Estimating shopping center visitor numbers based on a new hybrid fuzzy prediction method

2021 ◽  
pp. 1-14
Author(s):  
Cagatay Ozdemir ◽  
Sezi Cevik Onar ◽  
Selami Bagriyanik ◽  
Cengiz Kahraman ◽  
Burak Zafer Akalin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Large Scale ◽  
Prediction Method ◽  
Base Stations ◽  
Shopping Mall ◽  
Multi Criteria Decision Making ◽  
Shopping Malls ◽  
Data Production ◽  
Telecommunications Services

Companies started to determine their strategies based on intelligent data analysis due to stagey enhance data production. Literature reviews show that the number of resources where demand estimation, location analysis, and decision-making technique applied together with the machine learning method is low in all sectors and almost none in the shopping mall domain. Within this study’s scope, a new hybrid fuzzy prediction method has been developed that will estimate the customer numbers for shopping malls. This new methodology is applied to predict the number of visitors of three shopping malls on the Anatolian side of Istanbul. The forecasting study for corresponding shopping malls is made by using the daily signaling data from indoor base stations of large-scale technology and telecommunications services provider and the features to be used in machine learning models is determined by fuzzy multi criteria decision making method. Output revealed by the application of the fuzzy multi criteria decision making method enables the prioritization of features.

DEVELOPMENT OF VALUATION METHOD OF THE STATUS OF BASE STATIONS FOR DETERMINING THE NEED OF THEIR UPGRADING

2017 ◽  
pp. 60-70
Author(s):  
Natalya Ivanovna Shaposhnikova ◽  
Alexander Aleksandrovich Sorokin
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Cellular Network ◽  
New Technologies ◽  
Base Station ◽  
Base Stations ◽  
Valuation Method ◽  
The Status ◽  
Telecommunications Services ◽  
Theory Of Fuzzy Sets

The article consideres the problems of determining the need to modernize the base stations of the cellular network based on the mathematical apparatus of the theory of fuzzy sets. To improve the quality of telecommunications services the operators should send significant funding for upgrading the equipment of base stations. Modernization can improve and extend the functions of base stations to provide cellular communication, increase the reliability of the base station in operation and the functionality of its individual elements, and reduce the cost of maintenance and repair when working on a cellular network. The complexity in collecting information about the equipment condition is determined by a large number of factors that affect its operation, as well as the imperfection of obtaining and processing the information received. For a comprehensive assessment of the need for modernization, it is necessary to take into account a number of indicators. In the structure of indicators of the need for modernization, there were introduced the parameters reflecting both the degree of aging and obsolescence(the technical gap and the backlog in connection with the emergence of new technologies and standards). In the process of a problem solving, the basic stages of decision-making on modernization have been allocated. Decision-making on the need for modernization is based not only on measuring information that takes into account the decision-makers, but also on linguistic and verbal information. Therefore, to determine the need for upgrading the base stations, the theory of fuzzy sets is used, with the help of which experts can be attracted to this issue. They will be able to formulate additional fuzzy judgments that help to take into account not only measuring characteristics, but also poorly formalized fuzzy information. To do this, the main indicators of the modernization need have been defined, and fuzzy estimates of the need for modernization for all indicators and a set of indicators reflecting the need for upgrading the base stations have been formulated.

Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

2021 ◽  
Vol 11 (2) ◽  
pp. 38-52
Author(s):  
Abhinav Juneja ◽  
Sapna Juneja ◽  
Sehajpreet Kaur ◽  
Vivek Kumar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Healthy Lifestyle ◽  
Age Groups ◽  
Machine Learning Techniques ◽  
Multi Criteria Decision Making ◽  
Learning Techniques ◽  
Supervised Learning Algorithms ◽  
Unsupervised Algorithms ◽  
Early Diagnosis Of Diabetes

