A Beam Hardening Artifact Correction Method for CT Images Based on VGG Feature Extraction Networks by Hong Zhang, Zhaoguang Ma, Da Kang, Min Yang

“…These issues are particularly severe in high-density and irregularly shaped aerospace components, where accurate defect detection is critical. To mitigate beam hardening artifacts, this paper proposes a correction method based on the VGG16 feature extraction network. …”
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Enhanced Coronary Artery Disease Classification Through Feature Engineering and One-Dimensional Convolutional Neural Network by Atitaya Phoemsuk, Vahid Abolghasemi

“…Furthermore, we introduce the concept of feature engineering to improve the quality of the model training process and mitigate the challenge of ill-conditioned ECG data. …”
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Research on Blueberry Maturity Detection Based on Receptive Field Attention Convolution and Adaptive Spatial Feature Fusion by Bingqiang Huang, Zongyi Xie, Hanno Homann, Zhengshun Fei, Xinjian Xiang, Yongping Zheng, Guolong Zhang, Siqi Sun

“…Specifically, the C2F-RFAConv module is introduced to enhance spatial receptive field learning and a P2-level detection layer is introduced for small and distant targets and fused by a four-head adaptive spatial feature fusion detection head (Detect-FASFF). Additionally, the Focaler-CIoU loss is chosen to mitigate sample imbalance, accelerate convergence, and improve overall model performance. …”
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High-Precision Heterogeneous Satellite Image Manipulation Localization: Feature Point Rules and Semantic Similarity Measurement by Ruijie Wu, Wei Guo, Yi Liu, Chenhao Sun

“…The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. …”
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FEPA-Net: A Building Extraction Network Based on Fusing the Feature Extraction and Position Attention Module by Yuexin Liu, Yulin Duan, Xuya Zhang, Wen Zhang, Chang Wang

“…This substitution aims to broaden the receptive field, with the primary intention of enabling the output of each convolution layer to incorporate a broader spectrum of feature information. Additionally, a feature extraction module is added to mitigate the loss of detailed features. …”
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Detection of mare parturition through balanced multi-scale feature fusion based on improved Libra RCNN. by Buyu Wang, Weijun Duan, Jian Zhao, Dongyi Bai

“…This paper addresses the challenges of manual monitoring of parturition in large-scale equine facilities due to the unpredictability of mare parturition timing, proposing an algorithm for detecting mare parturition through a balanced multi-scale feature fusion based on an improved Libra RCNN. Initially, a ResNet101 backbone network incorporating the CBAM attention module was used to enhance parturition feature extraction capability; subsequently, a balanced content-aware feature reassembly feature pyramid, CARAFE-BFP, was employed to mitigate data imbalance effects while enhancing the quality of feature map upsampling; finally, the GRoIE module was utilized to merge CARAFE-BFP's multi-scale features, improving the model's perception of multi-scale objectives and minor feature changes. …”
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Power Line Segmentation Algorithm Based on Lightweight Network and Residue-like Cross-Layer Feature Fusion by Wenqiang Zhu, Huarong Ding, Gujing Han, Wei Wang, Minlong Li, Liang Qin

“…Finally, the Mish activation function is applied in deep convolutional layers to maintain feature extraction accuracy and mitigate overfitting. …”
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Feature selection using a multi-strategy improved parrot optimization algorithm in software defect prediction by Qi Fei, Guisheng Yin, Zhian Sun

“…Experiments conducted on eight benchmark test functions validate the superior performance of MEPO in terms of convergence speed and solution accuracy. Secondly, to mitigate the adverse impact of irrelevant features on model performance in traditional software defect prediction methods, this study introduces a binary multi-strategy enhanced Parrot Optimization algorithm (BMEPO) for optimizing feature selection. …”
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BBDetector: Intelligent border binary detection in IoT device firmware based on a multidimensional feature model. by Shudan Yue, Guimin Zhang, Qingbao Li, Wenbo Zhang, Xiaonan Li, Weihua Jiao

“…To mitigate these issues, we introduce BBDetector, a border binary detection method based on a multidimensional feature model. …”
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A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI by Rashiduzzaman Shakil, Sadia Islam, Bonna Akter

