Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF by Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI

“…Also, the BiLSTM model is constructed to capture long-distance and contextual semantic features. …”
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Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China by Ao Zhang, Xin-wen Zhao, Xing-yuezi Zhao, Xiao-zhan Zheng, Min Zeng, Xuan Huang, Pan Wu, Tuo Jiang, Shi-chang Wang, Jun He, Yi-yong Li

“…RF and XGB models were superior to LR and SVM models, indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.©2024 China Geology Editorial Office.…”
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Exploring the Causality of Accident Severity on Mountainous Freeways With a Two-Stage Approach by Lingzhi Kong, Changan Xiong, Wenchen Yang, Weiliang Zeng

“…Initially, a Gaussian mixture clustering algorithm is developed to categorize accident severity. …”
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Compensation of acetabular defects in primary and revision hip arthroplasty by Maria Yu. Udintseva, Elena A. Volokitina, Denis A. Kolotigin, Sergey M. Kutepov

“…Results Depending on the type of acetabular defects, an algorithm was developed for choosing a bone grafting method for acetabular defects and implanting the cup. …”

Development and validation of a pathological model predicting the efficacy of neoadjuvant therapy for breast cancer based on RCB scoring by Huan Li, Xianli Ju, Chuanfei Zeng, Zhengzhuo Chen, LinXin Yu, Ge Ke, Ziyin Huang, Youping Wang, Jingping Yuan, Mingkai Chen

“…Based on clinical and pathological characteristics along with the Residual Cancer Burden (RCB) score, we utilized a support vector machine (SVM) algorithm to construct a Pathomics Breast Cancer Signature (PBCS) prediction model. …”
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Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning by ZhanQiang Xie, YongLi Situ, Li Deng, Meng Liang, Hang Ding, Zhen Guo, QinYing Xu, Zhu Liang, Zheng Shao

“…This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating differential gene expression analysis, weighted gene co-expression network analysis, Mfuzz clustering, single-cell RNA sequencing, and machine learning algorithms including LASSO regression, SVM-RFE, and random forest, five hub genes related to AD, including PLCB1, NDUFAB1, KRAS, ATP2A2, and CALM3 were identified. …”
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Improved phase prediction of high-entropy alloys assisted by imbalance learning by Libin Zhang, Chang-Seok Oh, Yoon Suk Choi

“…Herein, machine learning and imbalance learning algorithms were combined together to improve the phase prediction of HEAs. …”
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Design and simulation of control strategy for medium and high speed zone of ship’s main propulsion motor by Yan Langtao, Liu Guangyin, Ren Yiran

“…Additionally, a Lyapunov function is constructed to demonstrate algorithm stability. The designed control strategy is simulated and verified using Matlab/Simulink software under various operating conditions. …”
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Adaptive Feedforward Vibration Control of Helicopter Cabin Floor Driven by Piezoelectric Stack Actuators: Modeling, Simulation and Experiments by Laishou Song, Yingquan Wang, Xiaoyu Shen

“…The model of PSA is integrated into the attached beam element based on the conditions of force equilibrium and displacement compatibility, and adaptive feedforward control is implemented by the filtered-x least mean square (Fx-LMS) algorithm. …”
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Automated Detection of Tailing Impoundments in Multi-Sensor High-Resolution Satellite Images Through Advanced Deep Learning Architectures by Lin Qin, Wenyue Song

“…While remote sensing enables large-scale monitoring, conventional methods relying on single-sensor data and traditional machine learning-based algorithm suffer from reduced accuracy in cluttered environments. …”
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Enhancing Relationship Link Prediction With Hierarchical Feature Enhancement by Zhouying Xu, Huiyue Wang

“…In this paper, we propose a novel methodology by regarding users’ attributes as node-level features, extracting them from tweet text using GPT-style models, and considering the structure of the constructed network as network-level features. The network-level features are obtained through a soft-partitioning overlapping community detection algorithm, namely BIGCLAM(Cluster Affiliation Model for Big Networks). …”
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Reliability Assessment via Combining Data from Similar Systems by Jianping Hao, Mochao Pei

“…To address this issue, integrating data from similar systems with test data can effectively expand data sources. …”
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Bearing remaining useful life prediction based on optimized VMD and BiLSTM-CBAM. by Wei Liu, Sen Liu

“…Subsequently, time-domain and frequency-domain features are extracted from the reconstructed data to construct a degradation feature set. Finally, the degradation feature set is input into a BiLSTM network integrated with the CBAM* for RUL prediction of bearings. …”
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Optimal Histogram Filter by A. V. Ausiannikau, V. M. Kozel

“…The histogram filter is an easy-to-implement tool that can be easily integrated into any open distribution law identification (recognition) algorithm.…”
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Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma by Beichen Cai, Beichen Cai, Beichen Cai, Qian Lin, Qian Lin, Qian Lin, Ruonan Ke, Ruonan Ke, Ruonan Ke, Jiaqi Yu, Jiaqi Yu, Jiaqi Yu, Lu Chen, Lu Chen, Xuejun Ni, Xuejun Ni, Hekun Liu, Xiuying Shan, Xiuying Shan, Biao Wang, Biao Wang, Biao Wang

“…Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. A KRAS-Macrophage Prognostic Associated Gene (KMPAG) signature was developed by integrating these findings, followed by validation using a least absolute shrinkage and selection operator (LASSO) regression model. …”
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Comprehensive Design and Experimental Validation of Tethered Fixed-Wing Unmanned Aerial Vehicles by Changjin Yan, Jinchuan Yang, Donghui Zhang, Shu Zhang, Taihua Zhang

“…Furthermore, the feasibility of the system was confirmed through the integration of UAV hardware with a constructed power supply system, incorporating open source flight control software. …”
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A novel stochastic power flow calculation and optimal control method for microgrid based on multivariate stochastic factors fusion – Sensitivity by HongTao Shi, Jiahao Zhu, Kun Feng, Zhuoheng He, Jiaming Chang, Tingting Chen

“…Furthermore, the fusion-sensitivity (F-sensitivity) of the power flow in the microgrid integrated into the distribution network is constructed to accurately characterize the influence degree of various stochastic factors in the microgrid on the power flow of the distribution network. …”
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EEG analysis of speaking and quiet states during different emotional music stimuli by Xianwei Lin, Xinyue Wu, Zefeng Wang, Zhengting Cai, Zihan Zhang, Guangdong Xie, Lianxin Hu, Laurent Peyrodie

“…In the construction of EEG classification models, the classification performance of deep neural network algorithms is superior to other machine learning algorithms.…”
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Teaching Additional Programming Languages on the Basis of Mapping in the System of Professional Development of Computer Science Teachers by Elisey Nigodin, Natalia Dobrovolskaia

“…On the basis of the matching mechanism and the block-modular principle of organizing the teaching material, the paper proposes a model of technology for teaching programming languages based on the matching approach, including normative-target, content, procedural and result modules. Typical algorithmic constructions corresponding to the main didactic lines of teaching programming to schoolchildren are singled out. …”
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Optimized window functions for spectral analysis based on digital filters by R.V. Petrosian

“…The proposed approach also shows promising potential for further development through the integration of machine learning techniques and heuristic algorithms, enabling automatic tuning of window parameters depending on analysis conditions.…”
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