Location-Aware Service Recommendation With Enhanced Probabilistic Matrix Factorization

Yin, Yuyu; Chen, Lu; Xu, Yueshen; Wan, Jian

doi:10.1109/access.2018.2877137

Cited by 119 publications

(91 citation statements)

References 27 publications

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“…Study of workflow scheduling using CGA is another future investigation. And we can also mine or forecast its potential relationships [32][33][34]. In addition, the method of task scheduling can consider many other parameters, such as the use of memory, peak of the demand, and overloads [10].…”

Section: Discussionmentioning

confidence: 99%

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Wang

Pang

et al. 2020

Scientific Programming

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Task scheduling plays a critical role in the performance of the edge-cloud collaborative. Whether the task is executed in the cloud and how it is scheduled in the cloud is an important issue. On the basis of satisfying the delay, this paper will schedule tasks on edge devices or cloud and present a task scheduling algorithm for tasks that need to be transferred to the cloud based on the catastrophic genetic algorithm (CGA) to achieve global optimum. e algorithm quantifies the total task completion time and the penalty factor as a fitness function. By improving the roulette selection strategy, optimizing mutation and crossover operator, and introducing cataclysm strategy, the search scope is expanded. Furthermore, the premature problem of the evolutionary algorithm is effectively alleviated. e experimental results show that the algorithm can address the optimal local issue while significantly shortening the task completion time on the basis of satisfying tasks delays.

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Section: Discussionmentioning

confidence: 99%

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Wang

Pang

et al. 2020

Scientific Programming

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“…By Dirac Delta function, the energy L g ðϕÞ represents the curve integral of the function g along the zero level set contour of the level set function ϕ. In (16), I represents the image on the domain Ω and g is the boundary indicator function. jÑG σ Ã Ij represents the gradient value of the image after Gaussian denoising, where G σ is the Gaussian kernel with standard deviation σ and '*' is the convolution operation for the purpose of noise reduction.…”

Section: Candidate Nuclei Refinementmentioning

confidence: 99%

“…Many scholars have proposed different methods for nucleus segmentation based on the ISBI2014 and ISBI2015 public datasets. The algorithms for nucleus segmentation are mainly divided into simple linear iterative clustering (SLIC) method [5][6][7][8], region-based segmentation method [7,[9][10][11][12][13], convolutional neural network (CNN) [14][15][16][17], and clustering method [18][19][20][21][22][23][24][25][26][27][28]. SLIC superpixel algorithm is one of the most popular nucleus segmentation methods currently.…”

Section: Introductionmentioning

confidence: 99%

Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm

et al. 2020

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In the screening of cervical cancer cells, accurate identification and segmentation of nucleus in cell images is a key part in the early diagnosis of cervical cancer. Overlapping, uneven staining, poor contrast, and other reasons present challenges to cervical nucleus segmentation. We propose a segmentation method for cervical nuclei based on a multi-scale fuzzy clustering algorithm, which segments cervical cell clump images at different scales. We adopt a novel interesting degree based on area prior to measure the interesting degree of the node. The application of these two methods not only solves the problem of selecting the categories number of the clustering algorithm but also greatly improves the nucleus recognition performance. The method is evaluated by the IBSI2014 and IBSI2015 public datasets. Experiments show that the proposed algorithm has greater advantages than the state-of-the-art cervical nucleus segmentation algorithms and accomplishes high accuracy nucleus segmentation results. ARTICLE HISTORY

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“…Deep learning brings tremendous opportunities for mobile computing and can greatly improve the performance of various applications. In addition, due to the rapid spread of smart portable devices and the development of mobile service technology [2,3], the possibility of introducing smart applications in mobile environments is receiving increased attention. Therefore, people are increasingly concerned about the possibility of applying deep neural networks (DNNs) in mobile environments [4].…”

Section: Introductionmentioning

confidence: 99%

A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices

Xia

2019

Mobile Netw Appl

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Deep learning (DL) is a hot topic in current pattern recognition and machine learning. DL has unprecedented potential to solve many complex machine learning problems and is clearly attractive in the framework of mobile devices. The availability of powerful pattern recognition tools creates tremendous opportunities for next-generation smart applications. A convolutional neural network (CNN) enables data-driven learning and extraction of highly representative, hierarchical image features from appropriate training data. However, for some data sets, the CNN classification method needs adjustments in its structure and parameters. Mobile computing has certain requirements for running time and network weight of the neural network. In this paper, we first design an image processing module for a mobile device based on the characteristics of a CNN. Then, we describe how to use the mobile to collect data, process the data, and construct the data set. Finally, considering the computing environment and data characteristics of mobile devices, we propose a lightweight network structure for optical character recognition (OCR) on specific data sets. The proposed method using a CNN has been validated by comparison with the results of existing methods, used for optical character recognition.

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Location-Aware Service Recommendation With Enhanced Probabilistic Matrix Factorization

Cited by 119 publications

References 27 publications

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm

A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices

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