Tag: neural networks

The adoption of machine learning and subsequent development of neural network applications has changed the way we perceive information from a business standpoint. If previously, the information was a commodity with a value limited to its instantly accessible features, now it is a resource the value of which depends on one’s skill to interpret it – the ability to make the most out of the available information.

The information can be used:

• Determine patterns and other significant features present in data
• Extract relevant insights
• Implement them into the business operation
• Predict future development

This process requires complex systems that consist of multiple layers of algorithms, that together construct a network inspired by the way the human brain works, hence its name – neural networks.

In this article, we will look at one of the most prominent applications of neural networks – recurrent neural networks and explain where and why it is applied and what kind of benefits it brings to the business.

What is a Recurrent Neural Network?

A Rcurrent Neural Network is a type of artificial deep learning neural network designed to process sequential data and recognize patterns in it (that’s where the term “recurrent” comes from).

The primary intention behind implementing RNN neural network is to produce an output based on input from a particular perspective.

The core concepts behind RNN are sequences and vectors. Let’s look at both:

• Vector is an abstract representation of raw data that reiterates its meaning into a comprehensive form for the machine. It is a kind of text-to-machine translation of data.
• The sequence can be described as a collection of data points with some defined order (usually, it is time-based, there can also be other specific criteria involved). An example of sequence can be time-series stock market data – a single point shows the current price while its sequence over a certain period shows the permutations of the cost.

Unlike other types of neural networks that process data straight, where each element is processed independently of the others, recurrent neural networks keep in mind the relations between different segments of data, in more general terms, context.

Given the fact that understanding the context is critical in the perception of information of any kind, this makes recurrent neural networks extremely efficient at recognizing and generating data based on patterns put into a specific context.

In essence, RNN is the network with contextual loops that enable the persistent processing of every element of the sequence with the output building upon the previous computations, which in other words, means Recurrent Neural Network enables making sense of data.

How Does Recurrent Neural Network work?

Just like traditional Artificial Neural Networks, RNN consists of nodes with three distinct layers representing different stages of the operation.

• The nodes represent the “Neurons” of the network.
• The neurons are spread over the temporal scale (i.e., sequence) separated into three layers.

The layers are:

1. Input layer represents information to be processed;
2. A hidden layer represents the algorithms at work;
3. Output layer shows the result of the operation;

The hidden layer contains a temporal loop that enables the algorithm not only to produce an output but to feed it back to itself.

This means the neurons have a feature that can be compared to short-term memory. The presence of the sequence makes them “remember” the state (i.e., context) of the previous neuron and pass that information to themselves in the “future” to further analyze data.

Overall, the RNN neural network operation can be one of the three types:

1. One input to multiple outputs – as in image recognition, image described with words;
2. Several contributions to one output – as in sentiment analysis, where the text is interpreted as positive or negative;
3. Many to many – as in machine translation, where the word of the text is translated according to the context they represent as a whole;

The key algorithms behind RNN are:

• Backpropagation Through Time to classify sequential input- linking one-time step to the next
• Vanishing/Exploding gradients – to preserve the accuracy of the results
• Long Short-Term Memory Units – to recognize the sequences in the data

Business Applications of Recurrent Neural Networks

RNN Text Generation – Text Summarization, Report Generation, Conversational UI

Generating text with recurrent neural networks is probably the most straightforward way of applying RNN in the context of the business operation.

From a business standpoint, text generation is valuable as a means for streamlining the workflow and minimizing the routine.

Natural language generation relies on Recurrent Neural Networks predictive algorithms. Since the language is sequentially organized with grammar and bound into cohesion with semantics – it is relatively easy to train a model to produce generic text documents for multiple purposes.

