2727 papers
This paper proposes an automated tomato leaf disease diagnosis framework using EfficientNetB3 transfer learning and Grad-CAM explainable analysis, which achieves high classification accuracy and interpretability for detecting Early Blight, Late Blight, Leaf Mold, and healthy leaves to support precision agriculture.
This paper introduces DYNAMICS-WEB, a pre-registered measurement demonstrating that a distinct "character" signal within the web's link graph, which is largely independent of authority and topic, can subconsciously influence reader conversion rates when aligned with user preferences.
This study proposes a hybrid MSCNN-LSTM model enhanced with Squeeze-and-Excitation attention to predict hourly Air Quality Index values, demonstrating superior accuracy and stability compared to traditional deep learning models through multi-scale feature extraction and adaptive channel weighting on data from Yiyang City.
NeviUNet is an efficient, attention-enhanced U-Net variant that achieves accurate and low-latency skin lesion segmentation in total body images by integrating Mish activation, Attention Gates, and SE blocks while omitting computationally expensive components.
The paper presents FlapFlowNet, a Transformer-based streaming framework that achieves high-accuracy, real-time perfusion monitoring for variable-length flap surgery sequences by combining a lightweight CNN, temporal modeling, and a medical-optimized loss function to significantly reduce critical false-negative rates.
The paper presents ORACOLO, a neuro-symbolic question-answering architecture that integrates ontology-constrained SPARQL query generation with dense retrieval fallback, demonstrating significantly improved factual accuracy over pure dense retrieval while maintaining comparable faithfulness.
This study proposes a hybrid CNN-LSTM deep learning model trained on a custom dataset combining global benchmarks and NS-3 simulated SDN traffic to achieve 97.8% accuracy in detecting cyberattacks against Sudan's e-government infrastructure, offering a scalable solution to enhance national cybersecurity.
This study presents an explainable machine learning framework using an XGBoost model to predict early-stage construction duration based on limited project information, demonstrating that while the model achieves strong predictive performance with project cost as the primary driver, its results require careful interpretation due to sensitivity to data partitioning.
This paper introduces Photonic Quantum-Enhanced Knowledge Distillation (PQKD), a hybrid framework that leverages the intrinsic stochasticity of photonic quantum processors to generate conditioning signals for a parameter-efficient student network, achieving competitive accuracy under aggressive compression across standard benchmarks while utilizing shot-noise scaling and feature smoothing to manage finite sampling limitations.
This paper proposes a certified selection method for ARINC-653 partition window counts that identifies near-optimal supply granularity with significantly reduced computational cost compared to exhaustive search, leveraging the observation that many window counts yield nearly identical performance values.