Aim & Scope
Advances in Data Science and Adaptive Analysis (ADSAA) is an interdisciplinary journal dedicated to report original research results on data analysis methodology developments and their applications, with a special emphasis on the adaptive approaches. The mission of the journal is to elevate data analysis from the routine data processing by traditional tools to a new scientific level, which encourages innovative methods development for data science and its scientific research and engineering applications. Data are the direct record of an event, such as a rocket launch, a phenomenon, nature, or engineering processes. The record can be taken by our eyes, ears, electronic sensors, or mechanical devices. We analyze the data, detect signals and make decisions. Thus, data are connections between the reality and us, and data analysis is for us to understand the reality and to find out its underlying driving mechanism. In this sense, data analysis is very different from data processing. The former emphasizes detailed decomposition and examinations of the data to extract physical understanding, while the latter often relies on established algorithms and machines to output values of mathematical parameters. In the big data era, science and technology advance in an unprecedented pace. The inadequacies of traditional data analysis methods based on a priori basis have become glaringly clear. The data from the complex nature cannot be well represented by a priori basis and are not linear and stationary. We have to face the reality of nonstationarity and nonlinearity in the data. Fortunately, some methods, such as empirical mode decomposition (EMD), have already been developed to analyze nonlinear and nonsationary data. It seems that a viable way for the method innovation is to break away from traditional limitations of a priori basis and to make a paradigms shift to adaptive analysis approaches, using an iterative algorithm based only on data not on a fixed basis. EMD, Bayesian method, Kalman filtering, and machine learning techniques may be considered adaptive analysis. A purpose of this journal is to encourage further development of data analysis methods for nonlinear and nonstationary processes. [1]
Continuations / Journal History
| ( 2009 - 2015 ) | Advances in Adaptive Data Analysis (AADA) | ( 2016 - 9999 ) | Advances in Data Science and Adaptive Analysis |
2024
A Correia , A Suzuki , P Ferreira , M Viola
Advances in Data Science and Adaptive Analysis , 2024
0
0
Graph Convolutional Gated Recurrent Unit Network for Traffic Prediction using Loop Detector Data
M Shoman , A Aboah , A Daud , Y Adu-Gyamfi
Advances in Data Science and Adaptive Analysis , 2024
1
0
Assessment Of Mars Analog Habitation Plans Using Network Analysis Methodologies
No authors listed.
Advances in Data Science and Adaptive Analysis , 2024
0
0
Editorial Retractions, Expressions of Concern and External Notices
An Analysis of the Influence of Internet Plus on Chorus Art
Advances in Data Science and Adaptive Analysis2022 - VOLUME 14, ISSUE 01N02
0 5
Z Huizhong , M Fanrong , W Gui , ... , T Puyalnithi
Advances in Data Science and Adaptive Analysis2022 - VOLUME 14, ISSUE 01N02
1 20
Social-Interactive Sports Monitor for Children Using Augmentative and Alternative Communication
Advances in Data Science and Adaptive Analysis2022 - VOLUME 14, ISSUE 03N04
1 0
No authors listed.
Advances in Data Science and Adaptive Analysis
2022
0 3
X Zheng , L Pan , B Lu , ... , A Khurshid
Advances in Data Science and Adaptive Analysis2022 - VOLUME 14, ISSUE 03N04
1 0
No authors listed.
Advances in Data Science and Adaptive Analysis
2022
0 3
Student Behavior Simulation in English Online Education Based on Reinforcement Learning
W Wang , S Sandaran , R Sabitha , K Deepa Thilak
Advances in Data Science and Adaptive Analysis2022 - VOLUME 14, ISSUE 03N04
2 0
No authors listed.
Advances in Data Science and Adaptive Analysis
2022
0 3