Earlier we discussed "What Neural Networks", "How do they work" and "Types of Neural Networks".
In this article, we are going to discuss the "Applications of Neural Networks"
Applications of Neural Networks are as follows:-
1. Facial Recognition
Facial Recognition Systems are serving as robust systems of surveillance. Recognition Systems match's the human face and compares it with digital images. They are used in offices for selective entries. The systems thus authenticate a human face and match it up with the list of IDs that are present in its database.
Convolutional Neural Networks (CNN) are used for facial recognition and image processing. A large number of pictures are fed into the database for training a neural network. The collected images are further processed for training.
Sampling layers in CNN are used for proper evaluations. Models are optimized for accurate recognition results.
2. Stock Market Prediction
Investments are subject to market risks. It is nearly impossible to predict the upcoming changes in the highly volatile stock market. The forever-changing bullish and bearish phases were unpredictable before the advent of neural networks. But well what changed it all? Neural Networks of course…
To make a successful stock prediction in real-time a Multilayer Perceptron MLP (class of feedforward artificial intelligence algorithm) is employed. MLP comprises multiple layers of nodes, each of these layers is fully connected to the succeeding nodes. Stock’s past performances, annual returns, and non-profit ratios are considered for building the MLP model.
3. Social Media
No matter how cliche it may sound, social media has altered the normal boring course of life. Artificial Neural Networks are used to study the behaviors of social media users. Data shared every day via virtual conversations is tacked up and analyzed for competitive analysis.
Neural networks duplicate the behaviors of social media users. Post analysis of individuals' behaviors via social media networks the data can be linked to people’s spending habits. Multilayer Perceptron ANN is used to mine data from social media applications.
MLP forecasts social media trends, it uses different training methods like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). MLP takes into consideration several factors like users’ favorite Instagram pages, bookmarked choices, etc. These factors are considered as inputs for training the MLP model.
In the ever-changing dynamics of social media applications, artificial neural networks can definitely work as the best fit model for user data analysis.
4. Aerospace
Aerospace Engineering is an expansive term that covers developments in spacecraft and aircraft. Fault diagnosis, high-performance auto-piloting, securing the aircraft control systems, and modeling key dynamic simulations are some of the key areas that neural networks have taken over. Time delay Neural networks can be employed for modeling non-linear time dynamic systems.
Time Delay Neural Networks are used for position-independent feature recognition. The algorithm thus built based on time-delay neural networks can recognize patterns. (Recognizing patterns are automatically built by neural networks by copying the original data from feature units).
Other than this TNN is also used to provide stronger dynamics to the NN models. As passenger safety is of utmost importance inside an aircraft, algorithms built using neural network systems ensure the accuracy of the autopilot system. As most of the autopilot functions are automated, it is important to ensure a way that maximizes security.
5. Signature Verification and Handwriting Analysis
Signature Verification, as the self-explanatory term goes, is used for verifying an individual’s signature. Banks and other financial institutions use signature verification to cross-check the identity of an individual.
Usually, signature verification software is used to examine the signatures. As cases of forgery are pretty common in financial institutions, signature verification is an important factor that seeks to closely examine the authenticity of signed documents.
Artificial Neural Networks are used for verifying the signatures. ANN is trained to recognize the difference between real and forged signatures. ANNs can be used for the verification of both offline and online signatures.
For training an ANN model, varied datasets are fed in the database. The data thus fed help the ANN model to differentiate. ANN model employs image processing for the extraction of features.
Handwriting analysis plays an integral role in forensics. The analysis is further used to evaluate the variations in two handwritten documents. The process of spelling words on a blank sheet is also used for behavioral analysis. Convolutional Neural Networks (CNN) is used for handwriting analysis and handwriting verification.
6. Healthcare
The age-old saying goes like “Health is Wealth”. Modern-day individuals are leveraging the advantages of technology in the healthcare sector. Convolutional Neural Networks are actively employed in the healthcare industry for X-ray detection, CT scans, and ultrasound.
As CNN is used in image processing, the medical imaging data retrieved from the aforementioned tests are analyzed and assessed based on neural network models. Recurrent Neural Network (RNN) is also being employed for the development of voice recognition systems.
Voice recognition systems are used these days to keep track of the patient’s data. Researchers are also employing Generative Neural Networks for drug discovery. Matching different categories of drugs is a hefty task, but generative neural networks have broken down the hefty task of drug discovery. They can be used for combining different elements which form the basis of drug discovery.
7. Defence
Defense is the backbone of every country. Every country’s state in the international domain is assessed by its military operations. Neural Networks also shape the defense operations of technologically advanced countries. The United States of America, Britain, and Japan are some countries that use artificial neural networks for developing an active defense strategy.
Neural networks are used in logistics, armed attack analysis, and object location. They are also used in air patrols, maritime patrol, and for controlling automated drones. The defense sector is getting the much-needed kick of artificial intelligence to scale up its technologies.
Convolutional Neural Networks(CNN), are employed for determining the presence of underwater mines. Underwater mines are the underpass that serves as an illegal commute route between two countries. Unmanned Airborne Vehicle (UAV), and Unmanned Undersea Vehicle (UUV) these autonomous sea vehicles use convolutional neural networks for image processing.
Convolutional layers form the basis of Convolutional Neural Networks. These layers use different filters for differentiating between images. Layers also have bigger filters that filter channels for image extraction.
8. Weather Forecasting
The forecasts done by the meteorological department were never accurate before artificial intelligence came into force. Weather Forecasting is primarily undertaken to anticipate the upcoming weather conditions beforehand. In the modern era, weather forecasts are even used to predict the possibilities of natural disasters.
Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Networks (RNN) are used for weather forecasting. Traditional ANN multilayer models can also be used to predict climatic conditions 15 days in advance. A combination of different types of neural network architecture can be used to predict air temperatures.
Various inputs like air temperature, relative humidity, wind speed, and solar radiations were considered for training neural network-based models. Combination models (MLP+CNN), (CNN+RNN) usually work better in the case of weather forecasting.
Hence in the above text, we discussed the "Major Applications of Neural Networks".
Now we'll discuss the next topic of AI that is "Evolutionary Computing"
Till then Stay Tuned Stay Safe.
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