scholarly journals Neural Network: An Improved FCM for Multimodal Cultural Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yan Wang ◽  
Di Liu ◽  
Lingling Tian ◽  
Aiping Tan
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Bp Neural Network ◽  
Clustering Algorithm ◽  
Cultural Analysis ◽  
Fuzzy C Means ◽  
Correlation Degree ◽  
Feature Information ◽  
Cultural Data Analysis ◽  
Cultural Data

With the development of cloud computing, big data, and artificial intelligence (AI) technology, there is a growing interest in “cultural analysis.” Cultural analysis requires different types of data such as texts, pictures, and videos. The richness and differences of resources in the cultural field lead to diverse modalities of cultural data. Traditional text analysis methods can no longer meet the data analysis needs of current multimedia cultural resources. This article starts from cultural data’s feature information to solve the heterogeneity problem faced by massive multimodal cultural data analysis. It analyzes it from geography, time, art, and thematic character, classified and aggregated to form a multimodal cultural feature information matrix. The corresponding correlation measurement methods for different matrices from the above dimensions are proposed, solved in turn, and substituted into the optimized training back propagation (BP) neural network to obtain the final correlation degree. The improved fuzzy C-means (FCM) clustering algorithm is used to aggregate the high correlation cultural data based on the degree. The algorithm proposed in this study is compared with the existing algorithm. The experimental results show that the optimized BP neural network is at least 58% more accurate than the current method for calculating different matrices’ correlation degrees. In terms of accuracy, the improved fuzzy C-means algorithm effectively reduces the random interference in the selection of the initial clustering center, which is significantly higher than other clustering algorithms.

Introduction to Applied Cross-Cultural Data Analysis

2021 ◽  
pp. 1-6
Author(s):  
Thanh V. Tran ◽  
Keith T. Chan
Keyword(s):  
Data Analysis ◽  
Cultural Analysis ◽  
Cross Cultural ◽  
Cultural Identification ◽  
Social Work Research ◽  
Race Ethnicity ◽  
Definition Of ◽  
Cultural Data Analysis ◽  
Cultural Data ◽  
Cross Cultural Analysis

This chapter introduces applied cross-cultural data analysis and addresses the concepts of culture and how culture can be integrated into social work research. We review the definition of culture and how it has been understood and examined in research across various disciplines. We present an overview of the theories and frameworks of cross-cultural analysis, and provide the lens through which culture is examined by means of the techniques and approaches that are used in this book. Cross-cultural analysis can be viewed as comparisons based on key demographic variables such as countries of origin, race, ethnicity, language, sex, religion, and related cultural identifications. The assumption is that people who share the same cultural identification also share similar values and behaviors.

Applied Cross-Cultural Data Analysis for Social Work

2021 ◽  
Author(s):  
Thanh V. Tran ◽  
Keith T. Chan
Keyword(s):  
Social Work ◽  
Data Analysis ◽  
Large Scale ◽  
Cultural Analysis ◽  
Cross Cultural ◽  
Bivariate Analysis ◽  
Practical Applications ◽  
Statistical Approaches ◽  
Cultural Data Analysis ◽  
Cultural Data

Applied Cross-Cultural Data Analysis for Social Work is a research guide which provides a hands-on approach for learning and understanding data analysis techniques for examining and interpreting data for the purpose of cultural group comparisons. This book aims to provide practical applications in statistical approaches of data analyses that are commonly used in cross-cultural research and evaluation. Readers are presented with step-by-step illustrations in the use of descriptive, bivariate, and multivariate statistics to compare cross-cultural populations using large-scale, population-based survey data. These techniques have important applications in health, mental health, and social science research relevant to social work and other helping professions, especially in providing a framework of evidence to examine health disparities using population-health data. For each statistical approach discussed in this book, we explain the underlying purpose, basic assumptions, types of variables, application of the Stata statistical package, the presentation of statistical findings, and the interpretation of results. Unlike previous guides on statistical approaches and data analysis in social work, this book explains and demonstrates the strategies of cross-cultural data analysis using descriptive and bivariate analysis, multiple regression, additive and multiplicative interaction, mediation, and SEM and HLM for subgroup analysis and cross-cultural comparisons. This book also includes sample syntax from Stata for social work researchers to conduct cross-cultural analysis with their own research.

scholarly journals A 4D Trajectory Prediction Model Based on the BP Neural Network

2019 ◽  
Vol 29 (1) ◽  
pp. 1545-1557 ◽  
Author(s):  
Zhi-Jun Wu ◽  
Shan Tian ◽  
Lan Ma
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Clustering Algorithm ◽  
Spline Interpolation ◽  
Absolute Error ◽  
Maximum Absolute Error ◽  
Trajectory Prediction ◽  
Model Based ◽  
Crossing Point

Abstract To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.

Analysis on Correlation Degree of Data Search Based on BP Neural Network Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3864-3867
Author(s):  
Li Hong Fan ◽  
Guo Hong Shi
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Search Time ◽  
Correlation Algorithm ◽  
Network Algorithm ◽  
Correlation Degree ◽  
Data Search ◽  
Financing Risk ◽  
Minimum Number ◽  
Neural Network Algorithm

Based on the correlation principles of three Hermite interpolation, combined with the optimal data correlation search method, we establish the correlation mathematical model of financing risk for supply chain management, and design the optimal search computer simulation system of financing risk data. In order to verify the availability and reliability of the system, we compare three different algorithms of risk data search times and search time. Through calculation and comparison, the minimum number of search times is the correlation algorithm designed with only 6 times; the maximum is the BP neural network algorithm with 13 times; the minimum time of search times is the correlation algorithm with only 1.8s; the maximum is the genetic algorithm with 2.9s.

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