arXiv subjects

Sathwik Narkedimilli

Publications and source records attributed to Sathwik Narkedimilli.

13 recordsLinked to original sources

arXiv2026

RSPC: A Benchmark for Modeling Stress and Psychiatric Conditions in Digitally Mediated Relationships using Psychiatrist Annotations

In NLP, mental health conditions are often modeled as isolated phenomena, without interpersonal context. We use Reddit posts about long-distance relationships to capture both mental health distress and associated relational triggers. We introduce the Relational Stress and Psychiatry Corpus (RSPC) containing 1,799 Reddit posts annotated by psychiatrists for diagnostic categories, including the most prevalent mood disorders (anxiety and depression), relational stressor triggers, and indications of relationship phase. We benchmark seven fine-tuned transformer models and five large language models across multi-label disorder classification, relational trigger detection, and temporal phase prediction tasks. We find clear task-dependent differences between model families, with Claude-3-Haiku achieving the best disorder classification performance (Macro-F1 = 0.538) and GPT-4o obtaining the strongest relational trigger detection performance (Macro-F1 = 0.519), suggesting distinct model capabilities. We further find strong associations between anxiety disorders and chronic relational uncertainty. Overall, RSPC establishes a benchmark for NLP tasks that consider relational context and supports a shift from individual-centric to context-aware mental health modeling that captures the social and temporal dynamics of distress.

cs.LG↗

arXiv2026

BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media

The promotion of betting applications on social media platforms has increased significantly in recent years. Many of these advertisements use persuasive techniques that may mislead users, encourage risky behavior, and potentially influence users' mental well-being. However, research on the automated detection of manipulative and deceptive betting advertisements remains limited due to the lack of publicly available annotated datasets. In this work, we introduce a new dataset of betting-related advertisements collected from two widely used social media platforms, Instagram and Reddit. The advertisements were manually annotated for manipulative and deceptive advertising practices. In addition to classification labels, the dataset includes human-provided explanations that describe the reasoning behind each annotation, enabling research into explainable approaches to detecting manipulative advertising. Furthermore, we analyze the strategies commonly used in betting advertisements and examine how these persuasive tactics may impact users' mental health. The proposed framework can also enable practical applications such as browser plugins that warn users about manipulative betting advertisements and automated web crawlers that help regulatory authorities monitor and detect such promotions online.

cs.LG↗

arXiv2026

From Design to Deorbit: A Solar-Electric Autonomous Module for Multi-Debris Remediation

The escalating accumulation of orbital debris threatens the sustainability of space operations, necessitating active removal solutions that overcome the limitations of current fuel-dependent methods. To address this, this study introduces a novel remediation architecture that integrates a mechanical clamping system for secure capture with a high-efficiency, solar-powered NASA Evolutionary Xenon Thruster (NEXT) and autonomous navigation protocols. High-fidelity simulations validate the architecture's capabilities, demonstrating a successful retrograde deorbit from 800 km to 100 km, <10m position Root Mean Square Errors (RMSE) via radar-based Extended Kalman Filter (EKF) navigation, and a 93\% data delivery efficiency within 1 second using Delay/Disruption Tolerant Network (DTN) protocols. This approach significantly advances orbital management by establishing a benchmark for renewable solar propulsion that minimizes reliance on conventional fuels and extends mission longevity for multi-target removal.

cs.DC↗

arXiv2026

Explainable Galaxy Interaction Prediction with Hybrid Attention Mechanisms

Galaxy interaction classification remains challenging due to complex morphological patterns and the limited interpretability of deep learning models. We propose an attentive neural ensemble that combines AG-XCaps, H-SNN, and ResNet-GRU architectures, trained on the Galaxy Zoo DESI dataset and enhanced with LIME to enable explainable predictions. The model achieves Precision = 0.95, Recall = 1.00, F1 = 0.97, and Accuracy = 96%, outperforming a Random Forest baseline by significantly reducing false positives (23 vs. 70). This lightweight (0.45 MB) and scalable framework provides an interpretable and efficient solution for large-scale surveys such as Euclid and LSST, advancing data-driven studies of galaxy evolution.

astro-ph.IM↗

arXiv2025

A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies

Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability. To address this, we propose a novel quantum adversarial framework that integrates a hybrid quantum neural network (QNN) with classical deep learning layers, guided by an evaluator model with LIME-based interpretability, and extended through quantum GAN and self-supervised variants. In the proposed model, an adversarial evaluator concurrently guides the QNN by computing feedback loss, thereby optimizing both prediction accuracy and model explainability. Empirical evaluations show that the Vanilla model achieves RMSE = 0.27, MSE = 0.071, MAE = 0.21, and R^2 = 0.59, delivering the most consistent performance across regression metrics compared to adversarial counterparts. These results demonstrate the potential of combining quantum-inspired methods with classical architectures to develop lightweight, high-performance, and interpretable predictive models, advancing the applicability of QML beyond current limitations.

