Fariha Tasmin

Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel

Wordle presents an algorithmically rich testbed for constraint satisfaction problem (CSP) solving. While existing solvers rely on information-theoretic entropy maximization or frequency-based heuristics without formal constraint treatment, we present the first comprehensive CSP formulation of Wordle with novel constraint-aware solving strategies. We introduce CSP-Aware Entropy, computing information gain after constraint propagation rather than on raw candidate sets, and a Probabilistic CSP framework integrating Bayesian word-frequency priors with logical constraints. Through evaluation on 2,315 English words, CSP-Aware Entropy achieves 3.54 average guesses with 99.9% success rate, a statistically significant 1.7% improvement over Forward Checking (t=-4.82, p<0.001, Cohen's d=0.07) with 46% faster runtime (12.9ms versus 23.7ms per guess). Under 10% noise, CSP-aware approaches maintain 5.3 percentage point advantages (29.0% versus 23.7%, p=0.041), while Probabilistic CSP achieves 100% success across all noise levels (0-20%) through constraint recovery mechanisms. Cross-lexicon validation on 500 Spanish words demonstrates 88% success with zero language-specific tuning, validating that core CSP principles transfer across languages despite an 11.2 percentage point gap from linguistic differences (p<0.001, Fisher's exact test). Our open-source implementation with 34 unit tests achieving 91% code coverage provides reproducible infrastructure for CSP research. The combination of formal CSP treatment, constraint-aware heuristics, probabilistic-logical integration, robustness analysis, and cross-lexicon validation establishes new performance benchmarks demonstrating that principled constraint satisfaction techniques outperform classical information-theoretic and learning-based approaches for structured puzzle-solving domains.

Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel et al.

Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adaptive Fair Federated Learning (AFFL) through three innovations: (1) Adaptive Knowledge Messengers dynamically scaling capacity based on heterogeneity and task complexity, (2) Fairness-Aware Distillation using influence-weighted aggregation, and (3) Curriculum-Guided Acceleration reducing rounds by 60-70%. Our theoretical analysis provides convergence guarantees with epsilon-fairness bounds, achieving O(T^{-1/2}) + O(H_max/T^{3/4}) rates. Projected results show 55-75% communication reduction, 56-68% fairness improvement, 34-46% energy savings, and 100+ institution support. The framework enables multi-modal integration across imaging, genomics, EHR, and sensor data while maintaining HIPAA/GDPR compliance. We propose MedFedBench benchmark suite for standardized evaluation across six healthcare dimensions: convergence efficiency, institutional fairness, privacy preservation, multi-modal integration, scalability, and clinical deployment readiness. Economic projections indicate 400-800% ROI for rural hospitals and 15-25% performance gains for academic centers. This work presents a seven-question research agenda, 24-month implementation roadmap, and pathways toward democratizing healthcare AI.

Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel

Multi-class wine classification presents fundamental trade-offs between model accuracy, feature dimensionality, and interpretability - critical factors for production deployment in analytical chemistry. This paper presents a comprehensive empirical study of One-vs-Rest logistic regression on the UCI Wine dataset (178 samples, 3 cultivars, 13 chemical features), comparing from-scratch gradient descent implementation against scikit-learn's optimized solvers and quantifying L1 regularization effects on feature sparsity. Manual gradient descent achieves 92.59 percent mean test accuracy with smooth convergence, validating theoretical foundations, though scikit-learn provides 24x training speedup and 98.15 percent accuracy. Class-specific analysis reveals distinct chemical signatures with heterogeneous patterns where color intensity varies dramatically (0.31 to 16.50) across cultivars. L1 regularization produces 54-69 percent feature reduction with only 4.63 percent accuracy decrease, demonstrating favorable interpretability-performance trade-offs. We propose an optimal 5-feature subset achieving 62 percent complexity reduction with estimated 92-94 percent accuracy, enabling cost-effective deployment with 80 dollars savings per sample and 56 percent time reduction. Statistical validation confirms robust generalization with sub-2ms prediction latency suitable for real-time quality control. Our findings provide actionable guidelines for practitioners balancing comprehensive chemical analysis against targeted feature measurement in resource-constrained environments.