PHARMACEUTICAL DEVELOPMENT OF DRUGS FOR PEDIATRIC PRACTICE: FUNDAMENTAL BASES AND SPECIFIC FEATURES by I. A. Narkevich, O. D. Nemyatykh, I. I. Basakina, D. D. Siukaeva

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Analyzing Key Features of Open Source Software Survivability with Random Forest by Sohee Park, Gihwon Kwon

“…Open source software (OSS) projects rely on voluntary contributions, but their long-term survivability depends on sustained community engagement and effective problem-solving. Survivability, critical for maintaining project quality and trustworthiness, is closely linked to issue activity, as unresolved issues reflect a decline in maintenance capacity and problem-solving ability. …”
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Features of the clinical course of novel coronavirus infection COVID-19 in children by T. B. Bikmetov, I. V. Zorin, R. S. Yakupova

“…Background. The problem of the clinical course and complications of a novel coronavirus infection (COVID-19) in children is given special attention in pediatrics. …”
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Method of detecting IRC Botnet based on the multi-features of traffic flow by Jian-en YAN, Chun-yang YUAN, Hai-yan XU, Zhao-xin ZHANG

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Morphological features of the periodontal status and their changes under the influence of modern sealers by Editorial Article

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Technical features and current trends of hip arthroplasty in patients with ankylosing spondylitis by I. V. Polevoy, A. A. Roskidaylo, A. M. Lila, M. A. Makarov

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Clinical diagnostic features and surgical outcomes of mesial temporal lobe epilepsy by O. P. Subbotina, L. M. Tibekina, S. N. Tonyan

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Research on language recognition algorithm based on improved CFCC feature extraction by Hua LONG, Zhangheng HUANG, Yubin SHAO, Qingzhi DU, Shumeng SU

“…Aiming at the problem of low language recognition rate under low signal-to-noise ratio, a language recognition method based on fractional wavelet transform was proposed.Firstly, the adaptive filtering algorithm was used to filter the noise of the noisy signal, so as to reduce the influence of noise on the feature extraction and improve the processing ability of the system for non-stationary signals.Secondly, the motion of the signal on the basilar membrane of the cochlea was simulated, and then the signal was compressed by a nonlinear power function.Finally, the improved CFCC were extracted by simulating the human hearing process.Experiments show that compared with the traditional CFCC, the language recognition rate is significantly improved, and the language recognition rate is increased by 11.1% on average under the 0 dB signal-to-noise ratio, which verifies the effectiveness and robustness of the proposed algorithm.…”
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Fault Feature Extraction Method of Gearbox based on Parameter Optimization VMD by Ding Chengjun, Fu Xiaoyang, Feng Yubo, Zhang Liang

“…In order to solve the problem that the signal-to-noise ratio of the gearbox fault signal is low and fault feature extraction is difficult，a method for extracting gearbox fault feature based on parameters optimized variational mode decomposition is proposed. …”
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Maximum Mutual Information Feature Extraction Method Based on the Cloud Platform by Shasha Wei, Huijuan Lu, Wei Jin, Chao Li

“…With the large-scale application of gene chip,gene expression data with high dimension which exists a large number of irrelevant and redundant features may reduce classifier performance problem.A maximum mutual information feature extraction method based on cloud platforms was proposed.Hadoop cloud computing platform could be a parallel computing after gene expression data segmentation,features was extracted at the same time combined with the maximum mutual information method and the characteristics of cloud computing platform filter model was realized.Simulation experiments show that the maximum mutual information feature extraction method based on the cloud platform can rapid extraction of features in a higher classification accuracy which save a lot of time resources to make a highly efficient gene feature extraction system.…”
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Fault Diagnosis Method of Gear based on VMD and Multi-feature Fusion by Wang Jianguo, Chen Shuai, Zhang Chao

“…Aiming at the problem that working condition is complex in fact so that it is difficult to extract the gear fault feature frequency,a method of gear fault diagnosis based on variational mode decomposition（ VMD） and multi- feature fusion is proposed. …”
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ROLLING BEARING WEAK FAULT FEATURE EXTRACTION METHOD WITH ALIF⁃NLM by WANG Ying, SONG YuBo, ZHU DaPeng

“…Aiming at the problem that the early weak fault feature was difficult to extract of rolling bearing under the strong noise background,combined with the advantages of adaptive local iterative filter(ALIF)and non⁃local means(NLM)method,an ALIF⁃NLM bearing weak fault feature extraction method was proposed.Firstly,a weighted kurtosis⁃energy ratio criterion was constructed to filter the intrinsic mode function(IMF)components of the ALIF decomposition and reconstruct the signal.Secondly,the minimum energy entropy⁃kurtosis ratio index was constructed by combining the sensitivity of kurtosis to the impact signal with the evaluation performance of energy entropy to the uniformity and complexity of signal energy distribution,and using this index as the fitness function,the adaptive selection of parameter combinations in NLM method was realized by particle swarm optimization(PSO)algorithm.Finally,the fault feature of the reconstructed signal was extracted with the adaptive NLM.The simulation and experimental results show that this method can effectively extract the weak fault feature information of rolling bearing under the strong noise background.…”
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Features of emotional burnout during the pandemic of nurses with different professional experience by V. V. Kozyreva

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Joint QoS prediction for Web services based on deep fusion of features by Jianxun LIU, Linghang DING, Guosheng KANG, Buqing CAO, Yong XIAO

“…In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed with considering of the hidden environmental preference information in QoS and the common features of multi-class QoS.First, QoS data was modeled as a user-service bipartite graph and multi-component graph convolution neural network was used for feature extraction and mapping, and the weighted fusion method was used for the same dimensional mapping of multi-class of QoS features.Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features, and high-order interactive features of the mapped feature vector.Finally, the results of each part were combined to achieve the joint QoS prediction.The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE).…”
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Deep blur detection network with boundary-aware multi-scale features by Xiaoli Sun, Qiwei Wang, Xiujun Zhang, Chen Xu, Weiqiang Zhang

“…To solve this problem, we newly establish a boundary-aware multi-scale deep network in this paper. …”
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Image Classification of Imbalanced Wood Microscope by Integrating Multi-source Features by Zhikang Tian, Na Zhang, Jiwei Wang, Liwei Sha, Hongping Liu, Li Zou

“…This paper presents a multi-source feature fusion classification, which combines traditional local feature description with automatic feature extraction by deep learning. …”
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Straight Line Features Detection and Mosaic of Wind Power Blades Image by MA Baoyan, TANG Lei, ZHAO Jing, HE Yongjun

“…A splicing method based on line features is proposed to solve this problem. This method first detects straight lines in the images of the wind power blade, then selects the deduplication as the feature for image registration, and finally splices the blade image according to translation rotation matrix.Experimental results show that the proposed method shows strong robustness and stability in complex situations such as single structure, small coincidence degree and diverse backgroundstability.…”
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Formation Features of the Natural and Post-Vaccination Anti-Diphtheria Antitoxic Immunity by E. A. Shmeleva, T. N. Popova, A. V. Safronova

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Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification by Edgar Lara-Arellano, Andras Takacs, Saul Tovar-Arriaga, Juvenal Rodríguez-Reséndiz

“…This algorithm can efficiently explore ample feature space and identify the most relevant features for the task. …”
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Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification by Keming Mao, Renjie Tang, Xinqi Wang, Weiyi Zhang, Haoxiang Wu

“…This paper focuses on the problem of lung nodule image classification, which plays a key role in lung cancer early diagnosis. …”
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