Simple Bayesian Algorithm Research Articles

Research on College Students’ Career Planning and Employment Guidance Strategies Assisted by Artificial Intelligence

Abstract The number of college graduates is increasing year by year, and college students’ career planning and employment have always been a high concern of the community. The article describes the specific role of artificial intelligence technology in assisting college students’ career planning and employment guidance and combines WEB technology to construct a college student’s career planning and employment guidance service platform. The information entropy is used to construct the feature importance set of student employment data, and the simple Bayesian algorithm is used to realize the construction of the classification model of student employment service data in combination with the feature importance. On the basis of considering the selection of students’ employment information, the time decay factor is introduced to improve the personalized recommendation algorithm, and the cosine similarity between students’ employment positions is calculated so as to realize the generation of students’ employment service information recommendation. The area of the classification ROC curve of the plain Bayesian model is more than 95%, the classification time for student employment service data is only 5.62ms, and the F1 value of the employment service information recommendation result is 6.03% higher than that of the traditional method. The college students’ career planning and employment guidance service platform was only satisfactory for 31.77% of the students, with the highest satisfaction score of 3.69 for getting information. Relying on artificial intelligence technology can help students make personalized recommendations for employment service information, which can improve the level of career planning and guidance for college students.

Research on the Construction and Application of Dynamic Early Warning Mechanism of Student Civics in Colleges and Universities

Abstract Aiming at the problems of lagging information feedback, imperfect early warning and intervention mechanisms, and weak risk control ability that exist in the current work of ideological education in colleges and universities, this paper establishes a combination of a simple Bayesian algorithm of ideological dynamic early warning mechanism. This study takes school A as an example, collects a large volume of log data features based on user behavior, and after preprocessing, fuses the decision tree algorithm and the plain Bayesian algorithm and integrates them into the early warning classifier to establish a dynamic early warning model for civic education, and carries out experimental simulation and practical application to test its performance and early warning results. The results show that the prediction error indexes of this paper’s algorithm are no more than 0.1, and the abnormal rate of Civics behavior in each dimension is basically within 10%, which shows high adaptability. This study is beneficial for universities to carry out student civic education work and help civic workers better guide students and serve them.

Extension of emission expectation maximization lookalike algorithms to Bayesian algorithms

We recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization (ML-EM) algorithm. In this study, we extend these algorithms to Bayesian algorithms. The family of emission-EM-lookalike algorithms utilizes a multiplicative update scheme. The extension of these algorithms to Bayesian algorithms is achieved by introducing a new simple factor, which contains the Bayesian information. One of the extended algorithms can be applied to emission tomography and another to transmission tomography. Computer simulations are performed and compared with the corresponding un-extended algorithms. The total-variation norm is employed as the Bayesian constraint in the computer simulations. The newly developed algorithms demonstrate a stable performance. A simple Bayesian algorithm can be derived for any noise variance function. The proposed algorithms have properties such as multiplicative updating, non-negativity, faster convergence rates for bright objects, and ease of implementation. Our algorithms are inspired by Green’s one-step-late algorithm. If written in additive-update form, Green’s algorithm has a step size determined by the future image value, which is an undesirable feature that our algorithms do not have.

Computer-aided interactive soil suitability classification - a simple Bayesian approach.

A simple Bayesian algorithm is used to calculate for each soil suitability class the probability that a soil individual belongs to it. This is done by comparing the values of the assessment factors to an existing set of already classified individuals using an interactive computer program. (Abstract retrieved from CAB Abstracts by CABI’s permission)