arXiv subjects

M. Ali Saif

Publications and source records attributed to M. Ali Saif.

10 recordsLinked to original sources

arXiv2025

Comparative study of Wavelet transform and Fourier domain filtering for medical image denoising

Denoising of images is a crucial preprocessing step in medical imaging, essential for improving diagnostic clarity. While deep learning methods offer state-of-the-art performance, their computational complexity and data requirements can be prohibitive. In this study we present a comprehensive comparative analysis of two classical, computationally efficient transform-domain techniques: Discrete Wavelet Transform (DWT) and Discrete Fourier Cosine Transform (DFCT) filtering. We evaluated their efficacy in denoising medical images which corrupted by Gaussian, Uniform, Poisson, and Salt-and-Pepper noise. Contrary to the common hypothesis favoring wavelets for their multi-resolution capabilities, our results demonstrate that a block-based DFCT approach consistently and significantly outperforms a global DWT approach across all noise types and performance metrics (SNR, PSNR, IM). We attribute DFCT's superior performance to its localized processing strategy, which better preserves fine details by operating on small image blocks, effectively adapting to local statistics without introducing global artifacts. This finding underscores the importance of algorithmic selection based on processing methodology, not just transform properties, and positions DFCT as a highly effective and efficient denoising tool for practical medical imaging applications.

cond-mat.stat-mech↗

arXiv2023

Crossover from dynamical percolation class to directed percolation class on a two dimensional lattice

We study the crossover phenomena from the dynamical percolation class (DyP) to the directed percolation class (DP) in the model of diseases spreading, Susceptible-Infected-Refractory-Susceptible (SIRS) on a two-dimensional lattice. In this model, agents of three species S, I, and R on a lattice react as follows: $S+I\rightarrow I+I$ with probability $λ$, $I\rightarrow R$ after infection time $τ_I$ and $R\rightarrow I$ after recovery time $τ_R$. Depending on the value of the parameter $τ_R$, the SIRS model can be reduced to the following two well-known special cases. On the one hand, when $τ_R \rightarrow 0$, the SIRS model reduces to the SIS model. On the other hand, when $τ_R \rightarrow \infty$ the model reduces to SIR model. It is known that, whereas the SIS model belongs to the DP universality class, the SIR model belongs to the DyP universality class. We can deduce from the model dynamics that, SIRS will behave as an SIS model for any finite values of $τ_R$. SIRS will behave as SIR only when $τ_R=\infty$. Using Monte Carlo simulations we show that as far as the $τ_R$ is finite the SIRS belongs to the DP university class. We also study the phase diagram and analyze the scaling behavior of this model along the critical line. By numerical simulation and analytical argument, we find that the crossover from DyP to DP is described by the crossover exponent $1/ϕ=0.67(2)$.

cond-mat.stat-mech↗

arXiv2023

Nonequilibrium phase transition of a one dimensional system reaches the absorbing state by two different ways

We study the nonequilibrium phase transitions from the absorbing phase to the active phase for the model of disease spreading (Susceptible-Infected-Refractory-Susceptible (SIRS)) on a regular one dimensional lattice. In this model, particles of three species (S, I and R) on a lattice react as follows: $S+I\rightarrow 2I$ with probability $λ$, $I\rightarrow R$ after infection time $τ_I$ and $R\rightarrow I$ after recovery time $τ_R$. In the case of $τ_R>τ_I$, this model has been found to has two critical thresholds separate the active phase from absorbing phases \cite{ali1}. The first critical threshold $λ_{c1}$ is corresponding to a low infection probability and second critical threshold $λ_{c2}$ is corresponding to a high infection probability. At the first critical threshold $λ_{c1}$, our Monte Carlo simulations of this model suggest the phase transition to be of directed percolation class (DP). However, at the second critical threshold $λ_{c2}$ we observe that, the system becomes so sensitive to initial values conditions which suggests the phase transition to be discontinuous transition. We confirm this result using order parameter quasistationary probability distribution and finite-size analysis for this model at $λ_{c2}$. Additionally, the typical space-time evolution of this model at $λ_{c2}$ shows that, the spreading of active particles are compact in a behavior which remind us the spreading behavior in the compact directed percolation.14

cond-mat.stat-mech↗

arXiv2023

Determination of the critical points for systems of directed percolation class using machine learning