“…In addition, two data balancing techniques—Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling—were used to mitigate the data imbalance issues. Furthermore, Chi-square and Least Absolute Shrinkage and Selection Operator are two distinct feature selection processes that have been applied to evaluate the feature rank, which are mostly correlated to identify the particular disease, and also integrate an explainable artificial intelligence technique, namely Shapley Additive Explanations, for clarifying the model outcome. …”
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Novel model for medium to long term photovoltaic power prediction using interactive feature trend transformer by Xiang Liu, Qingyu Liu, Shuai Feng, Yangyang Ge, Haoran Chen, Chunling Chen

“…The IFTformer model proposed in this paper is an effective approach for medium- to long-term PV power prediction, can mitigate the impact of outliers, enhance the feature extraction ability, and improve the prediction accuracy, generalizability and robustness of medium- to long-term predictions, providing a novel perspective on medium- to long-term PV power prediction methods based on deep learning methods.…”
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Family Physicians’ Feedback on the Feature Design of a Digital Health Platform to Streamline the Care of Older Adults by Marjan Abbasi, Sheny Khera, Julia Dabravolskaj, Amira Aissiou, Reza Abbasi-Dezfouly

“…This study examined the interface design and features of a digital health platform, Carmi, designed to mitigate administrative inefficiencies and cognitive overload by asynchronous patient data gathering and automated report generation. …”
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Photovoltaic fault detection algorithm using ensemble learning enhanced with deep neural network feature engineering by Maryam Parvin, Hossein Yousefi, Behnam Mohammadi-Ivatloo

“…The fault classification was conducted in the second stage using the optimum EL algorithm enhanced with Deep Neural Network (DNN) feature extraction. The capability of the DNN feature extraction, presented for the first time in this study, in identifying more complex and meaningful data features for small-sized input training datasets significantly enhanced the overall classification accuracy. …”
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UTILIZING QUANTUM-ENHANCED DYNAMIC CLUSTERING FOR OPTIMIZED PATH SELECTION IN WBANS FEATURING OPTICAL BIOSENSORS TECHNOLOGY by Poonam Sharma, Kamaljit Singh Saini, Prabhjot Kaur

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Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph by Imam Dad, JianFeng He, Zulqarnain Baloch

“…This study advances computational pathology by unifying Graph Neural Networks (GNN) with interpretable feature engineering, offering a scalable, efficient solution for cancer subtype classification. …”
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Time-Frequency Feature Extraction Method for Weak Acoustic Signals from Drill Pipe of Seafloor Drill by Jingwei Xu, Buyan Wan, Weicai Quan, Yi Xi, Xianglin Tian

“…The acoustic signals of the drill pipe of a seafloor drill present weak features under noise interference such as marine environmental noise and the mechanical vibration of the seafloor drill. …”
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Associations between appetite loss and clinical features as well as inflammatory cytokines in adolescents with major depressive disorder by Lewei Liu, Lewei Liu, Xi Zhang, Jinyue Xue, Lili Zhao, Pei Tang, Yinghan Tian, Haojie Fan, Mingru Hao, Xin Zhao, Feng Geng, Daming Mo, Lei Xia, Lei Xia, Huanzhong Liu, Huanzhong Liu, Huanzhong Liu

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LCFANet: A Novel Lightweight Cross-Level Feature Aggregation Network for Small Agricultural Pest Detection by Shijian Huang, Yunong Tian, Yong Tan, Zize Liang

“…In agricultural pest detection, the small size of pests poses a critical hurdle to detection accuracy. To mitigate this concern, we propose a Lightweight Cross-Level Feature Aggregation Network (LCFANet), which comprises three key components: a deep feature extraction network, a deep feature fusion network, and a multi-scale object detection head. …”
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The nanogreen revolution: Transforming CO2 capture through sustainable nanotechnology by Francis A. Ibekwe, Humphrey S. Samuel, David A. Undie, Oluwakemi O. Akinpelu, Onimisi P. Onotu, Emmanuel E. Etim

“…We present a new classification of nanomaterials, including metal-organic frameworks (MOFs), nanoporous carbons, and 2-dimensional materials based on their structural features, synthesis approaches, and capture mechanisms. …”
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CANini: In-Depth Traffic Analysis for Design and Robustness Evaluation of DTree-Based IDS in Automotive Networking Systems by Nicasio Canino, Pierpaolo Dini, Stefano Mazzetti, Daniele Rossi, Sergio Saponara

“…We proceeded to test these features by designing a simple anomaly-based IDS that employs a multiclass Decision Tree classifier. …”
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