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Researcher profile

Nitin Kumar

Nitin Kumar contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.mtrl-scicond-mat.softmath.PRcond-mat.stat-mechmath.NANumerical Analysisphysics.app-phphysics.flu-dyn

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Published work

10 published item(s)

preprint2026arXiv

A Machine Learning Model for Solving Lane-Emden Equation using Legendre Wavelet Neural Network

As we know differential equations are very useful for electrical engineers to solve a variety of problems like: voltage across a capacitor, input versus output voltage, etc. Therefore, the goal of this paper is to find the solutions of non-linear differential equations based on the Lane Emden equation of second order using the Legendre wavelet neural network (LWNN) method. Here all the considered equations are singular initial value problems. To manage the singularity challenge, we have employed an artificial neural network method. This approach utilizes a neural network of a single layer, where the hidden layer is omitted by enlarging the input using Legendre wavelets functions. We have applied a feed-forward neural network method to the proposed problem along with the principle of error backpropagation. The effectiveness of the Legendre wavelet Neural Network method is validated through Lane Emden equations..

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

ARMARecon: An ARMA Convolutional Filter based Graph Neural Network for Neurodegenerative Dementias Classification

Early detection of neurodegenerative diseases such as Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) is essential for reducing the risk of progression to severe disease stages. As AD and FTD propagate along white-matter regions in a global, graph-dependent manner, graph-based neural networks are well suited to capture these patterns. Hence, we introduce ARMARecon, a unified graph learning framework that integrates Autoregressive Moving Average (ARMA) graph filtering with a reconstruction-driven objective to enhance feature representation and improve classification accuracy. ARMARecon effectively models both local and global connectivity by leveraging 20-bin Fractional Anisotropy (FA) histogram features extracted from white-matter regions, while mitigating over-smoothing. Overall, ARMARecon achieves superior performance compared to state-of-the-art methods on the multi-site dMRI datasets ADNI and NIFD.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets

Medical imaging datasets are often characterized by extreme class imbalances, where rare pathologies are significantly underrepresented compared to common conditions. This imbalance poses a dual challenge for Open-Set Recognition (OSR): models must maintain high classification accuracy on known classes while reliably rejecting unknown samples unseen during training in the clinical settings. While recently proposed Deep Simplex Classifier (DSC)~\cite{cevikalp2024reaching} and UnCertainty-aware Deep Simplex Classifier (UCDSC)~\cite{Aditya_2026_WACV} successfully leverage Neural Collapse to ensure maximal inter-class separation, they rely on a uniform margin that does not account for the varying densities of medical classes. In this paper, we propose DMDSC an enhanced framework featuring a dynamic margin approach. Our approach automatically adapts class-specific margins based on label frequency, enforcing a higher penalty and tighter feature clustering for rare pathologies to counteract the effects of data imbalance. Extensive experiments conducted on diverse medical benchmarks on BloodMNIST\cite{medmnistv2}, OCTMNIST\cite{medmnistv2}, DermaMNIST\cite{medmnistv2}, and BreaKHis~\cite{spanhol2015dataset} datasets, demonstrate that our framework outperforms state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Application of machine learning for forced plume in linearly stratified medium

Direct numerical simulation (DNS) is very accurate however, the computational cost increases significantly with the increase in Reynolds number. On the other hand, we have the Reynolds Averaged Navier Stokes (RANS) method for simulating turbulent flows, which needs less computational power. Turbulence models based on linear eddy viscosity models (LEVM) in the RANS method, which use a linear stress-strain rate relationship for modelling the Reynolds stress tensor do not perform well for complex flows \cite{shih1995new} . In this work, we intend to study the performance of non linear eddy viscosity model (NLEVM) hypothesis for turbulent forced plumes in a linearly stratified environment and modify the standard RANS model coefficients obtained from machine learning. The general eddy viscosity hypothesis supported by the closure coefficients generated from the tensor basis neural network (TBNN) is used to develop TBNN based K-$ε$ model. The aforementioned model is used to evaluate the plume's mean velocity profile, and maximum height reached. The comparison between standard LEVM, NLEVM and the experimental results indicates a significant improvement in the maximum height achieved, and a good improvement in the mean velocity profile.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Quantifying nematic order in evaporation-driven self-assembly of Halloysite nanotubes: Nematic islands and critical aspect ratio

Halloysite nanotubes (HNTs) are naturally occurring clay minerals found in Earth's crust that typically exist in the form of high aspect-ratio nanometers-long rods. Here, we investigate the evaporation-driven self-assembly process of HNTs and show that a highly polydisperse collection of HNTs self-sort into a spatially inhomogeneous structure, displaying a systematic variation in the resulting nematic order. Through detailed quantification using nematic order parameter $S$ and nematic correlation functions, we show the existence of well-defined isotropic-nematic transitions in the emerging structures. We also show that the onset of these transitions gives rise to the formation of nematic islands - phase coexisting ordered nematic domains surrounded by isotropic phase - which grow in size with $S$. Detailed image analysis indicates a strong correlation between local $S$ and the local aspect ratio, $L/D$, with nematic order possible only for rods with $L/D \ge 6.5 \pm 1$. Finally, we conclude that observed phenomena directly result from aspect ratio-based sorting in our system. Altogether, our results provide a unique method of tuning the local microscopic structure in self-assembled HNTs using $L/D$ as an external parameter.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Self-Assembly of Magnetic Co Atoms on Stanene

