Search, Sales Assistance and Purchase Decisions: An Analysis Using Retail Video Data

2104 ◽  
Author(s):  
Aditya Jain ◽  
Sanjog Misra ◽  
Nils Rudi
Keyword(s):  
Video Data ◽  
Purchase Decisions

The Effect of Sales Assistance on Purchase Decisions: An analysis using retail video data

2020 ◽  
Vol 18 (3) ◽  
pp. 273-303 ◽  
Author(s):  
Aditya Jain ◽  
Sanjog Misra ◽  
Nils Rudi
Keyword(s):  
Video Data ◽  
Purchase Decisions

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Analisa Pengaruh Motivasi Konsumen, Persepsi Kualitas, Dan Sikap Konsumen Terhadap Keputusan Pembelian Sepeda Motor PT. Yamaha Indonesia MFG

CICES ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 188-203
Author(s):  
Ria Wulandari ◽  
M. Ifran Sanni ◽  
Dani Ramadhan
Keyword(s):  
Decision Making ◽  
Regression Analysis ◽  
Qualitative Analysis ◽  
Purchase Decisions ◽  
Analysis Method ◽  
Purchasing Decisions ◽  
Processing Data ◽  
Customer Purchases ◽  
Customer Attitudes ◽  
Quantitative And Qualitative Methods

This research is motivated by a decline in motorcycle sales produced by PT. Yamaha Indonesia MFG in the 2014-2018 period. In this research there was a decrease in the decision on the power of interest in customer purchases on PT. Yamaha Indonesia MFG so that later can be analyzed in the formulation of this paper, that how customer take motorcycle purchase decisions amid the phenomenon of competition and increasingly crowded sales rivalries. The purpose of this research was to analyze the influence of motivation, perceived quality, and customer attitudes toward decisions in purchasing Yamaha motorbikes. This research uses quantitative and qualitative methods. The respondents in this research were 100 people who could meet one to five criteria consisting of; initiator (initiator), influencer (influencer), decision making (decider), purchase (buyer), user (user) motorcycle production PT. Yamaha Indonesia MFG. There are 3 hypotheses formulated and tested using the Regression Analysis method. In qualitative analysis it is obtained from the interpretation of processing data by providing information and explanation. In the results of this research shows the results of Motivation, Quality Perception, and Customer Attitudes have a relationship that has a significant impact on Purchasing Decisions.

Selection of the efficient video data processing strategy based on the analysis of statistical digital images characteristics

2018 ◽  
Vol 91 (3) ◽  
pp. 107-114
Author(s):  
Mykhailo Palamar ◽  
Myroslava Yavorska ◽  
Mykhailo Strembitskyi ◽  
Volodymyr Strembitskyi
Keyword(s):  
Data Processing ◽  
Digital Images ◽  
Video Data ◽  
Processing Strategy ◽  
Efficient Video ◽  
Selection Of

Video Data Embedding. Part II

2013 ◽  
Vol 45 (4) ◽  
pp. 75-83
Author(s):  
Lyubov L. Nikitenko
Keyword(s):  
Video Data ◽  
Data Embedding

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

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