Let’s look at the most common:

• Text summarization – the process involves condensing the original text into a distillation of critical points and its subsequent reiteration into a cohesive summary. Summarization is used in project management to quickly onboard new members and keep an eye on the progress in general. This approach is also used to create news digests and streamline the news article production pipeline.
• Document generation – commonly used in banking and insurance to create custom forms based upon templates adapted for the specific client with relevant information.
• Report Generation – in this case, text generation serves as a form of data visualization. Except, instead of turning data into bars and charts, and graphs, the text is transformed into a formatted document with template sentences covering key points. Here’s an example of this kind of report: “There were 100 visitors on-site during 24 hour period, which is two visitors more compared with the previous 24 hour period. Twenty-five visitors came from Facebook, 10 of which bounced off instantly, while the other 15 made from 5 to 20 clicks on the following page”.
• Conversational Interfaces and chatbots are amongst the most prominent casual uses of text generation. In this case, the algorithm is trained on the knowledge base combined with the behavioral intent scenarios. For example, a lead generation scenario is designed to gather information about the potential client, while a customer support scenario is designed to assist customers with product use. In addition to text generation, Conversational UI also requires a sentiment analysis component to correctly dissect the input message (more on that later).  

RNN Application in Machine Translation – Content Localization

Machine translation is another field where RNN is widely applied due to its capability to determine the context of the message.

Here’s why – high-quality translation can be a bridge towards the expansion of the foreign language market. In a way, translated content can be considered as a broad form of service personalization.

From a technical standpoint, it seems like machine translation operation is a mere substitution of words representing certain concepts with the equivalent terms in the other language.

TВшуhe languages tend to have different structures of the sentences and modes of expression of the concepts, which makes it impossible to translate the message behind the words by deciphering the words. Instead, a machine translation algorithm needs to understand the meaning of the news first and then match it with the appropriate words.

These days, the most prominent machine translation application is Google Translate. Also, there are numerous custom recurrent neural network applications used to localize content by various platforms. Just look at eCommerce platforms like Amazon, AliExpress, and eBay. They all use machine translation to adapt content like product cards and helps with the efficiency of the search results.   

Visual Search, Face detection, OCR Applications – Image Recognition

Humans tend to think visually and have an extensive visual shorthand reference board that helps them to navigate in the world. Until recently, this peculiar feature of the human mind was not taken into consideration when it comes to customer services. Now it’s a full-fledged feature commonly used in a variety of fields, such as search engines, eCommerce stores, and OCR apps.

Image recognition is one of the major points of computer vision. It is also the most accessible form of RNN to explain.

At its core, the algorithm is designed to recognize one unit of input (the image) into multiple groups of output (the description of the image).

The image recognition framework includes

1. A convolutional neural network  that processes the image and recognizes the features of the pictures,
2. Recurrent neural networks that use the known features to make sense of the image and put together a cohesive description.

The benefits of image recognition for business are obvious – it is a streamlining tool that makes it easier for the customer to operate with the service, find relevant images, navigate through information, and make purchases.  

The most prominent industries for image recognition are Search engines, eCommerce, Social Media.

Let’s look at them closer.

• Search engines are the basic application of visual search. Google, Bing, and DuckDuckGo are the most prominent examples of recurrent neural network image recognition. The goal is to images that fit the input query or find images that look like an input image. To do that, the image is recognized and described. The resulting information is then used to find relevant search matches.
• In the case of eCommerce, image recognition is used for object detection and visual search purposes. The goal is to improve the product database and make it easier to navigate. In addition to that, visual search contributes to the product recommendation and consequently to the service personalization. The way Amazon and AliExpress use visual search results in vast streamlining of the user journey and better engagement with more possibilities of further purchases.
• In the cases of Social media like Facebook or Instagram, the primary use case of image recognition is face recognition. The difference between face recognition and basic image recognition is an additional layer of processing. There is a general recognition of the shape of the face, and then there is the matching of the unique credentials of the individual face based on available samples. The same principle is also used for transformative filters in photo editing applications.

Conversational UI, Speech-to-text – RNN Speech recognition

The adoption of conversation interfaces is growing with each passing day. It is easy to see why – it is a more practical way of doing things, one step further for machines and humans talking in the same language.

Virtual assistants like Alexa or Siri are becoming commonplace in everyday life, and the majority of eCommerce marketplaces and company websites integrate chatbots that can help users with their causes within a couple of casually formulated phrases.

The technology that brings them together is speech recognition with deep recurrent neural networks.

From a technical standpoint, Speech (or sound in general) recognition and image recognition have a lot in common. The basic framework of the algorithm is more or less the same.

The difference is in the way the sound is recognized. Unlike visual information, where shapes of the object are more or less constant, sound knowledge has an additional layer of the performance. This makes recognition more of an approximation based on a broad sample base.

