Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm

Li, Zhichao; Li, Yilin; Lu, Wanchun; Huang, Jilin

doi:10.1109/access.2020.2995063

Cited by 57 publications

(14 citation statements)

References 45 publications

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“…An improved DBSCAN algorithm is implemented at visible light communication systems to improve the signal-to-noise ratio and weaken the damage of noise to the communication quality [24]. A few more studies utilizing the DBSCAN algorithm in different fields of research can be found in very recent works [25][26][27][28]. On the other hand, importantly, the recent article [29] searches a way to determine the correct values of the DBSCAN parameters, by detecting the sharp increase in distance.…”

Section: Methodsmentioning

confidence: 99%

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

HABİBOĞLU,

HERNANDEZ,

UYAVER

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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Tyrosine, tryptophan, and phenylalanine are important aromatic amino acids for human health. If they are not properly metabolized, severe rare mental or metabolic diseases can emerge, many of which are not researched enough due to economic priorities. In our previous simulations, all three of these amino acids are discovered to be self-organizing and to have complex aggregations at different temperatures. Two of these essential stable formations are observed during our simulations: tubular-like and spherical-like structures. In this study, we develop and implement a clustering analyzing algorithm using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to measure the shapes of the formed structures by the self-assembly processes of these amino acids. We present the results in quantitative and qualitative ways. To the best of our knowledge, the geometric shapes of the formed structures by the self-assembly processes of these amino acids are not measured quantitatively in the literature. Analytical measurements and comparisons of these aggregations might help us to identify the self-aggregations quickly at early stages in our simulations and hence provide us with more opportunity to experiment with different parameters of the molecular simulations (like temperature, mixture rates, density etc.). We first implement DBSCAN method to identify the main self-aggregation cluster and then we develop and implement two algorithms to measure the shapes of the formed structures by the self-assembly processes of these amino acids. The measurements, which are completely in line with our simulation results, are presented in quantitative and qualitative ways.

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Section: Methodsmentioning

confidence: 99%

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

HABİBOĞLU,

HERNANDEZ,

UYAVER

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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“…In addition, employing regression methods, Li, Li [67] and Hao, Guo [65] examined the impacts of factors including workers' average distance from the given task point, distance between locations of tasks, workers' credit and reputation, workers' density within the given area, and tasks' density within the given area.…”

Section: Requester Pricingmentioning

confidence: 99%

A Systematic Literature Review on Pricing Strategies in the Sharing Economy

Yang

Xia

2021

Sustainability

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As an emerging business model, the sharing economy has gained a large amount of academic attention; the pricing problem in the sharing economy has also been widely investigated. Aiming to capture the current state-of-the-art research on pricing strategies in the sharing economy and foreseeing directions for future research, this article conducts a systematic literature review and content analysis of 158 articles from the Scopus and Web of Science databases. As a result, first, this review proposes an extended definition covering B2C and C2C models and a model structure covering the entire supply chain, based on which 158 articles are categorized into nine sub-models covering 30 scenarios. Second, the general characteristics (i.e., research fields, time and journal distributions, research themes and scenarios) and technical details (i.e., theories, methodologies, approaches, models, and conclusions) of the 158 articles are reviewed and summarized by the pricing party, business mode, and scenario. Finally, this review proposes some future research directions of existing scenarios from the perspectives of information asymmetry, market competition, and empirical approaches, and discusses some extensions, including uninvestigated scenarios and COVID-19-related topics; correspondingly, this review suggests some analytical models and empirical approaches that can be employed to fill these gaps. The proposed research directions and corresponding approaches can be references for future research.

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“…e methods used in this pricing model mainly include cluster analysis, multiple regression, bivariate models, and Bayesian models. Literature [18] uses a density-based spatial clustering algorithm to cluster the density areas of the tasks in the task price dataset to optimize the pricing strategy. Literature [19] uses the same clustering method as literature [18] and introduces a proportional sharing mechanism to establish a sensing pricing optimization model that can evaluate task success rates in advance.…”

Section: Related Workmentioning

confidence: 99%

Task Price Prediction Based on Clustering and DNN in Crowdsensing

Jia

Luo

Tao

et al. 2021

Security and Communication Networks

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With the popularization of mobile devices and the development of wireless networks, crowdsensing is devoted to providing universal Internet of Things services. A reasonable task pricing mechanism can not only motivate more users to participate in the sensing task but also help the benign development of crowdsensing platform, so it has gradually become a research hotspot in the field of crowdsensing. Aiming at the common problems of insufficient analysis of task pricing rules and large deviations of pricing prediction models, a task price prediction method based on clustering and DNN is proposed. Using the real historical trade price set as the data source, natural grouping and taxonomic description of task price are realized by exploring sensing task pricing law with complex constraint relation using two-step clustering analysis. On the basis of the above, the price interval prediction model based on DNN is implemented. The experimental results show that the predicting accuracy of the pricing mechanism is higher than 82.7%.

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Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm

Cited by 57 publications

References 45 publications

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

A Systematic Literature Review on Pricing Strategies in the Sharing Economy

Task Price Prediction Based on Clustering and DNN in Crowdsensing

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