Deep Learned Feature Technique for Human Action Recognition in the Military using Neural Network Classifier by Adeola O Kolawole, Martins E Irhebhude, Philip O Odion

“…There is the need to have a system that can easily recognize various human actions involved in obstacle crossing and also give a fair assessment of the whole process. In this paper, VGG16 model features with neural network classifier is used to recognize human actions in a military obstacle-crossing competition video sequence involving multiple participants performing different activities. …”
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Reconstructing missing data of damaged buildings from post-hurricane reconnaissance data using XGBoost by Hyunje Yang, Jun-Whan Lee, Steven Klepac, Armando Ulises Santos Cruz, Arthriya Subgranon, Junfeng Jiao

“…For each region, we analyzed the model’s performance depending on the missing structural features. …”
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Fast real-time detection and counting of thrips in greenhouses with multi-level feature attention and fusion by Zhangzhang He, Xinyue Chen, Ying Gao, Yu Zhang, Yuheng Guo, Tong Zhai, Xiaochen Wei, Huan Li, Haipeng Zhu, Yongkun Fu, Zhiliang Zhang, Zhiliang Zhang

“…Finally, we introduce the Adaptive Feature Mixer Feature Pyramid Network (AFM-FPN), where the Adaptive Feature Mixer (AFM) replaces the traditional element-wise addition at the P level, enhancing the model’s ability to select and retain thrips features, improving detection performance for extremely small objects. …”
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Time-Series Representation Feature Refinement with a Learnable Masking Augmentation Framework in Contrastive Learning by Junyeop Lee, Insung Ham, Yongmin Kim, Hanseok Ko

“…Time-series data pose challenges due to their temporal dependencies and feature-extraction complexities. To address these challenges, we introduce a masking-based reconstruction approach within a contrastive learning context, aiming to enhance the model’s ability to learn discriminative temporal features. …”
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Three Strategies Enhance the Bionic Coati Optimization Algorithm for Global Optimization and Feature Selection Problems by Qingzheng Cao, Shuqi Yuan, Yi Fang

“…With the advancement of industrial digitization, utilizing large datasets for model training to boost performance is a pivotal technical approach for industry progress. …”
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Deep Learning-Based Sign Language Recognition Using Efficient Multi-Feature Attention Mechanism by Esma Yenisari, Sirma Yavuz

“…Initially, general visual features are acquired through the EfficientNet model, leveraging the transfer learning paradigm. …”
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Innovative Approach to Detecting Autism Spectrum Disorder Using Explainable Features and Smart Web Application by Mohammad Abu Tareq Rony, Fatama Tuz Johora, Nisrean Thalji, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

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Attention-enhanced and integrated deep learning approach for fishing vessel classification based on multiple features by Xin Cheng, Jintao Wang, Xinjun Chen, Fan Zhang

“…Finally, the feature vector was fed into an ensemble model of a two-dimensional bidirectional long short-term memory network and a convolutional neural network with an attention mechanism for training, and the prediction results were obtained through a fully connected layer. …”
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Classification of Retinoblastoma Eye Disease on Digital Fundus Images Using Geometric Features and Machine Learning by Arif Setiawan

“…Therefore, this study aimed to propose a new method for classifying normal and retinoblastoma-affected retinas using geometric feature extraction and machine learning. The workflow consisted of (1) fundus image data collection for retinoblastomas, (2) image segmentation, (3) feature extraction process, (4) building a classification model using machine learning, (5) splitting testing and training data, (6) classification process using machine learning methods, and (7) evaluation of classification results using a confusion matrix. …”
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An Improved YOLOP Lane-Line Detection Utilizing Feature Shift Aggregation for Intelligent Agricultural Machinery by Cundeng Wang, Xiyuan Chen, Zhiyuan Jiao, Shuang Song, Zhen Ma

“…Multi-task learning (MTL) is increasingly employed to enhance the efficiency and performance of detection models in joint detection tasks, such as lane-line detection, pedestrian detection, and obstacle detection. …”
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Predicting drug-target interactions using machine learning with improved data balancing and feature engineering by Md. Alamin Talukder, Mohsin Kazi, Ammar Alazab

“…For the BindingDB-Kd dataset, the GAN+RFC model achieved remarkable performance metrics: accuracy of 97.46%, precision of 97.49%, sensitivity of 97.46%, specificity of 98.82%, F1-score of 97.46%, and ROC-AUC of 99.42%. …”
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Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis by Jianhua Sun, Ye Cao, Ying Zhou, Baoqiao Qi