Here’s how it works:

• The input information is first processed and recognized through the convolutional network. The result is a varied collection of input sound waves.
• The information contained in the sound wave is then classified by intent, key credentials (basically, keywords related to the query)
• Then input sound waves are recognized into phonetic segments and subsequently pieced together into cohesive words via RNN application. The result is a mosaic of phonetic segments seamlessly put together into a singular whole.

Just like image recognition, speech recognition is first and foremost, the tool to streamline the workflow and make it more comfortable for all categories of users – from tech-savvy ones to novices.

Let’s look at the most prominent applications of speech recognition RNN:

• Conversational UI is the biggest field of use for speech recognition these days. This kind of UI can be designed for a certain purpose, such as customer support (with custom generated responses from the knowledge base) and service navigation (with customized explanations of how to use certain features of the service or where to find certain kinds of content). They can also be more action-oriented organizers integrated with other applications (like Google Assistant);
• Chatbots are smaller relatives of fully-fledged Conversational interfaces. Their main purpose is to provide relevant information. Such applications can be used on-site and also on social networks like Facebook. In addition to providing information, chatbots can be used to generate leads and initiate the start of the communication (for example, such a service is provided by Hubspot marketing platform).
• Speech-to-text applications. Sound is another medium where content marketing can thrive. Due to a variety of reasons, not every user has time to read a blog post from start to finish, but they are likely to listen to it.  However, recording read-outs with voice actors can be a bit too much on the budget. Hopefully, modern speech-to-text applications are capable of doing a serviceable and cost-effective job without calling much attention to their mechanistic nature. Such claims have sample banks with phonetic segments performed in different languages that are arranged in the form of the input text. Blogging platforms like Medium are currently trying out these features, and many separate services provide speech-to-text transformations, such as SpeechNote and VoiceNotebook.

RNN Text Classification – Semantic Search

Navigating in the vast spaces of information is one of the major requirements in the data-driven world. As one of the premier recurrent neural network examples, semantic search is one of the tools that make it easier and much more productive. In addition to that, semantic search simplifies the continuous updates and revisions of the knowledge base.

These days, semantic search is widely used in a variety of fields that:

• involve high turnaround of sensitive information or vast knowledge bases;
• require accessibility and speed to provide a decent level of workflow efficiency.

Here’s how semantic search RNN application works:

1. The input message is analyzed for context. The process involves – feature extraction and context recognition.
2. The result is a deconstruction of the input message to its moving parts. In addition to that, the algorithm looks for the related queries and checks them for relevance to the current query.
3. Then, the processed input is checked and matched with the available knowledge base.
4. The matches are presented as output results.

Semantic Search is commonly used in the following fields:

• Customer support – semantic search is to navigate the product/service knowledge base and also customer cards. You can also read our case study for the project that involves the usage of a semantic search for customer support.
• Banking – in this case, semantic search is used to navigate through customer documents and double-check the validity of the proceedings at each step. It is also one of the tools for fraud detection when it comes to document or handwriting fraud.
• Project documentation – SS is used to navigate through documents and simplify access to the information. In addition to that, the Semantic search is often used to implement changes or corrections into a large amount of documentation quickly.
• Employee Onboarding and general Q&A – in this case, semantic search makes it easier to understand the ins and outs of the organization for the newbies and turns the knowledge base into an easily accessible reference tool.
• Search engines like Google and Amazon are amongst the most progressive types of semantic search. In this case, SS also involves a web scraper that looks for the relevant results. The input query is processed as usual, but it is also matched with the standard search patterns and more specific criteria such as corresponding region, language or type of content (depending on the query). In addition to that, the resulting information contributes to further service personalization.

 Check Out MSP Case Study: How Semantic Search Can Improve Customer Support

RNN Text Classification – Sentiment Analysis

Natural Language Processing is one of the core fields for Recurrent Neural Network applications due to its sheer practicality. A large chunk of business intelligence from the internet is presented in natural language form and because of that RNN are widely used in various text analytics applications. The most prominent field of recurrent neural network natural language processing is sentiment analysis.

Sentiment analysis is one of the most exciting applications of recurrent neural networks. The reason for that is simple – versatility.

Here’s why – RNN can be applied to a wide variety of different aspects of the RNN sentiment analysis operation.

