English abbreviations related to language teaching: problems of typology and translation into Ukrainian by N. Kovalchuk

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On solving problems of operational forecasting of main pipeline weld joint quality by A. E. Filyakov, M. A. Sholokhov

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Trichology features of alopecia in reproductive age women with polycystic ovary syndrome by L. V. Belenkaya, O. V. Ivanov, O. V. Panarina, M. A. Rashidova, L. F. Sholokhov

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Legal problems of ensuring equal conditions for realization the constitutional right to education by A. Matnenko

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Features of fault transient's in generator network of powerful electric power stations by E. P. Safonov, V. Ya. Frolov

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Problems in the formation and implementation of the methodology for investigating homicides related to domestic violence by R. L. Stepaniuk, V. O. Husieva, V. V. Kikinchuk

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Features of structural and technological solutions for receiving system of small radio telescopes by E. N. Gluschechenko, A. M. Pylypenko, E. V. Slonin, I. K. Sunduchkov, V. V. Chmil

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Features of application of immersive technologies of virtual and augmented reality in higher education by Anna Alexandrovna Paskova

“…The aim of the research is to study the features of the application of immersive learning programs using AR/VR technologies in higher education.The methodological basis of the study is the analysis of Internet resources and literary sources, the study and generalization of pedagogical experience, synthesis.The results of the research are: the main advantages of using AR and VR technologies have been considered, possible options for introducing immersive educational technologies into the educational process proposed, the problems of their integration into the educational process of higher educational institutions and ways to overcome these problems identified.Key conclusions: AR/VR technologies are a promising addition to the educational space due to their immersive nature; in higher education, the use of immersive technologies can increase student engagement in the learning process, help students understand abstract concepts, allow for more personalized learning approaches, and improve learning analytics; when introducing AR / VR into the educational process. …”
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FEATURES OF OXIDATIVE STRESS IN VARIOUS PATHOLOGICAL CONDITIONS IN MEN OF REPRODUCTIVE AGE by N. A. Kurashova

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FEATURES OF FORMATION AND EXECUTION OF THE FEDERAL BUDGET IN THE MILITARY-INDUSTRIAL COMPLEX OF RUSSIA by Anzhela Z. Namitulina, Olga V. Kalashnikova

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FEATURES OF PRELIMINARY AND PERIODIC MEDICAL EXAMINATIONS OF SHIFT PERSONNEL IN THE ARCTIC SHELF by N. I. Dubinina, G. N. Degteva, Y. A. Korneyeva

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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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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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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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