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Muhammad Rizwan

Publications and source records attributed to Muhammad Rizwan.

15 recordsLinked to original sources
arXiv2026

Population-Level Profiling of DSM-5 Depressive Symptoms Among Self-Reported ADHD and ASD Users on Twitter: An Exploratory Study Using Advanced NLP and Statistical Analysis

Background: Depression frequently co-occurs with ADHD and autism spectrum disorder (ASD), but population-level differences in symptom expression between these groups remain underexplored. Objective: We examined whether social media users with ADHD and ASD differ in how they express DSM-5 depressive symptoms in their tweets, and whether differences persist across varying levels of depressive-content filtering. Methods: We analysed 1,282,437 tweets from 792 users (622 ADHD; 170 ASD) with self-reported diagnoses on Twitter. Tweets were pre-filtered for depressive relevance using zero-shot NLI, then classified into nine DSM-5 symptoms using MentalRoBERTa fine-tuned on ReDSM5. Profiles were mean-centered per user. We applied L1-penalised logistic regression with cross-validation to distinguish ADHD from ASD users, complemented by Pearson correlations for symptom co-occurrence, and tested robustness across five filtering thresholds using bootstrapping. Results: MentalRoBERTa achieved macro-F1 of 0.901 on a held-out set, outperforming the original ReDSM5 benchmark. ADHD vs ASD classification yielded stable but modest performance (cross-validated ROC-AUC 0.645-0.653). Cognitive issues, sleep issues, appetite change, and fatigue leaned toward ADHD, while suicidal ideation and anhedonia leaned toward ASD. A largely shared symptom co-occurrence structure emerged between groups; no pair met our criterion for a robust disorder-specific difference. Conclusions: Population-level differences in depression-related language between ADHD and ASD social media users were consistently observed across thresholds, reflecting reproducibility rather than clinical validity. Findings are exploratory and do not establish differing phenomenology at the individual level.

cs.CL↗
arXiv2025

Large Language Models for Power System Applications: A Comprehensive Literature Survey

This comprehensive literature review examines the emerging applications of Large Language Models (LLMs) in power system engineering. Through a systematic analysis of recent research published between 2020 and 2025, we explore how LLMs are being integrated into various aspects of power system operations, planning, and management. The review covers key application areas including fault diagnosis, load forecasting, cybersecurity, control and optimization, system planning, simulation, and knowledge management. Our findings indicate that while LLMs show promising potential in enhancing power system operations through their advanced natural language processing and reasoning capabilities, significant challenges remain in their practical implementation. These challenges include limited domain-specific training data, concerns about reliability and safety in critical infrastructure, and the need for enhanced explainability. The review also highlights emerging trends such as the development of power system-specific LLMs and hybrid approaches combining LLMs with traditional power engineering methods. We identify crucial research directions for advancing the field, including the development of specialized architectures, improved security frameworks, and enhanced integration with existing power system tools. This survey provides power system researchers and practitioners with a comprehensive overview of the current state of LLM applications in the field and outlines future pathways for research and development.

eess.SY↗
arXiv2025

Text-Conditioned Diffusion Model for High-Fidelity Korean Font Generation

Automatic font generation (AFG) is the process of creating a new font using only a few examples of the style images. Generating fonts for complex languages like Korean and Chinese, particularly in handwritten styles, presents significant challenges. Traditional AFGs, like Generative adversarial networks (GANs) and Variational Auto-Encoders (VAEs), are usually unstable during training and often face mode collapse problems. They also struggle to capture fine details within font images. To address these problems, we present a diffusion-based AFG method which generates high-quality, diverse Korean font images using only a single reference image, focusing on handwritten and printed styles. Our approach refines noisy images incrementally, ensuring stable training and visually appealing results. A key innovation is our text encoder, which processes phonetic representations to generate accurate and contextually correct characters, even for unseen characters. We used a pre-trained style encoder from DG FONT to effectively and accurately encode the style images. To further enhance the generation quality, we used perceptual loss that guides the model to focus on the global style of generated images. Experimental results on over 2000 Korean characters demonstrate that our model consistently generates accurate and detailed font images and outperforms benchmark methods, making it a reliable tool for generating authentic Korean fonts across different styles.

