A Decision-Aid Model for Predicting Triple-Negative Breast Cancer ICI Response Based on Tumor Mutation Burden by Houda Bendani, Nasma Boumajdi, Lahcen Belyamani, Azeddine Ibrahimi

“…Then, four machine learning models were trained to classify TNBC patients based on histological features into high and low TMB. …”
Get full text

MHFS-FORMER: Multiple-Scale Hybrid Features Transformer for Lane Detection by Dongqi Yan, Tao Zhang

“…It fuses multi-scale features with the Transformer Encoder to obtain enhanced multi-scale features. …”
Get full text

Clinical features, treatment and prognosis analysis of distant metastatic esophageal cancer by Shuang Li, Yanwei Lu, Ruiqi Liu, Luanluan Huang, Ding Nan, Xiaoyan Chen, Wenjie Xia, Xiaodong Liang, Haibo Zhang

“…The AUC values for both the training and validation cohorts for the 1-year OS ranged from 0.50 to 0.70, and the AUC for the rest of the training and validation cohort ranged from 0.70 to 0.90, which suggests that the model is moderately discriminating. …”
Get full text

Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks by Jiayu Mai, Haonan Lin, Xuezhen Hong, Zhenbo Wei

Get full text

Neural radiance fields assisted by image features for UAV scene reconstruction by Zhihong Chen, Xueyun Chen, Chenghong Ye, Shaojie Wu, Xiang Wu

“…Abstract With the rapid advancement of Unmanned Aerial Vehicle applications, vision-based 3D scene reconstruction has demonstrated significant value in fields such as remote sensing and target detection. …”
Get full text

Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction by Oyebayo Ridwan Olaniran, Aliu Omotayo Sikiru, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi

“…The method first trains multiple LSTM/BiLSTM base models on dynamically sampled feature subsets to promote diversity, followed by a meta-learner that integrates predictions into a final robust output. …”
Get full text

Predicting the invasiveness of pulmonary adenocarcinoma using intratumoral and peritumoral radiomics features by Jingjing Hong, Liyang Yang, Jiekun Huo, Guoci Huang, Bowen Shan, Tingting Cai, Lianlian Zhang, Weikang Huang, Ge Wen

“…Radiomics models were trained using LASSO with 10-fold cross-validation in training dataset. …”
Get full text

A Fusion Dimension Reduction Method for the Features of Surface Electromyographic Signals by Luyao Ma, Qing Tao, Xiaodong Zhang, Qingzheng Chen

“…This method is constructed based on the unique correlation between the features of sEMG. …”
Get full text

A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [¹⁸F]FDG-PET Images During Follow-Up by Hanne S. Vries, Gijs D. van Praagh, Pieter H. Nienhuis, Lejla Alic, Riemer H. J. A. Slart

“…In total, 441 ML models were trained, using combinations of seven feature selection methods, seven classifiers, and nine different numbers of features. …”
Get full text

FEATURES OF THE INDIVIDUAL STYLE OF TRANSLATOR’S PROFESSIONAL ACTIVITY: THE RESULTS OF A SURVEY by Lavnikov Oleh

“…Based on the results, the author identified and characterized the prospects for further research, namely: to identify and justify the pedagogical conditions for the formation of the individual style of professional activity of a future translator in the process of professional training…”
Get full text

Evolving Clinical Features of Diabetic Ketoacidosis: The Impact of SGLT2 Inhibitors by Selin Genc, Bahri Evren, Onur Selcuk Yigit, Ibrahim Sahin, Ramazan Dayanan, Aleksandra Klisic, Ayse Erturk, Filiz Mercantepe

Get full text

TrainNet for locking state recognition of side door of railway freight car by Ning Xing, Cai Kangcheng, Zhou Shumin, Lan Xiangui, Lai Yihui

“…We designed an efficient layer aggregation network (ELAN)-S module in our TrainNet, which can be used with YOLOv7. The module efficiently extracts curvilinear features and is integrated into the backbone feature extraction network to enhance the feature representation capability. …”
Get full text

Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction by Long Han, Xinming Wu, Renjie Chen, Yunhua Shi, Zhanxuan Hu, Huijing Fang

“…We use the iTransformer's self‐attention mechanism to calculate attribute weights for selection, and through extensive experiments, we developed a windowed multi‐attribute data input method that incorporates neighboring information to ensure lateral consistency. Based on the geological understanding that different attributes computed from the same sample convey correlated features reflecting geological properties, we pre‐train the network on large‐scale unlabeled data using a self‐supervised learning strategy of attribute masking and recovery. …”
Get full text

Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning by Supasuta Busayakanon, Morakot Kaewthamasorn, Natchapon Pinetsuksai, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang, Veerayuth Kittichai

Get full text

The perception of naturalness correlates with low-level visual features of environmental scenes. by Marc G Berman, Michael C Hout, Omid Kardan, MaryCarol R Hunter, Grigori Yourganov, John M Henderson, Taylor Hanayik, Hossein Karimi, John Jonides

“…We then trained a machine-learning algorithm to predict whether a scene was perceived as being natural or not based on these low-level visual features and we could do so with 81% accuracy. …”
Get full text

BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation by Muhammad Abrar Ahmad Khan, Muhammad Attique Khan, Ateeq Ur Rehman, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Deepak Gupta, Saima Ahmed Rahin, Yudong Zhang

“…In the last step, the authors develop a posterior probability‐based MFO feature selection algorithm to select the best features. …”
Get full text

Linear Time Train Contraction Minor Labeling for Railway Line Capacity Analysis by Qinglun Zhong, Ruihua Xu

“…Abstract When no more than one train is feasibly contained in the separation headway times of two other trains, a triangular gap problem-based method is used to compute the consumed capacity in linear time. …”
Get full text

Detecting Driver Drowsiness Using Hybrid Facial Features and Ensemble Learning by Changbiao Xu, Wenhao Huang, Jiao Liu, Lang Li

“…Additionally, we use the SHAP method to ensure model explainability and analyze the correlations between features from various facial regions. Trained and tested on the UTA-RLDD dataset, our method achieves a video accuracy (VA) of 86.52%, outperforming similar techniques introduced in recent years. …”
Get full text

Multistage Training and Fusion Method for Imbalanced Multimodal UAV Remote Sensing Classification by Shihao Wang, Zhengwei Xu, Yun Lin

“…Building on the GCMT, we further introduce an information entropy measurement fusion (IEMF) module, which dynamically adjusts cross-modal feature fusion weights using entropy-based metrics to mitigate overreliance on dominant modalities while preserving synergistic interactions. …”
Get full text

Acquisition versus consolidation of auditory perceptual learning using mixed-training regimens. by David W Maidment, HiJee Kang, Emma C Gill, Sygal Amitay

“…Based on previous literature we predicted that acquisition would be disrupted by varying the task-relevant stimulus feature during training (stimulus interference), and that consolidation would be disrupted by varying the perceptual judgment required (task interference). …”
Get full text