“…The research aims to provide an efficient and reliable tool for clinicians to aid in the diagnosis of knee joint disorders, particularly focusing on Anterior Cruciate Ligament (ACL) tears.MethodsKneeXNet leverages the power of graph convolutional networks (GCNs) to capture the intricate spatial dependencies and hierarchical features in knee MRI scans. The proposed model consists of three main components: a graph construction module, graph convolutional layers, and a multi-scale feature fusion module. …”
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Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting by Mojtaba Khakpour Komarsofla, Kavian Khosravinia, Amirkianoosh Kiani

“…Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. …”
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Comprehensive Style Transfer for Facial Images Using Enhanced Feature Attribution in Generative Adversarial Nets by Yongseon Yoo, Seonggyu Kim, Jong-Min Lee

“…Our comprehensive evaluation, using distribution similarity metrics, classification-based assessments, and visual comparisons, demonstrates that our approach effectively captures and transfers complex style characteristics while preserving content integrity, outperforming state-of-the-art models. Specifically, our model achieves superior Fréchet Inception Distance (FID) scores (19.88 vs. 24.22) and recognition accuracy (0.966 vs. 0.941) compared to StarGAN v2, confirming the performance gains introduced by our enhanced feature attribution strategy.…”
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Individualized prediction of post-acute pancreatitis diabetes mellitus by combining lipid metabolism and anatomical features by Ling Ling Tang, Qi Zhang, Shuang Yi Song, Nian Liu, Qing Lin Du, Shu Ting Zhong, Xiao Hua Huang

“…Univariate and multivariate analyses showed B-P type in pancreaticobiliary junction (p = 0.017), the angle of junction (p = 0.041), non-high-density lipoprotein (p = 0.029), alcohol index (p < 0.001), body mass index (p = 0.042), inflammatory frequency (p = 0.016), fasting blood glucose (p = 0.002), concomitant hypertension (p < 0.001) were important predictive factors for the occurrence of PPDM. The model that integrated imaging features of the pancreaticobiliary junction has a higher predictive performance than models without imaging features, with an AUC of 0.882 (95% CI, 0.836–0.930). …”
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A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization. by Karim Rajaei, Seyed-Mahdi Khaligh-Razavi, Masoud Ghodrati, Reza Ebrahimpour, Mohammad Ebrahim Shiri Ahmad Abadi

“…To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. …”
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An effective heuristic for developing hybrid feature selection in high dimensional and low sample size datasets by Hyunseok Shin, Sejong Oh

“…Through the comparison of the benchmark dataset with existing methods, the proposed method reduced the average number of selected features from 37.8 to 5.5 and improved the performance of the prediction model, based on the selected features, from 0.855 to 0.927. …”
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Semi-supervised segmentation of cardiac chambers from LGE-CMR using feature consistency awareness by Hairui Wang, Helin Huang, Jing Wu, Nan Li, Kaihao Gu, Xiaomei Wu

“…The purpose of this manuscript was to develop a semi-supervised segmentation method to use unlabeled data to improve model performance. Methods This manuscript proposed a semi-supervised network that integrates triple-consistency constraints (data-level, task-level, and feature-level) for cardiac chambers segmentation from LGE-CMR. …”
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Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning by Bruno Matos Porto, Flavio Sanson Fogliatto

“…Feature engineering (FE) improved the performance of the ML algorithms. …”
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UAV-based multitier feature selection improves nitrogen content estimation in arid-region cotton by Fengxiu Li, Fengxiu Li, Chongqi Zhao, Chongqi Zhao, Yingjie Ma, Yingjie Ma, Ning Lv, Yanzhao Guo, Yanzhao Guo

“…Nevertheless, because high-dimensional remote-sensing data are inherently complex and redundant, accurately estimating cotton plant nitrogen concentration (PNC) from unmanned aerial vehicle (UAV) imagery remains problematic, which in turn constrains both model precision and transferability.MethodsAccordingly, this study introduces a hierarchical feature-selection scheme combining Elastic Net and Boruta–SHAP to eliminate redundant remote-sensing variables and evaluates six machine-learning algorithms to pinpoint the optimal method for estimating cotton nitrogen status.ResultsOur findings reveal that five critical features (Mean_B, Mean_R, NDRE_GOSAVI, NDVI, GRVI) markedly enhanced model performance. …”
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