1. The identification of opinion is usually relegated to convolutional
2. Polarity recognition is an example of multiple inputs gathered into one output. The algorithm processes the message and determines
3. Subject recognition is an example of various data to multiple outputs. This approach uses the capabilities of the Recurrent network to its fullest.

As such, RNN applications can gather vast amounts of diverse data that will bring more clarity regarding the perception of the product and will undoubtedly contribute to the decision-making process.

There are five major use cases for recurrent neural network sentiment analysis. Let’s take a closer look:

• Brand Management – sentiment analysis is used to track the perception of the general perception of the brand by customers from different audience segments. Subsequently, it is used to analyze the specific aspects of the perception – find patterns of interest and use it for the benefit of the business operation.
• Market Research – in this case, sentiment analysis is used to collect information regarding specific aspects of the market (use of technology, audience reaction, and involvements) across various platforms.
• Product Analytics – in this case, sentiment analysis is used to manage and analyze all sorts of customer feedback regarding the product or its specific aspects to plan further improvements.
• In the case of customer Support, sentiment analysis is used to analyze the feedback and manage the support operation. You get an intent analysis of the customer (i.e., what kind of help he needs), and then you get an insight into the customer’s opinion.  
• Voice of the customer analysis uses SA to define and specify target audience segments – this includes customer’s wants and needs, expectations from the product, and so on.

If you want to read more about Sentiment analysis – we have an article describing the technology itself and also a section detailing its business use.

Ad Fraud, Spam Detection, Bot Detection – Anomaly Detection

The lion’s share of fraudulent activities on the internet is performed via automated algorithms with clearly distinguishable patterns. In addition to that, traditional fraud like handwriting faking is widespread when it comes to document fraud.

In both cases, the recurrent neural network framework can be a powerful weapon against fraud of all walks, which is good in terms of effective budget spending and money-making.

Here’s why:

• Data consists of pattern sequences that can be explored and assessed.
• This enables the algorithm to assume what may come next and determine the probability of a particular turn of events.
• On the other hand, pattern analysis enables to identify of anomalies in the behavior of the entities,

Overall, Fraud Prevention relies on predictive algorithms to expose illegal activity.

• In the case of ad fraud, RNN is used to determine suspicious /abnormal behavioral patterns.
• In the case of spam detection, RNN applies NLP tools to expose general patterns and subsequently block the message.
• In the case of ad fraud bot detection, recurrent neural network anomaly detection is used to identify suspiciously generic behavior of the supposed user and take him out of the analytics.

Stock Price Forecasting – Predictive Analytics

In a way, recurrent neural network stock prediction is one of the purest representations of RNN applications. It is all tweaking numbers to understand what the next figure might be.

The critical term is time series prediction, which is a representation of the number figure fluctuation or transformation over time. Apps like Stock Market Sensei use this approach.

The transformation includes a specific criterion that affected the changes (for example, the connection of the special price to the other expenses). The combination of the elements above is then taken into consideration upon calculation of the predictions.

The predictions themselves range by probability from the most to the least possible from the available data. As a result, the stock market trader gets more solid grounds for decision making and reduces the majority of risks.

In Conclusion

Recurrent Neural Networks stand at the foundation of the modern-day marvels of artificial intelligence. They provide solid foundations for artificial intelligence applications to be more efficient, flexible in their accessibility, and most importantly, more convenient to use.

On the other hand, the results of recurrent neural network work show the real value of the information in this day and age. They show how many things can be extracted out of data and what this data can create in return. And this is incredibly inspiring.

Machine Learning and neural networks are expanding our understanding of data and the insights it holds. From a business standpoint, neural networks are engines of generating opportunities. They make sense of data and let you enjoy it.

Convolutional Neural Networks holds a special place in that regard. The development and implementation of Convolutional Neural Networks show us:

• how many different insights are behind visual content;
• how data impact customer satisfaction.

In this article, we will explain what CNN is, how it operates, and look at its common business cases.

What is CNN?

Convolutional Neural Network is an artificial deep learning neural network. It is used for computer vision/image recognition. This process includes the following operations:

• Image recognition and OCR

• Object detection for self-driving cars

• Face recognition on social media

• Image analysis in healthcare

The term “convolutional” means mathematical function derived by integration from two distinct functions. It includes rolling different elements together into a coherent whole by multiplying them. Convolution describes how the other function influences the shape of one function. In other words, it is all about the relations between elements and their operation as a whole.

