Location Tracking Prediction of Network Users Based on Online Learning Method With Python

2021 ◽  
pp. 790-806
Author(s):  
Xin Xu ◽  
Hui Lu
Keyword(s):  
Online Learning ◽  
Clustering Algorithm ◽  
Prediction Method ◽  
Recall Rate ◽  
User Preference ◽  
Time Dimension ◽  
Time Segment ◽  
Learning Network ◽  
Prediction Efficiency ◽  
User Location

Aiming at the problem that the precision and recall rate of traditional prediction methods are low and its low prediction efficiency, a Python-based trajectory tracking prediction method of online learning network user location is proposed. First, troubleshooting terminal programs of online learning network user by programming in Python (computer programming language) structure, the location trajectory data of online learning network user is spatially processed. In this way, features of time-related, spatial correlation, social relationship correlation, and user preference characteristics are extracted respectively to realize feature normalization processing. Second, on this basis, the cosine similarity is used to calculate the similarity of user behavior trajectory. According to K-MEANS (hard clustering algorithm), the time dimension is considered. Finally, the clustering result of users' behavior trajectory based on the sign-in data is compared with a preset threshold to predict the online user location trajectory. The experimental results show that the proposed method normalizes the user's trajectory, combines the time segment, and compares it with the preset threshold, which does not only improve the prediction efficiency but also obtains higher and more feasible precision and recall rate.

Location Tracking Prediction of Network Users Based on Online Learning Method With Python

2019 ◽  
Vol 10 (1) ◽  
pp. 49-64
Author(s):  
Xin Xu ◽  
Hui Lu
Keyword(s):  
Online Learning ◽  
Clustering Algorithm ◽  
Recall Rate ◽  
User Preference ◽  
Trajectory Data ◽  
Time Dimension ◽  
Time Segment ◽  
Learning Network ◽  
Prediction Efficiency ◽  
User Location

Aiming at the problem that the precision and recall rate of traditional prediction methods are low and its low prediction efficiency, a Python-based trajectory tracking prediction method of online learning network user location is proposed. First, troubleshooting terminal programs of online learning network user by programming in Python (computer programming language) structure, the location trajectory data of online learning network user is spatially processed. In this way, features of time-related, spatial correlation, social relationship correlation, and user preference characteristics are extracted respectively to realize feature normalization processing. Second, on this basis, the cosine similarity is used to calculate the similarity of user behavior trajectory. According to K-MEANS (hard clustering algorithm), the time dimension is considered. Finally, the clustering result of users' behavior trajectory based on the sign-in data is compared with a preset threshold to predict the online user location trajectory. The experimental results show that the proposed method normalizes the user's trajectory, combines the time segment, and compares it with the preset threshold, which does not only improve the prediction efficiency but also obtains higher and more feasible precision and recall rate.

A learning resource recommendation algorithm based on online learning sequential behavior

2019 ◽  
Vol 17 (02) ◽  
pp. 1940001 ◽  
Author(s):  
Xuebin Wang ◽  
Zhengzhou Zhu ◽  
Jiaqi Yu ◽  
Ruofei Zhu ◽  
DeQi Li ◽  
...  
Keyword(s):  
Online Learning ◽  
Collaborative Filtering ◽  
Clustering Algorithm ◽  
Recall Rate ◽  
Learning Resources ◽  
Recommendation Algorithm ◽  
Sequential Behavior ◽  
Learning Resource ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

The accuracy of learning resource recommendation is crucial to realizing precise teaching and personalized learning. We propose a novel collaborative filtering recommendation algorithm based on the student’s online learning sequential behavior to improve the accuracy of learning resources recommendation. First, we extract the student’s learning events from his/her online learning process. Then each student’s learning events are selected as the basic analysis unit to extract the feature sequential behavior sequence that represents the student’s learning behavioral characteristics. Then the extracted feature sequential behavior sequence generates the student’s feature vector. Moreover, we improve the H-[Formula: see text] clustering algorithm that clusters the students who have similar learning behavior. Finally, we recommend learning resources to the students combine similarity user clusters with the traditional collaborative filtering algorithm based on user. The experiment shows that the proposed algorithm improved the accuracy rate by 110% and recall rate by 40% compared with the traditional user-based collaborative filtering algorithm.

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

scholarly journals Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

2020 ◽  
Vol 9 (3) ◽  
pp. 181
Author(s):  
Banqiao Chen ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Clustering Algorithm ◽  
Recall Rate ◽  
Location Based Services ◽  
Trajectory Data ◽  
Intersection Detection ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Road Intersections ◽  
Road Maps ◽  
Gps Trajectory

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.

scholarly journals An Automatic Classification and Clustering Algorithm for Online Learning Goals Based on Cognitive Thinking

2018 ◽  
Vol 13 (11) ◽  
pp. 54 ◽  
Author(s):  
Ying Wang ◽  
Weifeng Jiang
Keyword(s):  
Online Learning ◽  
Student Learning ◽  
Learning Community ◽  
Clustering Algorithm ◽  
Automatic Classification ◽  
Learning Goals ◽  
Diagnostic Model ◽  
Process Data ◽  
Community Concept ◽  
Classification And Clustering

To improve the learning effect of online learning, an online learning target automatic classification and clustering analysis algorithm based on cognitive thinking was proposed. It was applied to a multi-dimensional learning community. A new form of virtual learning community concept was proposed. The design ideas of its multi-dimensional learning environment were elaborated. Ontology technology was used to collect student learning process data. A cognitive diagnostic model for assessing student learning status was generated. Finally, through the cluster analysis technology, the registered students in the curriculum center were automatically divided into different levels of community groups. The results showed that the proposed algorithm for automatic classification and clustering of online learning targets had a good application effect in the learning community. Therefore, this method has practical application value.

scholarly journals SK-FMYOLOV3: A Novel Detection Method for Urine Test Strips

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Rui Yang ◽  
Yonglin Zhang ◽  
Zhenrong Deng ◽  
Wenming Huang ◽  
Rushi Lan ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Test Strip ◽  
Recall Rate ◽  
Detection Algorithm ◽  
Urine Test ◽  
Feature Maps ◽  
Test Strips ◽  
Fuzzy C Means Clustering ◽  
Convolution Kernels ◽  
Clustering Center

To accurately detect small defects in urine test strips, the SK-FMYOLOV3 defect detection algorithm is proposed. First, the prediction box clustering algorithm of YOLOV3 is improved. The fuzzy C-means clustering algorithm is used to generate the initial clustering centers, and then, the clustering center is passed to the K-means algorithm to cluster the prediction boxes. To better detect smaller defects, the YOLOV3 feature map fusion is increased from the original three-scale prediction to a four-scale prediction. At the same time, 23 convolutional layers of size 3 × 3 in the YOLOV3 network are replaced with SkNet structures, so that different feature maps can independently select different convolution kernels for training, improving the accuracy of defect classification. We collected and enhanced urine test strip images in industrial production and labeled the small defects in the images. A total of 11634 image sets were used for training and testing. The experimental results show that the algorithm can obtain an anchor frame with an average cross ratio of 86.57, while the accuracy rate and recall rate of nonconforming products are 96.8 and 94.5, respectively. The algorithm can also accurately identify the category of defects in nonconforming products.

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