Research on Wood Defects Feature Imbalance Optimization and Recognition by Xiao Wang

“…The proposed loss functions improve the model performance through optimizing the model training process, providing a new idea for deep learning application in wood defects detection.…”
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A hybrid model based on transformer and Mamba for enhanced sequence modeling by Xiaocui Zhu, Qunsheng Ruan, Sai Qian, Miaohui Zhang

“…This approach successfully merges the advantages of the Transformer and Mamba, resulting in enhanced performance. Comprehensive experiments across various language tasks demonstrate that our proposed model consistently achieves competitive results, outperforming existing benchmarks.…”
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A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery by Zhen-qi LIAO, Yu-long DAI, Han WANG, Quirine M. KETTERINGS, Jun-sheng LU, Fu-cang ZHANG, Zhi-jun LI, Jun-liang FAN

“…A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. …”
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Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model by Changzhen XIONG, Hui ZHI

“…In order to improve the accuracy of weakly-supervised semantic segmentation method,a segmentation and optimization algorithm that combines multi-scale feature was proposed.The new algorithm firstly constructs a multi-scale feature model based on transfer learning algorithm.In addition,a new classifier was introduced for category prediction to reduce the failure of segmentation due to the prediction of target class information errors.Then the designed multi-scale model was fused with the original transfer learning model by different weights to enhance the generalization performance of the model.Finally,the predictions class credibility was added to adjust the credibility of the corresponding class of pixels in the segmentation map,avoiding false positive segmentation regions.The proposed algorithm was tested on the challenging VOC 2012 dataset,the mean intersection-over-union is 58.8% on validation dataset and 57.5% on test dataset.It outperforms the original transfer-learning algorithm by 12.9% and 12.3%.And it performs favorably against other segmentation methods using weakly-supervised information based on category labels as well.…”
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A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features by Xing‐bo Cai, Ze‐hui Lu, Zhi Peng, Yong‐qing Xu, Jun‐shen Huang, Hao‐tian Luo, Yu Zhao, Zhong‐qi Lou, Zi‐qi Shen, Zhang‐cong Chen, Xiong‐gang Yang, Ying Wu, Sheng Lu

“…The intelligent classifier achieved optimal performance when using the first 15 PCA‐extracted features, with a cumulative variance contribution rate exceeding 75%. …”
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Mask suitability recommendation based on facial image key feature dimension recognition by Xinjin YANG, Weiqun XIE, Zhenyu HUANG, Yixiong SHEN, Lin YE, Hai LI, Zhuobo YANG, Zhu LIAO, Simi LI

“…Dimensionality reduction and clustering were applied to the facial feature data, and a correlation model was established between facial clusters and mask sealing performance data. …”
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Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model by Jae-Young Choi, Min-Ji Song, You-Jin Shin

“…The final classification results are achieved through a weighted soft voting approach, where the generalization blocks are assigned higher weights to optimize performance. Through 15 experiments using various model configurations, we show that the weighted ABC ensemble model outperforms the baseline models, achieving the best performance with an accuracy of 0.9326 and an F1-score of 0.9302. …”
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Hybrid Deep Learning Models for Predicting Student Academic Performance by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga, Vikash Jugoo

“…The approach combines a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) network to enhance predictive capabilities. To improve the model’s performance, we address key data preprocessing challenges, including handling missing data, addressing class imbalance, and selecting relevant features. …”
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PERFORMANCE STUDY OF THE DTU MODEL FOR RELATIONAL DATABASES ON THE AZURE PLATFORM by Serhii Minukhin

