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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment.

Yalla Venkateswarlu1, K Baskar2, Anupong Wongchai3

  • 1Department of Computer Science and Engineering, BVC College of Engineering, Rajahmundry, East Godavari District, Andhra Pradesh, India.

Computational Intelligence and Neuroscience
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This study introduces an oppositional ant lion optimizer-based feature selection with machine learning classification (OALOFS-MLC) model for financial crisis prediction in big data environments. The OALOFS-MLC model enhances prediction accuracy for small and medium-sized enterprises.

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Area of Science:

  • Financial technology and Big Data analytics.
  • Machine learning applications in finance.

Background:

  • Big Data, IoT, and cloud computing are transforming business data analysis.
  • Effective financial crisis prediction (FCP) is crucial for economic stability.
  • Existing FCP methods lack optimal classifier efficiency and predictive accuracy.

Purpose of the Study:

  • To develop an advanced model for Financial Crisis Prediction (FCP) in a Big Data environment.
  • To improve the accuracy and efficiency of FCP for small and medium-sized enterprises.
  • To introduce a novel feature selection algorithm for enhanced classification.

Main Methods:

  • Utilized Hadoop MapReduce for Big Data management in the financial sector.
  • Developed an Oppositional Ant Lion Optimizer-based Feature Selection (OALOFS) algorithm for optimal feature subset selection.
  • Employed a Deep Random Vector Functional Links Network (DRVFLN) for the classification process.

Main Results:

  • The OALOFS-MLC model demonstrated superior performance compared to existing approaches.
  • Achieved improved classification results through optimized feature selection.
  • Validated the model's effectiveness using a baseline dataset.

Conclusions:

  • The OALOFS-MLC model offers a significant advancement in Financial Crisis Prediction.
  • The proposed approach enhances predictive accuracy and efficiency in Big Data settings.
  • This model provides a robust tool for forecasting financial failures in SMEs.