scholarly journals User Value Identification Based on Improved RFM Model and K -Means++ Algorithm for Complex Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jun Wu ◽  
Li Shi ◽  
Liping Yang ◽  
XiaxiaNiu ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Big Data ◽  
User Satisfaction ◽  
Clustering Algorithm ◽  
Strong Support ◽  
Complex Data ◽  
User Group ◽  
User Classification ◽  
Rfm Model ◽  
User Value ◽  
Real Purchasing

In recent years, with the development of machine learning and big data technology, user data has become an important element in the production process of enterprises. For today’s e-commerce platforms, the deep mining of user’s purchase behavior is helpful to understand user’s purchase preferences and accurately recommend products that meet user expectations, which can not only improve user satisfaction but also reduce platform marketing cost. To accurately identify the user value of online purchasing on an e-commerce platform, this paper uses an improved RFM model to extract user features and uses the K -means++ clustering algorithm to realize user classification. The indicators of the traditional RFM model characterize user features from three angles: recent purchase time ( R ), purchase frequency ( F ), and total consumption amount ( M ). The user group and scenarios studied in this paper are different from the previous literature: (1) the user group is relatively fixed, (2) the consumer goods are relatively single, and (3) the characteristics of repeated purchase are obvious. Therefore, based on the existing literature, this paper extracts the user characteristics studied and improves and models the traditional indicators. Based on the real purchasing data from September to December 2018, it calculates the indicators that improved RFM, empowers the weight to indicators, and finally classifies the value of users by using the K -means++ algorithm. The experimental results show that the user classification based on the improved RFM model is more accurate than the user classification based on the traditional RFM model, and the improved RFM model can identify the user value more accurately, which provides a strong support for the e-commerce platform to realize the accurate marketing strategy based on big data.

scholarly journals Clinical implementation of a 3D4K-exoscope (Orbeye) in microneurosurgery

2021 ◽  
Author(s):  
Judith Rösler ◽  
Stefan Georgiev ◽  
Anna L. Roethe ◽  
Denny Chakkalakal ◽  
Güliz Acker ◽  
...  
Keyword(s):  
User Satisfaction ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Intraoperative Complications ◽  
Rank Test ◽  
User Group ◽  
Clinical Implementation ◽  
Physical Strain ◽  
Conventional Microscope ◽  
The Impact

AbstractExoscopic surgery promises alleviation of physical strain, improved intraoperative visualization and facilitation of the clinical workflow. In this prospective observational study, we investigate the clinical usability of a novel 3D4K-exoscope in routine neurosurgical interventions. Questionnaires on the use of the exoscope were carried out. Exemplary cases were additionally video-documented. All participating neurosurgeons (n = 10) received initial device training. Changing to a conventional microscope was possible at all times. A linear mixed model was used to analyse the impact of time on the switchover rate. For further analysis, we dichotomized the surgeons in a frequent (n = 1) and an infrequent (n = 9) user group. A one-sample Wilcoxon signed rank test was used to evaluate, if the number of surgeries differed between the two groups. Thirty-nine operations were included. No intraoperative complications occurred. In 69.2% of the procedures, the surgeon switched to the conventional microscope. While during the first half of the study the conversion rate was 90%, it decreased to 52.6% in the second half (p = 0.003). The number of interventions between the frequent and the infrequent user group differed significantly (p = 0.007). Main reasons for switching to ocular-based surgery were impaired hand–eye coordination and poor depth perception. The exoscope investigated in this study can be easily integrated in established neurosurgical workflows. Surgical ergonomics improved compared to standard microsurgical setups. Excellent image quality and precise control of the camera added to overall user satisfaction. For experienced surgeons, the incentive to switch from ocular-based to exoscopic surgery greatly varies.

Big Data in the philippines: How do we actually use them?

2021 ◽  
pp. 1-30
Author(s):  
Lisa Grace S. Bersales ◽  
Josefina V. Almeda ◽  
Sabrina O. Romasoc ◽  
Marie Nadeen R. Martinez ◽  
Dannela Jann B. Galias
Keyword(s):  
Big Data ◽  
High Speed ◽  
The Philippines ◽  
Complex Data ◽  
Official Statistics ◽  
Group Discussions ◽  
The Public ◽  
The Government ◽  
The Many ◽  
Current Utilization