We have investigated the magnetic Co atoms self-assembled on the ultraflat stanene on Cu(111) substrate by utilizing scanning tunneling microscopy/spectroscopy (STM/STS) in conjunction with density functional theory (DFT). By means of depositing Co onto the stanene/Cu(111) held at 80 K, Co atoms have developed into the monomer, dimer, and trimer structures containing one, two, and three Co atoms respectively. As per atomically resolved topographic images and bias-dependent apparent heights, the atomic structure models based on Sn atoms substituted by Co atoms have been deduced, which are in agreement with both self-consistent DFT calculations and STM simulations. Apart from that, the projected density of states (PDOS) has revealed a minimum at around -0.5 eV from the Co-3d3z2-r2 minority band, which contributes predominately to the peak feature at about -0.3 eV in tunneling conductance (dI/dU) spectra taken at the Co atomic sites. As a result of the exchange splitting between the Co-3d majority and minority bands, there are non-zero magnetic moments, including about 0.60 uB in monomer, 0.56 uB in dimer, and 0.29 uB in trimer of the Co atom assembly on the stanene. Such magnetic Co atom assembly therefore could provide the vital building blocks in stabilizing the local magnetism on the two-dimensional (2D) stanene with non-trivial topological properties.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A queueing system with batch renewal input and negative arrivals

This paper studies an infinite buffer single server queueing model with exponentially distributed service times and negative arrivals. The ordinary (positive) customers arrive in batches of random size according to renewal arrival process, and joins the queue/server for service. The negative arrivals are characterized by two independent Poisson arrival processes, a negative customer which removes the positive customer undergoing service, if any, and a disaster which makes the system empty by simultaneously removing all the positive customers present in the system. Using the supplementary variable technique and difference equation method we obtain explicit formulae for the steady-state distribution of the number of positive customers in the system at pre-arrival and arbitrary epochs. Moreover, we discuss the results of some special models with or without negative arrivals along with their stability conditions. The results obtained throughout the analysis are computationally tractable as illustrated by few numerical examples. Furthermore, we discuss the impact of the negative arrivals on the performance of the system by means of some graphical representations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Analysis of an infinite-buffer batch-size-dependent service queue with discrete-time Markovian arrival process: D-$MAP/G_n^{(a,b)}/1$

Discrete-time queueing models find huge applications as they are used in modeling queueing systems arising in digital platforms like telecommunication systems, computer networks, etc. In this paper, we analyze an infinite-buffer queueing model with discrete Markovian arrival process. The units on arrival are served in batches by a single server according to the general bulk-service rule, and the service time follows general distribution with service rate depending on the size of the batch being served. We mathematically formulate the model using the supplementary variable technique and obtain the vector generating function at the departure epoch. The generating function is in turn used to extract the joint distribution of queue and server content in terms of the roots of the characteristic equation. Further, we develop the relationship between the distribution at the departure epoch and the distribution at arbitrary, pre-arrival and outside observer's epoch, which is used to obtain the latter ones. We evaluate some essential performance measures of the system and also discuss the computing process extensively which is demonstrated by a few numerical examples.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Phases and excitations of active rod-bead mixtures: simulations and experiments

We present a large-scale numerical study, supplemented by experimental observations, of a quasi-two-dimensional active system of polar rods and spherical beads confined between two horizontal plates and energised by vertical vibration. For low rod concentrations $Φ_r$ we observe a direct phase transition, as bead concentration $Φ_b$ is increased, from the isotropic phase to a homogeneous flock. For $Φ_r$ above a threshold value, an ordered band dense in both rods and beads occurs between the disordered phase and the homogeneous flock, in both experiments and simulations. Within the size ranges accessible we observe only a single band, whose width increases with $Φ_r$. Deep in the ordered state, we observe broken-symmetry "sound" modes and giant number fluctuations. The direction-dependent sound speeds and the scaling of fluctuations are consistent with the predictions of field theories of flocking, but sound damping rates show departures from such theories. At very high densities we see phase separation into rod-rich and bead-rich regions, both of which move coherently.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Performance limitation of Si Nanowire solar cells: Effects of nanowire length and surface defects

In Si nanowire (SiNW) solar cells enhanced light confinement property in addition to decoupling of charge carrier collection and light absorption directions plays a significant role to resolve the draw backs of bulk Si solar cells. In this report we have studied the dependence of the phovoltaic properties of Si NW array solar cells on the SiNW length and enhanced surface defect states as a result of enhanced surface area of the NWs. The SiNW arrays have been fabricated using metal catalyzed electroless etching (MCEE) technique. p-n junction has been produced by spin-on-dopant technique followed by thermal diffusion process. Front and rear electrodes have been deposited by e-beam evaporation techniques. SiNW lengths have been controlled from ~ 320 nm to 6.4 micro meter by controlling the parameters of MCEE technique. Photovoltaic properties of the solar cells have been characterized by measuring quantum efficiency and photocurrent density vs. voltage characteristics. Morphological studies have been carried out by using scanning electron microscopy. Reduction in light trapping capability comes at the benefit of reduced surface defects. The reduction of surface defects has been proved to be more advantageous in comparison to the decrement of light trapping capability. The major contribution to the changes in cell efficiency comes from the enhancement of short circuit current density with a very weak dependence on open circuit voltage. This work is beneficial for the production commercial Si solar cell where SiNW arrays could be used as a antireflection coating instead of using separate antireflection layers and thus could reduced the production cost.

0 citations0 reviewsNo code linkedOpen access