What is Artificial Neural Network? Five Main usage

An artificial neural network (ANN) is at the forefront of the computer systems for simulating the manner in the human brain analyses as a basis to develop algorithms that can be used to model complex patterns and prediction problems. ANNs have the capacity to learn themselves so that more data will yield better outcomes. Artificial neural networks are paving the way for very popular and useful mechanisms for solving many problems.

Let’s see types of Neural Networks and their usages.

1. Image recognition — Hopfield Network (HN)

Artificial Neural networks are computing systems designed to recognize patterns. Given the capacity of Artificial Neural Networks to obtain many inputs, process them in hidden and dynamic non-linear relationships, ANNs play a major role in image recognition. Image preprocessing, date reduction, segmentation, and recognition are the processes used in managing images with ANN. The identification of images is an ever-widening field that is already prevalent in many industries. With extensive applications from social media facial recognition, detention in medicine to agricultural, geotechnics, civil engineering, mechanics, industrial surveillance, automatics, transport, and defense, etc.

Medical image analysis

Technology based on Artificial Neural networks allows radiologists to deal with a vast number of medical images: computed tomography (CT) and ultrasound scans, magnetic resonance imaging (MRI), or x-rays. Medical pictures are analyzed and then compared with information from the medical records of the patients, which is used by the radiologists to planning treatment. Techniques for the diagnosis and detection of breast cancer are commonly used in medical imaging.

Facial recognition to improve the airport experience

Facial recognition is becoming mainstream among airlines that use it to enhance boarding and check-in. These enhancements have two main directives: to pursue self-service trends and biometric technology and to enhance the airport experience safer and faster. Also, Face recognition along with a 24-hour monitoring camera video stream will identify the passenger with their records. Which it claims can help validate a person’s identity quickly and efficiently using biometrics and facial recognition technology. It helps airports identify and authenticate passengers at the pedestrian gate as well as other security touchpoints turnstiles using facial recognition technology.

Visual product search

The gap between online and offline shopping has vanished since visual search entered the game. As an example, some apps can Scan + Shop feature which gives consumers the ability to scan and instantly order an item in a physical shop or printed it in magazines. The online shopping experience is enhanced by visual search. Apps with this capability are powered by neural networks. ANNs process images uploaded by users and generate image descriptions (tags), for features. Search results are presented based on a similarity score.

Online shopping sites like Amazon, AliExpress makes users buy more than one product that they came to buy thanks to the use of neural networks in their systems. When you search a product for example “RGB Light Strips” in the search box it recommends products most relevant to your search including Amazon’s Choices as well. This uses neural networks to learn from past patterns and phrases providing users with the best choice of selections. The recommendation system is another part that heavily uses neural networks to recommend a product to the user according to his/her likes. The neural networks learn user’s behavior and patterns which are then used to provide the best recommend results.

2. Audio analysis — Recurrent Neural Network (RNN)

Natural Language Processing (NLP) is a common technique used in RNNs to build voice-recognizing applications. If you’ve ever talked into a virtual assistant like Siri or Alexa, you have used an RNN. The Healthcare industry is being completely transformed using NLP and voice recognition application

Treat speech impaired children

Voices are an important fact for identifying autistic children. Speech recognition system for speech pathologists to identify speech impairments, pronunciation errors, emotion state, word count for the responding age range, to identify the tone issues of the children, and give a speech report.

Analyzing speech with PRAAT

PRAAT lets you record or interpret a sound from the sound on the disk using your microphone or another audio input device. Then you will be able to look at this sound ‘indoors.’ Certainly PRAAT is not limited to speech sounds: the sounds produced are studied by musicians and bio-acoustics.

Text-To-Speech

A Text-To-Speech (TTS) device uses written text as the input to translate it into an audible format for listening to what is in the text. It detects what is on the computer and reads it aloud. TTS can be used to overcome the literacy barrier of the common masses, increase the possibilities of improved man-machine interaction through online newspaper reading from the internet, and enhancing other information systems such as learning guides for students, IVR (Interactive Voice Recognition) systems, automated weather forecasting systems and so on by saving valuable time.

3. Natural Language Processing — Gated Recurrent Unit (GRU)

Handwriting Recognition or Handwritten Text Recognition (HTR) is still not considered a challenging problem. Artificial Neural Networks’ recent advances have quickly followed our success in solving the recognition of handwritten text.

Now it is affecting many sectors,

Banking

As a large number of checks need to be processed daily in a bank, a handwriting text recognition system can save costs and hours of human work. Requires a banker to read and enter, manually and check entries, such as signature and date, on the cheque present information. A verification is made by taking pictures of the signature by testing signatures or other handwriting.

Insurance

More than 20 million documents are received daily by the broad insurance industry and a delay in processing the claim can have a disastrous effect on the business. The claims document will contain different forms of handwriting and it will absolutely slow the pipeline down by pure manual automation of processing claims.

4. Data analysis — Restricted Boltzmann Machine (RBM)

The artificial neural network (ANN) analysis is a method of data analysis, that imitates the working method of the human brain. Over the years, the influence of ANNs has been demonstrated in several types of issues of varying complexity and in various fields of use.

Analysis of remotely sensed data.

In many industries, online calculation of the chemical composition in various operating conditions is a major issue. An approach for the estimation of composition from chemical-metric data was developed based on hybrid signal preparation and artificial neural network paradigms.

Using Deep Learning AI to Predict the Stock Market

Using an Artificial neural network, we can make estimated guesses and informed forecasts based on the information we have in the present and the past regarding any stock. An estimated guess from past movements and patterns in stock price is called Technical Analysis. We can use Technical Analysis (TA)to predict a stock’s price direction, Sales predictions, predict Bitcoin prices.

5. Medical analysis — Generative Neural Network

Healthcare is one of those industries which most apply these technologies. Since medical professionals often strive to find ways of applying emerging technology to produce impactful outcomes, wellbeing is a priority. Deep learning collects large data volumes including records of patients, medical reports, and insurance records, and uses its neural networks to produce the best results. The Generative Neural Network is the last neural network in the healthcare industry (GAN).

Drug discovery

The discovery of medicines in healthcare is a long and expensive process. But now profound healthcare education enables the discovery and development of medicines. The technology analyzes and delivers the right treatment for the medical history of the patient. In addition, this approach gains insight into the symptoms and tests of patients.

Insurance fraud

ANN is used to analyze medical insurance fraud claims. It can forecast fraud allegations that could arise in the future with predictive analytics. In addition, deep learning allows the insurance industry to give their targeted patients discounts and deals.

The aim is to find accurate treatments for a person based on his or her personal medical background, lifestyle choices, genetic information, and pathological tests which constantly change. Going beyond the prediction, AI tools might be able to model may help direct and train medical practitioners to intervene even before a person demonstrates symptoms.

Conclusion:

In this, I have given an overview of the artificial neural network and its main usage.👏

See you soon…