SHAP-Based Feature Selection for Enhanced Unsupervised Labeling by Mary Anne Walauskis, Taghi M. Khoshgoftaar

“…Lastly, unsupervised SHAP-based feature selection improves label quality when comparing feature subsets to the full-feature dataset while reducing computational complexity. …”
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Anomaly Detection Method for Hydropower Units Based on KSQDC-ADEAD Under Complex Operating Conditions by Tongqiang Yi, Xiaowu Zhao, Yongjie Shi, Xiangnan Jing, Wenyang Lei, Jiang Guo, Yang Meng, Zhengyu Zhang

“…The safe and stable operation of hydropower units, as core equipment in clean energy systems, is crucial for power system security. However, anomaly detection under complex operating conditions remains a technical challenge in this field. …”
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Analysis and Selection Method for Radar Echo Features in Challenging Scenarios by Yunlong Dong, Xiao Luo, Hao Ding, Ningbo Liu, Zheng Cao

“…These findings provide a reference for joint feature detection in complex scenarios. To optimize feature application, the Spearman correlation coefficient matrix was transformed into a generalized distance matrix, and spectral clustering was used to group features with strong correlations. …”
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Crashing Fault Residence Prediction Using a Hybrid Feature Selection Framework from Multi-Source Data by Xiao Liu, Xianmei Fang, Song Sun, Yangchun Gao, Dan Yang, Meng Yan

“…Maximal information coefficient analysis is then applied to rank features within each cluster and select the most relevant ones, forming an optimized feature subset. …”
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GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments by Yuhao Wang, Heran Zhu, Yirong Wang, Jianping Liu, Jun Xie, Bi Zhao, Siyue Zhao

“…However, existing detection methods face challenges such as varying target scales, large model parameters, and poor adaptability to complex backgrounds. …”
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Hydrodynamic Calculation and Analysis of a Complex-Shaped ROV Moving near the Wall Based on CFDs by Mengjie Jiang, Chaohe Chen, Zhijia Suo, Yingkai Dong

“…These findings provide valuable insights into calculating hydrodynamic coefficients and modeling the dynamics of ROVs with complex shapes operating near the wall.…”
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Development of a thrombin-antithrombin complex detection kit and study in venous thromboembolism complicated by cervical cancer by Yanru Qiu, Shuang Han, Yu Ji, Zhixian Lu, Xuan Huang

“…Thrombin-antithrombin complex (TAT), fibrinolytic enzyme-α2-fibrinolytic inhibitor complex (PIC), thrombomodulin (TM), and tissue-type plasminogen activator inhibitor 1 complex (t-PAIC) were detected using quantitative chemiluminescence immunoassay. …”
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A Quality Soft Sensing Method Designed for Complex Multi-process Manufacturing Procedures by Kaixiang PENG, Xin QIN, Jiahao WANG, Hui YANG

“…Four correlation-based feature selection algorithms, Pearson correlation coefficient (PCC), Spearman correlation coefficient (SCC), mutual information (MI), and minimum redundancy maximum relevance (mRMR), are used for feature selection, and the same number of features are selected for both experiments. …”
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Experimental Estimation of Two-Phase Flow Parameters in the Pneumatic System of a Sowing Complex by Airat M. Mukhametdinov, Salavat G. Mudarisov

“…Introduction. In modern sowing complexes, the process of transporting and distributing seeds and fertilizers is carried out using an air stream. …”
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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

“…The concept of explainable artificial intelligence was adopted by incorporating permutation feature importance ranking and Shapley Additive explanations values to identify the feature set that optimized a model's performance while reducing computational complexity. The proposed explainable artificial intelligence feature selection method was compared to conventional feature selection techniques, including mutual information, chi-square coefficient, maximal information coefficient, Fisher separation criterion and variance thresholding. …”
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Channel estimation method based on compressive sensing for FBMC/OQAM system by Weina YUAN, Qiu YAN

