Neural Networks & Its Use Cases

The first thing that one should learn before jumping into the concept of neural networks is what is the use of neural network and why are they important to use?

Importance of Neural Networks

Neural networks are also ideally suited to help people solve complex problems in real-life situations. They can learn and model the relationships between inputs and outputs that are nonlinear and complex; make generalizations and inferences; reveal hidden relationships, patterns and predictions; and model highly volatile data (such as financial time series data) and variances needed to predict rare events (such as fraud detection). As a result, neural networks can improve decision processes in areas such as:

• Credit card and Medicare fraud detection.
• Optimization of logistics for transportation networks.
• Character and voice recognition, also known as natural language processing.
• Medical and disease diagnosis.
• Targeted marketing.
• Financial predictions for stock prices, currency, options, futures, bankruptcy and bond ratings.
• Robotic control systems.
• Electrical load and energy demand forecasting.
• Process and quality control.
• Chemical compound identification.
• Ecosystem evaluation.
• Computer vision to interpret raw photos and videos (for example, in medical imaging and robotics and facial recognition).

What is a Neural Network?

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.

Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. (Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.)

What kind of problems does deep learning solve, and more importantly, can it solve yours? To know the answer, you need to ask questions:

• What outcomes do I care about? Those outcomes are labels that could be applied to data: for example, spam or not_spam in an email filter, good_guy or bad_guy in fraud detection, angry_customer or happy_customer in customer relationship management.
• Do I have the data to accompany those labels? That is, can I find labeled data, or can I create a labeled dataset (with a service like AWS Mechanical Turk or Figure Eight or Mighty.ai) where spam has been labeled as spam, in order to teach an algorithm the correlation between labels and inputs?

Types of Neural Networks

There are different kinds of deep neural networks — and each has advantages and disadvantages, depending upon the use. Examples include:

Convolutional Neural Networks

Convolutional neural networks (CNNs) contain five types of layers: input, convolution, pooling, fully connected and output. Each layer has a specific purpose, like summarizing, connecting or activating. Convolutional neural networks have popularized image classification and object detection. However, CNNs have also been applied to other areas, such as natural language processing and forecasting.

Recurrent Neural Networks

Recurrent neural networks (RNNs) use sequential information such as time-stamped data from a sensor device or a spoken sentence, composed of a sequence of terms. Unlike traditional neural networks, all inputs to a recurrent neural network are not independent of each other, and the output for each element depends on the computations of its preceding elements. RNNs are used in fore­casting and time series applications, sentiment analysis and other text applications.

Feedforward Neural Networks

Feedforward neural networks, in which each perceptron in one layer is connected to every perceptron from the next layer. Information is fed forward from one layer to the next in the forward direction only. There are no feedback loops.

Autoencoder Neural Networks

Autoencoder neural networks are used to create abstractions called encoders, created from a given set of inputs. Although similar to more traditional neural networks, autoencoders seek to model the inputs themselves, and therefore the method is considered unsupervised. The premise of autoencoders is to desensitize the irrelevant and sensitize the relevant. As layers are added, further abstractions are formulated at higher layers (layers closest to the point at which a decoder layer is introduced). These abstractions can then be used by linear or nonlinear classifiers.

Use Cases of Neural Networks

Here is the review from experts from engineering sector, they explain well the real use cases of neural networks.

People use wireless technology, which allows devices to connect to the internet or communicate with one another within a particular area, in many different fields to reduce costs and enhance efficiency. Huw Rees, VP of Sales & Marketing for KodaCloud, an application designed to optimize Wi-Fi performance, describes just some uses.

Rees offers some everyday examples of Wi-Fi use: “Supermarket chains use Wi-Fi scanners to scan produce in and out of their distribution centers and individual markets. If the Wi-Fi isn’t working well, entire businesses are disrupted. Manufacturing and oil and gas concerns are also good examples of businesses where Wi-Fi is mission critical, because ensuring reliability and optimization is an absolute requirement,” he says.

Wi-Fi is great, but it takes a lot of oversight to do its job. “Most enterprise or large-scale wireless local area network solutions require near-constant monitoring and adjustment by highly trained Wi-Fi experts, an expensive way to ensure the network is performing optimally,” Rees points out. “KodaCloud solves that problem through an intelligent system that uses algorithms and through adaptive learning, which generates a self-improving loop,” he adds.

Rees shares how KodaCloud technology takes advantage of neural networks to continuously improve: “The network learns and self-heals based on both global and local learning. Here’s a global example: The system learns that a new Android operating system has been deployed and requires additional configuration and threshold changes to work optimally. Once the system has made adjustments and measuring improvements necessitated by this upgrade, it applies this knowledge to all other KodaCloud customers instantaneously, so the system immediately recognizes any similar device and solves issues. For a local example, let’s say the system learns the local radio frequency environment for each access point. Each device then connects to each access point, which results in threshold changes to local device radio parameters. Globally and locally, the process is a continuous cycle to optimize Wi-Fi quality for every device.”

A fast-developing technology, drones are used in disaster relief, oil, gas, and mineral exploration, aerial surveillance, real estate and construction, and filmmaking. Neill McOran-Campbell is CEO of Aeiou.tech, which designs advanced drone technology for use in many different sectors. “Our Dawn platform is an on-board series of sensors and a companion computer that allows virtually any unmanned aerial system to utilize the wide range of benefits that AI offers, from flight mechanics, such as navigation and obstacle avoidance, to services such as infrastructure inspection or package delivery,” says McOran-Campbell.

McOran-Campbell explains how Dawn functions based on two levels of biology: “At the first level, we use ANNs to process raw information. There are three different types of networks we use: recurrent neural networks, which use the past to inform predictions about the future; convolutional neural networks, which use ‘sliding’ bundles of neurons (we generally use this type to process imagery); and more conventional neural networks, i.e., actual networks of neurons. Conventional neural networks are very useful for problems like navigation, especially when they are combined with recurrent elements.

“At the more sophisticated, second level, Dawn’s structure emulates the best architecture that exists for processing information: the human brain. This allows us to break down the highly complex problem of autonomy the same way biology does: with compartmentalized ‘cortexes,’ each one with their neural networks and each with their communication pathways and hierarchical command structures. The result is that information flows in waves through the cortexes in the same way that it does in the brain. [In both instances, the process is optimized] for effectiveness and efficiency in information processing,” he explains.

Here’s a list of other neural network engineering applications currently in use in various industries:

• Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulations
• Automotive: Improved guidance systems, development of power trains, virtual sensors, and warranty activity analyzers
• Electronics: Chip failure analysis, circuit chip layouts, machine vision, non-linear modeling, prediction of the code sequence, process control, and voice synthesis
• Manufacturing: Chemical product design analysis, dynamic modeling of chemical process systems, process control, process and machine diagnosis, product design and analysis, paper quality prediction, project bidding, planning and management, quality analysis of computer chips, visual quality inspection systems, and welding quality analysis
• Mechanics: Condition monitoring, systems modeling, and control
• Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems
• Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and control, network monitoring, real-time translation of spoken language, and pattern recognition (faces, objects, fingerprints, semantic parsing, spell check, signal processing, and speech recognition)

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