Constrained Voting Extreme Learning Machine and its Application

Project Code :TCMAPY466

Objective

The main objective of this application is to investigate a specific problem of whether it is valuable or not to use Extreme Learning machines for getting insights from complex patterns in data instead of using deep learning techniques.

Abstract

Extreme learning machine (ELM) has been proved to be an effective pattern classification and regression learning mechanism by researchers. However, its good performance is based on a large number of hidden layer nodes. In this paper, we propose a novel algorithm, named extreme learning machine (ELM). Compared with the Machine Learning algorithms, the ELM determines the input weight and bias based on the differences of between-class samples. The proposed method is evaluated on public benchmark datasets. The experimental results show that the proposed algorithm is superior to the other Machine Learning models. Further, we apply the ELM to the classification of superheat degree (SD) state in the aluminium electrolysis industry, and the recognition accuracy rate reaches 87.4%, and the experimental results demonstrate that the proposed method is more robust than the methods.

 KEYWORDS: extreme learning machine (ELM), Machine Learning, ensemble method, sample based learning, superheat degree (SD).

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE SPECIFICATIONS:

  • Processor- I3/Intel Processor
  •  RAM- 4GB (min)
  • Hard Disk- 128 GB
  • Key Board-Standard Window
  •  Keyboard. Mouse-Two or Three Button Mouse.
  • Monitor-Any.

SOFTWARE SPECIFICATIONS:

  • Operating System: Windows 7+
  • Technology: Python 3.6+
  •  IDE: PyCharm IDE
  •  Libraries Used: Pandas, NumPy, Scikit-Learn, Matplotlib.

Learning Outcomes

  • About Python.
  • About PyCharm.
  • About Pandas.
  • About Numpy.
  • About HTML.
  • About CSS.
  • About JavaScript.
  • About Database.
  • About Machine Learning.
  • About Artificial Intelligent.
  • About how to use the libraries.
  • Project Development Skills:
    • Problem analyzing skills.
    • Problem solving skills.
    • Creativity and imaginary skills.
    • Programming skills.
    • Deployment.
    • Testing skills.
    • Debugging skills.
    • Project presentation skills.
    • Thesis writing skills.

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