Value of dual-energy CT quantitative parameters combined with clinical features in diagnosis of stages T2 and T3 colorectal cancer by FANG Ni, WEI Xin, CHEN Weijuan

“…Objective‍　‍To investigate the diagnostic value of our regression model based on quantitative parameters of dual-energy CT and clinical features for stages T2 and T3 colorectal cancer. …”
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Finite Element Analysis of the Influence that Bone Density and Implant Fixation Features have on Total Ankle Tibial Component Stability by Joshua E. Johnson PhD, Gabriel Clarke MS, Cesar de Cesar Netto MD, PhD, Bopha Chrea MD, Donald D. Anderson PhD

“…Conclusion: We investigated the influence of bone density on implant-bone micromotions with varying primary fixation features using FEA. The model predicts that micromotions are less in average-density bone, regardless of implant fixation features. …”
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Flooded Infrastructure Change Detection in Deeply Supervised Networks Based on Multi-Attention-Constrained Multi-Scale Feature Fusion by Gang Qin, Shixin Wang, Futao Wang, Suju Li, Zhenqing Wang, Jinfeng Zhu, Ming Liu, Changjun Gu, Qing Zhao

“…Experimental results showed that the embedded MSFF performs better than the baseline model, demonstrating that MSFF can be used as a general multi-scale feature fusion component. …”
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Local-Global Feature Extraction Network With Dynamic 3-D Convolution and Residual Attention Transformer for Hyperspectral Image Classification by Qiqiang Chen, Zhengyang Li, Junru Yin, Wei Huang, Tianming Zhan

“…This prevents the vanishing of shallow spatial-spectral features. Finally, local and global HSI information may be efficiently integrated using the feature fusion module, which also improves performance during subsequent classification. …”
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OMSF2: optimizing multi-scale feature fusion learning for pneumoconiosis staging diagnosis through data specificity augmentation by Xueting Ren, Surong Chu, Guohua Ji, Zijuan Zhao, Juanjuan Zhao, Yan Qiang, Yangyang Wei, Yan Wang

“…Existing deep detection models utilize Feature Pyramid Networks (FPNs) to identify objects at different scales. …”
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Deep Pseudogene Categorization and Genome-Wide Transcription Prediction Using GANP-Based Feature Selection and TabNet Interpretability by Zeeshan Ahmed, Kashif Munir, Muhammad Usama Tanveer, Syed Rizwan Hassan, Ateeq Ur Rehman, Habib Hamam

“…The pipeline uses SMOTE to mitigate class imbalance and applies synthetic feature augmentation to boost classification performance. …”
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Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis by Valentina La Parola, Giancarlo Cusumano, Saverio Lombardi, Antonio Alessio Compagnino, Antonino La Barbera, Antonio Tutone, Antonio Pagliaro

“…Our methodology employs a Random Forest classifier with optimized hyperparameters, trained on a balanced dataset of gamma and hadron events. The model incorporates feature importance analysis to select the most discriminating temporal parameters from a comprehensive set of time-based features. …”
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Particle swarm optimization with YOLOv8 for improved detection performance of tomato plants by Sarah M. Ayyad, Nada M. Sallam, Samah A. Gamel, Zainab H. Ali

“…The experiments on the collection of different datasets reveal that the proposed model performs with the highest precision, recall, F1-score, and mean average precision (mAP) of 0.89, 0.9, 0.89, and 0.89 (mAP@0.5:0.95), respectively, exceeding other models. …”
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Feasibility of rabbit auricular VX2 tumor model as an experimental model for intra-arterial embolization. by Woosun Choi, Byung Kook Kwak, Jisung Jung, Serah Park, Soon Auck Hong, Sang Lim Choi

“…In conclusion, a rabbit auricular VX2 tumor model is easy to create, and its features can be conveniently observed on visual inspection. …”
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The Influence of the working fluid and Regenerator material on the Performance of the types Gamma Stirling Engine by yahya abbas, Ali A. F. Al-Hamadani

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Flaw-YOLOv5s: A Lightweight Potato Surface Defect Detection Algorithm Based on Multi-Scale Feature Fusion by Haitao Wu, Ranhui Zhu, Hengren Wang, Xiangyou Wang, Jie Huang, Shuwei Liu

