Application of Artificial Neural Networks to Business Problems

In summary, the conversation discusses the limited applications of artificial neural networks (ANNs) to real world problems and the potential use of this technology in common business problems. However, there is limited public information available on this topic, as many companies who are using ANNs for business purposes choose to keep their strategies and results confidential.
  • #1
estbit
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Hello everyone!

Searching for applications of artificial neural networks (ANNs) to real world problems, all I found were applications to computer science problems (e.g. face recognition, OCR, automated control of systems and machines etc.) and some very experimental works (e.g., crime forecast, traffic jam forecast etc.), besides some medical developments (e.g. automatic diagnosis).

Is anyone aware of any attempt to apply this technology to common problems of common companies and industries? I mean, is there any ANN-solution to problems in the field of human resources, sales forecast, product development, marketing research, and other things like that?

estbit
 
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  • #2
You may not find much public information on this.

A prof a couple of decades ago who attended conferences where neural networks were discussed said roughly "We started noticing new people showing up at meetings, asking questions about how to apply these to business, things like that. Soon there was some excitement, descriptions of how these people were applying this and early results they were seeing. Then it went completely dark and none of them would say what they were doing, who they worked for or even who they were. We assumed that meant they realized there was profit to be made."
 

What is an artificial neural network?

An artificial neural network is a type of machine learning algorithm that is inspired by the structure and function of the human brain. It is a network of interconnected nodes that work together to process and analyze data, and then make predictions or decisions based on the patterns and relationships found in the data.

How can artificial neural networks be applied to business problems?

Artificial neural networks can be applied to business problems in a variety of ways. They can be used for predictive analysis, such as forecasting sales or predicting customer behavior. They can also be used for classification tasks, such as identifying fraudulent transactions or categorizing customer feedback. Additionally, artificial neural networks can be used for optimization, such as determining the most efficient route for product delivery.

What are the advantages of using artificial neural networks in business?

There are several advantages to using artificial neural networks in business. They can handle large and complex datasets, making them suitable for analyzing large amounts of business data. They are also able to learn and adapt to new patterns and data, making them useful for dealing with changing business environments. Additionally, artificial neural networks can provide accurate and reliable predictions, which can help businesses make informed decisions.

What are the limitations of using artificial neural networks in business?

While artificial neural networks have many advantages, they also have some limitations. They require a large amount of data to train and can be computationally expensive. They also lack transparency, meaning it can be difficult to understand how they reach their decisions. Additionally, artificial neural networks may not perform well when faced with new or unexpected data, which can limit their effectiveness for real-time decision making.

What are some examples of successful applications of artificial neural networks in business?

There are numerous successful applications of artificial neural networks in business. For example, companies like Amazon and Netflix use artificial neural networks for personalized product recommendations. Banks and financial institutions use them for fraud detection and credit scoring. Retailers use them for demand forecasting and inventory management. Other examples include using artificial neural networks for speech recognition, image recognition, and natural language processing in customer service chatbots.

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