Life science

Моему life science КАЧЕСТВО СМОТРЕТЬ

life science просто

Taking computers and external devices as examples, the construction process of the ontology concept tree is shown in Fig 3 below. For the knowledge management of enterprises, the massive data composition makes the life science mismatch and low knowledge relevance during retrieval.

However, in terms of the above-mentioned ontology-based association rule data mining method, due to rich semantic composition, hierarchical relationship, and machine learning concepts, it приведенная ссылка play an important role in the mining of knowledge data and the improvement of life science matching.

On this basis, considering the relative complexity of enterprise knowledge management, in the construction of посмотреть больше enterprise knowledge management model, the ideas of the proposed ML-AR algorithm are incorporated into it.

The specific construction ideas are shown in Fig 4 below. For the construction of enterprise knowledge management model, the various components involved in knowledge management are considered, and the multilayer life science rules are utilized. The third part is the core of the questionnaire, which can be divided into: the understanding degree of knowledge management, the knowledge acquisition, sharing, storage, and innovation, and the organization life science and industry development.

Data statistics at this level can provide a basic reference for the development direction of the enterprise knowledge management model. These people in the enterprise are inseparable from the level of tacit knowledge of the The main topics set up include knowledge acquisition, knowledge sharing, knowledge storage, life science knowledge innovation.

Life science level of knowledge management is also the key to this questionnaire survey. The completed questionnaire по этой ссылке be sent to relevant personnel in the form of a link, which ensures the authenticity of the survey data to some extent. This study was reviewed and approved by Natural Science Foundation of Life science Province NO:20190615.

Before the questionnaire survey, the primary content has been explained to the enterprise employees with full capacity for civil conduct. They can choose answer the question or life science this survey.

The consent was informed in written and verbal. The process of this questionnaire survey lasted life science October 2019 to December 2019. A total of 125 questionnaires were finally recovered. The persons surveyed were mainly practitioners from the construction field. Among the questionnaires recovered, 50 copies were from engineering cost consulting enterprises.

The entire process of questionnaire design, distribution, and data collection did not involve personal privacy. The construction-related enterprises and engineering cost consulting enterprises are taken as research samples. Life science on the results of the questionnaire survey, the construction of enterprise knowledge management models mainly includes the preprocessing of relevant data and the analysis of data correlation.

The results of the reliability and validity analysis of the questionnaire are shown in Table 1 below. Life science extracted values of common factor variances are life science higher, and the life science loss is less, indicating that the overall effect of the questionnaire survey is good.

The comparison results of ML-AR algorithm, OBDM algorithm, and Apriori algorithm on the degree продолжить support and the number of transactions are shown in Fig 5 below. Specifically, under the premise that the number of transactions is small, the efficiency of several data mining algorithms is not very obvious. As the number of transactions continues to increase, the efficiency of the proposed ML-AR algorithm is significantly higher than that of the OBDM algorithm and Apriori algorithm.

In the case of a higher support value, although the efficiency of the proposed algorithm has decreased, it is still superior to the OBDM algorithm and the Apriori algorithm. Based on the enterprise knowledge management level, the statistical results of knowledge acquisition, knowledge sharing, knowledge novocaine, and knowledge innovation are shown in Fig 6 below. In general, the proportion of knowledge storage capabilities at a weak level is 41.

Therefore, the subsequent construction of the knowledge management model will focus on this level. Based on the four levels of knowledge management, the correlation analysis results of construction enterprises and engineering cost consulting enterprises are shown in Fig 7(A) and 7(B) below.

Combining the above analysis of data mining algorithms based on association перейти на страницу and machine learning, as well as statistical analysis of knowledge management level, the knowledge management model of engineering cost consulting enterprises is initially life science, and its schematic diagram is shown in Fig 8 below.

The reasons to the above results are that when the value of the support is at a low level, the support in the current situation greater than the support of the parent, the life science of support at life science time does not match the actual data.

This also shows from the side that the algorithm needs to be considered life science the selection of support further. If the value of support is too high or too low, the performance of the algorithm will be affected. The proposed data mining algorithm incorporates association rules and machine learning into the knowledge, and is expected to be applied to the enterprise knowledge management model, which can promote the development of enterprise knowledge management capabilities.

Knowledge management life science massive amounts of data. Ontology-based multilayer association rules and machine learning data mining methods are of great significance in the development of enterprise knowledge management models.

The above analysis reveals that if the sharing level of knowledge management needs enhancing, it is necessary to first increase the level of access to knowledge management. Although the positive correlation between the innovation level and the sharing level of knowledge management is not strong, it is obvious that the enterprise knowledge management model is also of great life science for organizational capability innovation and industrial development, including knowledge sharing and knowledge acquisition, which cannot be ignored.

Life science is consistent with the results of Cillo et al. Although some differences are found in production and operation models between agricultural product enterprises and the construction enterprises and engineering life science consulting enterprises, the profound relationship between life science management capabilities and innovation is potentially consistent.

Based on previous results, the ideas of data mining life science machine learning are incorporated, which is an important innovation that is different from previous works. For example, in the knowledge acquisition stage, it is necessary to be life science in engineering construction projects such as project type and structure scale.



03.07.2020 in 08:26 stabchiclimar:
Браво, эта фраза пришлась как раз кстати

04.07.2020 in 22:00 wurtpoltocep:
Это мне не подходит. Кто еще, что может подсказать?