Harnessing feature pruning with optimal deep learning based DDoS cyberattack detection on IoT environment by Eunmok Yang, Sooyong Jeong, Changho Seo

“…The FPODL-DDoSAD technique initially uses a min-max scalar for the data scaling into the standard layout. Besides, the feature pruning process is performed using an improved pelican optimization algorithm (IPOA), which enables the choice of an optimal subset of features. …”
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A Seasonal Fresh Tea Yield Estimation Method with Machine Learning Algorithms at Field Scale Integrating UAV RGB and Sentinel-2 Imagery by Huimei Liu, Yun Liu, Weiheng Xu, Mei Wu, Leiguang Wang, Ning Lu, Guanglong Ou

“…For autumn and annual yield estimation, voting regression models demonstrated superior performance, with the autumn model achieving an R² value of 0.42, an RMSE of 70.6 kg/acre, and an rRMSE of 39.77%, and the annual model attained an R² value of 0.47, an RMSE of 168.7 kg/acre, and an rRMSE of 34.62%. …”
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Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis by Ben Gabrielson, Hanlu Yang, Trung Vu, Vince Calhoun, Tulay Adali

“…We introduce methods for random and deterministic coresets, minimizing error via a measure of discrepancy between the coreset and full tensor. We perform the decompositions on simulated data, and perform on real-world fMRI data to demonstrate our method’s feature selection ability. …”
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PnPDA⁺: A Meta Feature-Guided Domain Adapter for Collaborative Perception by Liang Xin, Guangtao Zhou, Zhaoyang Yu, Danni Wang, Tianyou Luo, Xiaoyuan Fu, Jinglin Li

“…Guided by these meta features, the PnPDA module performs adaptive semantic conversion to enhance cross-agent feature alignment without modifying existing perception models. …”
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MSKFaceNet: A Lightweight Face Recognition Neural Network for Low-Power Devices by Peng Zhang, Qinghua Ma, Yi Li, Min Cui

“…However, deploying facial recognition models on low-power devices (with power consumption below 10 watts) remains challenging. …”
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Postoperative myocardial infarction in lung cancer patients with: incidence rate, clinical features, prognostic factors by O. A. Bolshedvorskaya, K. V. Protasov, P. S. Ulybin, V. V. Dvornichenko

“…The features associated with MI in the univariateregression model were introduced into multivariate stepwise logistic regression. …”
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Prediction of Early Diagnosis in Ovarian Cancer Patients Using Machine Learning Approaches with Boruta and Advanced Feature Selection by Tuğçe Öznacar, Tunç Güler

“…This research aims to assess the feature extraction process from various machine learning techniques for better modelling of ovarian cancer and the selection process in ovarian cancer analysis. …”
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Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media by Imran Shafi, Sadia Din, Zahid Hussain, Imran Ashraf, Gyu Sang Choi

“…However, text classification techniques are limited by visualization, pre-processing, features extraction, and larger features space. Additionally, change in criminal content require the learning models to identify altered malicious textual contents which poses extra challenge. …”
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Smart insole-based abnormal gait identification: Deep sequential networks and feature ablation study by Beomjoon Park, Minhye Kim, Dawoon Jung, Jinwook Kim, Kyung-Ryoul Mun

“…Ablation studies were also conducted to assess the impact of combining features from different modalities. Results Our results demonstrate that models incorporating IMU features outperform those using different combinations of modalities including individual feature sets, with the top-performing models achieving F1-scores of up to 90% in sample-wise classification and 92% in subject-wise classification. …”
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Remote sensing image semantic segmentation network based on multi-scale feature enhancement fusion by Feiting Wang, Yuan Zhang, Qiongqiong Hu, Yu Zhu

“…However, due to the problems of varying target scale and difficulty in determining the edges of small-scale targets in remote sensing images, traditional semantic segmentation models perform poorly. To address this issue, we propose a multi-scale feature enhancement network (MFENet) to improve the segmentation accuracy of small-scale objects in HRRSIs. …”
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Automated classification of chondroid tumor using 3D U-Net and radiomics with deep features by Tuan Le Dinh, Seungeun Lee, Hyemin Park, Sungwon Lee, Hyeondeok Choi, Keum San Chun, Joon-Yong Jung

“…From these ROIs, we extracted a set of radiomics features and deep learning-derived features. After feature selection, we identified 15 radiomics and 15 deep features to build classification models. …”
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Features of immune homeostasis disorders and their role in the pathogenesis of the development of adrenaline damage to the myocardium and experimental periodontitis by R. Ilyk, M. Regeda, Y. Sushinsky

“…Statistical analysis of digital results was performed by the Student method. Results. The results of the studies showed that under the conditions of APM development, there was a decrease in the level of T-lymphocytes and an increase in B-lymphocytes with a predominance on the 1st day of the experiment compared to the control, and under the conditions of comorbid pathology (APM and EP), suppression of cellular immunity was observed against the background of stimulation of humoral immunity with dominance on the 3rd and 7th days, which indicated significant violations of immune homeostasis indicators and their important role in the pathogenesis of the development of the indicated experimental disease models. …”
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Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning by Rubaiya Hafiz, Mohammad Reduanul Haque, Aniruddha Rakshit, Mohammad Shorif Uddin

“…After removing backgrounds and segment out only the region of interest from the image a deep CNN-based transfer learning model is employed for the drink classification. Finally, the size of each drink bottle is estimated using the bag-of-feature (BoF) and distance ratio calculation to find the nutrition value from the nutrition fact table. …”
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Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches by Davronbek Malikov, Pilsu Jung, Jaeho Kim

“…To address these challenges, this study adopts machine learning (ML) techniques that model player salaries based on a combination of performance metrics and contextual features. …”
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Computational Investigation of the Performance Enhancement of SR3 Propeller With Swirl Recovery Vanes by Santhi Raviselvam, Vasanthakumar Parthasarathy

“…Integrating SRVs into a propeller-only model has provided a 3.06% enhancement in propeller propulsive efficiency.…”
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Research on multi dimensional feature extraction and recognition of industrial and mining solid waste images based on mask R-CNN and graph convolutional networks by Shuqin Wang, Na Cheng, Yan Hu

“…Its overall performance was superior to other mainstream models. …”
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Prediction of Diabetes in Middle-Aged Adults: A Machine Learning Approach by Gideon Addo, Bismark Amponsah Yeboah, Michael Obuobi, Raphael Doh-Nani, Seidu Mohammed, David Kojo Amakye

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Improved IEC performance via emotional stimuli-aware captioning by Zibo Zhou, Zhengjun Zhai, Xin Gao, Jiaqi Zhu

“…This gap limits corresponding semantic representations and hinders the resulting model performance. In this study, we draw inspiration from psychological findings and advances in natural language processing. …”
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Performer-KAN-Based Failure Prediction for IGBT with BO-CEEMDAN by Yue Xiao, Fanrong Wang

“…In this paper, Bayesian-optimized CEEMDAN is adopted to extract fault features efficiently, and a prognostic model named Performer-KAN is proposed for IGBT failure prediction. …”
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Evaluation of early student performance prediction given concept drift by Benedikt Sonnleitner, Tom Madou, Matthias Deceuninck, Filotas Theodosiou, Yves R. Sagaert

“…We demonstrate that (i) LASSO, a shrinkage estimator that reduces complexity and overfitting, outperforms several machine learning models under these circumstances, (ii) a linear regression relying on only two handcrafted features achieves higher accuracy and substantially less predictive bias than commonly used, more complex models with large feature sets. …”
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