scholarly journals Precise Marketing Strategy Optimization of E-Commerce Platform Based on KNN Clustering

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Benfang Yang ◽  
Jiye Li
Keyword(s):  
Multidimensional Data ◽  
Mobile Phone Use ◽  
Consumption Behavior ◽  
Packet Data ◽  
User Data ◽  
Implementation Scheme ◽  
User Demand ◽  
Use Behavior ◽  
Ip Packet ◽  
Dimension Index

With the development of computer technology and the arrival of the era of artificial intelligence, the analysis of user demand bias is of great significance to the operation optimization of e-commerce platforms. Combined with CS domain signaling data, IP packet data of PS domain, and customer CRM data provided by operators, this research studies each dimension index of operator user portrait, after that the operator user portrait platform is divided into some individual subunits, and then the corresponding data mining technology is carried out to study the implementation scheme of each subunit. The system can process and mine multidimensional data of operators’ users and form user portraits on the basis of user data aggregation. Finally, based on the operator user portrait platform studied in this paper, the operator user data are analyzed from both the user’s mobile phone use behavior and user consumption behavior. Furthermore, the application value of this research in the precision marketing and personalized service of operators is illustrated.

scholarly journals Naturalistic Driving Study in Brazil: An Analysis of Mobile Phone Use Behavior while Driving

2020 ◽  
Vol 17 (17) ◽  
pp. 6412 ◽  
Author(s):  
Jorge Tiago Bastos ◽  
Pedro Augusto B. dos Santos ◽  
Eduardo Cesar Amancio ◽  
Tatiana Maria C. Gadda ◽  
José Aurélio Ramalho ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Average Frequency ◽  
The United States ◽  
International Studies ◽  
Middle Income ◽  
Mobile Phone Use ◽  
Trip Time ◽  
European Values ◽  
Naturalistic Driving ◽  
Use Behavior

Mobile phone use (MPU) while driving is an important road safety challenge worldwide. Naturalistic driving studies (NDS) emerged as one of the most sophisticated methodologies to investigate driver behavior; however, NDS have not been implemented in low- or middle-income countries. The aim of this research is to investigate MPU while driving and compare the results to those reported in international studies. An analysis of 61.32 h and 1350 km driven in Curitiba (Brazil) showed that MPU lasted for an average of 28.51 s (n = 627) and occurred in 58.71% of trips (n = 201) with an average frequency of 8.37 interactions per hour (n = 201). The proportion of the trip time using a mobile phone was 7.03% (n = 201), and the average instantaneous speed was 12.77 km/h (n = 627) while using the phone. Generally, drivers spent less time on more complex interactions and selected a lower speed when using the phone. MPU was observed more during short duration than longer trips. Drivers in this study engaged in a larger number of MPU compared to drivers from Netherlands and the United States; and the percentage of trip time with MPU was between North American and European values.

scholarly journals Analysis of User Energy Consumption Patterns Based on Data Mining

2020 ◽  
Vol 213 ◽  
pp. 02040
Author(s):  
Weitao Liu ◽  
Fuqing Wang ◽  
Hang Shi ◽  
Yan Zhang ◽  
Ruobo Chen
Keyword(s):  
Energy Consumption ◽  
Behavior Analysis ◽  
Energy Use ◽  
Electricity Consumption ◽  
Energy System ◽  
Management Service ◽  
Consumption Behavior ◽  
User Classification ◽  
Integrated Energy System ◽  
Use Behavior

The energy use behavior analysis method can dig out the user’s energy use behavior rules from the energy use big data, thereby improving the quality of the grid-side management service in the integrated energy system. Firstly, it summarizes the characteristics of the integrated energy system and constructs the integrated energy system service system; secondly, it summarizes the data-driven electricity consumption behavior analysis research model. Then, it elaborates on the collection and aggregation of electricity consumption information, and refined user classification. Next, the comprehensive application of energy consumption behavior analysis in load forecasting, demand response modeling and other typical scenarios is deeply analyzed. Finally, the challenges that may be encountered in further research are clarified and the follow-up work is prospected.

ANALYTICAL PERFORMANCE EVALUATION OF LOW-BITRATE REAL-TIME TRAFFIC MULTIPLEXING IN UMTS OVER IP NETWORKS

2001 ◽  
Vol 02 (01) ◽  
pp. 147-174
Author(s):  
MICHAEL MENTH
Keyword(s):  
Real Time ◽  
Substantial Effect ◽  
Ip Networks ◽  
Time Analysis ◽  
Network Resources ◽  
Real Time Traffic ◽  
User Data ◽  
Ip Packet ◽  
Traffic Multiplexing ◽  
Data Multiplexing

Voice and circuit switched data will be carried over IP networks in the wireline part of the future UMTS. The commonly used protocol suite for low-bitrate real-time traffic transmission in the Internet leads to large header overhead, i.e., to low utilization of the network resources by user data. Multiplexing packets of different flows into a single IP packet reduces this effect. We model packet tunneling and multiplexing with subsequent spacing in IP networks. We derive a discrete-time analysis based on a framework for solving Markov chains. The numerical results show the superiority of multiplexing schemes provided that parameters are set in an appropriate way since there are many performance tradeoffs that have substantial effect on the efficiency of the system.