The primary tasks of convolutional neural networks are the following:

• Classify visual content (describe what they “see”),
• Recognize objects within is scenery (for example, eyes, nose, lips, ears on the face),
• Gather recognized objects into clusters (for example, eyes with eyes, noses with noses);

The other prominent application of CNNs is preparing the groundwork for different types of data analysis.

CNN uses Optical Character Recognition (OCR) to classify and cluster peculiar elements like letters and numbers. Optical Character Recognition puts these elements together into a coherent whole. Also, CNN is applied to recognize and transcribe the spoken word.

The sentiment analysis operation uses the classification capabilities of CNN.

Now, let’s explain the mechanics behind the Convolutional Neural Network.

How Does Convolutional Neural Network work?

Convolutional Neural Network architecture consists of four layers:

• Convolutional layer – where the action starts. The convolutional layer is designed to identify the features of an image. Usually, it goes from the general (i.e., shapes) to specific (i.e., identifying elements of an object, the face of a certain man, etc.).  
• Then goes the Rectified Linear Unit layer (aka ReLu). This layer is an extension of a convolutional layer. The purpose of ReLu is to increase the non-linearity of the image. It is the process of stripping an image of excessive fat to provide a better feature extraction.
• The pooling layer is designed to reduce the number of parameters of the input i.e., perform regression. In other words, it concentrates on the meaty parts of the received information.
• The connected layer is a standard feed-forward neural network. It is a final straight line before the finish line where all the things are already evident. And it is only a matter of time when the results are confirmed.

Let’s explain how CNN works in the case of image recognition.

• CNN perceives an image as a volume, a three-dimensional object. Usually, digital color images contain Red-Blue-Green, aka RGB encoding. What it means is that convolutional networks understand images as three distinct channels of color stacked on top of each other.

• CNN groups pixels and processes them through a set of filters designed to get certain kinds of results. For example, to recognize geometrical shapes on an image). The number of filters applied usually depends on the complexity of an image and the purpose of recognition.

• The pooling layer is designed to reduce the number of parameters of the input, i.e., perform regression.

As a result, you can a recognized image by identifying credentials and data layout that represents a blueprint of a picture of a specified kind.

Now let’s take a look at the most prominent business applications of CNNs.

Business applications of Convolutional Neural Networks

Image Classification – Search Engines, Recommender Systems, Social Media

Image recognition and classification is the primary field of convolutional neural networks use. It is also the one use case that involves the most progressive frameworks (especially, in the case of medical imaging).

The purpose of the CNN image classification is the following:

• Deconstruct an image and identify its distinct feature. For that, the system uses a supervised machine learning classification algorithm.
• Reduce the description of its essential credentials. It is done by unsupervised machine learning algorithm)

The following fields are using this process:

• Image tagging algorithms are the most basic type of image classification. The image tag is a word or a word combination that describes the images and makes them easier to find. Google, Facebook, and Amazon are using this technique. It is also one of the foundation elements of visual search. Tagging includes recognition of objects and even sentiment analysis of the picture tone.

• Visual Search – this technique involves matching an input image with the available database. Besides, the visual search analyzes the image and looks for images with similar credentials. For example, this is how Google can find versions of the same model but in different sizes.
• Recommender engines is another field to apply image classification and object recognition. For example, Amazon uses CNN image recognition for suggestions in the “you might also like” section. The basis of the assumption is the user’s expressed behavior. The products themselves are matched on visual criteria — for example, red shoes and red lipstick for the red dress. Pinterest uses image recognition CNN in a different way. The company relies on visual credentials matching, and this results in a simple visual matching supplemented with tagging.

Face Recognition Applications of RNN is Social Media, Identification procedures, Surveillance

Face recognition deserves a separate mention. This subdivision of image recognition comprehends more complex images. Such images might include human faces or other living beings, animals, fish, and insects included.

The difference between straight image recognition and face recognition lays in operational complexity — the extra layer of work involved.

• First goes basic object recognition – the shape of the face and its features are recognized.