“…In mobile-to-mobile sensor networks,the channel estimation for FBMC/OQAM system can be investigated as a compressive sensing problem to raise frequency spectrum efficiency by exploiting the sparse nature of wireless channels.Firstly,a novel orthogonal matching pursuit algorithm with selection weak strategy and regularization based on Tanimoto coefficient (T-SWROMP) was proposed to improve the accuracy of LS channel estimation.Then,T-SWROMP methods with auxiliary pilot and coding were used to estimate channel frequency response for FBMC/OQAM system.The experimental results demonstrate the proposed method has lower complexity than the traditional SWOMP method.In addition,it achieve best performance among the traditional OMP,SWOMP and ROMP methods under dual-selective channels.…”
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Detection of Effective Devices in Information Dissemination on the Complex Social Internet of Things Networks Based on Device Centrality Measures by Wei Deng, Junqi Deng, Peyman Arebi

“…The Complex Social Internet of Things (CSIoT) integrates the connectivity of IoT with the relational dynamics of complex social networks, creating systems where devices autonomously form and manage relationships. …”
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Biotinylated fusion protein-streptavidin-peroxidase complex based non-competitive magnetic immunoassay for rapid detection of organophosphorus pesticides by Ruirui Shi, Jiangke Li, Wenli Yu, Tengfei Yin, Haiyan Lin, Juan Liu, Wenyu Cui

“…This study employed the SIC2 peptide, which recognized the immune complex of single-chain variable fragment 5 (scFv5) and OPs, to reveal its potential for detecting OPs residues. …”
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A Dynamic Kalman Filtering Method for Multi-Object Fruit Tracking and Counting in Complex Orchards by Yaning Zhai, Ling Zhang, Xin Hu, Fanghu Yang, Yang Huang

“…For fruit counting, the method attains a high coefficient-of-determination of 0.85 and a low root-mean-square error (RMSE) of 1.57, exhibiting high accuracy and stability of fruit detection, tracking and counting in video sequences under complex orchard environments.…”
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Mechanism–Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection by Wenhao Chen, Yong Zou, Zhengzhou Li, Shengrong Zhong, Haolin Gan, Aoran Li

“…These achievements highlight the importance of accurate sea clutter modeling and optimal training sample selection in improving target detection performance and ensuring the reliability of radar-based maritime surveillance.…”
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Exploring human mutuality in cyber and physical spaces using mobile big data and network analysis by Minglei Liao, Xintao Liu

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A data-driven hybrid framework for voltage transformer ratio error prediction: Addressing challenges in complex power systems by Jiuxi Cui, Zhenhua Li, Heping Lu, Feng Zhou, Haoyu Chen, Zhenxing Li

“…Then, the Maximum Information Coefficient (MIC) quantifies the nonlinear relationship between environmental factors and ratio errors, reducing redundant features. …”
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A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection by K. Anup Kumar, C. Vanmathi

“…When the condition of illness worsens, the chance of survival is reduced, and thus detection of skin cancer is extremely difficult. Hence, this paper introduces a new model, known as Parallel Convolutional Spiking Neural Network (PCSN-Net) for detecting skin cancer. …”
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Detection of hidden defects of thermal protection of buildings and determination of some thermal properties of structural building materials by Non-destructive Thermal Imaging meth... by D. F. Karpov, M. V. Pavlov, A. G. Gudkov

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Assessing the Role of EEG Biosignal Preprocessing to Enhance Multiscale Fuzzy Entropy in Alzheimer’s Disease Detection by Pasquale Arpaia, Maria Cacciapuoti, Andrea Cataldo, Sabatina Criscuolo, Egidio De Benedetto, Antonio Masciullo, Marisa Pesola, Raissa Schiavoni

“…Quantitative electroencephalography (QEEG) has emerged as a promising tool for detecting Alzheimer’s disease (AD). Among QEEG measures, Multiscale Fuzzy Entropy (MFE) shows great potential in identifying AD-related changes in EEG complexity. …”
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