cs.CV↗
arXiv2025

Thermodynamic Topological Classes of Family of Black Holes with NUT-Charge

{We introduce new analytical methods for treating black-hole thermodynamic topology, and thus depart from the usual treatment known in the literature. As application we investigate the universal thermodynamic topological classes of a family of black holes with NUT-charge. A charge-dependent thermodynamic topological transition from one class to another class is discussed.

gr-qc↗
arXiv2025

Universal thermodynamic topological classes of black holes in perfect fluid dark matter background

In this paper, we study the universal thermodynamic topological classes of a family of black holes in a perfect fluid dark matter (PFDM) background. Recent research on black hole thermodynamics suggests that all black holes can be classified into four universal thermodynamic classes, denoted by $W^{1-}$, $W^{0+}$, $W^{0-}$, and $W^{1+}$. Our study reveals that the Schwarzschild black hole in PFDM belongs to the $W^{1-}$ class, and independence of black hole size thermodynamically unstable at both low- and high-temperature limits. The Reissner-Nordstr\"om, Kerr, and Kerr-Newman black holes in the PFDM background belong to the same universal thermodynamic class, $W^{0+}$, which represents small, stable black holes and large, unstable black holes at low-temperature limits, whereas no black hole state exists at high temperatures. The AdS black holes behave differently compared to their counterparts in PFDM. The Schwarzschild-AdS black hole belongs to the $W^{0-}$ class, indicating no black hole state at low temperatures, but small, unstable and large, stable black hole states at high temperatures. Furthermore, the Kerr-AdS black hole belongs to the $W^{1+}$ class, characterized by small, stable black holes at low temperatures, large, stable black holes at high temperatures, and unstable, intermediate-sized black holes at both low and high temperatures. These findings uncover the universal topological classifications underlying black hole thermodynamics, offering profound insights into the fundamental principles of quantum gravity.

gr-qc↗
arXiv2024

Prevalent Frequency of Emotional and Physical Symptoms in Social Anxiety using Zero Shot Classification: An Observational Study

Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptoms becomes pivotal for comprehensive diagnosis and tailored therapeutic interventions. This study analyze prevalence and frequency of social anxiety symptoms taken from Mayo Clinic, exploring diverse human experiences from utilizing a large Reddit dataset dedicated to this issue. Leveraging these platforms, the research aims to extract insights and examine a spectrum of physical and emotional symptoms linked to social anxiety disorder. Upholding ethical considerations, the study maintains strict user anonymity within the dataset. By employing a novel approach, the research utilizes BART-based multi-label zero-shot classification to identify and measure symptom prevalence and significance in the form of probability score for each symptom under consideration. Results uncover distinctive patterns: "Trembling" emerges as a prevalent physical symptom, while emotional symptoms like "Fear of being judged negatively" exhibit high frequencies. These findings offer insights into the multifaceted nature of social anxiety, aiding clinical practices and interventions tailored to its diverse expressions.

cs.CL↗
arXiv2024

Automated Bi-Fold Weighted Ensemble Algorithms and its Application to Brain Tumor Detection and Classification

The uncontrolled and unstructured growth of brain cells is known as brain tumor, which has one of the highest mortality rates among diseases from all types of cancers. Due to limited diagnostic and treatment capabilities, they pose significant challenges, especially in third-world countries. Early diagnosis plays a vital role in effectively managing brain tumors and reducing mortality rates. However, the availability of diagnostic methods is hindered by various limitations, including high costs and lengthy result acquisition times, impeding early detection of the disease. In this study, we present two cutting-edge bi-fold weighted voting ensemble models that aim to boost the effectiveness of weighted ensemble methods. These two proposed methods combine the classification outcomes from multiple classifiers and determine the optimal result by selecting the one with the highest probability in the first approach, and the highest weighted prediction in the second technique. These approaches significantly improve the overall performance of weighted ensemble techniques. In the first proposed method, we improve the soft voting technique (SVT) by introducing a novel unsupervised weight calculating schema (UWCS) to enhance its weight assigning capability, known as the extended soft voting technique (ESVT). Secondly, we propose a novel weighted method (NWM) by using the proposed UWCS. Both of our approaches incorporate three distinct models: a custom-built CNN, VGG-16, and InceptionResNetV2 which has been trained on publicly available datasets. The effectiveness of our proposed systems is evaluated through blind testing, where exceptional results are achieved. We then establish a comparative analysis of the performance of our proposed methods with that of SVT to show their superiority and effectiveness.

