Case Study Of Neural Network

What is Neural Network ?

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. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.

History of Neural Networks

Although the study of the human brain is thousands of years old. The first step towards neural networks took place in 1943, when Warren McCulloch, a neurophysiologist, and a young mathematician, Walter Pitts, wrote a paper on how neurons might work. They modeled a simple neural network with electrical circuits.

In 1949, Donald Hebb reinforced the concept of neurons in his book, The Organization of Behavior. It pointed out that neural pathways are strengthened each time they are used.

In the 1950s, Nathanial Rochester from the IBM research laboratories led the first effort to simulate a neural network.

In 1956 the Dartmouth Summer Research Project on Artificial Intelligence provided a boost to both artificial intelligence and neural networks. This stimulated research in AI and in the much lower level neural processing part of the brain.

In 1957, John von Neumann suggested imitating simple neuron functions by using telegraph relays or vacuum tubes.

In 1958, Frank Rosenblatt, a neuro-biologist of Cornell, began work on the Perceptron. He was intrigued with the operation of the eye of a fly. Much of the processing which tells a fly to flee is done in its eye. The Perceptron, which resulted from this research, was built in hardware and is the oldest neural network still in use today. A single-layer perceptron was found to be useful in classifying a continuous-valued set of inputs into one of two classes. The perceptron computes a weighted sum of the inputs, subtracts a threshold, and passes one of two possible values out as the result.

In 1959, Bernard Widrow and Marcian Hoff of Stanford developed models they called ADALINE and MADALINE. These models were named for their use of Multiple ADAptive LINear Elements. MADALINE was the first neural network to be applied to a real-world problem. It is an adaptive filter which eliminates echoes on phone lines. This neural network is still in commercial use.

Several other steps have been taken to get us to where we are now; today, neural networks discussions are prevalent; the future is here! Currently most neural network development is simply proving that the principal works. This research is developing neural networks that, due to processing limitations, take weeks to learn.

Why Do We Use Neural Networks?

Neural networks’ human-like attributes and ability to complete tasks in infinite permutations and combinations make them uniquely suited to today’s big data-based applications. Because neural networks also have the unique capacity (known as fuzzy logic) to make sense of ambiguous, contradictory, or incomplete data, they are able to use controlled processes when no exact models are available.

According to a report published by Statista, in 2017, global data volumes reached close to 100,000 petabytes (i.e., one million gigabytes) per month; they are forecasted to reach 232,655 petabytes by 2021. With businesses, individuals, and devices generating vast amounts of information, all of that big data is valuable, and neural networks can make sense of it.

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.
• 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.

Real-World and Industry Applications of Neural Networks

As an August 2018 New York Times article notes, “The companies and government agencies that have begun enlisting the automation software run the gamut. They include General Motors, BMW, General Electric, Unilever, MasterCard, Manpower, FedEx, Cisco, Google, the Defense Department, and NASA.” We’re just seeing the beginning of neural network/AI applications changing the way our world works.

Engineering Applications of Neural Networks

Engineering is where neural network applications are essential, particularly in the “high assurance systems that have emerged in various fields, including flight control, chemical engineering, power plants, automotive control, medical systems, and other systems that require autonomy.”

(Source: Application of Neural Networks in High Assurance Systems: A Survey.)

Let’s see what expert are saying from engineering sector

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.”

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)

Business Applications of Neural Networks:

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.

“SkinVision uses our proprietary mathematical algorithm to build a structural map that reveals the different growth patterns of the tissues involved,” says Matthew Enevoldson, SkinVision’s Public Relations Manager.

Enevoldson adds that the phone app works fast: “In just 30 seconds, the app indicates which spots on the skin need to be tracked over time and gives the image a low, medium, or high-risk indication. The most recent data shows that our service has a specificity of 80 percent and a sensitivity of 94 percent, well above that of a dermatologist (a sensitivity of 75 percent), a specialist dermatologist (a sensitivity of 92 percent), or a general practitioner (a sensitivity of 60 percent). Every photo is double-checked by our team of image recognition experts and dermatologists for quality purposes. High-risk photos are flagged, and, within 48 hours, users receive personal medical advice from a doctor about next steps.” The app has 1.2 million users worldwide.

Here are further current examples of NN business applications:

• Banking: Credit card attrition, credit and loan application evaluation, fraud and risk evaluation, and loan delinquencies
• Business Analytics: Customer behavior modeling, customer segmentation, fraud propensity, market research, market mix, market structure, and models for attrition, default, purchase, and renewals
• Defense: Counterterrorism, facial recognition, feature extraction, noise suppression, object discrimination, sensors, sonar, radar and image signal processing, signal/image identification, target tracking, and weapon steering
• Education: Adaptive learning software, dynamic forecasting, education system analysis and forecasting, student performance modeling, and personality profiling
• Financial: Corporate bond ratings, corporate financial analysis, credit line use analysis, currency price prediction, loan advising, mortgage screening, real estate appraisal, and portfolio trading
• Medical: Cancer cell analysis, ECG and EEG analysis, emergency room test advisement, expense reduction and quality improvement for hospital systems, transplant process optimization, and prosthesis design
• Securities: Automatic bond rating, market analysis, and stock trading advisory systems
• Transportation: Routing systems, truck brake diagnosis systems, and vehicle scheduling

The use of neural networks seems unstoppable. “With the advancement of computer and communication technologies, the whole process of doing business has undergone a massive change. More and more knowledge-based systems have made their way into a large number of companies,” researchers Nikhil Bhargava and Manik Gupta found in “Application of Artificial Neural Networks in Business Applications.”

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