cs.LG↗

arXiv2025

REDDIX-NET: A Novel Dataset and Benchmark for Moderating Online Explicit Services

The rise of online platforms has enabled covert illicit activities, including online prostitution, to pose challenges for detection and regulation. In this study, we introduce REDDIX-NET, a novel benchmark dataset specifically designed for moderating online sexual services and going beyond traditional NSFW filters. The dataset is derived from thousands of web-scraped NSFW posts on Reddit and categorizes users into six behavioral classes reflecting different service offerings and user intentions. We evaluate the classification performance of state-of-the-art large language models (GPT-4, LlaMA 3.3-70B-Instruct, Gemini 1.5 Flash, Mistral 8x7B, Qwen 2.5 Turbo, Claude 3.5 Haiku) using advanced quantitative metrics, finding promising results with models like GPT-4 and Gemini 1.5 Flash. Beyond classification, we conduct sentiment and comment analysis, leveraging LLM and PLM-based approaches and metadata extraction to uncover behavioral and temporal patterns. These analyses reveal peak engagement times and distinct user interaction styles across categories. Our findings provide critical insights into AI-driven moderation and enforcement, offering a scalable framework for platforms to combat online prostitution and associated harms.

cs.CY↗

arXiv2025

Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches

In the case of Type-2 AGNs, estimating the mass of the black hole is challenging. Understanding how galaxies form and evolve requires considerable insight into the mass of black holes. This work compared different classical and quantum machine learning (QML) algorithms for black hole mass estimation, wherein the classical algorithms are Linear Regression, XGBoost Regression, Random Forest Regressor, Support Vector Regressor (SVR), Lasso Regression, Ridge Regression, Elastic Net Regression, Bayesian Regression, Decision Tree Regressor, Gradient Booster Regressor, Classical Neural Networks, Gated Recurrent Unit (GRU), LSTM, Deep Residual Networks (ResNets) and Transformer-Based Regression. On the other hand, quantum algorithms including Hybrid Quantum Neural Networks (QNN), Quantum Long Short-Term Memory (Q-LSTM), Sampler-QNN, Estimator-QNN, Variational Quantum Regressor (VQR), Quantum Linear Regression(Q-LR), QML with JAX optimization were also tested. The results revealed that classical algorithms gave better R^2, MAE, MSE, and RMSE results than the quantum models. Among the classical models, LSTM has the best result with an accuracy of 99.77%. Estimator-QNN has the highest accuracy for quantum algorithms with an MSE of 0.0124 and an accuracy of 99.75%. This study ascertains both the strengths and weaknesses of the classical and the quantum approaches. As far as our knowledge goes, this work could pave the way for the future application of quantum algorithms in astrophysical data analysis.

astro-ph.IM↗

arXiv2025

Enhancing IoT Network Security through Adaptive Curriculum Learning and XAI

To address the critical need for secure IoT networks, this study presents a scalable and lightweight curriculum learning framework enhanced with Explainable AI (XAI) techniques, including LIME, to ensure transparency and adaptability. The proposed model employs novel neural network architecture utilized at every stage of Curriculum Learning to efficiently capture and focus on both short- and long-term temporal dependencies, improve learning stability, and enhance accuracy while remaining lightweight and robust against noise in sequential IoT data. Robustness is achieved through staged learning, where the model iteratively refines itself by removing low-relevance features and optimizing performance. The workflow includes edge-optimized quantization and pruning to ensure portability that could easily be deployed in the edge-IoT devices. An ensemble model incorporating Random Forest, XGBoost, and the staged learning base further enhances generalization. Experimental results demonstrate 98% accuracy on CIC-IoV-2024 and CIC-APT-IIoT-2024 datasets and 97% on EDGE-IIoT, establishing this framework as a robust, transparent, and high-performance solution for IoT network security.

cs.CR↗

arXiv2025

FAPL-DM-BC: A Secure and Scalable FL Framework with Adaptive Privacy and Dynamic Masking, Blockchain, and XAI for the IoVs

The FAPL-DM-BC solution is a new FL-based privacy, security, and scalability solution for the Internet of Vehicles (IoV). It leverages Federated Adaptive Privacy-Aware Learning (FAPL) and Dynamic Masking (DM) to learn and adaptively change privacy policies in response to changing data sensitivity and state in real-time, for the optimal privacy-utility tradeoff. Secure Logging and Verification, Blockchain-based provenance and decentralized validation, and Cloud Microservices Secure Aggregation using FedAvg (Federated Averaging) and Secure Multi-Party Computation (SMPC). Two-model feedback, driven by Model-Agnostic Explainable AI (XAI), certifies local predictions and explanations to drive it to the next level of efficiency. Combining local feedback with world knowledge through a weighted mean computation, FAPL-DM-BC assures federated learning that is secure, scalable, and interpretable. Self-driving cars, traffic management, and forecasting, vehicular network cybersecurity in real-time, and smart cities are a few possible applications of this integrated, privacy-safe, and high-performance IoV platform.