cs.LG↗
arXiv2023

Topological classes of thermodynamics of black holes in perfect fluid dark matter background

In this paper we explore the topological classes of thermodynamics of a family of black holes. In particular we investigate the influence of distinct fields, including the electric field, non-linear magnetic field, along with the perfect fluid matter background that can mimic dark matter in large distances. In light of these considerations, we shall henceforth denote this fluid as perfect fluid dark matter (PFDM). Our analysis reveals that the winding and topological numbers for the Schwarzschild and Kerr black holes in PFDM background are the same as the Schwarzschild and Kerr black holes, however for the Kerr-AdS background in PFDM we obtain a different topological number compared to the Kerr black hole in PFDM. Furthermore, we explore in details the interplay of electric charge and nonlinear magnetic charge, impacting the topological classes of thermodynamics both in the absence and presence of PFDM. Interestingly, it is demonstrated that the topological numbers associated with the static Hayward black holes, both in the absence and presence of PFDM, deviate from those of the Schwarzschild black hole. This observation shows that the presence of a magnetic charge introduces an additional role and can alter the topological numbers. Finally, our study culminates with the comprehensive analysis of the topological numbers pertaining to the Hayward black hole, considering the combined effects of PFDM and rotation.

gr-qc↗
arXiv2019

A New Approach: Cognitive Multi-Level Authentication (CMLA) in Nuclear Command and Control

Nuclear monitoring must considered as high precedence against national security. Now with the increasing nuclear threats it is crucial to ensure that malicious entity never procure nuclear warheads. Which comprises the prevention of illegal or terrorist access to nuclear weapons. The disastrous damage that could be the consequence of unauthorized unapproved utilization of nuclear weapon and from the expansion of nuclear technologies to unacceptable states has driven the nuclear forces to spend epic measures of securing nuclear warheads as well as the supporting materials infrastructure and industries. The procedure of ratifying users credentials is known as authentication. Cognitive based authentication is a type of authentication that is actually the amalgamation of neuron biological and psychological techniques. This research is intended to provide human inspired Cognitive Multi-level Authentication utilizing the extensive quantum processing capabilities. Simulation is being done on online Q U V I S quantum simulator using quantum cryptography B B 8 4 algorithm where the intended person is successfully authenticated while considering different scenarios. So the proposed scheme will come up with self learning intellect based secure speedy and reliable authentication systems against nuclear command and control.

cs.CR↗
arXiv2019

On the possibility of wormhole formation in the galactic halo due to dark matter Bose-Einstein condensates

It has been recently claimed that dark matter could be in the form of a Bose-Einstein condensate (BEC) in order to explain the dynamics at large distances from the galactic center [Boehmer, Harko, JCAP {\bf 0706}, 025 (2007)]. In this paper we explore the possibility of wormhole formation in galactic halos due to the dark matter BEC. In particular we have found a new wormhole solution supported by BEC dark matter using the expressions for the density profile of the BEC and rotation velocity along with the Einstein field equations to calculate the wormhole red shift function as well as the shape function. To this end, we show that for a specific choose of the central density of the condensates our wormhole solution satisfies the flare our condition. Furthermore we check the null, weak, and strong condition at the wormhole throat with a radius $r_0$, and shown that in general the energy condition are violated by some arbitrary quantity at the wormhole throat. Using the volume integral quantifier, and choosing reasonable values of parameters we have calculate the amount of BEC exotic matter near the wormhole throat, such that the wormhole extends form $r_0$ to a a cut off radius situated at $`a'$. Moreover we have introduced a Kerr-like metric for a rotating BEC wormhole to study the effect of BEC dark matter on the Lense-Thirring precession frequencies. Namely, we have shown that the obtained precession frequencies lie within a range of typical quasi-periodic oscillations (QPOs).

