A Lightweight Network With Embedded Soft Constraints on Approximate Spectral Features for Real-Time Water Body Segmentation in Remote Sensing Images by Qingqing Cao, Boya Zhao, Zijin Li, Fangfang Zhang, Yuanfeng Wu

“…First, we introduce approximate spectral feature indices as auxiliary interband feature information. …”
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Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq. by Bingbing Lei, Xuanjun Zhu, Tao Zhou, Yuxi Zhang

“…This model leverages hybrid perception of multi-scale features. …”
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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis by Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise

“…In subsequent analysis, a large benchmark dataset is also utilized to evaluate the performance of learning algorithms in order to investigate the generalization power of the proposed model. …”
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Integrating VGG Re-trained Feature Extraction with Machine Learning for Knee Osteoarthritis Severity Levels Detection Using X-ray Images by Simeon Yuda Prasetyo, Ghinaa Zain Nabiilah

“…These models' performance is assessed using metrics including F1-score, recall, accuracy, and precision. …”
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Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China by Shaowu Lin, Sicheng Li, Ya Fang

“…Four machine learning techniques including Logistic Regression, Support Vector Machine, Gradient Boosting Decision Tree, and Random Forest were employed to develop predictive models for depression. SHapley Additive exPlanations were used to explain the feature importance. …”
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PID-CD: PID Control is What Change Detection Needs by Zhiwei Ye, Shirui Sheng, Qiyi He, Mingwei Wang, Chuan Xu, Zhina Song, Ziqian Fang, Liye Mei, Lingyu Yan, Xudong Lai

“…This article adopts the proportional-integral-derivative (PID) control concept and designs two modules: PID feature alignment (PIDFA) and PID feature fusion (PIDFF), which makes up the PID-CD model. …”
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Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method by Lichan Zhou, Jianghui Cai, Haifeng Yang, Ali Luo, Yinbi Li, Chenhui Shi, Boyu Zhang, Jianing Tian, Yinan Yuan, Yuqing Yang

“…This paper provides 7809 carbon star candidates identified from the Large-Area Multi-Object Fiber Optic Spectroscopic Telescope (LAMOST) DR11 and Sloan Digital Sky Survey (SDSS) DR18, using a parallel carbon star feature recognition method. This method deploys a parallel multi-interval feature representation model and a k-nearest neighbor classification model, effectively characterizing local and global molecular bands of carbon stars and enhancing feature discrimination, especially for weak features that are highly susceptible to noise interference. …”
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Artificial intelligence networks for assessing the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features: a systematic review and meta-analysis by Narges Norouzkhani, Hesam Mobaraki, Shirin Varmazyar, Hadis Zaboli, Zhina Mohamadi, Golnaz Nikeghbali, Kamyar Bagheri, Newsha Marivany, Mirmehdi Najafi, Mahdiyeh Nozad Varjovi, Mohamed Abouzeid, Hanieh Zeidi Baghrabad, Pooya Eini, Aida Azhdarimoghaddam, Farbod Khosravi, Mahsa Asadi Anar

“…The pooled results of AI model performance for gastrointestinal cancers based on genetic mutation features were: AUC = 0.86 (95% CI: 0.86–0.87), Sensitivity = 83% (95% CI: 83%-84%), Specificity = 72% (95% CI: 72%-73%), and Accuracy = 82% (95% CI: 82%-83%). …”
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LeONet: A Hybrid Deep Learning Approach for High-Precision Code Clone Detection Using Abstract Syntax Tree Features by Thanoshan Vijayanandan, Kuhaneswaran Banujan, Ashan Induranga, Banage T. G. S. Kumara, Kaveenga Koswattage

“…This study underscores the potential of hybrid deep learning models and feature extraction techniques in improving the accuracy of code clone detection, providing a promising direction for future research in this area.…”
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Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data by Jian-bin TAO, Wen-bin WU, Yong ZHOU, Yu WANG, Yan JIANG

