arXiv subjects

Kavya Bhand

Publications and source records attributed to Kavya Bhand.

6 recordsLinked to original sources

arXiv2026

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not

Multi-horizon latent consistency is a common training knob in video predictors and world models, but practitioners rarely know what it does to transition geometry. We treat lambda, the weight on multi-step latent agreement, as a diagnostic control and measure an empirical expansion proxy L20,q95 together with horizon-20 prediction error E20. On Moving-MNIST (n=6 seeds at the critical pair), raising lambda from 0 to 0.8 cuts L20 from 4.96 +/- 2.01 to 1.01 +/- 0.06 (paired t p=0.005, Wilcoxon p=0.031) and halves E20 (0.365 to 0.177, paired t p=1.1e-13). Four of six seeds cross L<1 at lambda=0.8. The same loss does not produce population L<1 on action-conditioned Pendulum-v1 or CartPole-v1, nor on KTH Actions video, even when E20 improves. An associational mediation analysis on MMNIST gives r-hat=0.94 (95% CI [0.88, 1.00], n=27, B=2000); lambda was not randomized. Defensive checks (architectural baselines, exogenous stress, WorldTest, MPC, scaling) mostly support a narrow claim: soft consistency can push passive video toward a near-contractive band, and that band is domain-limited. A stochastic-forcing law L20 ~ 1.23 + 1.82 eta at lambda=0.8 (bootstrap slope CI [1.73, 1.92], R^2=0.96) unifies control domains on the same curve via calibrated eta_eff. Complete joint slices at lambda in {0.4, 1.2} (30/30 cells, 5 eta x 3 seeds) show comparable linear L20(eta) slopes (~1.69 and ~2.00); we do not fit a continuous (lambda, eta) surface. We do not report DreamerV3 or TD-MPC2 returns.

cs.LG↗

arXiv2026

Adaptive Fuzzy Logic-Based Steganographic Encryption Framework: A Comprehensive Experimental Evaluation

Digital image steganography requires a careful trade-off among payload capacity, visual fidelity, and statistical undetectability. Fixed-depth least significant bit embedding remains attractive because of its simplicity and high capacity, but it modifies smooth and textured regions uniformly, thereby increasing distortion and detectability in statistically sensitive areas. This paper presents an adaptive steganographic framework that combines a Mamdanitype fuzzy inference system with modern authenticated encryption. The proposed method determines a pixel-wise embedding depth from 1 to 3 bits using local entropy, edge magnitude, and payload pressure as linguistic inputs. To preserve encoder-decoder synchronization, the same feature maps are computed from lower-bit-stripped images, making the adaptive control mechanism invariant to the least significant modifications introduced during embedding. A cryptographic layer based on Argon2id and AES-256-GCM protects payload confidentiality and integrity independently of steganographic concealment.

cs.CR↗

arXiv2026

A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning

Foreign key discovery and related schema-level prediction tasks are often modeled using graph neural networks (GNNs), implicitly assuming that relational inductive bias improves performance. However, it remains unclear when multi-hop structural reasoning is actually necessary. In this work, we introduce locality radius, a formal measure of the minimum structural neighborhood required to determine a prediction in relational schemas. We hypothesize that model performance depends critically on alignment between task locality radius and architectural aggregation depth. We conduct a controlled empirical study across foreign key prediction, join cost estimation, blast radius regression, cascade impact classification, and additional graph-derived schema tasks. Our evaluation includes multi-seed experiments, capacity-matched comparisons, statistical significance testing, scaling analysis, and synthetic radius-controlled benchmarks. Results reveal a consistent bias-radius alignment effect.

cs.LG↗

arXiv2025

A Fuzzy Logic-Based Cryptographic Framework For Real-Time Dynamic Key Generation For Enhanced Data Encryption

With the ever-growing demand for cybersecurity, static key encryption mechanisms are increasingly vulnerable to adversarial attacks due to their deterministic and non-adaptive nature. Brute-force attacks, key compromise, and unauthorized access have become highly common cyber threats. This research presents a novel fuzzy logic-based cryptographic framework that dynamically generates encryption keys in real-time by accessing system-level entropy and hardware-bound trust. The proposed system leverages a Fuzzy Inference System (FIS) to evaluate system parameters that include CPU utilization, process count, and timestamp variation. It assigns entropy level based on linguistically defined fuzzy rules which are fused with hardware-generated randomness and then securely sealed using a Trusted Platform Module (TPM). The sealed key is incorporated in an AES-GCM encryption scheme to ensure both confidentiality and integrity of the data. This system introduces a scalable solution for adaptive encryption in high-assurance computing, zero-trust environments, and cloud-based infrastructure.

cs.CR↗

arXiv2025

AI-Powered Early Detection of Critical Diseases using Image Processing and Audio Analysis

Early diagnosis of critical diseases can significantly improve patient survival and reduce treatment costs. However, existing diagnostic techniques are often costly, invasive, and inaccessible in low-resource regions. This paper presents a multimodal artificial intelligence (AI) diagnostic framework integrating image analysis, thermal imaging, and audio signal processing for early detection of three major health conditions: skin cancer, vascular blood clots, and cardiopulmonary abnormalities. A fine-tuned MobileNetV2 convolutional neural network was trained on the ISIC 2019 dataset for skin lesion classification, achieving 89.3% accuracy, 91.6% sensitivity, and 88.2% specificity. A support vector machine (SVM) with handcrafted features was employed for thermal clot detection, achieving 86.4% accuracy (AUC = 0.89) on synthetic and clinical data. For cardiopulmonary analysis, lung and heart sound datasets from PhysioNet and Pascal were processed using Mel-Frequency Cepstral Coefficients (MFCC) and classified via Random Forest, reaching 87.2% accuracy and 85.7% sensitivity. Comparative evaluation against state-of-the-art models demonstrates that the proposed system achieves competitive results while remaining lightweight and deployable on low-cost devices. The framework provides a promising step toward scalable, real-time, and accessible AI-based pre-diagnostic healthcare solutions.

cs.CV↗

arXiv2025

Enhancing Financial Decision-Making: Machine Learning and AI-Powered Predictions and Analysis

The proposed system aims to use various machine learning algorithms to enhance financial prediction and generate highly accurate analyses. It introduces an AI-driven platform which offers inflation-analysis, stock market prediction, and E-learning module powered by a chatbot. It has achieved high accuracy where the Inflation Analysis depicts 0.8% MAE, 1.2% RMSE and the Stock Prediction shows 98% and 96% accuracy for Apple and Google stock prices respectively. Key features include historical price trends, inflation rates, short-term future stock prediction, where the data has been extracted using real-world financial datasets. Additionally, the E-learning feature contributes to bridging financial gaps and promoting informed decisions. We have implemented algorithms like linear regression, ARIMA, LSTM where the accuracy has been evaluated using metrics such as MAE, RMSE and the like.

cs.CE↗