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The application of data mining technology to food services and the restaurant industry has certain social value. By predicting customer traffic and needs, a restaurant can prepare a reasonable amount of meals for customers according to predicted needs which is conducive to improving the dining experience of customers and also improving the quality of food preparation and making the restaurant itself operate more efficiently. In recent years, we have seen the use of collaborative robots for use in the fast food industry. In Asia and more specifically in Japan, we have seen many fast-food chains implement the use of robots to better serve their customers. By studying the linear regression algorithm and the random forest algorithm, this paper proposes a new interwoven novel fusion approach of combining both algorithms and applies the new model to restaurant data to assist in the prediction of customer traffic in the restaurant industry. This predictive algorithm using cognitive techniques can assist these newly place robots in the food industry better serve their client base and in doing so make the industry more efficient. Experimental, comparison, and analysis are reported in the paper. The error rate of the fusion solution is reduced by approximately 5.503% compared with the linear regression algorithm and is approximately 3.719% lower than the error rate of the random forest algorithm. Results show that the new fusion algorithm can achieve better prediction results of customer traffic prediction for the restaurant industry. Furthermore, we also provide a new take on the application of data mining technology in the restaurant industry itself. • The application of data mining technology to the restaurant industry has certain social value. • Propose a new interwoven novel fusion approach applied to restaurant data to assist in the prediction of customer traffic in the restaurant industry. • This predictive algorithm using cognitive techniques can assist these newly place robots in the food industry better serve their client base. [ABSTRACT FROM AUTHOR]

Aims: Child trafficking in South and Southeast Asia is widespread and deeply troubling. While several agencies have initiated anti-child-trafficking interventions, it is unknown whether they address the 21 social determinants of child trafficking identified by Perry and McEwing (2013). The aim of this integrative review was to explore the anti-child-trafficking strategies employed by governments and non-governmental organizations (NGOs) that target the social determinants of child trafficking in South and Southeast Asia, and identify which levels of the socio-ecological model (SEM) they address. Methods : The authors performed a literature search for journal articles and reports by researchers, local or national governments, and international organizations on the prevention of child trafficking in eight South Asian and eight Southeast Asian nations. Identified manuscripts were analyzed to determine which social determinants and socio-ecological levels were addressed by the programs described. Results : Sixteen journal articles and 31 reports from the gray literature were identified. The government and NGO anti-child-trafficking strategies in 16 South and Southeast Asian nations addressed 15 and 12 of the 21 social determinants, respectively. Social determinants at the intrapersonal, interpersonal, community, and policy levels of SEM were addressed, but failed to address the organizational level. Conclusions : The gravity of child trafficking necessitates that interventions address all of the 21 social determinants of child trafficking at all levels of the SEM. The authors found no account on any anti-child-trafficking intervention from four South Asian and five Southeast Asian nations. Dissemination and continuous evaluation of anti-trafficking interventions are warranted.