Reference no: EM132254122

Assignment

Task 1: 150-180 words with references

What is data warehousing characteristics and functions of data?

Task 2: 150-180 words with references

Data warehouse architecture employs a three-tier structure. Please describe all the three-tier.

Task 3: 150-180 words with references

Topic Question:

Describe one unique and specific example where you would use Nonlinear Regression methods or Neural Networks and explain WHY. Use references and justification to support your point of view.

Discussion Post on Topic:

The BP neural network is a widely-used parallel processing connection network that simplifies and abstractly simulates basic functions of the human brain through mathematical methods derived from studying how the human brain thinks (Xiaolan& Wenjun, 2018, p. 2770). Its simulated functions include learning, memory, calculation, and intelligent processing.

This artificial neural network has largely been applied to research on innovation within high tech fields, with minimal attention paid to the use of neural networks in research involving creative talent in cultural industries.

The authors' study, which focuses on evaluating creative talent, defines the creative process as a series of behavioral processes of creative production, creative cooperation, and creative realization that display the flash of individual inspiration to create new consciousness and material results (Xiaolan& Wenjun, 2018, p. 2769).

Using samples taken from evaluations of the innovation of creative talents employed in cultural industries by nine experts in the field of innovation, the study uses the nonlinear mapping, self-learning, and strong fault-tolerant abilities of the BP neural network to establish the evaluation model and to carry out case analysis and verification (Xiaolan& Wenjun, 2018, p. 2773).

The authors concluded that, in comparison to conventional evaluation methods, the BP neural network is able to simulate experts that conduct quantitative evaluation, which is achieved through repeated learning and training, and thus avoids human error normally associated with the evaluation of creative talent in cultural industries.

However, the artificial neural network is unlikely to replace Academy Award voters any time soon, especially since the model requires numerous training samples and does not involve the same degree of accuracy as conventional algorithms (Xiaolan& Wenjun, 2018, p. 2775).

Reference:

Xiaolan Chang, & Wenjun Li. (2018). Evaluation of Creative Talents in Cultural Industry based on BP Neural Network. International Journal of Performability Engineering, 14(11), 2769-2776.

Reply to Discussion Post:

Task 4: 150-180 words with references

Topic Question:

Describe one unique and specific example where you would use Nonlinear Regression methods or Neural Networks and explain WHY. Use references and justification to support your point of view.

Discussion Post on Topic:

The use of science and technology to predict atmospheric conditions for a given location is weather forecasting. The forecasting involves quantitatively measuring the atmospheric conditions and utilizing the measured parameters and relevant scientific understanding to predict future changes in the weather.

Doing this requires massive computational power to solve the mathematical equations for prediction and a comprehensive understanding of the current and past weather conditions. The accuracy of prediction increases with the measurement accuracy of measuring initial conditions (Abhishek, Singh, Ghosh, & Anand, 2012)

Artificial Neural Networks (ANN) are a set of algorithms, modeled loosely after the human brain, that is designed to recognize patterns. ANN is one of the major tools used for machine learning and it is highly used in the field of weather forecasting.

The advantage that ANN has over other methods of weather forecasting is that the ANN minimizes the error with different algorithms and gives us a predicted value that is almost equal to the actual value.

Some of the examples of using ANN for weather forecasting include the quantitative prediction of rainfall amounts in Dallas-Ft. Worth area (Hall,1999). ANN was used to process output from numerical weather prediction models to provide more accurate and localized predictions of rainfall in four separate regions of the Mid - Atlantic United States( Kuligowski, & Barros, 1998)

REFERENCES

Abhishek, K., Singh, M. P., Ghosh, S., & Anand, A. (2012). Weather forecasting model using an artificial neural network. Procedia Technology, 4, 311-318.

Hall, T., Brooks, H. E., & Doswell III, C. A. (1999). Precipitation forecasting using a neural network. Weather and Forecasting, 14(3), 338-345.

Kuligowski, R. J., & Barros, A. P. (1998). Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather and Forecasting, 13(4), 1194-1204.