a novel granular support vector machine based on mixed kernel function

The constaints of time and memory will reduce the learning performance of Support Vector Machine (SVM) when it is used to solve the large number of samples. In order to solve this problem, a novel algorithm called Granular Support Vector Machine based on Mixed Kernel Function (GSVM-MKF) is proposed. Firstly, the granular method is propsed and then the judgment and extraction methods of support vector particles are given. On the above basis, we propose a new granular support vector machine learning model. Secondly, in order to further improve the performance of the granular support vector machine learning model, a mixed kernel function which effectively uses the global kernel function having the good generalization ability and the local kernel function having good learning ability is proposed. Finally, the theoretical analysis and experimental results show the effectiveness of the method.The constaints of time and memory will reduce the learning performance of Support Vector Machine (SVM) when it is used to solve the large number of samples. In order to solve this problem, a novel algorithm called Granular Support Vector Machine based on Mixed Kernel Function (GSVM-MKF) is proposed. Firstly, the granular method is propsed and then the judgment and extraction methods of support vector particles are given. On the above basis, we propose a new granular support vector machine learning model. Secondly, in order to further improve the performance of the granular support vector machine learning model, a mixed kernel function which effectively uses the global kernel function having the good generalization ability and the local kernel function having good learning ability is proposed. Finally, the theoretical analysis and experimental results show the effectiveness of the method

When more is less : the other side of artificial intelligence recommendation

Based on consumers' preferences, AI (artificial intelligence) recommendation automatically filters information, which provokes scholars' debate. Supporters believe that by analyzing the consumers' preferences, AI recommendation enables consumers to choose products more quickly and with lower cost. Critics deem that consumers are more easily trapped in information cocoons because of the use of AI recommendation. This reduces the possibility of consumers contacting with a variety of commodities, thus lowering the consumer decision quality. Based on experiments, this paper discusses the moderating role of AI recommendation on the relationship of consumers' preferences and information cocoons. Moreover, it examines the relationship between information cocoons and consumer decision quality. The findings are: AI recommendation strengthens consumers' preferences; consumers' preferences are positively correlated with information cocoons and further leads to the decline of consumers’ decision quality. In the AI era, this paper contributes to revealing the dark sides of AI recommendation and provides empirical evidence for the regulation of AI behaviors.peerReviewe

Improvement of NH₃-SCR Performance by Exposing Different Active Components in a VCeMn/Ti Catalytic System

The physicochemical properties of active components play a key role in enhancing catalytic performance. In multi-component catalysts, different components offer a wide range of structural possibilities and catalytic potential. However, determining the role of specific components in enhancing efficiency may be blurry. This study synthetized a range of catalysts with various metal compositions on their external surfaces to investigate their catalytic activity on NH3-SCR. The V/CeMn/Ti catalysts exhibited exceptional catalytic efficiency and strong tolerance to SO2 during the SCR process. In the system, Mn and Ce facilitated electron transfer during the catalytic removal of NOx. As an assisting agent, increased the number of active species and acidic sites, playing a crucial role in oxidizing NO to NO2 and facilitating the denitrogenation reaction process at low temperatures. Further studies showed that the three ingredients exhibited unique adsorbent behaviors on the reacting gases, which provided different catalytic possibilities. This work modeled the particular catalysis of V and Ce (Mn) species, respectively, and offers experimental instruction for improving the activity and excellent tolerance to SO2 by controlling active ingredients