2833 papers
This paper proposes a non-invasive Parkinson's disease severity classification system using triaxial IMU sensor data and ensemble learning, demonstrating that the LightGBM model outperforms other machine learning algorithms with approximately 97% accuracy.
This study evaluates five supervised machine learning models for detecting zero-day attacks on imbalanced data, ultimately selecting XG Boost as the optimal solution due to its superior balance of high accuracy and fast processing time compared to the more accurate but slower Random Forest.
This study introduces a three-stage machine learning pipeline, validated on over 670,000 Kenyan magistrate court cases, which utilizes LightGBM and CatBoost models to predict case processing durations and classify same-day disposals, thereby enabling proactive backlog management through an empirically derived risk scheme and a deployed web application.
This study demonstrates that ImageNet-pretrained EfficientNet-B0 significantly outperforms random initialization for four-class brain tumor MRI classification only under extreme data scarcity (5% of 5,600 images), while highlighting critical limitations such as potential patient-level data leakage and the lack of statistical significance for transfer learning benefits at larger data fractions, ultimately arguing against autonomous deployment without further rigorous validation.
This study demonstrates that a Random Forest model utilizing combined PPG and inter-beat interval features can achieve accurate, interpretable, and computationally efficient five-class sleep stage classification from wearable PPG signals, offering a viable alternative to resource-intensive deep learning approaches.
The paper proposes LDMFNet, a lightweight dual-modal fusion network featuring a dual-feature backbone and specialized attention modules to achieve accurate, efficient multispectral object detection across various datasets while outperforming existing algorithms in precision and generalization.
The paper proposes BMTransUNet, a boundary-aware gated multimodal Transformer U-Net that integrates RGB and DSM data through adaptive fusion, Vision Transformer-based context modeling, and edge-enhanced skip connections to achieve superior semantic segmentation accuracy in remote sensing images.
This paper proposes a robust, two-phase filtering scheme that combines fuzzy cellular automata with Jaccard similarity and Hamming distance measures to effectively detect and remove salt and pepper noise while preserving fine image details and outperforming existing techniques.
This paper proposes the STAM framework, which integrates contrastive learning for scene-aware embeddings with a transformer encoder guided by Multi-Instance Learning attention, to effectively address contextual and temporal limitations in video anomaly detection and achieve superior accuracy on benchmark datasets.
SemPix3D proposes a hybrid framework combining a Neural Radiance Field for 3D-aware label map generation and a StyleGAN-based generator to synthesize high-quality, consistent multi-view RGB images, achieving significant improvements in diversity and FID scores over existing methods.