity due to its calculation on the subinterval [ (n-1)/2^(k-1) , n/2^(k-1)) of [0,1]. The main highlight of this method is to converts the system of the differential equation into an equivalent system of nonlinear algebraic equations, which greatly simplifies approximation. Some numerical example shows that the proposed scheme is very efficient and reliable.
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