Applications of Neural Network

Now that we are familiar with neural networks and their working, it’s now time to acknowledge the various fields of interest in which ANN plays an important role. In the previous article, we came across some of the applications of ANN in brief. Hence, PythonGeeks brings to you, a fascinating and elaborative list of applications of ANN in various fields. With the help of this article, you will learn about the applications of Artificial Neural Network in a jiffy. So, let us dive straight into it.

Applications of Neural Network

1. Social Media

As we have studied earlier, Artificial Neural Network is a great tool for processing images. It captures the detailing of the features and acknowledges them quite effectively. This feature allows ANN to find extensive use in the area of social networking. Major social networking applications make use of ANN extensively.

As an example, let us consider the “People you might know” element on the leading networking sites. As you may have experienced, social networking sites suggest people have accounts on the network based on the pictures that you post online. This feature is nothing but the application of Artificial Neural Networks in real life.

With the help of facial recognition, the algorithm is able to suggest people that you are friends with. It also helps you tag the people based on their facial features.

The algorithm analyzes your profile, interests as well as friend list to suggest to you a list of people that you might know who have a social presence on the network. It uses data points in the images and identifies a person on the basis of the previously stored database.It tends to look for 100 reference points on the image and then matches it with the database information. This helps industries in providing efficient services to their users using ANNs.

2. Marketing And Sales

Product recommendation and image search are some of the most common applications of ANNs in the field of Marketing and Sales. Whenever you try searching for some products online, the e-commerce sites automatically start suggesting products that are similar to your previous interests or things that may compliment the product that you have bought previously. The algorithm stores the information about your interests on the database and the trained models then identify a pattern in your shopping style based on ANN algorithms.

Not only e-commerce sites but food delivery services have also started adopting this model for providing effective services to their customers. The tech giants not only suggest you with similar products on their sites, they even pester you with the same on social networking sites. The collected database of the algorithm helps the neural network in finding effective marketing strategies. This is what paves the way for a new-age marketing schema and is done by implementing personalized marketing.

Artificial Neural Network proves beneficial in identifying the customer’s likes, interests, and needs based on previous order history and search results. This helps industries in identifying the surge in demand for a certain product and tailoring effective marketing strategies accordingly.

Apart from the marketing schema, ANNs are extensively used for stock exchange prediction. The accuracy of the prediction model is commendable. Companies make use of ANN to predict the prices of the stocks by analyzing and identifying the patterns of the stock price and the environment.

The trained ANN model finds the relationship between stock prices and political, marketing, and other factors that may affect the stock pricing schema. Companies like MJ Futures assured a wholesome of 199.2% of returns over a time span of 2 years. All because of ANN, consumers, as well as stockholders, are able to enjoy the immense benefits from the predictions.

3. Facial Recognition

To date, biometric authentication is one of the most secure methods for user authentication. ANN is proving to do a significant job in recognizing faces on the basis of the images we feed to the system. Well-trained ANN models are also capable of classifying images on the basis of the faces it recognizes through the dataset.

First, it takes in the image as input, and pre-processing begins on the image. This processed image then goes to the next stage for dimensionality reduction. Furthermore, this reduced image is then used for the recognition of the various faces in the image. The two networks that we use while developing this network are fully connected feed forever neural network and PCA for dimensionality reduction.

4. Healthcare

Trained ANN models are an emerging star in the field of biological research. ANNs capture standardized datasets and try to find the pattern in the growth of symptoms and the viruses. The models approach the functioning of the biological clusters in a very effective manner. Apart from these, ANNs find a wide range of applications in the cardiology sector.

ANNs are extensively used for diagnosis, medical imaging, analysis, and radiology in various disciplines of medicines. Applications of ANN are quite effective in pharmacoepidemiology and medical data mining. Apart from these, Artificial Neural Networks also find their applications in the discipline of Oncology. The ANN models are trained so that they can identify and detect cancerous cells at microscopic levels with accuracy.

Facial analysis is used widely for studying the effects of various rare diseases that may manifest in physical characteristics. So, extensive implementation of ANNs in the medical discipline helps in accurate diagnostics abilities and leads to medical evolution.

5. Text Classification and Categorization

Text classification is an important aspect of many applications like web searching, information filtering, readability, language identification, and sentiment analysis. In an attempt to build a CNN on top of word2vec, Convolutional Neural Networks for Sentence Classification was presented by Yoon Kim. This suggested model had the task of filtering negative as well as positive comments on a sentimental basis.

The results of the large number of tests that were conducted revealed that when you tune the hyperparameters to a smaller extent, the model depicts amazing suggestions that are used by the pre-trained vectors. The results were quite commendable for a thesaurus as well. The model showed a requirement of 99.96% of training and depicted 98.40% accuracy.