Recently, machine learning algorithms have been used remarkably to study the equilibrium phase transitions, however there are only a few works have been done using this technique in the nonequilibrium phase transitions. In this work, we use the supervised learning with the convolutional neural network (CNN) algorithm and unsupervised learning with the density-based spatial clustering of applications with noise (DBSCAN) algorithm to study the nonequilibrium phase transition in two models. We use CNN and DBSCAN in order to determine the critical points for directed bond percolation (bond DP) model and Domany-Kinzel cellular automaton (DK) model. Both models have been proven to have a nonequilibrium phase transition belongs to the directed percolation (DP) universality class. In the case of supervised learning we train CNN using the images which are generated from Monte Carlo simulations of directed bond percolation. We use that trained CNN in studding the phase transition for the two models. In the case of unsupervised learning, we train DBSCAN using the raw data of Monte Carlo simulations. In this case, we retrain DBSCAN at each time we change the model or lattice size. Our results from both algorithms show that, even for a very small values of lattice size, machine can predict the critical points accurately for both models. Finally, we mention to that, the value of the critical point we find here for bond DP model using CNN or DBSCAN is exactly the same value that has been found using transfer learning with a domain adversarial neural network (DANN) algorithm.

cond-mat.stat-mech↗

arXiv2023

SIR model on one dimensional small world networks

We study the absorbing phase transition for the model of epidemic spreading, Susceptible- Infected- Refractory (SIR), on one dimensional small world networks. This model has been found to be in the universality class of the dynamical percolation class, the mean field class corresponding to this model is d = 6. The one dimensional case is special case of this class in which the percolation threshold goes to one (boundary value) in the thermodynamic limit. This behavior resembles slightly the behavior of one dimensional Ising and XY models where the critical thresholds for both models go to zero temperature (boundary value) in the thermodynamic limit. By analytical arguments and numerical simulations we demonstrate that, increasing the connectivity (2k) of this model on regular one dimensional lattice does not alter the criticality of this model. Whereas we find that, this model crosses from a one dimensional structure to mean field type for any finite value of the rewiring probability (p), in manner is similarly to what happened in the Ising and XY models on small world networks. In additional to that, we calculate the critical exponents and the full critical phase space of this model on small world network. We also introduce the crossover scaling function of this model from one dimensional behavior to mean field behavior. Furthermore we reveal the similarity between this model, and the Ising and XY models on the small world networks.

cond-mat.stat-mech↗

arXiv2020

Universal Power Law Scaling Near the Turning Points

We show analytically and numerically that, the velocity $v_\pm$ of a particle near the turning points $x_0$ vanishes, i. e. $v_\pm\rightarrow 0$ as $x\rightarrow x_0$, according to the power law scaling $\left|v_\pm\right| \propto \left|x_0-x\right|^β$, where the exponent $β=1/2$ is independent of the particle mass and the force acting on it. We also show that, the time spends it any particle at each small interval $dx$ near the turning points diverges as $τ\propto \left|x_0-x\right|^ν$, with the exponent $ν=-1/2$. Behavior we find here is very similar to power law scaling that had been found near the critical points for systems which undergo a phase transition.

cond-mat.stat-mech↗

arXiv2019

Epidemic Threshold for the SIRS Model on Networks

We study the phase transition from the persistence phase to the extinction phase for the SIRS (susceptible/ infected/ refractory/ susceptible) model of diseases spreading on the networks. We derive an analytical expression of the probability for the descendants nodes to re-infect their ancestors nodes. We find that, in the case of the recovery time $τ_R$ is larger than the infection time $τ_I$, the infection will flow directionally from the ancestors to the descendants however, the descendants will not able to reinfect their ancestors during their infection time. This behavior leads us to deduce that, for this case and when the infection rate $λ$ is high enough in such that, any infected node on the network infects all of its neighbors during its infection time, SIRS model on the network evolves to extinction state, where all the nodes on the network become susceptible. Moreover, we assert that, in order to the infection occurs repeatedly inside the network, the loops on the network are necessary, which means the clustering coefficient will play an important role for this model. Hence, unlike the other models such as SIS model and SIR model, SIRS model has a two critical threshold which separate the persistence phase from the extinction phase when $τ_I<τ_R$. That means, for fixed values of $τ_I$ and $τ_R$ there are a two critical points for infection rate $λ_1$ and $λ_2$, where epidemic persists in between of those two points. We confirm those results numerically by the simulation of regular one dimensional SIRS system.

q-bio.PE↗

arXiv2010

Dynamic Phase Transition in Prisoner's Dilemma on a Lattice with Stochastic Modifications

We present a detailed study of prisoner's dilemma game with stochastic modifications on a two-dimensional lattice, in presence of evolutionary dynamics. By very nature of the rules, the cooperators have incentive to cheat and the fear to being cheated in prisoner's dilemma and may cheat even when not dictated by evolutionary dynamics. We consider two variants. In either case, the agents mimic the action (cooperation or defection) in the previous timestep of the most successful agent in the neighborhood. Over and above this, the fraction p of cooperators spontaneously change their strategy to pure defector at every time step in the first variant. In the second variant, there are no pure cooperators. All cooperators keep defecting with probability p at every time-step. In both cases, the system switches from coexistence state to an all-defector state for higher values of p. We show that the transition between these states unambiguously belongs to directed percolation universality class in 2 + 1 dimension. We also study the local persistence and the persistence exponents are higher than ones obtained in previous studies underlining their dependence on details of dynamics

cond-mat.stat-mech↗

arXiv2009

Emergence of Power Law in a Market with Mixed Models

We investigate the problem of wealth distribution from the viewpoint of asset exchange. Robust nature of Pareto's law across economies, ideologies and nations suggests that this could be an outcome of trading strategies. However, the simple asset exchange models fail to reproduce this feature. A yardsale(YS) model in which amount put on the bet is a fraction of minimum of the two players leads to condensation of wealth in hands of some agent while theft and fraud(TF) model in which the amount to be exchanged is a fraction of loser's wealth leads to an exponential distribution of wealth. We show that if we allow few agents to follow a different model than others, {\it i.e.} there are some agents following TF model while rest follow YS model, it leads to distribution with power law tails. Similar effect is observed when one carries out transactions for a fraction of one's wealth using TF model and for the rest YS model is used. We also observe a power law tail in wealth distribution if we allow the agents to follow either of the models with some probability.

q-fin.TR↗

arXiv2009

Effects of introduction of new resources and fragmentation of existing resources on limiting wealth distribution in asset exchange models

Pareto law, which states that wealth distribution in societies have a power-law tail, has been a subject of intensive investigations in statistical physics community. Several models have been employed to explain this behavior. However, most of the agent based models assume the conservation of number of agents and wealth. Both these assumptions are unrealistic. In this paper, we study the limiting wealth distribution when one or both of these assumptions are not valid. Given the universality of the law, we have tried to study the wealth distribution from the asset exchange models point of view. We consider models in which a) new agents enter the market at constant rate b) richer agents fragment with higher probability introducing newer agents in the system c) both fragmentation and entry of new agents is taking place. While models a) and c) do not conserve total wealth or number of agents, model b) conserves total wealth. All these models lead to a power-law tail in the wealth distribution pointing to the possibility that more generalized asset exchange models could help us to explain emergence of power-law tail in wealth distribution.

q-fin.TR↗