gr-qc↗
arXiv2018

Distinguishing a Kerr-like black hole and a naked singularity in perfect fluid dark matter via precession frequencies

We study a Kerr-like black hole and naked singularity in perfect fluid dark matter (PFDM). The critical value of spin parameter $a_c$ is presented to differentiate the black hole from naked singularity. It is seen that for any fixed value of dark matter parameter $\alpha$ the rotating object is black hole if $a\leq a_c$ and naked singularity if $a>a_c$. Also for $-2\leq\alpha<2/3$ the size of the black hole horizons decrease whereas for $2/3<\alpha$ it increases. We also study spin precession frequency of a test gyroscope attached to stationary observer to differentiate a black hole from naked singularity in perfect fluid dark matter. For the black hole, spin precession frequency blows up as the observer reaches the central object while for naked singularity it remains finite except at the ring singularity. Moreover, we study Lense-Thirring precession for a Kerr-like black hole and geodetic precession for Schwarzschild black hole in perfect fluid dark matter. To this end, we have calculated the Kepler frequency (KF), the vertical epicyclic frequency (VEF), and the nodal plane precession frequency (NPPF). Our results show that, the PFDM parameter $\alpha$ significantly affects those frequencies. This difference can be used by astrophysical observations in the near future to shed some light on the nature of dark matter.

gr-qc↗
arXiv2018

Charged fermions tunneling from stationary axially symmetric black holes with generalized uncertainty principle

In this paper, we study the tunneling of charged fermions from the stationary axially symmetric black holes using the generalized uncertainty principle (GUP) via Wentzel, Kramers, and Brillouin (WKB) method. The emission rate of the charged fermions and corresponding modified Hawking temperature of Ker-Newman black hole, Einstein-Maxwell-Dilaton-Axion (EMDA) black hole, Kaluza-Klein dilaton black hole, and then, charged rotating black string are obtained and we show that the corrected thermal spectrum is not purely thermal because of the minimal scale length which cause the black hole's remnant.

physics.gen-ph↗
arXiv2018

Distinguishing rotating Kiselev black hole from naked singularity using spin precession of test gyroscope

We study the critical values of the quintessential and spin parameters, to distinguish a rotating Kiselev black hole (RKBH) from a naked singularity. For any value of the dimensionless quintessential parameter $\omega_{q} \in (-1, -1/3)$, when increasing the value of quintessential parameter $\alpha$, the size of the event horizon increases, whereas the size of the outer horizon decreases. We then study the spin precession of a test gyroscope attached to a stationary observer in this spacetime. Using the spin precessions we differentiate black holes from naked singularities. If the precession frequency becomes large, as approaching to the central object in the quintessential field along any direction, then the spacetime is a black hole. A spacetime will contain a naked singularity if the precession frequency remains finite everywhere except at the singularity itself. Finally, we study the Lense-Thirring precession frequency for rotating Kiseleb black hole and the geodetic precession for Kiselev black hole.

gr-qc↗
arXiv2011

Performance of Short-Commit in Extreme Database Environment

Atomic commit protocols are used where data integrity is more important than data availability. Two-Phase commit (2PC) is a standard commit protocol for commercial database management systems. To reduce certain drawbacks in 2PC protocol people have suggested different variance of this protocol. Short-Commit protocol is developed with an objective to achieve low cost transaction commitment cost with non-blocking capability. In this paper we have briefly explained short-commit protocol executing pattern. Experimental analysis and results are presented to support the claim that short-commit can work efficiently in extreme database environment.

cs.DB↗