• Then the features of the face are further analyzed to identify its essential credentials. For example, it can be the shape of the nose, its skin tone, texture, or presence of scar, hair or other anomalies on the surface;

• Then the sum of these credentials is calculated into the image data perception of the appearance of a particular human being. This process involves studying many samples that present the subject in a different form. For example, with or without sunglasses).

• Then the input image is compared with the database, and that’s how the system recognizes a particular face.

 Social media like Facebook use Face recognition for both social networking and entertainment.

• In social networking, face recognition serves as a streamlining of the often dubious process of tagging people in the photo. This feature is especially helpful when you need to tag through a couple of hundred images from the conference, or there are way too many faces to tag. So if you are going to build your social network, think about this feature.

• In entertainment, face recognition lays the groundwork for further transformations and manipulations. Facebook Messenger’s filters and Snapchat Looksery filters are the most prominent examples. The filters jump from the autogenerated basic layout of the face and attach new elements or effects.

Facial recognition technology is establishing itself as a viable option for personal identification. 

Face recognition can’t serve as a verification of the persona on par with fingerprints and legal documents. Face recognition is constructive in identifying the person in cases of limited information. For example, from the surveillance camera footage or sneak video recording.   

Legal, Banking, Insurance, Document digitization – Optical Character Recognition

Optical Character Recognition was designed for written and print symbol processing. Like face recognition, it involves a more complicated process with move moving parts.

At its core, OCR is a combination of computer vision with natural language processing. First, the image is recognized and deconstructed into characters. Then, the characters are extracted together into a coherent whole.

Here’s how it works:

• The first step, there is image recognition involved. The image is scanned for elements that resemble written characters (it can be specific characters or in general).

• In the second step, each character is broken down to critical credentials that identify it as such (for example, a particular shape of letters “S” or “Z.”)

• In the third step, the image is matched with the respective character encoding.

• In the fourth step, the recognized characters are compiled into the text according to the visual layout of an input image.

Image tagging and further descriptions of the image content for better indexing and navigation are using CNN. The eCommerce platforms, such as Amazon, are using it for a more significant impact.

The legal organizations, as banking and insurance, use Optical Character Recognition of handwriting.

The recognition of personal signature becomes an extra validating and verifying layer. The process resembles face recognition bar the generalization.

like the face, a signature contains unique features that make it distinct from the others.

Signatures contain a minimal amount of generic elements with unique credential data. For example, the infamous Donald Trump “demon screaming” signature.

The system concentrates on the particular sample and the credentials of the specific person’s signature.

But, the first use case of Optical Character Recognition is digitizing documents and data.

The formatting of the text plays a significant role, as it is crucial to transcribe the document’s content. OCR algorithms reference the document templates. It means the whole operation resembles an elaborate “connect the dots” game.

Medical Image Computing – Healthcare Data Science / Predictive Analytics

Healthcare is the industry where all the cutting edge technologies get their trial on fire.

If you want to determine the practical worth of a particular technology – try using it for some healthcare purposes. Image recognition is no different.

Medical Image Computing is the most exciting image recognition CNN use case. 

The medical image involves a whole lot of further data analysis that spurs from initial image recognition.

CNN medical image classification detects the anomalies on the X-ray or MRI images with higher precision than the human eye.

Such systems can show how the sequence of images and the differences between them. This feature prepares the grounds for further predictive analytics.

Medical image classification relies on vast databases that include Public Health Records. It serves as a training basis for the algorithms and patients’ private data and test results. Together they make an analytical platform that keeps an eye on the current patient state and predicts outcomes.

Predictive Analytics – Health Risk Assessment

Saving lives is a top priority in healthcare. And it is always better to have the power of foresight at hand. Because when it comes to handling the patient treatment, you need to be ready for anything.

A case in point is the health risk assessment.

This field is the one where Convolutional Neural Network Predictive Analytics are applied.

Here’s how Health Risk Assessment CNN works:

• CNN process data with a grid topology approach – a set of spatial correlations between data points. In the case of images, the grid is two-dimensional. In the case of time series textual data – the grid is one-dimensional.

• Then the convolution algorithm is applied to recognize some aspects of the input

• Take into consideration the variations of an input 

• Determine sparse interactions between variables

• Apply same settings for many functions of a model

Health Risk Assessment applications are a broad term, so let’s explain the most prominent:

• HRA is a predictive application that calculates the probability of certain events. This use case includes disease progression or complications happening based on patient data. It matches similar PHR, analyzes the patient’s data, finds patterns, and calculate possible outcomes. Routine health checks can enjoy using this system;

• The framework can expand by adding the treatment plan. In this case, the prediction determines the optimal way of symptoms treatment.

• HRA system also can be used to study the specific environment and explore possible risks for people working there. The assessment of dangerous situations is using this approach. For example, in Australia, the officials are studying sun activity and determine the level of radiation threat.

Predictive Analytics – Drug Discovery

Drug discovery is another major healthcare field with the extensive use of CNNs. It is also one of the most creative applications of convolutional neural networks in general.

Like RNN (Recurrent Neural Network) and stock market prediction, drug discovery, and CNN is pure data tweaking.

The thing is – drug discovery and development is a lengthy and expensive process. Scalability and cost-effectiveness are essential in drug discovery.

The very method of creating new drugs is very convenient for the implementation of neural networks. There are a lot of data to take into consideration during the development of the new drug.

The process of drug discovery involves the following stages:

• Analysis of observed medical effects – this is a clustering and classification problem.

• Hit discovery – that’s where machine learning anomaly detection may come in handy. The algorithm goes through the compound database and tries to uncover new activities for specific purposes.

• Then the selection of results is narrowed down to the most relevant via Hit to Lead process. That’s dimensionality reduction and regression.

• Next goes Lead Optimization – the process of combining and testing the lead compounds and finding the most optimal approaches to them. The stages involve the analysis of chemical and physical effects on the organism.

After that, the development shifts in living testing. Machine learning algorithms took a back seat and used to structure incoming data.

CNN streamlines and optimizes the drug discovery process on the critical stages. It allows compressing the timeframe for the development of cures for emerging diseases.

Predictive Analytics – Precision Medicine

A similar approach also can be used with the existing drugs during the development of a treatment plan for patients. Precision medicine was designed to determine the most effective way of treating the disease.

Precision medicine includes supply chain management, predictive analytics, and user modeling.

Here’s how it works:

• From the data point of view, the patient is the set of states that depend on a variety of factors (symptoms and treatments).

• The addition of the variables (types of treatment) causes specific effects in short and long-term perspectives.

• Each variable has its own set of stats about its effect on a symptom.

• Data is combined to create an assumption of what is the best course of action according to the available information.

• Then various results and changes in the patient’s state are put into perspective. That’s how the assumption is verified. Recurrent neural networks handle this stage as it requires the analysis of the sequences of the data points.

Conclusion

Convolutional Neural Networks uncover and describe the hidden data in an accessible manner.

Even in its most basic applications, it is impressive how much is possible with the help of a neural network.

The way CNN recognizes images says a lot about the composition and execution of the visuals. But, Convolutional Neural Networks also discover newer drugs, which is one of the many inspiring examples of artificial neural networks making the world a better place.

CNN creates the way we see the world and operate within it – think about how many times you’ve met an interesting person because of the tag on the photo? Or how many times you’ve found the thing you’ve been looking for via Google’s visual search.

That’s all Convolutional Neural Networks in action.

 

Back in the mid-00s, when machine learning algorithms were at the very beginning of the road towards widespread modern use – it seemed almost surreal to think that one-day complex systems that resemble the structure of the human brain would be anything more than another science-fiction trope.

Now neural network applications are commonplace – the universal tool for all things data analysis and generation – from natural language processing and image recognition to more complex operations like predictive analytics and sentiment analysis.

In this article, we will explain classical Artificial Neural Networks (aka ANN) and look at significant neural network examples.

What are Artificial Neural Networks?

ANN is a deep learning operational framework designed for complex data processing operations. The “neural” part of the term refers to the initial inspiration of the concept – the structure of the human brain. Conceptually, the way ANN operates is indeed reminiscent of the brainwork, albeit in a very purpose-limited form.

The thing is – Neural Network is not some approximation of the human perception that can understand data more efficiently than human – it is much simpler, a specialized tool with algorithms designed to achieve specific results.

The critical component of the artificial neural network is perceptron, an algorithm for pattern recognition. Perceptrons can classify and cluster information according to the specified settings.