scholarly journals Sustainable Consumption Behavior at Home and in the Workplace: Avenues for Innovative Solutions

2020 ◽  
Vol 12 (16) ◽  
pp. 6564 ◽  
Author(s):  
Jūratė Banytė ◽  
Laura Šalčiuvienė ◽  
Aistė Dovalienė ◽  
Žaneta Piligrimienė ◽  
Włodzimierz Sroka
Keyword(s):  
Online Survey ◽  
Sustainable Consumption ◽  
Collaborative Consumption ◽  
Consumption Behavior ◽  
The United Kingdom ◽  
Innovative Solutions ◽  
Private And Public ◽  
Use Behavior ◽  
Consumption Theory ◽  
At Home

Companies which offer innovative solutions to aid the achievement of sustainable consumption behavior of individuals in home environment gain a competitive advantage. The study aims to uncover the relationship between the engagement in sustainable consumption and sustainable consumption behavior of individuals at home and in the workplace environments enabling companies to provide innovative solutions to advance sustainability management. This research holds that sustainable consumption behavior is a process and the focus of this study is use behavior. An online survey was employed to collect data from 407 respondents in the United Kingdom. Consumers working in both private and public sectors were surveyed. Data analysis suggests that one dimension of engagement in sustainable consumption, namely, Enthusiasm and Attention, mostly influences sustainable consumption behavior at home and in the workplace. Further, females feature higher sustainable consumption behavior at home and in the workplace most of the time in comparison to males. Also, there are age differences apropos sustainable consumption behavior at home and in the workplace. Social Learning Theory and Collaborative Consumption Theory are used to raise hypotheses and explain findings. The findings lead to practical implications for companies regarding engagement and sustainable consumption behavior in both environments in terms of incentives, green product and service innovation that may be offered to individuals to enhance sustainability.

scholarly journals Identifying Energy-Use Behavior and Energy-Use Profiles of Hotel Guests

2021 ◽  
Vol 11 (13) ◽  
pp. 6093
Author(s):  
Hevar Palani ◽  
Aslihan Karatas
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Research Study ◽  
The United States ◽  
Consumption Behavior ◽  
Effective Interventions ◽  
Energy Behavior ◽  
Good Energy ◽  
Use Behavior ◽  
Behavior Models

There are about 47,000 hotels in the United States that spend an average of $2200 per room on energy annually. Studies found that hotel guests’ energy consumption behavior is one of the key reasons that affects hotel buildings’ energy consumption. However, there has been little research study that provides efficient energy-use reduction interventions based on guests’ energy-related behavior in hotel buildings. To address this research gap, this research study aims to develop an integrated energy-use framework in four steps: (1) integrating four energy-related behavior models (i.e., Motivation-Opportunity-Ability, Norm Activation Model, Theory of Planned Behavior, and Pro-environmental Behavior); (2) developing a set of hypotheses and their relevant measures to examine the relationship between the energy-related behavior models and hotel guests’ energy-use behavior; (3) conducting an energy-use survey to analyze the effect of each determined measure on hotel guests’ energy-use behavior; (4) analyzing the energy behavior data to identify energy-use behavior of hotel guests in hotel buildings; (5) analyzing the energy behavior data to identify energy-use profiles (i.e., Prone, Indifferent, or Resistant to Change) of hotel guests in hotel buildings. In this study, Prone to Change refers to good energy consumption behavior, Indifferent to Change refers to moderate energy consumption behavior, and Resistant to Change refers to bad or dark energy consumption behavior. From the energy-use survey, 370 responses were collected. Then, the 370 responses were analyzed to identify the energy-use profiles of hotel guests. The results from the analysis indicated that 168 out of 370 (45%) respondents have Resistant to Change energy-use profile, 146 out of 370 (40%) respondents have Indifferent to Change energy-use profile, and 56 out of 370 (15%) respondents have Prone to Change energy-use profile. The findings can provide decision-makers in hospitality industry with a better understanding of their guests’ energy-related behavior; and accordingly develop effective interventions to reduce energy consumption in hotel buildings.

scholarly journals Determinants of Maternal Behavior of Mobile Phone Use during Pregnancy

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Min Li ◽  
Xia Wu ◽  
Guoqiang Sun ◽  
Min Peng
Keyword(s):  
Mobile Phone ◽  
Perceived Risk ◽  
Structural Equation ◽  
Healthcare Providers ◽  
Equation Modeling ◽  
Applied Theory ◽  
Psychological Consequences ◽  
Mobile Phone Use ◽  
Model Based ◽  
Use Behavior

Excessive use of mobile phones might bring negative physical and psychological consequences to pregnant women. This study aims to explore the potential determinants of pregnant women’s mobile phone use behavior to assist healthcare providers in the development of guideline programs. In order to explain the behavior, we developed a theoretical model based on the widely applied theory of planned behavior (TPB) by incorporating two additional constructs of personal habit and perceived risk. Structural equation modeling technique is employed to estimate the model based on questionnaire survey. Research results clearly show that behavior attitude and perceived behavior control play dominant roles in determining the intention and behavior. It is interesting to find that perceived risk and personal habit are less important in determining pregnant women’s behavior of mobile phone use. Finally, suggestions are put forward to reduce the risk of mobile phone use during pregnancy.

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