Image-based anomaly detection in low-light industrial environments with feature enhancement by Dinh-Cuong Hoang, Phan Xuan Tan, Anh-Nhat Nguyen, Son-Anh Bui, Ta Huu Anh Duong, Tuan-Minh Huynh, Duc-Manh Nguyen, Viet-Anh Trinh, Quang-Huy Ha, Nguyen Dinh Bao Long, Duc-Thanh Tran, Xuan-Tung Dinh, Van-Hiep Duong, Tran Thi Thuy Trang

“…The ablation study further highlights the synergistic contributions of FFE and IFE, with their combined effect driving the model's superior performance across diverse and complex scenarios.…”
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Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor by Xian Wu, Xueyi Guo, Hang Peng, Bin Su, Sabbir Ahamod, Fenglin Han

“…In this article, we use local feature descriptors as a preprocessing method to extract features from point cloud data and propose a new neural network architecture aligned with these local features, effectively enhancing performance even in extreme cases of data corruption. …”
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Agricultural Cultivation Cost Prediction Using Neural Networks and Feature Importance Analysis by Salmania Putri, Tora Fahrudin, Asti Widayanti

“…Evaluation techniques such as MAE, MSE, and R² were used to assess the effectiveness of the model. The results showed that the prediction model almost achieved optimal performance, with the Cost of Cultivation C2 factor having the greatest influence in understanding the data and guiding improvements to the significant factors affecting cultivation cost prediction. …”
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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

“…The preprocessing pipeline was designed to address the complexities inherent in medical data, including strategic management of missing values and standardizing heterogeneous features. The model demonstrated an overall accuracy of 96.3%, with a sensitivity of 66.7% and a specificity of 97.6%. …”
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ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases by Sheng Yu, Li Xie, Liang Dai

“…The high accuracy and F1 scores, in conjunction with low loss values, further validate the model’s efficacy in learning discriminative features. …”
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Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation by Xianzhi Deng, Xiaolong Hu, Liangsheng Shi, Chenye Su, Jinmin Li, Shuai Du, Shenji Li

“…The best performance of our model in R2 was 0.86 and 0.81 for maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax), respectively. …”
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A Moving Object Detection Method Based on Conditional Information and Feature Deepening by Hongrui Zhang, Luxia Yang

“…To solve these issues, this paper proposes a moving object detection model that integrates conditional information and feature deepening techniques. …”
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Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data by Simone A. Ludwig

“…It involves selecting a subset of relevant features for use in model construction. Feature selection helps in improving model performance by reducing overfitting, enhancing generalization, and decreasing computational cost. …”
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Integrating spatiotemperporal features into fault prediction using a multi-dimensional method by Chun-Yi Lin, Yu-Chuan Tseng, Wu-Sung Yao

“…The study evaluates the model using metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic area under curve (ROC-AUC), and visualizes performance through confusion matrices, ROC curves, and precision-recall curves. …”
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Towards a minimum universal features set for IoT DDoS attack detection by Osama Ebrahem, Salah Dowaji, Suhel Alhammoud

“…The minimal number of features are chosen by using feature selection methods (ANOVA, Variance Threshold, Information Gain, Chi Square) which performed on the IoT-23 dataset. …”
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Federated and ensemble learning framework with optimized feature selection for heart disease detection by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

“…We used particle swarm optimization (PSO) for feature selection, which optimized the most relevant features in conjunction with voting and stacking approaches to further increase the model's performance. …”
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Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features by Kennette James Basco, Alana Singh, Daniel Nasef, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Model performance was evaluated on a reserved testing set using metrics such as accuracy, precision, recall, and F1-score. …”
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MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach by Miada Murad, Ameur Touir, Mohamed Maher Ben Ismail

“…The experiments demonstrated that associating the BiGAN encoder, for unsupervised feature extraction, with a depth-wise separable deep learning model enhances the classification performance. …”
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Siamese Network-Based Feature Transformation for Improved Automated Epileptic Seizure Detection by Tayebeh Iloon, Ramin Barati, Hamid Azad

“…This paper examines the Siamese network’s contribution as a learning-based feature transformation in improving seizure detection performance. …”
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Machine learning–based feature prediction of convergence zones in ocean front environments by Weishuai Xu, Lei Zhang, Hua Wang

“…Furthermore, among the input features, the turning depth emerged as a crucial determinant, contributing more than 25% to the model’s effectiveness in predicting the convergence zone’s distance. …”
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Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification by Tianlang Lan, Chengfei Jiang, Xiaofan Luo, Wentao An

“…The results show that MRSD polarization features significantly improve model performance, particularly distinguishing among sea ice categories. …”
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Aeroengine Remaining Life Prediction Using Feature Selection and Improved SE Blocks by Hairui Wang, Shijie Xu, Guifu Zhu, Ya Li

“…Considering that the RUL of equipment changes in a progressively more complex manner as the equipment is used over time, we propose an improved squeeze and excitation block (SSE) and combine it with a convolutional neural network (CNN). By enhancing the feature selection ability of CNN through segmented squeeze and excitation block, the model can focus on important information within features to effectively improve prediction performance. …”
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Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features by Gang Li, Chen Lin, Yupeng Li

“…The case study shows that the model proposed in this paper improves the interval prediction performance by at least 12.3% and reduces the deterministic prediction root mean square error (RMSE) by at least 19.4% relative to the benchmark model.…”
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Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining by Shenyu Chen, Meng Li, Weifeng Yang

“…In this paper, we propose a novel graph convolution network model, named multilevel feature fusion-based graph convolution network (MFF-GCN) which can employ multiple streams of GCNs to learn different level features of rumor data, respectively. …”
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An effective brain stroke diagnosis strategy based on feature extraction and hybrid classifier by Maha Samir Elsayed, Gehad Ahmed Saleh, Ahmed I. Saleh, Abeer Twakol Khalil

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