“…Finally, a regression model was built to model the relationship of the phenological feature and the sample data. …”
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Soil Moisture Content Prediction Using Gradient Boosting Regressor (GBR) Model: Soil-Specific Modeling with Five Depths by Tarek Alahmad, Miklós Neményi, Anikó Nyéki

“…The statistical analysis revealed significant variation in SMC across depths in loam soil (<i>p</i> < 0.05), while silt loam exhibited more stable moisture distribution. The GBR model demonstrated high performance in both soil textures, achieving R² values of 0.98 and 0.94 for silt loam and loam soils, respectively, with low prediction errors (RMSE 0.85 and 0.97, respectively). …”
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FF-ResNet-DR model: a deep learning model for diabetic retinopathy grading by frequency domain attention by Chang Yu, Qian Ma, Jing Li, Qiuyang Zhang, Jin Yao, Biao Yan, Zhenhua Wang

“…Extensive experiments on multiple public datasets demonstrate the superior performance of FF-ResNet-DR compared to state-of-the-art models. …”
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RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism by Chuanlu Li, Xiaorong Xue, Caijia Zeng, Yifan Xu, Xingbiao Xu, Siyue Zhao

“…The model employs RegNet as the backbone network for feature extraction, using a simple and efficient strategy to fuse shallow features. …”
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A REMARKABLE FEATURE OF BIODIVERSITY OF THE COASTAL, MARINE AND ISLAND ECOSYSTEMS OF THE CASPIAN SEA. A NEW LOOK AT THE AGE OF ISLANDS AND LEVEL MODE by G. M. Abdurahmanov, A. A. Teymurov

“…Removal of the collected material was performed at intervals of 3 days. The need for a causal interpretation of the contemporary configuration of habitat in the species composition of the biota of the study area, has led us to develop a hydrodynamic GIS model of the Middle and North of the Caspian sea. …”
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A Comparison of Feature-Based MLR and PLS Regression Techniques for the Prediction of Three Soil Constituents in a Degraded South African Ecosystem by Anita Bayer, Martin Bachmann, Andreas Müller, Hermann Kaufmann

“…Regression models are developed based on extensive ground reference data of 163 sampled sites collected in the Thicket Biome, South Africa, where land changes are observed due to intensive overgrazing. …”
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New State Identification Method for Rotating Machinery under Variable Load Conditions Based on Hybrid Entropy Features and Joint Distribution Adaptation by Xiaoming Xue, Nan Zhang, Suqun Cao, Wei Jiang, Jianzhong Zhou, Liyan Liu

“…Finally, five cases with the training and testing set under variable load conditions were used to demonstrate the performance of the proposed method, and comparisons with some other diagnosis models combined with the same features and other dimensionality reduction methods were also discussed. …”
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Interplay between MRI radiomics and immune gene expression signatures in oral squamous cell carcinoma by Anna Corti, Deborah Lenoci, Valentina D. A. Corino, Davide Mattavelli, Marco Ravanelli, Tito Poli, Stefano Cavalieri, Lisa Licitra, Loris De Cecco, Luca Mainardi

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Analytical and numerical computations for bioconvective flow Maxwell nanofluid with variable thermal properties using homotopy analysis method and finite difference scheme by Jawaher Alzahrani

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PAMFPN: Position-Aware Multi-Kernel Feature Pyramid Network with Adaptive Sparse Attention for Robust Object Detection in Remote Sensing Imagery by Xiaofei Yang, Suihua Xue, Lin Li, Sihuan Li, Yudong Fang, Xiaofeng Zhang, Xiaohui Huang

“…To address these limitations, we propose a Dynamic Multi-Kernel Position-Aware Feature Pyramid Network (PAMFPN), which integrates adaptive sparse position modeling and multi-kernel dynamic fusion to achieve robust feature representation. …”
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Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods by Saiprasad Potharaju, Swapnali N. Tambe, G. Madhukar Rao, M. V. V. Prasad Kantipudi, Kalyan Devappa Bamane, Mininath Bendre

“…These features were used to train classification models (Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)) with a 70:30 train-test split and tenfold cross-validation. …”
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