A Human-Centered Activity Tracking System: Toward a Healthier Workplace

Zhao, Wenbing; Lun, Roanna; Gordon, Connor; Fofana, Abou-Bakar M.; Espy, Deborah D.; Reinthal, M. Ann; Ekelman, Beth; Goodman, Glenn; Niederriter, Joan; Luo, Xiong

doi:10.1109/thms.2016.2611825

Cited by 70 publications

(31 citation statements)

References 22 publications

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“…At the beginning stage when the number of neighbors is within [10,30], the results of the algorithm proposed by this paper are close to those of PIP but slightly better than that of PIP in later period. The experimental results of NHSM are good when the number of neighbors is within [10,30] but worse in later period. The experimental results of PCC and Acosine are worse than other algorithms.…”

Section: Experiments 3: Experimental Analysis Of the Comparison With Omentioning

confidence: 58%

“…On Netflix data set, from Figure 10 we know that the algorithm proposed in this paper outperforms other algorithms gradually with the increase of neighbors. NHSM outperforms others when the number of neighbors is within [10,40] but performs not so well as the algorithm proposed by this paper later.…”

Section: Experiments 3: Experimental Analysis Of the Comparison With Omentioning

confidence: 70%

“…From the perspective of tourism recommendation, Wang et al 's [9] experiments proved that the combination of machine learning method (Naive Bayes, Bayesian network, and SVM) and demographic characteristics can improve the prediction accuracy of tourism recommendations. Zhao et al [10] used visual tracking sensors to acquire biometric information and then used machine learning based biometrics to improve the accuracy of recognition. Combined with user attribute features, Al-Shamri [11] constructed five similarity measures, respectively, based on user preference modeling method, and the experimental results showed that the combination of user attribute features improves the recommendation accuracy of recommender systems.…”

Section: Related Workmentioning

confidence: 99%

“…According to (10), in order to get the values of and which will generate relatively good experimental results, we used the RIT-UA algorithm to carry From results shown by Figures 7 and 8 we can see that, for ML-100k data set, when = 0.75 and = 0.25, we get relatively better experimental results. And, for Netflix data set, when = 0.7 and = 0.3, the relatively better experimental results are obtained.…”

Section: Experiments 2: Experimental Analysis Of the Weightmentioning

confidence: 99%

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A Novel Hybrid Similarity Calculation Model

Fan

Chen

Zhu

et al. 2017

Scientific Programming

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This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference. Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculation model is proposed in this paper. This model consists of two parts, on the one hand the model uses the function fitting to describe users' rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account. Furthermore, the paper combines the two parts to build a new hybrid similarity calculation model for user recommendation. Experimental results show that, for data sets of different size, the model's prediction precision is higher than the traditional recommendation algorithms.

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Section: Experiments 3: Experimental Analysis Of the Comparison With Omentioning

confidence: 58%

Section: Experiments 3: Experimental Analysis Of the Comparison With Omentioning

confidence: 70%

Section: Related Workmentioning

confidence: 99%

Section: Experiments 2: Experimental Analysis Of the Weightmentioning

confidence: 99%

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A Novel Hybrid Similarity Calculation Model

Fan

Chen

Zhu

et al. 2017

Scientific Programming

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“…(2) "Limited time problem": within a limited time, activities designed for staff members who tracked consent using depth sensors were proposed and they were carefully reminded of noncompliant activities [5], and a collaborative smartphone task model is proposed, which is called Collaboration-Based Intelligent Perception Task Model (CMST) [6].…”

Section: Introductionmentioning

confidence: 99%

Routing Optimization Algorithms Based on Node Compression in Big Data Environment

Yang

Chen

Wang

et al. 2017

Scientific Programming

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Shortest path problem has been a classic issue. Even more so difficulties remain involving large data environment. Current research on shortest path problem mainly focuses on seeking the shortest path from a starting point to the destination, with both vertices already given; but the researches of shortest path on a limited time and limited nodes passing through are few, yet such problem could not be more common in real life. In this paper we propose several time-dependent optimization algorithms for this problem. In regard to traditional backtracking and different node compression methods, we first propose an improved backtracking algorithm for one condition in big data environment and three types of optimization algorithms based on node compression involving large data, in order to realize the path selection from the starting point through a given set of nodes to reach the end within a limited time. Consequently, problems involving different data volume and complexity of network structure can be solved with the appropriate algorithm adopted.

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