Research on the Effect of Online Marketing Based on Multimodel Fusion and Artificial Intelligence in the Context of Big Data

Traditional online marketing methods use a single model to predict the advertising conversion rate, but the prediction results are not accurate, and users are not satisfied with the recommendation results. Therefore, this paper proposes an online marketing method based on multimodel fusion and artificial intelligence algorithms under the background of big data. First, it introduces big data technology and analyzes the characteristics of network advertising marketing model (RTB). Second, combined with multitask learning and fusion technology to improve the single model in advertising conversion rate prediction effect, prediction results to further improve the accuracy of results. Then, tF-IDF technology in artificial intelligence algorithm is used to measure the importance of advertising words in online marketing and calculate the contribution degree. Finally, according to XGBoost technology, the multitask fusion model of online marketing effect is classified. Experiments are used to analyze the effect of online marketing. Experimental results show that the proposed method can improve the accuracy of advertising conversion rate prediction and online sales of goods.

Influencia de la intervención humana en procesos modernos de manufactura: un enfoque de simulación de procesos centrado en el factor humano

El presente proyecto se basa en la modelación de la relación hombre-máquina centrado en el factor humano y en los errores que no necesariamente llevan a fatalidades, pero si a pérdidas de productividad. Este caso de estudio se basa en un proceso automatizado típico en una empresa metalmecánica. Error del set-ut, especificación y medidas tienen una probabilidad de ocurrencia de 0.1, 0.05 y 0.2 respectivamente. Dependiendo de la etapa en el proceso así será el efecto del error humano ya sea en el tiempo de terminación o en la cantidad de unidades producidas buenas. La simulación de proceso es usada en la evaluación del impacto del error humano en los diferentes puntos del proceso. La técnica Technique for Human Error Rate Prediction (THERP) es usada para la estimación del error humano. Los programas de entrenamiento y estandarización de procesos son herramientas claves para la prevención de estos errores.

Prediction of College Students’ Employment Rate Based on Gray System

College students’ employment is affected by many factors such as economy and policy, which makes the prediction error of college students’ employment rate large. In order to solve this problem, a prediction method of college students’ employment rate based on the gray system is designed. Firstly, it analyzes the current research status of college students’ employment rate prediction, finds out the causes of errors, then collects the historical data of college students’ employment rate, fits the change characteristics of college students’ employment rate through the gray system, and establishes the prediction model of college students’ employment rate. Finally, the simulation test is realized by using the employment rate data of college students. The results show that the gray system can reflect the change characteristics of college students’ employment rate and obtain high-precision college students’ employment rate prediction results. The prediction error is less than that of other college students’ employment rate prediction methods. We achieved an average accuracy of 95.22% as compared to 92.3% and 87.7% of other proposed systems. The prediction results can provide some reference information for the university employment management department.

Evaluation of human factor engineering influence in nuclear safety using probabilistic safety assessment techniques

This paper provides the empirical basis to support predictions of the Human Factor Engineering (HFE) influences in Human Reliability Analysis (HRA). A few methods were analyzed to identify HFE concepts in approaches of Performance Shaping Factors (PSFs): Technique for Human Error Rate Prediction (THERP), Human Cognitive Reliability (HCR) and Cognitive Reliability and Error Analysis Method (CREAM), Success Likelihood Index Method (SLIM) Plant Analysis Risk – Human Reliability Analysis (SPAR-H), A Technique for Human Error Rate Prediction (ATHEANA) and Man-Machine-Organization System Analysis (MMOSA). Also, in order to identify other necessary PSFs in HFE, an additional investigation process of human performance (HPIP) in event occurrences was used. Thus, the human error probability could be reduced and its evaluating can give out the information for error detection and recovery. The HFE analysis model developed using BHEP values (maximum and pessimistic) is based on the simplifying assumption that all specific circumstances of HFE characteristics are equal in importance and have the same value of influence on human performance. This model is incorporated into the PSA through the HRA methodology. Finally, a clarification of the relationships between task analysis and the HFE is performed, ie between potential human errors and design requirements.