Diabetes has become one of the common health issues in people of all age groups. The disease is responsible for many difficulties in lifestyle and is represented by imbalance in hyperglycemia. If kept untreated, diabetes can raise the chance of heart attack, diabetic nephropathy, and other disorders. Early diagnosis of diabetes helps to maintain a healthy lifestyle. Machine learning is a capability of machine to learn from past pattern and occurrences and converge with experience to optimise and give decision. In the current research, the authors have employed machine learning techniques and used multi-criteria decision-making approach in Pima Indian diabetes dataset. To classify the patients, they examined several different supervised and unsupervised predictive models. After detailed analysis, it has been observed that the supervised learning algorithms outweigh the unsupervised algorithms due to the output class being a nominal classified domain.

scholarly journals Human Origin-Destination Flow Prediction Based on Large Scale Mobile Signal Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiuyang Huang ◽  
Yongjian Yang ◽  
Yuanbo Xu ◽  
En Wang ◽  
Kangning Zhu
Keyword(s):  
Machine Learning ◽  
Mobile Phone ◽  
Disease Transmission ◽  
Large Scale ◽  
Population Distribution ◽  
Base Stations ◽  
Human Origin ◽  
Safety Control ◽  
Fine Grained ◽  
Flow Prediction

The human origin-destination (OD) flow prediction is of great significance for urban safety control, stampede prevention, disease transmission control, urban planning, and many other aspects. Most of the existing methods generally divide the urban area into grids and use vehicle GPS trajectories and metrocard check-in data, combined with machine learning or deep learning models to predict human OD flow. However, these kinds of methods are challenging to capture fine-grained human mobility patterns. Moreover, these methods usually deviate from the actual human OD transfer patterns on a citywide scale due to the particularity of different datasets. To this end, in this paper, we use large-scale mobile phone signal data to achieve human OD flow prediction between the coverage of varying signal base stations. Many signal base stations are distributed in urban geographical space, collecting all the mobile phone user’s location information to obtain large-scale fine-grained unbiased human OD flow data. Due to the lack of natural topology structure between base stations, this paper adopts a TGCN model combined with a graph fusion module to pretrain the dynamic population distribution prediction task. The parameters of the graph fusion module are employed to capture the different semantic information in the proposed hybrid machine learning method and finally achieve citywide human OD flow prediction. Extensive experiments on the real-world signal datasets in Changchun, China, demonstrate the effectiveness of our model.

scholarly journals Spatial Logic of Shopping Malls: Application of Space Syntax in understanding Economics of Architecture

2020 ◽  
Vol 7 (2) ◽  
pp. 109-117
Author(s):  
Sumanta Deb ◽  
Keya Mitra
Keyword(s):  
Decision Making ◽  
Quantitative Description ◽  
Space Syntax ◽  
Shopping Mall ◽  
Perfect Competition ◽  
Movement Behavior ◽  
Verbal Description ◽  
Management Decision ◽  
Shopping Malls ◽  
Economic Success

Research findings of architecture and environmental psychology espouse the supremacy of built environment in influencing human behavior in general and movement behavior within buildings and urban areas in particular. Retail management studies on the other hand highlight the importance of influencing human movement as a determining factor for tenant-mix design. Identifying a proper mix of tenant stores in a shopping mall is responsible for its economic performance and is considered a strategic mall management decision. In practice, this decision is taken by management professionals, based mostly on gut feeling or rule of thumb. So, there is a scope for integration of knowledge of these two different disciplines for significantly enhancing tenanting decision making in shopping malls, which will ultimately lead to its economic success. A proper methodology is required in this juncture to relate spatial configuration with movement. Verbal description of space, prevalent in the architectural practice, makes it difficult for correlating with measurable variables like footfall. Space syntax analysis is a potential evidence based approach for quantitative description of configuration in explaining movement through space. The purpose of this paper is twofold: identifying the supremacy of space syntax measures over normal metric measures and establishing a spatial rationale behind tenanting decision making (optimal area and rent of tenant stores) through developing the standard bid-rent model with tenant store specific variables and solving under the conditions of maximizing profit and situation of perfect competition. Consequently, retail space planning will not only be an accommodator of functional requirements but will be a potential tool for economic success through generating, controlling and predicting movement.

Sign in / Sign up

Export Citation Format

Share Document