“…Then, the SPPF in the backbone network is replaced by SPPELAN, which combines SPP with ELAN to enable the model to perform multi-scale pooling and feature extraction, optimizing detection capacity for small targets in potatoes. …”
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MFDAFF-Net: Multiscale Frequency-Aware and Dual Attention-Guided Feature Fusion Network for UAV Imagery Object Detection by Shu Tian, Bingxi Zhang, Lin Cao, Lihong Kang, Jing Tian, Xiangwei Xing, Bo Shen, Chunzhuo Fan, Kangning Du, Chong Fu, Ye Zhang

“…Specifically, we construct a multiscale frequency-aware feature pyramid network as the neck of the model, which facilitates thorough top-down fusion of multiscale features through a meticulously designed feature fusion architecture. …”
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Numerical Study to Evaluate the Performance of Nonuniform Stepped Spillway Using ANSYS-CFX by Shawnm M. Saleh, Sarhang M. Husain

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Extraction of Garlic in the North China Plain Using Multi-Feature Combinations from Active and Passive Time Series Data by Chuang Peng, Binglong Gao, Wei Wang, Wenji Zhu, Yongqi Chen, Chao Dong

“…The findings reveal that: (1) Better results in garlic extraction can be achieved using active–passive time series remote sensing. The performance of the classification model can be further enhanced by incorporating short-wave infrared bands or spliced time series data into the classification features. (2) Examination of garlic cultivation fragmentation using the garlic fragmentation index aids in elucidating variations in accuracy across the study area’s six counties. (3) Comparative analysis with validation samples demonstrated superior garlic extraction outcomes from the six primary garlic-producing counties of the North China Plain in 2021, achieving an overall precision exceeding 90%. …”
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Identifying performance differences among trail cameras used to monitor forest mammals by Kelsey Wellington, Christopher Bottom, Cody Merrill, John A. Litvaitis

“…ABSTRACT As a result of the increasing popularity of remotely triggered (trail) cameras among hunters, amateur naturalists, and field biologists, a large variety of equipment and technologies have become available. Among various models of cameras, such features as the size of detection zone, trigger speed, and sensitivity of infrared‐motion detector can influence performance, including animal detection rates. …”
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Perform-therapy as a method for restoring identity under conditions of postmodernist fragmentation by Anatoliy A. Grebenyuk

“…Elements of cognitive neuroscience and the anthropology of ritual serve as empirical support for interpreting therapeutic mechanisms. Results. Perform-therapy is presented as an independent therapeutic model, distinct from narrative and existential therapies across several parameters: the nature of narrative, the role of artificiality, the function of irony, the client's mode of participation, and the ontological metaphor of therapy. …”
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Optimizing feature selection and deep learning techniques for precise detection of low-rate distributed denial of service (LDDoS) attack by Naeem Ali Al-Shukaili, Miss Laiha M. Kiah, Ismail Ahmedy

“…Feature encoding to train the model with k-best feature is done by label encoder. …”
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MFPI-Net: A Multi-Scale Feature Perception and Interaction Network for Semantic Segmentation of Urban Remote Sensing Images by Xiaofei Song, Mingju Chen, Jie Rao, Yangming Luo, Zhihao Lin, Xingyue Zhang, Senyuan Li, Xiao Hu

“…The MCFEM enhances local feature modeling through parallel multi-kernel convolutions, forming a complementary relationship with the Swin Transformer’s global perception capability. …”
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Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures by Hari Mohan Rai, B. Omkar Lakshmi Jagan, N. Thiruapthi Rao, Thayyaba Khatoon Mohammed, Neha Agarwal, Hanaa A. Abdallah, Saurabh Agarwal

“…Data collection, preprocessing, normalization, and scaling are part of the framework, considering leukemia cell images, genomic features, and clinical records. Using the preprocessed data, training is performed on the various models with thorough testing and validation to fine-tune the best-performing architecture. …”
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A novel multiscale feature enhancement network using learnable density map for red clustered pepper yield estimation by Chenming Cheng, Chenming Cheng, Jin Lei, Jin Lei, Zicui Zhu, Lijian Lu, Lijian Lu, Zhi Wang, Zhi Wang, Jiali Tao, Jiali Tao, Xinyan Qin, Xinyan Qin

“…It also achieved the highest R-squared (R²) value of 0.9802 on the test dataset, beating the best performing DSNet by 0.98%. Notably, despite its multi-column structure, the model has a significant advantage in terms of parameters, with only 13.08M parameters (a reduction of 3.18M compared to the classic single-column network CSRNet). …”
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