4157 papers
This paper proposes IFR-3WD-CD, an incremental fuzzy-rough three-way decision framework that effectively handles concept drift in data streams by combining adaptive boundary learning, selective forgetting, and uncertainty-aware deferral to maintain high predictive accuracy without the computational cost of full model reconstruction.
This study provides evidence that the widespread adoption of generative AI has caused a durable, population-scale shift in web search query syntax toward more conversational forms—a phenomenon termed "promptification"—which emerged prior to and independently of search platforms integrating generative features, indicating a behavioral spillover where users are reverting to natural language due to their familiarity with LLMs.
This study presents a leakage-free evaluation of machine learning models for liver disease prediction using the Indian Liver Patient Dataset, revealing that rigorous nested cross-validation yields lower but more realistic performance metrics than previous studies, highlights the critical importance of handling class imbalance and sex-specific validation, and identifies key liver function biomarkers as primary predictors.
This controlled evaluation demonstrates that a Slater-determinant exclusion gating mechanism fails to improve polyp segmentation performance or reduce false positives compared to matched controls, primarily because it excessively attenuates deep features, whereas a log-determinant penalty successfully reduced redundancy without sacrificing accuracy.
This study evaluates GPT-5's translation of literary metaphors from English to Arabic using a stage-aware framework, revealing that while the model excels at recognizing and interpreting metaphors, it significantly struggles with rendering them compared to both DeepL and human translation, thereby exposing a critical gap between comprehension and creative production in AI.
This paper demonstrates that a fine-tuned CodeBERT model, optimized with a 64-token input, achieves superior accuracy and efficiency over other transformer models in simultaneously detecting SQL injection and XSS attacks, making it a cost-effective solution for lightweight web application firewalls.
This paper proposes ST-MRC, a three-stage service-oriented probabilistic forecasting framework combining Set Transformer-based interaction encoding and multi-scale residual correction to accurately predict coupled loads and quantify uncertainty for Virtual Power Plant market participation.
This paper reconstructs the CAPilot Model, a Human–AI co-piloting framework that continuously bridges observable reality and organized knowledge by positioning humans as explorers of the world through experience and AI as organizers of knowledge through language.
This paper proposes a robust CNN-based multimodal framework that integrates Red Teaming, Prompt Engineering, and Retrieval-Augmented Generation to effectively mitigate Large Language Model hallucinations across text, audio, and image modalities, achieving high performance metrics on diverse tasks.
This paper proposes a hardware-aware, multi-objective evolutionary search framework using NSGA-II to optimize EfficientNet-Lite architectures for crop disease classification on edge devices, achieving high accuracy (0.98) with significantly reduced latency and model size compared to existing models like EfficientNet-B0 and MobileNetV3.