In a study conducted as an Amazon Review data set, the researchers tried to construct a sentiment polarity dataset with two negative as well as positive datasets. The results were a mix of 97.57% of training accuracy as well as 95.07% of testing accuracy.

One more advantage of this network is paraphrasing the given sentences. While developing quizzical applications involving questions and answers, it becomes very important for the developer to analyze if two different sentences are having the same meaning. Paraphrasing helps the machine in understanding the similarity between the sentences by rearranging the words to form the same sentence. ANN is a crucial part of the development of this process.

6. Part of Speech Tagging

POS or Part-of-Speech tagging is used in many disciplines like parsing, text-to-speech conversion, information extraction, and many others. In the prescribed model Part-of-Speech Tagging with Bidirectional Long Short-Term Memory, the testing by Wall Street Journal data from Penn Treebank III showed a 97.40% tagging accuracy.

7. Aerospace

Due to the effective ability to establish a non-linear relationship between input and output, ANNs find huge applications in Aerospace Engineering. You can train ANN models to perform fault diagnosis, high-performance auto-piloting, securing aircraft controls, and modeling key dynamic simulations.

8. Image Compression

The main idea behind image compression is equalizing the size of the input and output layers. The intermediate layer while producing the output is comparatively smaller. Hence, the compression ratio is the comparison between the input layer and the intermediate layer.

9. Compression Ratio= Input Layer/ Intermediate Layer

The main focus of image compression is to store, encrypt and re-generate the actual image again. Hence, in such networks, the input image is itself used for model training.

10. Speech Recognition

From times unknown, speech has been a key factor of communication for humans. So, as a matter of fact, humans would expect it from the machines as well to be capable enough to connect with them with the help of speech. Despite the technical advancements, humans still feel the need for a sophisticated high-level language to communicate with their machines. In order to help machines have more humane behavior, we have to help them communicate through various spoken languages.

ANN is playing a major part in the development of these speeches recognizing techniques for the machines. ANNs like Multilayer Networks, Kohonen Self-Organizing Maps, and Multilayer Network with Recurrent connections play a significant role in achieving the expected results.

11. Handwriting Recognition

In the introduction of ANN in the previous article, we came across the application of ANN in handwriting recognition. Handwriting recognition is a milestone in the field of fraud detection. The algorithm takes in the bitmap pattern of handwritten characters and outputs the correct letter or digit. The two most widely used applications of handwriting recognition are

• Optical character recognition for data entry
• Validation of signature on a bank cheque

The algorithm accurately analyzes the handwritten characters and thus provides a surge in the security system. The model is trained in such a way that we first feed the input to the first layer. The first layer handles the input queries while the last layer handles the output. The middle layers are the processing layers and have no connecting link to the outside world. The flow for data is unidirectional and thus we call it a feed-forward network. There is no link between the perceptrons of the same layer.

Apart from this, handwriting recognition is also an important part of authenticating users for any system. To date, signatures are a crucial part of the authentication of users into the system. This technique provides a high level of security since you cannot breach the system easily.

12. Personal Assistance

Though Machine Learning has tremendously left its impact on a variety of disciplines, the most fascinating one amongst them has to the way personal assistants work.

Amazon Alexa, Google Home, and Siri are some of the most heard of names in the arena of Personal Assistants. These assistants can help you in the making of plethora works more convenient and accountable. Their work can range from helping you find something on the Internet over just a voice command to helping you look for your holiday destinations.

Personal Assistants can take care of all these in just a jiffy. The major factor working behind these assistants is Natural Language Processing (NLP). NLP makes use of Artificial Neural Networks to attain results with high accuracy and reliability.

13. Weather Forecasting

Conventionally, the meteorological department made forecasts that were never accurate before artificial intelligence came into force. Weather Forecasting is primarily undertaken to anticipate the upcoming weather conditions with prior knowledge of the weather by observing the past weather patterns. In the times of MAchine Learning, weather forecasts are even used to predict the possibilities of natural disasters.

Other Fields of Applications

Apart from the various disciplines that we have mentioned above, we use ANN in a plethora of other disciplines as well. Using the Multilayer Perceptron Network, we can deploy models for Machine Diagnostics and Medical Diagnostics. They use the tan sigmoid function as the activation function for processing.

Classification Supervised Algorithms along with ANNs are a perfect combination for Portfolio Management. Like Medical Diagnostics, Portfolio Management also uses the tan sigmoid function as the activation function for processing. The list of the applications of ANN goes on with the addition of fields like Fraud Detection, Credit Rating, Targeted Marketing, and much more.

Conclusion

ANN is a very effective model in various fields. Problem-solving is the crux of applying the ANN models in these fields. ANN is a very flexible algorithm since it uses the user data to train itself for advancement. Thus, this PythonGeeks article is beneficial as it gives you an insight into the applications of the neural network and helps you in understanding them in a comprehensive way.