8 Basic Applications of The Neural Networks

A neural network is a series of algorithms that ventures to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. A neural network works similarly to the human brain neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture.

The concept of the neural network is gaining much popularity in today’s world as it can be used in various fields. They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.

Neural networks, mostly artificial neural networks, are broadly applied in functional approximation or regression analysis, classification, and data processing. Artificial neuron network is also applied in areas like non-linear system identification and control, game playing and decision making, pattern recognition, and sequence recognition.

8 Basic Application Areas of Neural Networks

Here are some areas where neural networks are being widely used.

1. Image Processing and Character Recognition

Neural networks play a big role in image processing and character recognition. Character recognition, such as handwriting, has many applications in the identification of fraud (e.g. bank fraud) and even domestic safety assessments. Image recognition is also an ever-growing field with widespread applications. The artificial neural network can also do facial recognition in social media, cancer detention in medicine to satellite imagery processing for agricultural and defense usage.

2. Forecasting

Artificial neural networks have become objects of everyday use, although few people know of it. Forecasting is important for everyday business decisions, in economic and monetary policy, in finance and the stock market. Mostly, forecasting problems are complex. Predicting inventory prices, for instance, is a complicated issue with many underlying variables (some recognized, some unseen). Traditional forecasting models create constraints to take these complicated, non-linear interactions into consideration. For its capacity to model and extract invisible characteristics and interactions, ANNs, implemented correctly, can provide a solid solution.

3. Medical Diagnosis

We can also use a neural network in medical diagnosis and can solve many medical problems. Neural networks are ideal in recognizing diseases using scans there is no need to provide a specific algorithm on how to identify the diseases. They are used experimentally to model the human cardiovascular system. Artificial neural networks can also detect complex medical conditions by fusing the data from individual biomedical sensors. An ANN-based hybrid lung cancer detection system named HLND improves the accuracy of diagnosis and the speed of lung cancer radiology. ANNs are also used to detect early-stage breast cancers through image processing. We take mathematical modeling of neural networks in analyzing medical diagnoses using the allowable range of parameters as future work.

4. Pattern Recognition

Neural networks are widely used in pattern recognition as well. The prominent characteristics of the neural networks are that they can learn complex nonlinear input-output relationships, use sequential training procedures, and adapt themselves to the data. Feed-forward network, which includes multilayer perceptron and Radial-Basis Function (RBF) networks are the most commonly used family of neural networks for pattern classification tasks. The growing popularity of neural network models to solve pattern recognition issues was mainly owing to their low reliance on domain-specific expertise and the accessibility of effective learning algorithms for professionals to use. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons.

5. Data Mining

Data mining, also known as data discovery and knowledge discovery, is the process of analyzing hidden patterns of data against criteria to categorize the data into useful information. Neural Network is used for effective data mining to turn raw data into useful information. Neural networks search for patterns in large batches of data, enabling a business to learn more about their customers, which guides their marketing strategies, increases revenues, and lowers expenses.

6. Marketing

Neural networks can also be viewed as an enabling tool for marketing professionals to work smarter and achieve higher levels of effectiveness. In recent years, the neural network has been moving from research laboratories into the business world and is already at work in the world of banking and finance. They are being used in making marketing decisions. A marketing-based neural network is a software decision tool that helps decision-makers in the selection of an appropriate response to a particular situation.

7. Control Systems

The main areas of application of neural networks for control of processes are identification, optimization, cancellation of nonlinearities and adaptive control of complex processes with variable and non-stable parameters. Neural networks can be used for control in both reinforcement learning and supervised learning settings.

8. Speech Recognition

An artificial neural network that uses anatomical and physiological findings on the afferent pathway from the ear to the cortex is presented and the roles of the constituent functions in recognition of continuous speech are examined. The network deals with successive spectra of speech sounds by a cascade of several neural layers: lateral excitation layer (LEL), lateral inhibition layer (LIL), and a pile of feature detection layers (FDL’s). These layers are shown to be effective in recognizing spoken words.

In Conclusion

Neural networks are considered simple mathematical models to enhance existing data analysis technologies. Although it is not comparable with the power of the human brain, still it is the building block of artificial intelligence.

Many of the things we all do every day involve recognizing patterns and using them to make decisions, so neural networks can help us out in zillions of different ways. They can help us forecast the stock market or the weather, operate radar scanning systems that automatically identify enemy aircraft or ships, and even help doctors to diagnose complex diseases based on their symptoms.

Neural networks have even proved effective in translating text from one language to another. Neural networks can be used in various fields in the future and can make our lifestyle easier.

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