Industry Use Cases of Neural Networks!!!
➡Neural networks-an overview:-
“Neural networks” is a very evocative one. It suggests machines that are something like brains and is potentially laden with the science fiction connotations of the Frankenstein mythos. One of the main tasks of this book is to demystify neural networks and show how, while they indeed have something to do with brains, their study also makes contact with other branches of science, engineering and mathematics. The aim is to do this in as non-technical a way as possible, although some mathematical notation is essential for specifying certain rules, procedures and structures quantitatively.
➡What are neural networks??
A neural network is an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal neuron. The processing ability of the network is stored in the interunit connection strengths, or weights, obtained by a process of adaptation to, or learning from, a set of training patterns.
The human brain consists of an estimated 10 11 (100 billion) nerve cells or neurons, a highly stylized .Neurons communicate via electrical signals that are short-lived impulses or “spikes” in the voltage of the cell wall or membrane. The interneuron connections are mediated by electrochemical junctions called synapses, which are located on branches of the cell referred to as dendrites. Each neuron typically receives many thousands of connections from other neurons and is therefore constantly receiving a multitude of incoming signals, which eventually reach the cell body.
The artificial equivalents of biological neurons are the nodes or units in our preliminary definition and a prototypical . Synapses are modelled by a single number or weight so that each input is multiplied by a weight before being sent to the equivalent of the cell body.
➡Why study neural networks??
Neural networks are often used for statistical analysis and data modelling, in which their role is perceived as an alternative to standard nonlinear regression or cluster analysis techniques (Cheng & Titterington 1994). Thus, they are typically used in problems that may be couched in terms of classification, or forecasting.
Neuroscientists and psychologists are interested in nets as computational models of the animal brain developed by abstracting what are believed to be those properties of real nervous tissue that are essential for information processing.
➡How do Artificial Neural Networks Work??
As we have seen Artificial Neural Networks are made up of a number of different layers. Each layer houses artificial neurons called units. These artificial neurons allow the layers to process, categorize, and sort information. Alongside the layers are processing nodes. Almost all artificial neural networks are fully connected throughout these layers. Each connection is weighted. The heavier the weight, or the higher the number, the greater the influence that the unit has on another unit. The first layer is the input layer. This takes on the information in various forms.
✒️ Use Cases of Neural Networks in Business:-
⚫Artificial Neural Networks are Improving Marketing Strategies:-
By adopting Artificial Neural Networks businesses are able to optimise their marketing strategy. Systems powered by Artificial Neural Networks all capable of processing masses of information. This includes customers personal details, shopping patterns as well as any other information relevant to your business. Once processed this information can be sorted and presented in a useful and accessible way. This is generally known as market segmentation. This application of Artificial Neural Networks can save businesses both time and money. It can also help to increase profits.
⚫Developing Targeted Marketing Campaigns:-
Through unsupervised learning, Artificial Neural Networks are able to identify customers with a similar characteristic. This allows businesses to group together customers with similarities, such as economic status or preferring vinyl records to downloaded music. Supervised learning systems allow Artificial Neural Networks to set out a clear aim for your marketing strategy. Like unsupervised systems, they can also segment customers into similar groupings. The company has integrated its rewards system location and purchase history on their app. This allows them to offer an incredibly personalised experience, helping to increase revenue by $2.56 billion.
⚫Reducing Email Fatigue and Improving Conversion Rates:-
By only advertising relevant products to interested customers, you also reduce the chances of customers developing email fatigue. In short, if your advertisements are relevant and interesting customers are more likely to interact. This drives visits to your website, potentially increasing sales, and helps you to build a strong client-business relationship. According to dragon360.com 61% of customers say that they are most likely to use companies that send them targeted content. Each group then received targeted emails. Consequently, the business reported an open rate increase of 244%. The traffic driven from emails to the website also increased by 161%. These statistics show that personalised marketing campaigns can deliver real results, benefiting businesses.
⚫Improving Search Engine Functionality:-
During 2015 Google I/O keynote address in San Francisco, Google revealed they were working on improving their search engine. These improvements are powered by a 30 layer deep Artificial Neural Network. This depth of layers, Google believes, allows the search engine to process complicated searches such as shapes and colours. Google’s application shows that neural networks can help to improve search engine functionality. Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites. This means that many companies can improve their website search engine functionality. This allows customers with only a vague idea of what they want to easily find the perfect item. Amazon has reported sales increases of 29% following improvements to its recommendation systems.
⚫The Effects on Insurance Provision:-
Artificial Neural Networks have a number of different applications in the insurance industry. Firstly, as in marketing applications, Artificial Neural Networks allow for segmentation of policyholders. This grouping allows companies to determine and offer appropriate pricing plans. Consequently applying Artificial Neural Networks allows for the correct level of provision to be offered. It also allows for special offers to be made to encourage customers to renew policies.
⚫Facial Recognition Software:-
Technology companies have long been working toward developing reliable facial recognition software. One company leading the way is Facebook. For a number of years now they have been using the facial recognition technology to auto-tag uploaded photographs. They have also developed DeepFace. ▪️DeepFace:- DeepFace is a form of facial recognition software-driven by Artificial Neural Networks. It is capable of mapping 3D facial features. Once the mapping is complete the software turns the information into a flat model. The information is then filtered, highlighting distinctive facial elements. To be able to do this DeepFace implements 120 million parameters. This technology hasn’t just emerged overnight. DeepFace has been trained with a pool of 4.4 million tagged faces.
During the training process, tests were carried out presenting the system with side-by-side images. The system was then asked to identify if the images are of the same person. In these tests, DeepFace returned an accuracy rating of 97.25%. Human participants taking the same test scored, on average, 97.5%. Facebook has also taken its software to computing and technology conferences. This is done with the purpose of allowing academics and researchers to assess and inspect the technology.
⚫Neural Network for Elderly Could Make Healthcare Savings:-
If healthcare providers could accurately predict how their services would be used, they could save large sums of money by not having to allocate funds unnecessarily. Deep learning artificial intelligence models can be good at predicting the future given previous behavior, and researchers based in Finland have developed one that can predict when and why elderly people will use healthcare services.
Risk-adjustment models make use of data from previous years, and are used to allocate healthcare funds in a fair and effective way. These models are already used in countries like Germany, the Netherlands, and the US. However, this is the first proof-of-concept that deep neural networks have the potential to significantly improve the accuracy of such models.
⚫Is AI superficial when it comes to using it for cybersecurity purposes:-
Businesses use AI to improve their security while hackers are using it to launch even more sophisticated attack Data is central to today’s digital economy and it has intrinsic value to businesses and consumers. However, organisations worldwide are facing severe challenges when it comes to protecting data from cybercrime and data leakage.
Technology can of course provide many solutions to help protect data from leakage. Yet, some technologies such as artificial intelligence (AI) can also arm cybercriminals with new ways to expand upon malicious attacks.
To better understand whether AI is a help or a hindrance to cybersecurity, TechRadar Pro sat down with Ensighten’s Chief Revenue Officer, Ian Woolley.
- BlackBerry signs $1.4bn AI cybersecurity deal
- Microsoft: The future of security is AI
- Staying one step ahead of the cyber security
➡What security risks are out there for businesses??
Businesses must take notice and realise that cybercrime is becoming increasingly common especially given more consumers are using online channels to transact and share data.
As a result, there have been plenty of cybersecurity incidents that have leaked businesses’ data — both its own and of its customers. Microsoft Office 365 is just one example with some of its users’ accounts being compromised by hackers — exposing personal content from emails as part of a data breach. Although the number of users affected has not been disclosed, Microsoft confirmed that around 6% of those involved would have had their emails hacked. It’s clear cybercriminals find value in any type of data for monetary return. For example, Facebook logins are sold for just $5.20 (£4.09) each on the dark web.
It’s clear that data breaches need to be prevented for various reasons — such as avoiding financial or reputational implications. IBM found the global average cost of a data breach is £3 million, and estimated that a breach of 50 million records or more can cost a company as much as £273 million.
What’s even more dangerous is the fact they are mirroring the way businesses work. Cybercriminals are identifying where to spend their time and effort based on the return-on-investment. For instance, if businesses only focus security on one channel, such as phones, criminals will turn their attention to pursue internet platforms, such as company websites. We’re seeing this behaviour in the banking and finance sector. Some attackers are turning away from internet and telephone banking to turn their attention towards mobile banking fraud. According to RSA, 60% percent of digital banking fraud now originates from the mobile channel.
⚫AI is being chosen by many businesses to help protect against cybercrime. But is it a cure or a risk:-
Technology for many businesses is a silver bullet, especially as AV-TEST Institute found 856 million malware variants were created last year alone. Traditional cybersecurity systems and techniques cannot handle the many variations of malware which is why evolving technologies, such as AI, are being chosen to tackle potential cybercrime risk — thanks to its ability to automate businesses’ threat prevention, detection and response. Gartner found the number of organisations implementing forms of artificial intelligence (AI), has grown exponentially over the past four years by 270%.
Businesses hope that by adding an intelligent, machine-based layer to its traditional firewall approach they will arm themselves with the necessary tools to keep their networks impenetrable. For example, AI is being used to help with securing existing business log-ins and passwords thanks to its biometric login techniques, such as fingerprint scans, retina and palm prints.
🔘Conclusion:-
The network structure developed for the experiments proved the practical ability of the neural network in the orthopaedic diseases recognition. Very encouraging results have also been noticed in the area of neurology. This area does not define any formulas that allow describing the abnormalities of walking in neurological measuring systems. The neural network algorithms are very calculation intensive. They require highly efficient computing machines. Large datasets take a significant amount of runtime on R. We need to try different types of options and packages. Currently, there is a lot of exciting research going on, around neural networks.
