Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm.
Applications of Neural Networks
Neural Networks are regulating some key sectors including finance, healthcare, and automotive. As these artificial neurons function in a way similar to the human brain. They can be used for image recognition, character recognition and stock market predictions. Let’s understand the diverse applications of neural networks
1. Facial Recognition
Facial Recognition Systems are serving as robust systems of surveillance. Recognition Systems matches the human face and compares it with the 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. 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
Neural networks duplicate the behaviours of social media users. Post analysis of individuals' behaviours 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 user’s favourite instagram pages, bookmarked choices etc. These factors are considered as inputs for training the MLP model.
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 modelling non linear time dynamic systems.
5. Defence
Defence 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 defence 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 defence strategy.
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 Scan and ultrasound.
7. Signature Verification and Handwriting Analysis
Artificial Neural Networks are used for verifying the signatures. ANN are trained to recognize the difference between real and forged signatures. ANNs can be used for the verification of both offline and online signatures.
8. Weather Forecasting
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.
You must be logged in to post a comment.