Advances in intelligent information processing

Li, Ling; Warfield, John; Guo, Shuo Jia; Guo, Wen Dong; Qi, Jia

doi:10.1016/j.is.2006.10.001

Cited by 15 publications

(5 citation statements)

References 8 publications

(8 reference statements)

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“…In this regard, the genetic algorithm is a choice for choosing a right subset of variables. Genetic algorithms, is a newer form of artificial intelligence based on inductive learning technique (Xu, 1999(Xu, , 2000(Xu, , 2006Chaudhry et al, 2000;Li et al, 2007Li et al, , 2008Xu et al, 2008). The integration of genetic algorithms and other techniques, such as neural networks and fuzzy logic, within the intelligent systems, has been the topic of interest to the researchers.…”

Section: Applying Genetic Algorithms To Ratio Selectionmentioning

confidence: 99%

Influencing factors for predicting financial performance based on genetic algorithms

Jiang

Wang

et al. 2009

Syst. Res.

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In this paper, considering the financial performance of China's listed companies as the dependent variable, a computational intelligence method based on genetic algorithms and discriminant analysis is employed to screen variables that influence financial performance and forecast the change of financial performance. Specifically, a new model based on genetic algorithms is developed to screen factors that influence financial performance of Chinese listed companies. The empirical results show that variables selected by genetic algorithms can predict financial performance well.

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Section: Applying Genetic Algorithms To Ratio Selectionmentioning

confidence: 99%

Influencing factors for predicting financial performance based on genetic algorithms

Jiang

Wang

et al. 2009

Syst. Res.

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“…More recent developments suggest that big data problems are identified by the “5V”: volume (quantity of data), variety (data from different categories), velocity (rapid generation of new data), veracity (quality of the data), and value (in the data) (Chen et al, 2020; Terzo et al, 2013). Therefore, new technologies are strongly needed since most of the traditional data mining methods or data analytics developed for a centralized data analysis process may not be able to be applied directly to big data (Li et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Study on the interaction between big data and artificial intelligence

Zhang

2022

Syst Res Behav Sci

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The explosive growth of information has rapidly ushered people into the era of big data. Due to the large volume, high variety, and rapid velocity characteristics of big data, most traditional data mining methods developed for a centralized data analysis process cannot be applied directly. AI leverages machine intelligence to provide insights, automation, and new methods to interact with data, thereby promoting data literacy throughout the organization. Based on the theory of Big Data Cycle, this paper discusses the relationship between big data and AI and how they interact and influence each other. It adopts the integrative review research method to screen latest literature and summarizes the role of AI in different phases of big data cycle. We also provide an insight into the applications of big data and AI in three different areas, that is, social network, health care, and finance.

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“…A considerable interest in computer vision has been witnessed in past decades (Li et al 2007;Xu 1999Xu , 2006Zhou et al 2003Zhou et al , 2007. Examples of applications of computer vision systems include systems controlling processes, event detection, information organizing, objects modeling, etc (Bennett et al 2008;Juang and Chen 2008;Kerner et al 2004;Wei et al 2006).…”

Section: Introductionmentioning

confidence: 99%