Feature enhanced cascading attention network for lightweight image super-resolution by Feng Huang, Hongwei Liu, Liqiong Chen, Ying Shen, Min Yu

“…This demonstrates a better trade-off between SR performance and model complexity.…”
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A Novel Deep Hybrid Model for Automatic Femoral Stem Classification in Hip Arthroplasty From Radiographs: MSFT-Net With CBAM and Transformer Modules by Emre Gogus, Atinc Yilmaz, Meric Enercan

“…The experimental results demonstrated that the proposed architecture enhances classification performance while reducing overfitting through attention and transformer-based feature refinement. …”
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The Comparison of Activation Functions in Feature Extraction Layer using Sharpen Filter by Oktavia Citra Resmi Rachmawati, Ali Ridho Barakbah, Tita Karlita

“… Activation functions are a critical component in the feature extraction layer of deep learning models, influencing their ability to identify patterns and extract meaningful features from input data. …”
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EnhanceCenter for improving point based tracking and rich feature representation by Hyun-Sung Yang, Sung-Wook Park, Se-Hoon Jung, Chun-Bo Sim

“…Experiments on various MOT benchmarks demonstrated the performance of EnhanceCenter against models using high-performance detectors. …”
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Federated cross-view e-commerce recommendation based on feature rescaling by Ruiheng Li, Yuhang Shu, Yue Cao, Yiming Luo, Qiankun Zuo, Xuan Wu, Jiaojiao Yu, Wenxin Zhang

“…By addressing data heterogeneity and optimizing feature utilization through dynamic rescaling, Fed-FR-MVD effectively mitigates the impact of noisy data, with performance maintained across noise levels of 5%–15%. …”
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Discriminative Features Extraction for Plant Disease Classification Using Deep CNN by Hira Farman, usman Amjad

“…This paper uses a CNN approach for identifying plant diseases using the Plant Village, web-based dataset that has on average 35 classes, with 29281 images, comprising of both healthy and diseased leaves. To improve the model performance further techniques like data augmentation, contrast enhancement, noise reduction techniques were used. …”
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An Mcformer encoder integrating Mamba and Cgmlp for improved acoustic feature extraction by Nurmemet Yolwas, Yongchao Li, Lixu Sun, Jian Peng, Zhiwu Sun, Yajie Wei, Yineng Cai

“…On the TED-LIUM 3 English public dataset, the word error rates (WER) for the validation and test sets are 7.26% and 6.95%, respectively, without a language model. These experimental outcomes substantiate the efficacy of Mcformer in enhancing speech recognition performance.…”
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Two-branch Shape Complement Network for Feature Missing Splicing Mode by SUNJin, MAHaotian, LEIZhenting, LIANGLi

“…Then , on the basis of the skeleton point cloud , the local point cloud generation is further refined to ensure the local features of the target point cloud. Finally , a two-branch shape completion network is built for the feature missing splicing model. …”
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Improved Estimation of Parkinsonian Vowel Quality through Acoustic Feature Assimilation by Amr Gaballah, Vijay Parsa, Daryn Cushnie-Sparrow, Scott Adams

“…Results showed that the RPDE measure performed the best among all individual features, while a regression model incorporating a subset of features produced the best overall correlation of 0.80 between the predicted and actual vowel quality ratings. …”
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A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis by Tarneem Elemam, Mohamed Elshrkawey

“…For the ovarian cancer dataset, the SVM model achieves the highest accuracy at 100% using only 6 features. …”
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Kernel to computation: identifying optimal feature set for red rice classification by Suma D, Narendra V G, Darshan Holla M, Shreyas, Raviraja Holla M

“…Feature selection was performed using Recursive Feature Elimination and Backward Feature Elimination to enhance model efficiency. …”
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Optimisation-Based Feature Selection for Regression Neural Networks Towards Explainability by Georgios I. Liapis, Sophia Tsoka, Lazaros G. Papageorgiou

“…However, they are often criticised for their lack of interpretability and commonly referred to as black-box models. Feature selection approaches address this challenge by simplifying datasets through the removal of unimportant features, while improving explainability by revealing feature importance. …”
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Feature Reweighting-Based Factorization Machine for Effective Learning Latent Representation by Xiebing Chen, Bilian Chen, Yue Wang, Langcai Cao

“…However, existing research has largely overlooked the potential correlations and attributes among features in FMs. These inherent relationships between features can enhance our understanding and facilitate the modeling of meaningful feature representations. …”
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Thermal features prediction in asphalt pavements using ANFIS-based regression by Mohammad Ali Khasawneh, Hiren Mewada, Ahmad Ali Khasawneh, Ansam Adnan Sawalha

“…Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). …”
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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

“…The combined intra-peri-clinical model demonstrated superior predictive performance compared to other models, with an AUC of 0.93, sensitivity of 0.91, and specificity of 0.86.ConclusionThe combined model incorporating intratumor and peritumor radiomics features with clinical data showed significant value in predicting the invasiveness of nodular pulmonary adenocarcinoma, aiding in the precise selection of surgical methods.…”
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Enhancing UAS-Based Multispectral Semantic Segmentation Through Feature Engineering by Elena Vollmer, Mishal Benz, James Kahn, Leon Klug, Rebekka Volk, Frank Schultmann, Markus Gotz

“…This article investigates how feature engineering (FE), the process of adapting raw data to serve as DL training data, can impact performance when transferring prevalent model architectures to combined red, green, blue (RGB) thermal imagery. …”
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JDroid: Android malware detection using hybrid opcode feature vector by Recep Sinan Arslan

“…In this study, we propose a tool called JDroid that treats opcodes (Dalvik Opcode and Java ByteCode) as features based on static analysis. The proposed tool aims to detect malicious applications with a unique ensemble model in a stacked generalised structure that uses different opcode sequences as a hybrid, and where each feature is first trained separately and then used by an ensemble decision. …”
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Ensemble Learning-Based Person Re-identification with Multiple Feature Representations by Yun Yang, Xiaofang Liu, Qiongwei Ye, Dapeng Tao

“…It essentially focuses on extracting or learning feature representations followed by a matching model using a distance metric. …”
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Temporal Features-Fused Vision Retentive Network for Echocardiography Image Segmentation by Zhicheng Lin, Rongpu Cui, Limiao Ning, Jian Peng

“…Our model is based on an encoder–decoder architecture and consists of two modules: the Temporal Feature Fusion Module (TFFA) and the Vision Retentive Network (Vision RetNet) encoder. …”
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Investigating feature extraction by SIFT methods for prostate cancer early detection by Shadan Mohammed Jihad, Ali A. Alsaud, Firas H. Almukhtar, Shahab Kareem, Raghad Zuhair Yousif

“…The adopted methodology was based on the comparative analysis and benchmarking of the performance of feature extraction based on SIFT against traditional image processing techniques with a generic representation on a number of metrics: sensitivity, specificity, and overall diagnostic accuracy. …”
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