Applications of Neural Networks 2023 : Aalpha
Artificial Intelligence has brought a revolutionary change in the wide IT field. Many times, you will see adverts or suggestions of adverts of the things you use in common, and you will wonder how that is possible. Again, you may wonder how Siri and Google assistants listen and follow your prompts as directed. All these scenarios are a result of AI, where artificial neural networks (ANN) form an element to guide the machines on how to respond to prompts just like human beings would.
Defining neural network
Just the way the human brain functions, it is the same way neural networks function. Therefore, the neural network is a way through which AI prompts the computer to process information and act like a human brain.
It is simply a machine learning process known as deep learning, and it depends on neurons or nodes that are layered like a human brain. Through neural networks, computers can adapt to the system, learn continuously, and keep improving for a better experience. This, therefore, means that artificial neural networks try as much as possible to recognize faces, sum up documents, and solve complex problems in a more accurate way.
What are the applications of Neural Networks?
Neural networks play significant roles in regulating and controlling how some sectors, including automotive, healthcare, and finance, among many others, operate. For a better understanding of the neural networks, below are some of the applications you should know:
Social media, being wide, has now improved the overall user experience, thanks to neural networks. Artificial neural networks have the ability to study and analyze different social media users through the data they share daily. Besides, data mining from different social media platforms is achieved through multilayer perceptron ANN.
What’s more, MLP forecasts social medial trends through varied training methods such as MSE -mean squared error, MAE (Mean Absolute Error) to analyze user’s favorite pages when using social media. With this said, it is evident that an artificial neural network is the best approach when performing user data analysis.
The facial recognition system is now the order of modern technology. The system is programmed to recognize and match the faces of human beings with related digital pictures. This technology is commonly applied in offices to perform specific entries where the system is programmed to verify the human face and match it with the listed IDs available in the database.
CNN -convolutional neural networks play a key role in processing the image and recognizing the faces. Through neural networks, it is possible to process and feed a large number of pictures into the database for further processing and then later for training.
The CNN has sampling layers that play a key role in proper evaluations where the models are used for producing accurate recognition results.
Aerospace Engineering is a general term covering the wide aircraft and spacecraft field. Among the significant areas where neutral networks apply in aerospace engineering include modeling key dynamic simulations, securing aircraft control systems, high-performance autopiloting, and fault diagnosis.
TNN- time delay neural network is applied when modeling systems with non-linear dynamics, providing stronger dynamics to the neural networks and positioning independent feature recognition. This implies that the algorithm built on tome delay neural networks is programmed to recognize and verify patterns.
In the spacecraft, the most critical aspect is the safety of the passengers. Therefore, the algorithm of such a system is programmed to enhance an accurate autopilot system.
Investment is a wide subject where risks are involved on a higher percentage. As a result, predicting what will change and what will remain constant can be a bit hard. However, the unpredictability rate was high before the invention of neural networks. With neutral networks, everything changed for the better.
Through neutral networks, a multilayer perceptron (a system that provides artificial intelligence feedback) can predict a successful stock market in real time. An MLP is made up of varied node layers, with each layer connected to a successful node. As a result, it becomes easy to analyze past performance on the stock market, do annual returns, and calculate non-profit ratios.
Healthcare is another sector where neutral networks apply. Through neutral networks, we can see the manifestation of the old saying: health is wealth. People are now taking advantage of the advanced technology to enhance the healthcare sector.
Among the major healthcare sectors where neutral network plays a big role include ultrasound, CT scan, and X ray detection.
Through CNN, the system can process images, then the data retrieved from the tests and accessed through neural network models. This depends on the service being done.
RNN- recurrent neural network plays a key role in recognizing and verifying voices from the systems.
Nowadays, healthcare personnel can easily use voice recognition features to get the patient’s information, thanks to neural networks.
Currently, experts are on the move to research GNN- generative neutral networks meant for discovering and categorizing different drugs accordingly.
Ideally, every country must have a solid team for defense. As a result, the international domain can access every country’s data in military operations through neutral networks. What’s more, advanced countries use neutral networks to strengthen their defense system. For instance, Japan, Britain, and the USA are among the technologically advanced countries using neural networks to shape their defense system.
Among the areas where neutral networks play key roles in the defense sector include object location, armed attack analysis, and logistics. Besides, neutral networks are used in controlling automated drones, maritime patrols, and air patrols to strategize a perfect defense system.
Through CNN, it is easy to detect the existence of illegal commute routes (underwater mines) between countries.
Before the rise of artificial intelligence, weather forecasts were just estimates and not quite accurate. The aim of weather forecasts is to predict the upcoming weather conditions and take precautionary measures if need be. Information on weather is crucial as it can help predict possible calamity in the near future, thus helping the people surrounding a given environment take the necessary safety measures.
Through MLP, RNN, and CNN, it is now possible to forecast accurate weather conditions beforehand. What’s more, ANN multilayer models have the ability to predict the weather 15 days in advance, hence improving the overall experience. Combining varied neutral networks can enhance the prediction of temperatures.
Signature verifications are crucial in determining the viability of an individual’s data. Institutions like banks use this technology to verify the identity of people.
Signature forgery is quite common among many financial institutions, hence the need for a sophisticated signature software solution to verify the details.
Through ANN, it is now possible to differentiate between the actual and the fake signatures. The best part is that ANN can be used for offline and online applications.
What is the future of neural networks?
Many companies are now opting to use AI and machine learning in their operations. As a result, the future of neural networks is anticipated to grow globally. Users across the globe will now enjoy customized neutral network applications for a better experience in different fields. The best examples include:
- Mobile and web apps provide personalized options based on social media history and activity
- The rise of hyper-intelligent VA tools such as Siri and Google Assistant, thus making work easier
The technology is advancing at a steady rate. If you are planning to use a neutral networks application to scale and customize your business, you are sure on the right move.
For more information connect with out AI development company : Aalpha information systems!