Industry Use-Cases on Neural Networks

What are Neural Networks?

Artificial Neural Networks(ANNs) usually are called Neural Networks(NN) are computing systems inspired by the biological neural networks that constitute animal brains.

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons. Neural networks are just one of many tools and approaches used in machine learning algorithms. The neural network itself may e used as a piece in many different machine learning algorithms to process complex data inputs into a space that computers can understand.

NN is being applied to many real-life problems today, including speech and image recognition, spam email filtering, finance, and medical diagnosis, etc.

How Artificial Neural Networks Function

Neural networks 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 output through a transfer function to the output layer.

Machine learning (ML) is the instrument through which these newborn computer-based bits of synthetic intelligence process all the info they’re nourished with, much like the five senses help a human toddler learn and experience the world.

Eventually, all this information becomes enough to help these AIs provide us with new answers to our questions, and many solutions that are much smarter than those that a human mind might conceive. So, what are some examples where neural networks and machine learning are being effectively used in practice today? Let’s have a look.

Neural Networks: 5 Use Cases to Better Understand

Self-Driving Cars

Is there anything that screams “future” more than a self-driving car? We’ve spent the last 30 years dreaming of cyberpunk dystopian worlds where androids who dream of electric sheep run from captors by jumping on driverless vehicles. Okay, maybe those vehicles were also able to fly, but you get the point.

Autonomous vehicles aren’t just a dream anymore. Albeit most of them are still just prototypes, they’re definitely a reality nowadays. Dozens of different companies have already invested a substantial amount of money to fuel this technology. And now, self-driven vehicles seem even more indispensable in a world where the coronavirus strengthened the use case for robot drivers and contactless societies. Even a simple algorithm-driven steel bucket on wheels can make the difference if a new pandemic forces the world into a new lockdown.

How else could those vehicles learn how to drive if not through machine learning? Deep learning algorithms are employed by software developers to power computer vision, understand all the details about their surrounding environment, and make smart, human-like decisions. For years, human-driven cars have been equipped with an array of cameras and sensors that record everything from driving patterns to road obstacles, traffic lights, and road signs.

Cybersecurity

ANNs can also be used to protect organizations from several types of attacks, such as DDoS and malicious software. Malware itself is a huge problem, with at least 325,000 new malicious files being generated every day. Yet, no more than 10 percent of the files change from iteration to iteration, so algorithm-based learning models that can predict these variations are able to detect which files are malware with amazing accuracy.

AI is better than humans at cybersecurity because they automate the most complex processes required for detecting attacks and analyzing the best way to react to breaches. More in general, neural nets could be used to detect any change or anomaly in network traffic, including the newest 5G networks. AI can avoid the risk of false positives and identify potentially malicious activities such as brute-force attacks, unusual failed logins and file exfiltration with some experiments reaching a 96.4% detection rate.

Obviously, hackers started developing their own adaptive AI to deceive security software and exploit vulnerabilities, in a never-ending arms race between attackers and defenders. However, all of this actually benefits AIs, which get smarter and smarter every day they are deployed in the battlefield

Fighting Against Future Pandemics

The 2020 COVID-19 pandemic shocked the whole world, forcing us to rethink our entire society and face a cataclysm that no one ever expected. Whether this specific viral pandemic is over not, there’s one thing that we learned the hard way: that we cannot be found unprepared anymore in the future if another biological threat emerges.

AI technologies can help us (and already helped us) in many different ways. Complex neural networks can be used to coordinate the efforts of multiple cameras simultaneously and send a warning whenever a person with elevated temperature is found. The AI can take immediate action such as stopping the person from accessing critical places and ensure better workplace safety.

Radiological imaging can be coupled with advanced AI to allow for immediate recognition of X-ray images suggesting that a patient is affected by the disease, even in remote or less served areas.

Network Efficiency

The idea of using artificial intelligence to optimize the efficiency of networks and improve their security dates back to the early ’80s. However, modern technologies have made a huge leap forward, and revolutionary machine learning algorithms can mundanely perform complex tasks such as predicting faults and scheduling fixes.

AI is exceptionally efficient at allocating network resources where they’re most needed by autonomously analyzing traffic data, and they possess the agility required to integrate themselves with the many internet of things (IoT) devices connected to the network architecture. No one can talk to a machine better than another machine.

Business and Advertising

This can be summed up in just one word (well… three): personalized product recommendations. Every time we search for something on Google or any other search engine, eventually we start seeing a ton of precisely targeted ads about these things. How could the software understand so well what our interests are and how to entice us into buying those extremely cheap goods we want so badly?

Once again, deep learning is the answer. These highly reactive programs learn by watching our behaviors, such as when we skip to page two of the search results when none of those found on page one satisfied our needs. Machines can crunch demographic data about customers’ habits and preferences at a speed that no human analyst can ever hope to reach, and can consume it to optimize pricing, offers, customer experience, and profitability. It should not surprise anyone that one of the biggest lovers of AIs and smart algorithms is none other than Amazon itself.

Yet, the retail giant is using advanced heuristics to optimize its services in many other ways. One of the reasons why Jeff Bezos’ creature is so successful, is, in fact, the amazing efficiency of its logistics planning. Other giants such as Walmart and Honda as well as many small-to-medium businesses and factories vastly improved their efficiency by implementing machine learning in the management of orders, stocking, inventory control and warehousing.

Final Thoughts

We live in an age where many of the newest digital technologies are assisting many lazy humans in discarding their abilities to learn, communicate and interact with real life. Ironically enough, these same technologies are helping artificial intelligence grow and move forward at an incredibly fast pace.

Just like young and promising kids eager to learn new things every day, our machines are still “attending school” right now. We can only look forward to the day when they will be able to build and perfect their own learning methods and reach their university phase, but in the meantime, the goals they have already achieved are nonetheless amazing

Fuzzy Logic using Neural Networks

Fuzzy logic refers to the logic developed to express the degree of truthiness by assigning values between 0 and 1, unlike traditional Boolean logic that represents 0 and 1.

Fuzzy logic and Neural networks have one thing in common. They can be used to solve problems of pattern recognition and others that do not involve any mathematical model.

Systems combining both fuzzy logic and neural networks are neuro-fuzzy systems.

These systems (Hybrid) can combine the advantages of both neural networks and fuzzy logic to perform in a better way.

Fuzzy logic and Neural Networks have been integrated to use in the following applications –

• Automotive engineering
• Applicant screening of jobs
• Control of crane
• Monitoring of glaucoma

In a hybrid (neuro-fuzzy) model, Neural Networks Learning Algorithms are fused with the fuzzy reasoning of fuzzy logic.

The neural network determines the values of parameters, while if-then rules are handled by fuzzy logic.