Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach by Xiayuan Huang, Shushun Ren, Xinyue Mao, Sirui Chen, Elle Chen, Yuqi He, Yun Jiang

“…ResultsOur framework evaluated the prediction performance of explainable machine learning models, with the MLP model demonstrating the best performance. …”
Get full text

Predicting the Risk of Lumbar Prolapsed Disc: A Gene Signature-Based Machine Learning Analysis by Fengfeng Wang, Fei Meng, Stanley Sau Ching Wong

“…Overall, the random forest model exhibited the most robust performance in predicting the risk of LPD. …”
Get full text

Full event particle-level unfolding with variable-length latent variational diffusion by Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson

“…Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. …”
Get full text

GCSA-SegFormer: Transformer-Based Segmentation for Liver Tumor Pathological Images by Jingbin Wen, Sihua Yang, Weiqi Li, Shuqun Cheng

“…On the liver dataset, the GCSA-SegFormer achieved a 1.12% increase in MIoU and a 1.15% increase in MPA compared to baseline models. On the BACH dataset, it improved MIoU by 1.26% and MPA by 0.39% compared to baseline models. …”
Get full text

CDA-mamba: cross-directional attention mamba for enhanced 3D medical image segmentation by Jiashu Xu, Yihua Lan, Yingqi Zhang, Chi Zhang, Sergii Stirenko, Huizhong Li

“…This paper introduces CDA-Mamba (Cross-Directional Attention Mamba), a novel hybrid architecture that combines the efficiency of SSMs with the strengths of convolutional and attention mechanisms to address the unique challenges of 3D medical image segmentation. CDA-Mamba features three key innovations: a Multi-Frequency Gated Convolution (MFGC) module to enhance spatial and frequency-domain feature integration, a Tri-Directional Mamba module to capture volumetric dependencies across orthogonal dimensions, and Selective Self-Attention integration in high-semantic layers to balance computational efficiency with global context modeling. …”
Get full text

Uncertainty-Aware Parking Prediction Using Bayesian Neural Networks by Alireza Nezhadettehad, Arkady Zaslavsky, Abdur Rakib, Seng W. Loke

“…This research shows that modelling both types of uncertainty leads to significantly improved predictive performance in intelligent transportation systems and highlights the potential of uncertainty-aware approaches as a foundation for future work on integrating BNNs with hybrid neuro-symbolic reasoning to enhance decision making under uncertainty.…”
Get full text

A Wearable Silent Text Input System Using EMG and Piezoelectric Sensors by John S. Kang, Kee S. Moon, Sung Q. Lee, Nicholas Satterlee, Xiaowei Zuo

“…Results showed that non-feature-based models, particularly the Fea-Shot Learning with fused EMG and PZT signals, achieved the highest accuracy (95.63%) and F1-score (95.62%). …”
Get full text

Review of Research on Trajectory Prediction of Road Pedestrian Behavior by YANG Zhiyong, GUO Jieru, GUO Zihang, ZHANG Ruixiang, ZHOU Yu

“…Based on these data, this paper systematically reviews both physics-based and data-driven prediction approaches, with a focus on the development of statistical models, deep learning, and reinforcement learning models. …”
Get full text

Annotated intraoral image dataset for dental caries detection by Syed Muhammad Faizan Ahmed, Muhammad Huzaifa Ghori, Aamna Khalid, Ayesha Nooruddin, Niha Adnan, Abhishek Lal, Fahad Umer

“…Five AI models (YOLOv5s, YOLOv8s, YOLOv11, SSD-MobileNet-v2, and Faster R-CNN) were trained and evaluated, with YOLOv8s achieving the best performance (mAP = 0.841 @ 0.5 IoU). …”
Get full text

Enhancing fraud detection in imbalanced motor insurance datasets using CP-SMOTE and Random Under-Sampling by Pornpawee Komsrimorakot, Thitirat Siriborvornratanakul

“…The findings underscore the critical impact of class distribution and feature dimensionality on model performance and offer actionable insights for deploying high-recall fraud detection systems in real-world insurance applications.…”
Get full text

Lubrication state identification of vibration time-frequency characteristics based on CWT and CNN by Haijie Yu, Haijun Wei

“…Results demonstrated exceptional performance: the model achieved 99.8% training accuracy and 100% test accuracy, significantly outperforming traditional methods (RMS + CNN: 63.8%; PSD + CNN: 73.4%; CWT + SVM: 76.3%). t-SNE visualization confirmed distinct feature separation among lubrication states, and the confusion matrix revealed flawless classification on the test set. …”
Get full text

