Common web problems where Neural Networks could help

Question

I was wondering if you creative minds out there could think of some situations or applications in the web environment where Neural Networks would be suitable or an interesting spin.

Edit: Some great ideas here. I was thinking more web-centric. Maybe bot detectors or AI in games.

Answer 1

Here is some application in the web development where I think neural networks would be suitable:

• Image Processing and Character recognition: Artificial Neural Network can have a lot of inputs, process them to infer hidden as well as complex, non-linear relationships, ANNs are playing a big role in image and character recognition. Character recognition like handwriting, correcting mistakes has a lot of applications in fraud detection (e.g. bank fraud, ) and even national security work. Image recognition also has large applications from facial recognition in social media, cancer detention in medicine to satellite image processing for agricultural and defense usage. The research on ANN now has paved the way for deep neural networks that forms the basis of “deep learning” and which has now opened up all the exciting innovations in computer vision, speech recognition, natural language processing — famous examples being self-driving cars.

• Human Face Recognition:  If a neural network is well trained, then it can be divided into two classes namely images having faces and images that do not have faces. First, all the input images must be preprocessed. Then, the dimensionality of that image must be reduced. And, at last, it must be classified using neural network training algorithm. Following neural networks are used for training purposes with preprocessed image −

-Fully-connected multilayer feed-forward neural network trained with the help of the back-propagation algorithm.

-For dimensionality reduction, Principal Component Analysis (PCA) is used.

• Forecasting: Forecasting is required in everyday business decisions (e.g. sales, the financial allocation between products, capacity utilization), in economic policy, in finance and the stock market. More often, forecasting problems are complex, for example, predicting the stock prices is a complex problem with a lot of underlying factors (some known, some unseen). Traditional forecasting models provide limitations in terms of taking into account these complex models. ANNs can be applied in the right way, can provide better alternatives, giving the ability to the model to extract unseen features and relationships. Also, unlike these traditional models, ANN doesn’t have any restrictions on input and residual distributions.

• Healthcare: MetaMind uses deep learning networks for image recognition and text analysis. Their image recognition software enables ad targeting, prediction of customer preferences and automated data entry using pictures. Their textual analysis software supports identification and tracking of customer sentiment, opinion and attitude monitoring across different online channels, and automated customer service and support.

• Robotics: Nowadays, we have different kinds of robots, such as the Dog robot. The dog robot acts just like a Dog. It will bark, it will growl, it can jump, and etc. These robots again use AI to understand the behavior of the Dog.

• Voice Generation: Products like Amazon Alexa uses deep learning to generate voice and interact with humans.

• Self Driving Vehicles: Google’s self-driving car is based on Machine Learning and Deep Learning algorithms. It can drive at a precision of 98% in dark, while its raining and in high terrain areas.

• Producing Music: Deep Learning can be used to produce music by feeding in music patterns and letting it analyze on its own. It can also be used to restore audio voices in silent movies.

You can refer to this blog for further web-based application: https://intellipaat.com/blog/what-is-artificial-neural-network/

Watch this video to learn about Neural Networks: