Industry use cases of Neural Networks and elaborating how it works.

Biological Model of Neural Networks Functions

All mammalian brains consist of interconnected neurons that transmit electrochemical signals. Neurons have several components: the body, which includes a nucleus and dendrites; axons, which connect to other cells; and axon terminals or synapses, which transmit information or stimuli from one neuron to another. Combined, this unit carries out communication and integration functions in the nervous system. The human brain has a massive number of processing units (86 billion neurons) that enable the performance of highly complex functions.

What is Neural Networks?

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models — essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques. Neural networks have provided us with greater convenience in numerous ways, including through ridesharing apps, Gmail smart sorting, and suggestions on Amazon.

How Artificial Neural Networks Function

ANNs are statistical models designed to adapt and self-program by using learning algorithms in order to understand and sort out concepts, images, and photographs. For processors to do their work, developers arrange them in layers that operate in parallel. The input layer is analogous to the dendrites in the human brain’s neural network. The hidden layer is comparable to the cell body and sits between the input layer and output layer (which is akin to the synaptic outputs in the brain). The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate an output through a transfer function to the output layer.

A Brief History of Neural Networks

Neural networks date back to the early 1940s when mathematicians Warren McCulloch and Walter Pitts built a simple algorithm-based system designed to emulate human brain function. Work in the field accelerated in 1957 when Cornell University’s Frank Rosenblatt conceived of the perceptron, the groundbreaking algorithm developed to perform complex recognition tasks. During the four decades that followed, the lack of computing power necessary to process large amounts of data put the brakes on advances. In the 2000s, thanks to the advent of greater computing power and more sophisticated hardware, as well as to the existence of vast data sets to draw from, computer scientists finally had what they needed, and neural networks and AI took off, with no end in sight. To understand how much the field has expanded in the new millennium, consider that ninety percent of internet data has been created since 2016. That pace will continue to accelerate, thanks to the growth of the Internet of Things (IoT).

Attributes of Neural Networks

With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:

• Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, software uses adaptive learning to teach math and language arts.
• It helps in auto selection of features provided in the dataset.
• Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
• Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
• Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
• Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.

Tasks Neural Networks Perform

Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:

• Classification: NNs organize patterns or datasets into predefined classes.
• Prediction: They produce the expected output from given input.
• Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
• Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.

Business Applications of Neural Networks:

Real-world business applications for neural networks are booming. In some cases, NNs have already become the method of choice for businesses that use hedge fund analytics, marketing segmentation, and fraud detection. Here are some neural network innovators who are changing the business landscape.

At a time when finding qualified workers for particular jobs is becoming increasingly difficult, especially in the tech sector, neural networks and AI are moving the needle. Ed Donner, Co-Founder and CEO of untapt, uses neural networks and AI to solve talent and human resources challenges, such as hiring inefficiency, poor employee retention, dissatisfaction with work, and more. “In the end, we created a deep learning model that can match people to roles where they’re more likely to succeed, all in a matter of milliseconds,” Donner explains.

“Neural nets and AI have incredible scope, and you can use them to aid human decisions in any sector. Deep learning wasn’t the first solution we tested, but it’s consistently outperformed the rest in predicting and improving hiring decisions. We trained our 16-layer neural network on millions of data points and hiring decisions, so it keeps getting better and better. That’s why I’m an advocate for every company to invest in AI and deep learning, whether in HR or any other sector. Business is becoming more and more data driven, so companies will need to leverage AI to stay competitive,” Donner recommends.

The field of neural networks and its use of big data may be high-tech, but its ultimate purpose is to serve people. In some instances, the link to human benefits is very direct, as is the case with OKRA’s artificial intelligence service.“OKRA’s platform helps healthcare stakeholders and biopharma make better, evidence-based decisions in real-time, and it answers both treatment-related and brand questions for different markets,” emphasizes Loubna Bouarfa, CEO and Founder of Okra Technologies and an appointee to the European Commission’s High-Level Expert Group on AI. “In foster care, we apply neural networks and AI to match children with foster caregivers who will provide maximum stability. We also apply the technologies to offer real-time decision support to social caregivers and the foster family in order to benefit children,” she continues.

Like many AI companies, OKRA leverages its technology to make predictions using multiple, big data sources, including CRM, medical records, and consumer, sales, and brand measurements. Then, Bouarfa explains, “We use state-of-the-art machine learning algorithms, such as deep neural networks, ensemble learning, topic recognition, and a wide range of non-parametric models for predictive insights that improve human lives.”

