Destripe Any Scale: Effective Stripe Removal Via Multiscale Decomposition Using the Luojia3-02 Stripe-Noise Dataset by Ru Chen, Mi Wang, Yingdong Pi, Ru Wang, Tao Peng, Fan Yang, Rongfan Dai

“…This network employs an independent-weight learning strategy to separately capture and optimize large-scale and small-scale noise features, facilitating the precise extraction of multiscale noise features. …”
Get full text

Sensor-Based Yield Prediction in Durum Wheat Under Semi-Arid Conditions Using Machine Learning Across Zadoks Growth Stages by Süreyya Betül Rufaioğlu, Ali Volkan Bilgili, Erdinç Savaşlı, İrfan Özberk, Salih Aydemir, Amjad Mohamed Ismael, Yunus Kaya, João P. Matos-Carvalho

“…The LightGBM model also showed remarkable performance during the ZD30 stage, achieving an R²% of 78.0, an RMSE of 0.52, and an MAE of 0.40. …”
Get full text

Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization by Wenli Liu, Yang Chen, Tianxiang Liu, Wen Liu, Jue Li, Yangyang Chen

“…These metrics collectively reflect the model’s excellent performance in prediction accuracy, ability to explain data variability, and control of prediction bias. …”
Get full text

Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions by John Adeoye, Yuxiong Su

“…Machine learning classifiers using structured (tabular) data have been employed to predict malignant transformation in OL and OLD. However, current models require improved discrimination, and their frameworks may limit feature fusion and multimodal risk prediction. …”
Get full text

TPL-DA: A Novel Threshold-Free Pseudolabel Learning Framework for Domain Adaptive Semantic Segmentation of High-Resolution Remote Sensing Images by Yan Ren, Jie Long, Xiaowen Gao, Ming Zhang, Guoqing Liu, Nan Su

“…In addition, we model uncertainty using relative entropy and incorporate it into the optimization objective to manage high-confidence noise. …”
Get full text

Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC by Lukas Delasos, Mohammadhadi Khorrami, Vidya S. Viswanathan, Khalid Jazieh, Yifu Ding, Pushkar Mutha, Kevin Stephans, Amit Gupta, Nathan A. Pennell, Pradnya D. Patil, Kristin Higgins, Anant Madabhushi

“…This study analyzed 293 patients from two institutions, with 140 experiencing pneumonitis (RTP: 84, IIP: 56). Two models were developed: M1 predicted pneumonitis risk using seven radiomic features, achieving high accuracy across internal and external datasets (AUCs: 0.76 and 0.85). …”
Get full text

Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness by Junhui Song, Zhangqi Zheng, Afei Li, Zhixin Xia, Yongshan Liu

“…Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. …”
Get full text

An adaptive filter for anemia screening using deep convolutional neural network by Jose B. Lazaro, Jr., Jennifer C. Dela Cruz, Jocelyn F. Villaverde

“…Performance evaluation metrics indicate an accuracy of 95.27%, with a 100% training-to-validation ratio, demonstrating high classification reliability. …”
Get full text

Tri-band vehicle and vessel dataset for artificial intelligence research by Yingjian Liu, Gangnian Zhao, Shuzhen Fan, Cheng Fei, Junliang Liu, Zhishuo Zhang, Liqian Wang, Yongfu Li, Xian Zhao, Zhaojun Liu

“…After training with YOLOv8 and SSD object detection algorithms, all models have mAP values above 0.6 at an IoU threshold of 0.5, which indicates good recognition performance for this dataset. …”
Get full text

Temporal waveform denoising using deep learning for injection laser systems of inertial confinement fusion high-power laser facilities by Wei Chen, Xinghua Lu, Wei Fan, Xiaochao Wang

“…We train the model using simulated datasets and evaluate it on both the simulated and experimental temporal waveforms. …”
Get full text

Deep learning for real-time P-wave detection: A case study in Indonesia's earthquake early warning system by Adi Wibowo, Leni Sophia Heliani, Cecep Pratama, David Prambudi Sahara, Sri Widiyantoro, Dadan Ramdani, Mizan Bustanul Fuady Bisri, Ajat Sudrajat, Sidik Tri Wibowo, Satriawan Rasyid Purnama

“…This paper proposes a novel deep learning-based model with three convolutional layers, enriched with dual attention mechanisms—Squeeze, Excitation, and Transformer Encoder (CNN-SE-T) —to refine feature extraction and improve detection sensitivity. …”
Get full text

Organizational Structure and Its Impact on the Effectiveness of Private Medical Clinics by Yu. Yu. Snegireva, N. Sh. Sarkhadov, G. B. Padiev

“…In the course of the research, analytical and comparative methods were used to study the impact of various models of organizational structures on the effectiveness of private medical institutions. …”
Get full text

Scalable machine learning framework for predicting critical links in urban networks by Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen

“…Random Forest and Gradient Boosting emerged as the top-performing models, consistently delivering the best precisions and lowest number of errors. …”
Get full text

Single-Image Super-Resolution via Cascaded Non-Local Mean Network and Dual-Path Multi-Branch Fusion by Yu Xu, Yi Wang

“…The ADMFB enhances texture reconstruction by adaptively aggregating multi-scale features through dual attention paths. The experimental results demonstrate that our method achieves superior performance on multiple benchmarks. …”
Get full text

A data driven framework for optimizing droplet microfluidics with residual block and Fourier enhanced networks by Alireza Samari, Kamal Jannati, Azadeh Jafari

“…We developed two innovative machine learning models: the Fourier-Enhanced Network (FEN), which utilizes Fourier series to decompose input features into harmonic components; and the Residual Block Network (ResBNet), which incorporates skip connections within residual layers to capture complex nonlinear patterns. …”
Get full text

Graph neural network approach with spatial structure to anomaly detection of network data by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…Additionally, the scarcity of labelled anomaly data for training supervised models can hinder the accuracy and effectiveness of anomaly detection methods. …”
Get full text

A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages by Xinghong Kuang, Biyun Yan

“…The key advantage of this system lies in its ability to simultaneously extract features from both the time and frequency domains and capture the variable correlations among multi-channel measurements, thereby enhancing the modeling capabilities for sensor data. …”
Get full text

Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning by Motahare Shabestari, Akram Mehrabbeik, Sebastiano Barbieri, Pedro Marques-Vidal, Poria Heshmati-nasab, Reyhaneh Azizi

“…The performance of the models was evaluated by different metrics. …”
Get full text

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. …”
Get full text

Machine learning algorithms for prediction of cerebrospinal fluid leakage after posterior surgery for thoracic ossification of the ligamentum flavum by Ruizhou Guo, Ben Liu, Yunqi Wu, Yilu Zhang, Xiyang Wang, Dingyu Jiang, Zheng Liu

“…Abstract To develop and validate a machine-learning (ML) model that pre-operatively predicts cerebrospinal-fluid leakage (CSFL) after posterior decompression for thoracic ossification of the ligamentum flavum (TOLF), and to elucidate the key risk factors driving model decisions. …”
Get full text