“…The aim of the study is to develop a system of indicators for monitoring the current state of work with the database for reasonable decision-making on the choice of a certain price category of the DTU model of the MS Azure cloud service, which will optimize the results of working with the database. platforms Achieving the set goals involves the following tasks: to analyze modern tools and services for working with databases, in particular relational databases, on Azure and AWS cloud platforms, the features of their application and implementation; develop software for generating test relational databases of different sizes; test the generated databases on a local resource; taking into account the characteristics of the levels of the Azure DTU model, develop a new system of performance indicators, which includes 2 groups - time indicators and indicators of the load on existing platform resources; develop and implement queries of varying complexity for the generated test database for different levels of the DTU model and analyze the results. …”
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An improved performance model for artificial intelligence-based diabetes prediction by Ugwu Hillary Okwudili, Oparaku Ogbonna Ukachukwu, V. C. Chijindu, Michael Okechukwu Ezea, Buhari Ishaq

“…The proposed model achieved an area under the curve (AUC) of 0.946, surpassing the previously best-performing model, extreme gradient boosting (XB) by 0.7%. …”
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Object detection model design for tiny road surface damage by Chenguang Wu, Min Ye, Hongwei Li, Jiale Zhang

“…This study proposes a novel road surface damage object detection model (RSDD) to address these challenges. Firstly, a backbone applied to road surface damage feature extraction is designed to solve the problems of feature loss and insufficient extraction of tiny damage during feature extraction. …”
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Application of Radiomics in Predicting the Prognosis of Medulloblastoma in Children by Jiashu Chen, Wei Yang, Zesheng Ying, Ping Yang, Yuting Liang, Chen Liang, Baojin Shang, Hong Zhang, Yingjie Cai, Xiaojiao Peng, Hailang Sun, Wenping Ma, Ming Ge

“…A total of five prognostic radiomic features were selected. The radiomics model could discriminate different risk hierarchies with good performance power in the training and testing datasets (training set <i>p</i>= 0.0009; test set <i>p</i> = 0.0286). …”
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Recursive Effects to Study Feature-Based Capabilities in Supply Chain Management by Pietro De Giovanni

“…This latter is carried out by investigating the recursive effects in structural equation modeling. Our findings reveal that feature-based capabilities entail an economically feasible loop through competitors and supply chain partners but not also through facilitators and operational performance.…”
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Bayesian Neural Networks With Robust Feature Interpretation for Enhanced Compressive Strength Prediction of Ultra‐High‐Performance Concrete by Tao Zhang, Ji Hao, Jie Zhang, Lina Du, Yunhao Zhang, Wenbin Jiao

“…ABSTRACT Ultra‐high‐performance concrete (UHPC) has emerged as a revolutionary material in civil engineering due to its superior strength, durability, and longevity. …”
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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

“…With feature selection, model performance can be maximized and hardware requirements reduced, which are essential for real-world applications with resource constraints. …”
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FPE–Transformer: A Feature Positional Encoding-Based Transformer Model for Attack Detection by Hande Çavşi Zaim, Esra Nergis Yolaçan

“…Traditional deep learning methods often require large amounts of data and struggle to understand relationships between features effectively. With their self-attention mechanism, Transformers excel in modeling complex relationships and long-term dependencies. …”
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Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA by Yaohua Deng, Huiqiao Zhou, Kexing Yao, Zhiqi Huang, Chengwang Guo

“…Performance feature extraction is the primary problem in equipment performance degradation assessment. …”
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Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…Classifying all domain 22 significant features using a random forest model improved classification accuracy to 84.9%. …”
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Transformers and large language models are efficient feature extractors for electronic health record studies by Kevin Yuan, Chang Ho Yoon, Qingze Gu, Henry Munby, A. Sarah Walker, Tingting Zhu, David W. Eyre

“…A zero-shot OpenAI GPT4 model matches the performance of traditional NLP models without the need for labelled training data (F1 = 0.71 and 0.86) and a fine-tuned GPT3.5 model achieves similar performance to the fine-tuned BERT-based model (F1 = 0.95 and 0.97). …”
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Degradation Assessment of the Rolling Bearing Performance based on AR-FCM by Zhou Jianmin, Guo Huijuan, Zhang Long

“…The test data are put into the FCM model by keeping the model invariant and continuously iterating,and the performance degradation index is obtained. …”
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