750 papers
This study evaluates machine learning models for predicting ICU mortality and length of stay in Ethiopian hospitals using real and synthetic data, finding that hybrid training with CTGAN-generated data significantly outperforms other methods despite its high computational cost, while highlighting oxygen requirement and SpO2 as the strongest mortality predictors.
This paper proposes an interactive query-based algorithm for generating abnormal event synopses in surveillance videos that utilizes a rule-based classifier to handle complex user queries and introduces an "improved overlapping ratio" metric for evaluation, demonstrating superior accuracy and quality over existing methods on the PETS09 dataset.
This paper proposes an LLM-based test case generation approach enhanced by a project memory repository that integrates historical data and release context to significantly improve risk coverage, reduce duplication, and increase defect discovery in large-scale software systems.
This paper establishes a preliminary MATLAB-based CNN baseline for binary malignant versus non-malignant breast ultrasound classification using the BrEaST dataset, achieving improved test accuracy while explicitly framing exploratory subtype labeling and explainability as future research directions rather than validated results.
This paper evaluates bounded-horizon local training for 24-layer byte-level Transformers on many-core CPUs, finding that while the proposed readout-gradient consensus method achieves a 38% throughput increase over global backpropagation, it fails to meet a 1% non-inferiority criterion for model quality across all tested datasets.
This study analyzes 1.57 million GitHub repositories to demonstrate that common filtering criteria in Mining Software Repositories (MSR) research introduce significant maintenance, ecosystem, and relational biases that distort project abandonment rates and variable relationships, advocating for a shift toward stratified sampling and refined noise detection.
This paper proposes and evaluates a predictive, adaptive scheduling model that integrates workload forecasting with Ceph-aware storage decisions to significantly reduce resource contention and I/O latency in Kubernetes–Ceph hyperconverged infrastructure running on Proxmox VE, achieving superior load balancing and performance compared to the default Kubernetes scheduler.
This paper proposes a federated reinforcement learning framework for mobile edge networks that combines a VAE-LSTM model for content popularity prediction with a multi-agent deep reinforcement learning algorithm to optimize cooperative caching decisions, thereby significantly reducing latency and improving cache hit rates compared to existing baseline methods.
This paper proposes DMPB-Net, a novel semi-supervised framework that integrates a Mamba-driven encoder with specialized modules for semantic decoupling, prototype attention, and boundary refinement to achieve state-of-the-art breast lesion segmentation in ultrasound images under limited supervision.
This paper proposes a robust tri-modal framework that integrates CNNs, Vision Transformers, and Graph Neural Networks via an adaptive gated fusion mechanism to achieve state-of-the-art forgery detection accuracy (99.10%) and interpretability on social media images.