According to the World Cancer Research Fund, melanoma is the 19th most common cancer worldwide. One in five people on the planet develop skin cancer, and early detection is essential to prevent skin cancer-related death. There’s an app for that: a phone app to perform photo self-checks using a smartphone.

Applications of ANN in Bankruptcy Prediction

Bankruptcy prediction has long been an important and widely studied topic. The main impact of such research is in bank lending. Banks need to predict the possibility of default of a potential counter-party before they extend a loan. This can lead to sounder lending decisions, and therefore result in significant savings [10]. The forecast of bankruptcies belong to classification problems. With input variables, generally financial and accounting data on a firm, we try to find out in which category the firm enters, bankrupt or not bankrupt [11, 12]. The availability of a large amount of accounting and financial data on computerize databases, facilitates the use of artificial neural networks with quantitative data. They are tested as substitutes of traditional statistical tools such as multivariate discriminate analysis. There are two main approaches to loan default/bankruptcy prediction. The first approach, the structural approach, is based on modeling the underlying dynamics of interest rates and firm characteristics and deriving the default probability based on these dynamics. The second approach is the empirical or the statistical approach. Instead of modeling the relationship of default with the characteristics of a firm, this relationship is learned from the data.

In early empirical approaches, Altman used the classical multivariate discriminant analysis technique with following financial ratios as input variables:

1. Working capital/total assets

2. Retained earnings/total assets

3. Earnings before interest and taxes/total assets

4. Market capitalization/total debt

5. Sales/total assets These particular financial ratios have been widely used as inputs, even for NNs and other nonlinear models.

Ohlson introduced the logistic regression approach (LR) to the bankruptcy prediction problem. It is essentially a linear model with a sigmoid function at the output (it is thus similar to a single-neuron network). Because the output is in between 0 and 1, the model has a nice probabilistic interpretation. Ohlson used a novel set of financial ratios as inputs. Both the MDA model and the LR model have been widely used in practice and in many academic studies. They have been standard benchmarks for the loan default prediction problem.

Common Applications of Neural Networks

1. Handwriting Recognition
2. Traveling Salesman Problem
3. Stock Exchange Prediction

Handwriting Recognition

The idea of using feedforward networks to recognize handwritten characters is straightforward. The bitmap pattern of the handwritten character is input, with the correct letter or digit as the desired output. Such programs need the user to train the network by providing the program with their handwritten patterns.

The two common applications of handwriting recognition are:

• Optical character recognition for data entry
• Validation of signatures on a bank cheque

Traveling Salesman Problem

The traveling salesmen problem refers to the finding the shortest possible path to travel all cities in a given area. We can use Neural Networks to solve this problem.

A neural network algorithm such as a genetic algorithm starts with random orientation of the network, to solve the problem. This algorithm chooses a city in a random manner each time and finds the nearest city. Thus, this process continues several times. After every iteration, the shape of the network changes and network converges to a ring around all the cities.

The used algorithm minimizes the length of rings. In this way, we can estimate the traveling problem.

Stock Exchange Prediction

The prediction accuracy of neural networks has made them useful in making a stock market prediction. For large business companies, making predictions for stock exchange is common. This is by using parameters, such as current trends, political situation, public view, and economists’ advice.

Application of Neural Network in Healthcare

1. Imaging analytics and diagnostics
2. Drug discovery and precision medicine
3. Clinical decision support and predictive analytics

Imaging analytics and diagnostics

One type of deep learning, known as convolutional neural networks (CNNs), is particularly well-suited to analyzing images, such as MRI results or x-rays.

CNNs are designed with the assumption that they will be processing images, according to computer science experts at Stanford University, allowing the networks to operate more efficiently and handle larger images.

As a result, some CNNs are approaching — or even surpassing — the accuracy of human diagnosticians when identifying important features in diagnostic imaging studies.

Drug discovery and precision medicine

Precision medicine and drug discovery are also on the agenda for deep learning developers. Both tasks require processing truly enormous volumes of genomic, clinical, and population-level data with the goal of identifying hitherto unknown associations between genes, pharmaceuticals, and physical environments.

Deep learning is an ideal strategy for researchers and pharmaceutical stakeholders looking to highlight new patterns in these relatively unexplored data sets — especially because many precision medicine researchers don’t yet know exactly what they should be looking for.

Clinical decision support and predictive analytics

Google is also on the leading edge of clinical decision support, this time for eye diseases. The company’s UK-based subsidiary, DeepMind, is working to develop a commercialized deep learning CDS tool that can identify more than 50 different eye diseases — and provide treatment recommendations for each one.

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