Transformer-Based Student Engagement Recognition Using Few-Shot Learning by Wejdan Alarefah, Salma Kammoun Jarraya, Nihal Abuzinadah

“…In this research, we propose an innovative FSL model that employs a prototypical network with the vision transformer (ViT) model pre-trained on a face recognition dataset (e.g., MS1MV2) for spatial feature extraction, followed by an LSTM layer for temporal feature extraction. …”
Get full text

Prioritizing image processing and big data analytics factors for software development using DEMATEL-choquet method in bipolar complex fuzzy context by Ruimin Ma

“…These findings offer actionable insights for decision-makers (DMKs), emphasizing the importance of intelligent feature handling and scalable analytics workflows in designing adaptive and high-performance software systems.…”
Get full text

Advancing Hate Speech Detection in Indonesian Language Using Graph Neural Networks and TF-IDF by Syaikha Amirah Zikrina, Fitriyani

“…However, these approaches are limited in their ability to capture the relational and contextual nuances inherent in the data, resulting in suboptimal performance. This study introduces an approach by combining Graph Neural Networks (GNN) with Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction to improve hate speech detection on Twitter (platform X). …”
Get full text

MRD: A Linear-Complexity Encoder for Real-Time Vehicle Detection by Kaijie Li, Xiaoci Huang

“…To address these limitations, this study introduces Mamba RT-DETR (MRD), an optimized architecture featuring three principal innovations: (1) We devise an efficient vehicle detection Mamba (EVDMamba) network that strategically integrates a linear-complexity state space model (SSM) to substantially mitigate computational overhead while preserving feature extraction efficacy. (2) To counteract the constrained receptive fields and suboptimal spatial localization associated with conventional SSM sequence modeling, we implement a multi-branch collaborative learning framework that synergistically optimizes channel dimension processing, thereby augmenting the model’s capacity to capture critical spatial dependencies. (3) Comprehensive evaluations on the BDD100K benchmark demonstrate that MRD architecture achieves a 3.1% enhancement in mean average precision (mAP) relative to state-of-the-art RT-DETR variants, while concurrently reducing parameter count by 55.7%—a dual optimization of accuracy and efficiency.…”
Get full text

Color image hybrid noise filtering algorithm based on deep convolution neural network by Yongfei Yu, Yuanjian Yan

“…The color image hybrid noise filtering model designed in this study has good filtering performance, good image cleanliness, and high filtering efficiency.…”
Get full text

Prediction of stroke severity: systematic evaluation of lesion representations by Anna K. Bonkhoff, Alexander L. Cohen, William Drew, Michael A. Ferguson, Aaliya Hussain, Christopher Lin, Frederic L. W. V. J. Schaper, Anthony Bourached, Anne‐Katrin Giese, Lara C. Oliveira, Robert W. Regenhardt, Markus D. Schirmer, Christina Jern, Arne G. Lindgren, Jane Maguire, Ona Wu, Sahar Zafar, John Y. Rhee, Eyal Y. Kimchi, Maurizio Corbetta, Natalia S. Rost, Michael D. Fox, MRI‐GENIE and GISCOME Investigators and the International Stroke Genetics Consortium

“…Results We found that prediction models trained on small single‐center datasets could perform well using within‐dataset cross‐validation, but results did not generalize to independent datasets (median R2N1 = 0.2%). …”
Get full text

Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates by Chayanon Sub-r-pa, Rung-Ching Chen

“…However, traditional feature-based methods struggle with templates that feature simple geometries and lack rich textures, making reliable feature matching and alignment difficult, even under controlled conditions. …”
Get full text

OriLoc: Unlimited-FoV and Orientation-Free Cross-View Geolocalization by Boni Hu, Haowei Li, Shuhui Bu, Lin Chen, Pengcheng Han

“…Code and trained models are publicly available on <uri>https://github.com/boni-hu/OriLoc</uri>.…”
Get full text

The Persistent Threat of Chronic Inflammation on the Mortality Among Cervical Cancer Survivors: A Mendelian Randomization and Machine Learning Analysis Using UK Biobank and Chinese... by Wang J, Chen Z, Guan M, Ma Z, Peng L, Chen J, Fiori PL, Carru C, Capobianco G, Coradduzza D, Zhou L

“…Similarly, the GBM model delivered the best overall performance in 5-year OS prediction with an AUC of 0.893 and a Brier score of 0.089. …”
Get full text