cs.CR↗

arXiv2024

FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios

This study proposes an advanced Federated Learning (FL) framework designed to enhance data privacy and security in IoT environments by integrating Decentralized Attribute-Based Encryption (DABE), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), and Blockchain technology. Unlike traditional FL, our framework enables secure, decentralized authentication and encryption directly on IoT devices using DABE, allowing sensitive data to remain locally encrypted. Homomorphic Encryption permits computations on encrypted data, and SMPC ensures privacy in collaborative computations, while Blockchain technology provides transparent, immutable record-keeping for all transactions and model updates. Local model weights are encrypted and transmitted to fog layers for aggregation using HE and SMPC, then iteratively refined by the central server using differential privacy to safeguard against data leakage. This secure, privacy-preserving FL framework delivers a robust solution for efficient model training and real-time analytics across distributed IoT devices, offering significant advancements in secure decentralized learning for IoT applications.

cs.CR↗

arXiv2024

Predicting Stellar Metallicity: A Comparative Analysis of Regression Models for Solar Twin Stars

The research focuses on determining the metallicity ([Fe/H]) predicted in the solar twin stars by using various regression modeling techniques which are, Random Forest, Linear Regression, Decision Tree, Support Vector, and Gradient Boosting. The data set that is taken into account here includes Stellar parameters and chemical abundances derived from a high-accuracy abundance catalog of solar twins from the GALAH survey. To overcome the missing values, intensive preprocessing techniques involving, imputation are done. Each model will subjected to training using different critical observables, which include, Mean Squared Error(MSE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE), and R-squared. Modeling is done by using, different feature sets like temperature: effective temperature(Teff), surface gravity: log g of 14-chemical-abundances namely, (([Na/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [Ca/Fe], [Sc/Fe], [Ti/Fe], [Cr/Fe], [Mn/Fe], [Ni/Fe], [Cu/Fe], [Zn/Fe], [Y/Fe], [Ba/Fe])). The target variable considered is the metallicity ([Fe/H]). The findings indicate that the Random Forest model achieved the highest accuracy, with an MSE of 0.001628 and an R-squared value of 0.9266. The results highlight the efficacy of ensemble methods in handling complex datasets with high dimensionality. Additionally, this study underscores the importance of selecting appropriate regression models for astronomical data analysis, providing a foundation for future research in predicting stellar properties with machine learning techniques.

astro-ph.SR↗

arXiv2024

Photometric Analysis for Predicting Star Formation Rates in Large Galaxies Using Machine Learning and Deep Learning Techniques

Star formation rates (SFRs) are a crucial observational tracer of galaxy formation and evolution. Spectroscopy, which is expensive, is traditionally used to estimate SFRs. This study tests the possibility of inferring SFRs of large samples of galaxies from only photometric data, using state-of-the-art machine learning and deep learning algorithms. The dataset adopted in this work is the one collected by Delli Veneri et al. (2019): it includes photometric data of more than 27 million galaxies coming from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). The algorithms we implemented and tested for comparing the performances include Linear Regression, Long Short-Term Memory (LSTM) networks, Support Vector Regression (SVR), Random Forest Regressor, Decision Tree Regressor, Gradient Boosting Regressor, and classical deep learning models. Our results mention that the Linear Regression model predicted an impressive accuracy of 98.97 percent as measured by the Mean Absolute Error (MAE), demonstrating that machine-learning approaches can be effective when it comes to photometric SFR estimation. Besides, the paper also reported the results for other intelligent algorithms, which predicted the SFRs, providing a detailed comparison of the performance of different machine learning algorithms in the photometric SFR estimation. This study not only shows the estimated SFR from photometric data is promising but also opens a door toward the application of machine learning and deep learning in astrophysics.

astro-ph.GA↗

arXiv2024

FL-DECO-BC: A Privacy-Preserving, Provably Secure, and Provenance-Preserving Federated Learning Framework with Decentralized Oracles on Blockchain for VANETs

Vehicular Ad-Hoc Networks (VANETs) hold immense potential for improving traffic safety and efficiency. However, traditional centralized approaches for machine learning in VANETs raise concerns about data privacy and security. Federated Learning (FL) offers a solution that enables collaborative model training without sharing raw data. This paper proposes FL-DECO-BC as a novel privacy-preserving, provably secure, and provenance-preserving federated learning framework specifically designed for VANETs. FL-DECO-BC leverages decentralized oracles on blockchain to securely access external data sources while ensuring data privacy through advanced techniques. The framework guarantees provable security through cryptographic primitives and formal verification methods. Furthermore, FL-DECO-BC incorporates a provenance-preserving design to track data origin and history, fostering trust and accountability. This combination of features empowers VANETs with secure and privacy-conscious machine-learning capabilities, paving the way for advanced traffic management and safety applications.

cs.CR↗