With the advancement of technology, digitalization, and the internet of things, large amounts of complex data are being produced daily. This vast quantity of various data produced at high speed is referred to as Big Data. The utilization of Big Data is being implemented with success in the private sector, yet the public sector seems to be falling behind despite the many potentials Big Data has already presented. In this regard, this paper explores ways in which the government can recognize the use of Big Data for official statistics. It begins by gathering and presenting Big Data-related initiatives and projects across the globe for various types and sources of Big Data implemented. Further, this paper discusses the opportunities, challenges, and risks associated with using Big Data, particularly in official statistics. This paper also aims to assess the current utilization of Big Data in the country through focus group discussions and key informant interviews. Based on desk review, discussions, and interviews, the paper then concludes with a proposed framework that provides ways in which Big Data may be utilized by the government to augment official statistics.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

scholarly journals A Comprehensive Survey on Local Differential Privacy

2020 ◽  
Vol 2020 ◽  
pp. 1-29 ◽  
Author(s):  
Xingxing Xiong ◽  
Shubo Liu ◽  
Dan Li ◽  
Zhaohui Cai ◽  
Xiaoguang Niu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Learning ◽  
Data Privacy ◽  
Statistical Estimation ◽  
Differential Privacy ◽  
Future Research ◽  
Reference Source ◽  
Complex Data ◽  
Comprehensive Survey

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

scholarly journals Three faces of the online leftists: An exploratory study based on case observations and big-data analysis

2020 ◽  
Vol 6 (1) ◽  
pp. 67-101
Author(s):  
Yong Gui ◽  
Ronggui Huang ◽  
Yi Ding
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Strong Support ◽  
Negative Attitude ◽  
Two Dimensions ◽  
Supervised Machine Learning ◽  
Interaction Patterns ◽  
Left Wing ◽  
Mixed Approach ◽  
Method Approach

Left-leaning social thoughts are not a unitary and coherent theoretical system, and leftists can be divided into divergent groups. Based on inductive qualitative observations, this article proposes a theoretical typology of two dimensions of theoretical resources and position orientations to describe left-wing social thoughts communicated in online space. Empirically, we used a mixed approach, an integration of case observations and big-data analyses of Weibo tweets, to investigate three types of left-leaning social thoughts. The identified left-leaning social thoughts include state-centered leftism, populist leftism, and liberal leftism, which are consistent with the proposed theoretical typology. State-centered leftism features strong support of the state and the current regime and a negative attitude toward the West, populist leftism is characterized by unequivocal affirmation of the revolutionary legacy and support for disadvantaged grassroots, and liberal leftism harbors a grassroots position and a decided affirmation of individual rights. In addition, we used supervised machine learning and social network analysis techniques to identify online communities that harbor the afore-mentioned left-leaning social thoughts and analyzed the interaction patterns within and across communities as well as the evolutions of community structures. We found that during the study period of 2012–2014, the liberal leftists gradually declined and the corresponding communities dissolved; the interactions between populist leftists and state-centered leftists intensified, and the ideational cleavage between these two camps increased the online confrontations. This article demonstrates that the mixed method approach of integrating traditional methods with big-data analytics has enormous potential in the sub-discipline of digital sociology.

Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation

2021 ◽  
pp. 016555152110137
Author(s):  
N.R. Gladiss Merlin ◽  
Vigilson Prem. M
Keyword(s):  
Big Data ◽  
Similarity Measure ◽  
Patient Information ◽  
Complex Data ◽  
Mapreduce Framework ◽  
Maximum Value ◽  
User Query ◽  
Indexing And Retrieval ◽  
Sine Cosine Algorithm ◽  
Disparate Source

Large and complex data becomes a valuable resource in biomedical discovery, which is highly facilitated to increase the scientific resources for retrieving the helpful information. However, indexing and retrieving the patient information from the disparate source of big data is challenging in biomedical research. Indexing and retrieving the patient information from big data is performed using the MapReduce framework. In this research, the indexing and retrieval of information are performed using the proposed Jaya-Sine Cosine Algorithm (Jaya–SCA)-based MapReduce framework. Initially, the input big data is forwarded to the mapper randomly. The average of each mapper data is calculated, and these data are forwarded to the reducer, where the representative data are stored. For each user query, the input query is matched with the reducer, and thereby, it switches over to the mapper for retrieving the matched best result. The bilevel matching is performed while retrieving the data from the mapper based on the distance between the query. The similarity measure is computed based on the parametric-enabled similarity measure (PESM), cosine similarity and the proposed Jaya–SCA, which is the integration of the Jaya algorithm and the SCA. Moreover, the proposed Jaya–SCA algorithm attained the maximum value of F-measure, recall and precision of 0.5323, 0.4400 and 0.6867, respectively, using the StatLog Heart Disease dataset.

Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

2021 ◽  
pp. 1-12
Author(s):  
Li Qian
Keyword(s):  
Big Data ◽  
Fuzzy Clustering ◽  
Classification Accuracy ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Data Classification ◽  
Fisher Discriminant Analysis ◽  
Fuzzy Clustering Algorithm ◽  
Local Fisher Discriminant Analysis ◽  
Big Data Classification

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

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