Be-dataHIVE: a base editing database by Lucas Schneider, Peter Minary

“…However, the overall robustness and performance of those models is limited. One way to improve the performance is to train models on a diverse, feature-rich, and large dataset, which does not exist for the base editing field. …”
Get full text

From lab to field with machine learning – Bridging the gap for movement analysis in real-world environments: A commentary by Carlo Dindorf, Fabian Horst, Djordje Slijepčević, Bernhard Dumphart, Jonas Dully, Matthias Zeppelzauer, Brian Horsak, Michael Fröhlich

“…Finally, automated classification (e) refers to the process of developing a predictive model that assigns input features of data samples to predefined categories or classes using supervised ML. …”
Get full text

A new approach for nuclear forensics investigations of uranium dioxide: Application of laboratory-based photoelectron spectroscopy with hard and Soft X-ray sources by Stuart A. Dunn, Paul Roussel, Aaron Wood, Ben F. Spencer, Robert W. Harrison, Philip Kaye, Matthew Higginson, Matthew R. Gilbert, Simon C. Middleburgh, Wendy R. Flavell

“…Inelastic background analysis is performed to determine the in-depth distribution of atoms, developing a consistent model to describe the surface overlayer, correlated to the chemical and stoichiometric differences over the excitation range. …”
Get full text

Research on Fault Detection Technology for Circuit Breaker Operating Mechanism Combinations Based on Deep Residual Networks by Hongping Shao, Yizhe Jiang, Jianeng Zhao, Xueteng Li, Mingzhan Zhang, Mingkun Yang, Xinyu Wang, Hao Yang

“…The convolutional layer strategy, which first performs dimensionality reduction followed by dimensionality expansion, combined with the use of the ReLU activation function, contributes to superior performance. …”
Get full text

Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning by Abdelrahman Abdallah, Mahmoud Salaheldin Kasem, Ibrahim Abdelhalim, Norah Saleh Alghamdi, Ayman El-Baz

“…In the first stage, the model differentiates non-cancerous from potentially cancerous mammograms using SelfPatch, an innovative self-supervised learning task that enhances patch-level feature learning by enforcing consistency among spatially correlated patches. …”
Get full text

IP SafeGuard–An AI-Driven Malicious IP Detection Framework by Abdullah Al Siam, Moutaz Alazab, Albara Awajan, Md Rakibul Hasan, Areej Obeidat, Nuruzzaman Faruqui

“…It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. …”
Get full text

An Interpretability Method for Broken Wire Detection by Hailong Wu, Shaoqing Liu, Zhanghou Xu, Zhenshan Ji, Mengpeng Qian, Xiaolin Yuan, Yong Wang

“…Therefore, it is necessary to perform broken wire detection. Deep learning has powerful feature-learning capabilities and is characterized by high accuracy and efficiency, and the YOLOv8 object detection model has been adopted to detect wire breaks in electromagnetic signal images of wire rope, achieving better results. …”
Get full text

Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data by Jiannan He, Yongbin Zhang, Mingyue Liu, Lin Chen, Weidong Man, Hua Fang, Xiang Li, Xuan Yin, Jianping Liang, Wenke Bai, Fuping Li

“…The results show that the following. 1) The prediction accuracy is improved by classifying the data into three types: unlodging <italic>S. alterniflora</italic> (ULSA), lodging <italic>S. alterniflora</italic> (LSA), and mudflats. 2) XGBoost outperformed RF and SVM in accurately predicting SOC content, with <italic>R</italic>²; values of 0.743 for ULSA, 0.731 for LSA, and 0.705 for mudflats; 3) In the XGBoost models constructed for ULSA, LSA, and mudflats, spectral features contributed 75.7&#x0025;, 73.1&#x0025;, and 63.1&#x0025;, respectively, with the normalized difference vegetation index emerging as the most critical spectral feature. …”
Get full text

Pattern transition recognition based on transfer learning for exoskeleton across different terrains by Yifan Gao, Jianbin Zheng, Yang Gao, Ziyao Chen, Jing Tang, Liping Huang

“…To address the problem of pattern transition recognition, transfer learning adapts a model from the source domain to the target domain. …”
Get full text

Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification by Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed

“…The results show that the models integrating multi-omics data outperformed the models trained on single omics data, where LASSO-MOGAT achieved the best overall performance, with an accuracy of 95.9%. …”
Get full text

Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network by Salih Taha Alperen Özçelik, Hüseyin Üzen, Abdulkadir Şengür, Hüseyin Fırat, Muammer Türkoğlu, Adalet Çelebi, Sema Gül, Nebras M. Sobahi

“…It combines the InceptionV3 model for encoding with a custom decoder for feature integration and segmentation, using pointwise convolution and an attention mechanism. …”
Get full text

Hardware/Software Implementation of a Chip-to-Chip Communication Protocol Based on SPDM by Kais Belwafi, Abdulhadi Shoufan, Mariam Alsafi, Ashfaq Ahmed, Kyusuk Han

“…The ZTP interfaces with the connected peripherals, allowing the execution of the Security Protocol and Data Model (SPDM). SPDM is a reliable option for achieving zero-trust security on the hardware level. …”
Get full text

ECG filtering and QRS extraction under steep pulse interference by Xi-tong YAO, Yu DAI, Jian-xun ZHANG, Jin-tao GE, Tong CHEN, Hao YANG

“…To eliminate the interference caused by the steep pulse, we analyzed the characteristics of steep pulse interference and established the mathematical model of steep pulse noise. Moreover, we proposed an ECG signal filtering algorithm based on variational mode decomposition (VMD) to extract the steep pulse interference component superimposed on the ECG signal. …”
Get full text

Social Determinants Influencing Internet‐Based Service Adoption Among Female Family Caregivers in Bangladesh: A Sociodemographic and Technological Analysis by Mohammad Ishtiaque Rahman, Jahangir Alam, Khadija Khanom, Forhan Bin Emdad

“…Additionally, the feature importance of the best‐performing model was assessed using permutation importance and Shapley Additive Explanations (SHAP) analysis. …”
Get full text

Gait Optimization Method for a Large Heavy Load Biped Robot Based on Particle Swarm Optimizer Algorithm by Huanhuan Ren, Xuanji Guo, Mingzhibo He, Chengshuai Ma, Chengzhi Su

“…In this paper, we present the innovative design of a highly robust bipedal robot featuring parallel legs and delve into its intricate gait planning strategy. …”
Get full text

Effect of Mesopore Structural Parameters in Alumina Supports on Catalytic Hydrodeoxygenation of Guaiacol to Cycloalkanes via Ni-Supported Al₂O₃ Catalysts by Wen Huang, Chengyan Wen, Yanting Su, Xinghua Zhang, Longlong Ma

“…During the upgrading of lignin-derived oil, the Ni/meso-Al₂O₃-F-200 catalyst, featuring a mesopore size of 4.07 nm and a mesopore volume of 0.286 cm³/g, exhibited outstanding performance. …”
Get full text

Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness by Yalin Gao, Dongwei Xu, Huiyan Zhu, Qi Xuan

“…To solve these problems, we propose a super-resolution denoising residual network (SDRNet), which combines the advantages of the super-resolution convolutional neural network (SRCNN) and the denoising convolutional neural network (DnCNN) to construct a pilot-based OFDM signal model, train SDRNet using OFDM pilot data containing Gaussian noise, and optimize its feature enhancement ability in frequency-selective fading channels. …”
Get full text

A multi-biomarker machine learning approach for early prediction of interstitial lung disease in rheumatoid arthritis by Jiaojiao Xu, Wei Zhang, Weili Bai, Nannan Gai, Jing Li, Yunqi Bao

“…Results The XGBoost model demonstrated superior predictive performance (AUC = 0.891, 95% CI: 0.847–0.935). …”
Get full text

Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network by Mengjuan Xu, Xiang Chen, Yuwen Ruan, Xu Zhang

“…Given that high-density surface EMG (HD-sEMG) signal contains rich temporal and spatial information, the multi-view spatial-temporal graph convolutional network (MSTGCN)is adopted as the basic classifier, and a feature extraction convolutional neural network (CNN) module is designed and integrated into MSTGCN to generate a new model called CNN-MSTGCN. …”
Get full text

Structural Attributes Injection Is Better: Exploring General Approach for Radar Image ATR with a Attribute Alignment Adapter by Xiaolin Zhou, Xunzhang Gao, Shuowei Liu, Junjie Han, Xiaolong Su, Jiawei Zhang

“…However, existing data-driven approaches frequently ignore prior knowledge of the target, leading to a lack of interpretability and poor performance of trained models. To address this issue, we first integrate the knowledge of structural attributes into the training process of an ATR model, providing both category and structural information at the dataset level. …”
Get full text