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1.
Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.  相似文献   
2.
Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.  相似文献   
3.
Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today.  相似文献   
4.
Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.  相似文献   
5.
The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.  相似文献   
6.
Abstract

The purpose of this study is to analyse the new processes of tourism growth and its conflicts from the perspective of social movements. First, the urban growth machine analysis model is applied by the systematisation of six projects. Second, the resistance movements against those projects and whether this resistance could be the start of local tourism degrowth policies are examined. The methodology is qualitative, based on documentary analysis, participatory observation, discussion groups and interviews. The case study is the destination of Costa del Sol-Málaga. The results enable the development of the urban growth machine model in tourist destinations. Meanwhile, social movements demystify the argument based on neoclassical economic progress. The social movements condemn the effects of large-scale top-down projects, and implement alternative bottom-up proposals. Although the social movements do not reject tourism, they call for greater control over its impact, denounce unlimited growth, overtourism and the loss of urban quality of life. These movements advocate a lifestyle linked to the everyday space, which they believe is threatened by excessive urban-tourism growth. They are a symptom of the need to devise a proposal using the principles of degrowth.  相似文献   
7.
This empirical study analyzes the relationship between the sentiments in online media with regard to travel destinations and corresponding tourist arrivals. We expect the media reports on political and economic instability and turmoil to enhance tourist arrival nowcasts and forecasts, as they can probably complement them with information on disruptions and shocks. Therefore, we believe this research will help to build better models for tourism demand nowcasting and forecasting. We use the sentiment in the German-speaking online media because the German-speaking region is the most populated in Europe and has the largest group of travelers visiting destinations in and around Europe.

An artificial neural network is used to analyze the mood of the media. The software classifies news items regarding potential tourist destinations with either positive or negative labels. The number of positive and negative news items is used to build sentiment indices for popular tourist destinations for Europeans.

Our results show strong correlations between the mood concerning tourist destinations and tourist arrivals in these countries. Indeed, disruptions and shocks prevalent in the news are reflected in similar ratios in both tourist arrivals and sentiment indices. These results can be used as a new explanatory variable for tourism demand modelling.  相似文献   
8.
针对大规模机器类通信中拥塞导致的时延敏感设备时延高和接入成功率低的问题,提出将小区中设备按时延要求分组,对不同组设备引入不同的退避模型,分析时延敏感设备的时延和吞吐量,按照不同组中设备的时延需求动态分配前导数目,同时通过调整接入类限制因子实现吞吐量的优化。仿真结果表明,在给定时延敏感设备的时延限制条件时,与统一退避的机制对比,所提分组机制的时延敏感设备能够满足时延要求,并且提高了接入效率。  相似文献   
9.
随着我国社会主义市场经济的迅速发展以及科技水平的不断提升,交通运输行业得到了蓬勃发展。当前,随着我国地下隧道等各项工程建设数量的不断上升,盾构机的重要性不言而喻。加强对盾构机自动控制技术的研究工作,不断解决该技术应用过程中存在的问题,才能有效地促进技术应用,为提升我国地下工程施工安全性能奠定技术支持。  相似文献   
10.
This paper evaluates ways of instilling project management skills into accounting-based learning by the use of an iterative A3 planner to plan, monitor and review assignment progress. The application of an A3 planner to facilitate a project-based learning (PBL) group assignment in undergraduate accounting education has been critically evaluated in terms of both the student and tutor experience. The study uses a mix of qualitative and quantitative data. Quantitative data assisting exploration of perceptions were collected through 100 undergraduate students. A series of focus group discussions were carried out to investigate students’ engagement and tutors’ teaching experiences regarding the adoption of the A3 planner. The results suggest that the A3 planner promotes active planning and effective management of a PBL group assignment. It makes students’ thought processes more visible thereby facilitating and enhancing the tutoring/mentoring process. Moreover a more interactive and transparent approach by doing assignment via the use of an iterative A3 planner has ensured more feedback points and action based efficiency in the doing approach for learners.  相似文献   
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