801 papers
This paper provides a comprehensive review and comparative performance analysis of classical and quantum algorithms for factoring and primality testing, concluding that while quantum methods like Shor's algorithm offer significant advantages for factoring, they do not provide comparable benefits for primality testing.
This paper empirically evaluates various clustering methods on hyperspectral remote sensing data, finding that computationally efficient centroid-based algorithms like K-Means consistently offer the best balance of quality, robustness, and speed compared to more complex alternatives when combined with effective dimensionality reduction.
This paper introduces the "Scaffold Jump" design pattern, an operational framework that enables organizations to systematically and safely transition AI systems from full human oversight to autonomy by defining four distinct phases linked by evidence-gated transitions that ensure performance thresholds are met while maintaining accountability and ethical constraints.
This paper proposes an adaptive Fuzzy-GAN system that utilizes Generative Adversarial Networks and fuzzy logic to classify face orientations into low, medium, and high angles (including clockwise and anti-clockwise variations) to enhance pose-invariant face recognition for law enforcement applications.
This paper evaluates the feasibility of NVIDIA's RTX Neural Shaders for real-time fluid rendering, demonstrating that while neural networks successfully approximate pixel-level BSDFs at high frame rates, significant framework limitations currently prevent their effective deployment for vertex displacement tasks.
This study proposes and validates a partial-correctness and progress-aware formulation for early success prediction in programming practice, demonstrating that it significantly outperforms traditional binary and ordinal approaches by more effectively capturing learner progression and enabling targeted instructional support.
This systematic review of 122 studies (2015–2025) demonstrates that machine learning models, particularly ensemble and hybrid approaches integrated with non-destructive sensing technologies, significantly enhance post-harvest management of perishable fruits by improving quality assessment and loss reduction across the entire supply chain, while highlighting the need for standardized datasets and real-world validation to overcome current deployment challenges.
This paper introduces Validation-Aligned Coreset Selection (VACS), a method that selects the optimal class-balanced subset selector by evaluating a portfolio on internal validation splits, demonstrating that repeated validation can significantly improve few-shot classification accuracy under extreme budget constraints, though it does not universally surpass the best static selection rules.
This paper proposes a lightweight two-stage framework utilizing YOLO11s-seg for instance-level candidate generation and YOLOv8n-cls for hard-negative and occlusion-aware classification, achieving high harvesting decision accuracy and zero false harvest rates in complex oil palm plantation environments.
This paper proposes a neuro-symbolic fuzzy rule-based framework that integrates deep learning-based dermoscopic attribute estimation with automatically extracted clinical rules to improve melanoma classification accuracy and interpretability compared to image-only deep learning approaches.