Calculating the user-item similarity using Pearson's and cosine correlation

Dharaneeshwaran,; Nithya, S.; Srinivasan, A.; Senthilkumar, M.

doi:10.1109/icoei.2017.8300858

Cited by 17 publications

(3 citation statements)

References 15 publications

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“…Thus, the accuracy of the prediction model and results, simulation process and results are verified. The Pearson correlation coefficient ξ is used to verify the correlation between calculated data and experimental data, and the correlation between simulated data and experimental data, respectively 32 . The Pearson correlation coefficient ξ is able to measures the degree of correlation between two kinds of data.…”

Section: Discussionmentioning

confidence: 99%

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Orthodontic force/torque modeling and experiment of Kitchon root‐controlled auxiliary archwire

Yao,

Jiang,

Zhang

et al. 2023

Numer Methods Biomed Eng

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The incidence of oral malocclusion is increasing and is seriously damaging the oral health of human beings. The Kitchon root‐controlled auxiliary archwire is an individualized orthodontic arch. It is used clinically for the treatment of tooth‐lingual tilt/root‐lip tilt phenomenon of the central incisors. However, the bending parameters of the Kitchon root‐controlled auxiliary archwire used in different patients are based on the clinical experience of the dentists. Therefore, this orthodontic treatment has a high risk and unpredictability. In this paper, the loading performance and orthodontic process of Kitchon root‐controlled auxiliary archwire are analyzed. And the prediction model of support resistance and correction torque are established. The bending parameters of the Kitchon root‐controlled auxiliary archwire, as well as the effect of the bending parameters on the support resistance and the correction torque, are all quantified. And the prediction models for the support resistance and the correction torque are calculated separately. The correlation coefficients of calculated data and experimental data are ξT1 > .97 and ξA1 > .96, respectively; the correlation coefficients of simulated data and experimental data are ξT2 > .96 and ξA2 > .96, respectively. The accuracy and reliability of the established prediction models are verified. It provides an effective theoretical guide for dentists to safely and efficiently perform root‐controlled rotation orthodontic treatment on patients' central incisors.

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Section: Discussionmentioning

confidence: 99%

“…The Pearson correlation coefficient ξ is used to verify the correlation between calculated data and experimental data, and the correlation between simulated data and experimental data, respectively. 32 The Pearson correlation .014 inch .016 inch 04 mm 05 mm 14 mm 16 mm…”

Section: Correlation Analysis Of Calculated Data Simulation Data and ...mentioning

confidence: 99%

Orthodontic force/torque modeling and experiment of Kitchon root‐controlled auxiliary archwire

Yao,

Jiang,

Zhang

et al. 2023

Numer Methods Biomed Eng

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“…The Pearson correlation coefficient (PCC) mirrors the level of direct relationship between two factors, that is, the degree to which the factors are connected, and extends from + 1 to -1. 21 A connection of + 1 implies that there is a great positive direct relationship between factors or in different words two clients have fundamentally the same tastes, while a negative connection shows that the clients have divergent tastes. Pearson's connection coefficients are utilized to determine whether the level of the relationship is dynamic.…”

Section: User Request-similarity Modelmentioning

confidence: 99%

User preference–based QoS-aware service function placement in IoT-Edge cloud

Mutichiro

Kim

2021

International Journal of Distributed Sensor Networks

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In the Internet of Things-Edge cloud, service provision presents a challenge to operators to satisfy user service-level agreements while meeting service-specific quality-of-service requirements. This is because of inherent limitations in the Internet of Things-Edge in terms of resource infrastructure as well as the complexity of user requirements in terms of resource management in a heterogeneous environment like edge. An efficient solution to this problem is service orchestration and placement of service functions to meet user-specific requirements. This work aims to satisfy user quality of service through optimizing the user response time and cost by factoring in the workload variation on the edge infrastructure. We formulate the service function placement at the edge problem. We employ user service request patterns in terms of user preference and service selection probability to model service placement. Our framework proposal relies on mixed-integer linear programming and heuristic solutions. The main objective is to realize a reduced user response time at minimal overall cost while satisfying the user service requirements. For this, several parameters, and factors such as capacity, latency, workload, and cost constraints, are considered. The proposed solutions are evaluated based on different metrics and the obtained results show the gap between the heuristic user preference placement algorithm and the optimal solution to be minimal.

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