Day-ahead photovoltaic power forecasting with multi-source temporal-feature convolutional networks by Ziming Ouyang, Zhaohui Li, Xiangdong Chen

“…Based on this, redundant information is suppressed by a cascading channel compression approach, and a temporal segmentation strategy is applied to model fine-grained temporal features. We conducted experiments on two publicly available datasets, and the results demonstrate that the proposed data augmentation method effectively improves the forecasting performance of the deep learning model. …”
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Enhancing voice spoofing detection in noisy environments using frequency feature masking augmentation by Soyul Han, Jaejin Seo, Sunmook Choi, Taein Kang, Sanghyeok Chung, Seungeun Lee, Seoyoung Park, Seungsang Oh, Il-Youp Kwak

“…Our exploratory data analysis revealed that for a given speech sample, noisy signals tend to occur within similar frequency bands. If a model is heavily reliant on data within frequency ranges that contains noise, its performance will be suboptimal. …”
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Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques by Sahas V. Swamy, Bijay Mihir Kunar, Karra Ram Chandar, Mamdooh Alwetaishi, Shashikumar Krishnan, Sudhakar Reddy

“…Four supervised machine learning models viz., Multiple Linear Regression (MLR), k-Nearest Neighbor Regression (k-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR) were developed for UCS prediction, with hyperparameter optimization performed using RandomisedSearchCV technique. …”
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Identification of Gingival Inflammation Surface Image Features Using Intraoral Scanning and Deep Learning by Wei Li, Linlin Li, Wenchong Xu, Yuting Guo, Min Xu, Shengyuan Huang, Dong Dai, Chang Lu, Shuai Li, Jiang Lin

“…The performance of the model was evaluated by the Dice coefficient (Dice), intersection over union (IoU), and pixel accuracy (PA), and the correlation between the identification performance and the periodontal examination index was analysed. …”
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An improved Red-billed blue magpie feature selection algorithm for medical data processing. by Chenyi Zhu, Zhiyi Wang, Yinan Peng, Wenjun Xiao

“…In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. …”
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Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion by Junwei Ma, Meiru Huo, Jinfeng Han, Yunfeng Liu, Shunfa Lu, Xiaokun Yu

“…Due to the variability of different neural networks, the prediction results of the integrated model are often higher than the best‐performing individual model. …”
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A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles by Qiao Meng, Huansheng Song, Gang Li, Yu’an Zhang, Xiangqing Zhang

“…However, network performance usually degrades when small objects are detected. …”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…Compared to traditional DNNs, the proposed method offers a more efficient approach to feature extraction, resulting in a notable enhancement in model performance, albeit with a moderate increase in computational complexity. …”
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Ensemble Learning-Based Wine Quality Prediction Using Optimized Feature Selection and XGBoost by Sonam Tyagi, Ishwari Singh Rajput, Bhawnesh Kumar, Harendra Singh Negi

“…High-dimensional data from several sources may impede processing and classification model performance. Feature selection increases learning and reduces computational costs by picking subsets of features and deleting irrelevant ones. …”
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Neighborhood Information Aggregation and Multi-View Feature Extraction-Based Contrastive Graph Clustering by Liulong Yao, Jinrong Cui, Yazi Xie, Chengli Sun

“…While these strategies perform well on image data, they often tend to lead to semantic drift when used on graph-structured data, thus limiting the performance of the model. …”
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Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction by Ahmad Houkan, Ashwin Kumar Sahoo, Sarada Prasad Gochhayat, Prabodh Kumar Sahoo, Haipeng Liu, Syed Ghufran Khalid, Prince Jain

“…What sets this study apart from previous ones is its novel demonstration of how these techniques significantly reduce training time and model complexity while maintaining or even improving performance, confirming the effectiveness of strategic feature utilization in strengthening Industrial IoT security by balancing accuracy, speed, and model size.…”
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Comparing UNet configurations for anthropogenic geomorphic feature extraction from land surface parameters. by Sarah Farhadpour, Aaron E Maxwell

“…However, limitations such as small sample sizes and class imbalance in anthropogenic geomorphic feature extraction tasks have necessitated the exploration of advanced modifications to improve model performance. …”
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Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification by Ida A. P. A. Crisdayanti, Sung Woo Nam, Seong Kwan Jung, Seong-Eun Kim

“…<italic>Results:</italic> The proposed CNN model with knowledge propagation demonstrated superior performance compared to existing state-of-the-art models. …”
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Prediction of genetic status and grading in glioma based on fusion of macro⁃ and micro⁃imaging features by LI Zhen, SONG Peng⁃fei, ZHU Rui⁃ze, JIANG Shan, CAO Shi⁃wen, YU Jin⁃hua, SHI Zhi⁃feng

“…Objective To develop a dual⁃layer feature distillation multiple instance learning (DLFD⁃ MIL) model integrating MRI and whole slide image (WSI) features for precise prediction of IDH1 mutation, 1p/19q codeletion, and World Health Organization (WHO) grading in adult⁃type diffuse gliomas. …”
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Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2. by Praveen Talari, Bharathiraja N, Gaganpreet Kaur, Hani Alshahrani, Mana Saleh Al Reshan, Adel Sulaiman, Asadullah Shaikh

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Clustering OKU Timur Script Images using VGG Feature extraction and K-Means by Liu Toriko, Susan Dian Purnamasari, Yesi Novaria Kunang, Ilman Zuhri Yadi, Andri Andri

“…The dataset comprises 2,280 images, representing 19 characters and 228 variations with different diacritics. Features are extracted using the VGG16 model, which are then clustered with the K-Means algorithm. …”
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Towards Transparent Deep Learning in Medicine: Feature Contribution and Attention Mechanism-Based Explainability by Thanveer Shaik, Xiaohui Tao, Haoran Xie, Lin Li, Niall Higgins, Juan D. Velásquez

“…Attention weights and Shapley values were computed for each input feature to provide global and local explanations, offering insights into the models’ behavior and feature importance. …”
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IMPACT OF FEATURE SELECTION ON DECISION TREE AND RANDOM FOREST FOR CLASSIFYING STUDENT STUDY SUCCESS by Firdaus Amruzain Satiranandi Wibowo, Heri Retnawati, Muhammad Lintang Damar Sakti, Asma Khoirunnisa, Angella Ananta Batubara, Miftah Okta Berlian, Zulfa Safina Ibrahim, Jailani Jailani, Sumaryanto Sumaryanto, Lantip Diat Prasojo

“…This study investigates the influence of feature selection on the performance of machine learning models, particularly Decision Tree and Random Forest, in classifying student academic success. …”
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Hierarchical Feature Fusion and Enhanced Attention Mechanism for Robust GAN-Generated Image Detection by Weinan Zhang, Sanshuai Cui, Qi Zhang, Biwei Chen, Hui Zeng, Qi Zhong

“…Specifically, on the StarGAN dataset, the model attained outstanding performance, with accuracy (Acc) and average precision (AP) both reaching 100%. …”
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Heart Sound Classification Based on Multi-Scale Feature Fusion and Channel Attention Module by Mingzhe Li, Zhaoming He, Hao Wang

“…In this paper, we propose a heart sound classification model named CAFusionNet, which fuses features from different layers with varying resolution ratios and receptive field sizes. …”
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