Classical neural network applications consist of numerous combinations of perceptrons that together constitute the framework called multi-layer perceptron.

The multilayer perceptron is the original form of artificial neural networks. It is the most commonly used type of NN in the data analytics field. MLP is the earliest realized form of ANN that subsequently evolved into convolutional and recurrent neural nets (more on the differences later).

The primary purpose of the MLP neural network is to create a model that can solve complex computational problems from large sets of data and with multiple variables that are beyond human grasp.

So, what are neural networks good for? The key goals of using MLP in the data processing and analysis operation are:

1. Study the data and explore the nuances of its structure;
2. Train the model on the representative dataset;
3. Predict the possible outcomes based on the available data and known patterns in it.

Now let’s explain the difference between MLP, Recurrent NN, and Convolutional NN.

What is the difference between MLP, RNN, and CNN?

There are three major types of deep learning artificial neural networks currently in use.

• Classical Neural Networks aka multilayer perceptron – the one that processes input through a hidden layer with the specific model;
• Recurrent NN – got the repetitive loop in the hidden layer that allows it to “remember” the state of the previous neuron and thus perceive data sequences;
• Convolutional NN – contains multiple layers of processing different aspects of data input.

The main difference between them is the purpose of the application. The thing is – the choice of the solution depends on the needs of the operation.

When to use different types of neural networks:

• Multilayer perceptron classical neural networks are used for basic operations like data visualization, data compression, and encryption. It is more of a practical swiss army knife tool to do the dirty work.
• If your business needs to perform high-quality complex image recognition – you need CNN.
• If you need predictive analytics and statistical analysis – it is the job of RNN.

How does a Basic Multiplayer Perceptron work?

Basic multilayer perceptron consists of at least three nodes arranged in three functional layers:

1. Input layer – where information comes in;
2. Hidden layer – the one where all the action is;
3. Output layer – the results of the operation;

The hidden layer and output layer uses a non-linear activation function that models the behavior of the neurons by combining input with the weights of the neurons and adding bias. In other words – it is a mapping of the weighted inputs to the output.

The learning algorithm for perceptrons is backpropagation – continuous adjustment of the weights of the connections after each bout of processing. The adjustment is based on the error in the output. In other words, the system is learning from mistakes. The process continues until the cost of the error is at the lowest as possible.

There are two types of backpropagation

• Forward pass – where output correlating to the given input is evaluated
• Backing pass – where partial derivatives of the cost function (with different parameters) are propagated back through the network.

Now let’s explain the major neural network applications used.

Multilayer Perceptron Neural Networks Examples in Business

Data Compression, Streaming Encoding – Social media, Music Streaming, Online Video Platforms

In the days of virtually unlimited disc storage and cloud computing the whole concept of data compression seems very odd – why bother? Your company can upload data without such compromises.

This attitude comes from the misconception of the term “compression” – it is not actually “making data smaller” but restructuring data while retaining its original shape and thus making more efficient use of operational resources. The purpose of data compression is to make data more accessible in a specific context or medium where the full-scale presentation of data is not required or unnecessary.

To do that, neural networks for pattern recognition are applied. The file’s structure and content are analyzed and assessed. Subsequently, it is transformed to fit specific requirements.

Data compression came out of necessity to shorten the time of transferring information from one place to another. In plain terms – smaller things get to the destination faster. Because the internet is not transmitting the data instantly and sometimes, that’s a major requirement.

There are two types of compression:

• Lossy – inexact approximations and partial data discarding to represent the content.
• Lossless – when the file is compressed in a way, that the exact representation of the original file.

These days, social media and streaming services are using data compression the most prominently. It includes all forms of media – sound, image, video. Let’s look at them one by one:

• Instagram is a mobile-first application. This means, its image encoding is specifically designed for the most effective presentation on the mobile screen. This approach allows Instagram to perform lossy compression of an image content so that the load time and resource use would be as little and possible. Instagram’s video encoding algorithm is similarly designed for mobile-first and thus applies the lossy method.
• Facebook’s approach can’t be any different. Since Facebook’s users are spread equally over mobile and desktop platforms – Facebook is using different types of compression for every presentation. In the case of images, this means that each image is present in several variations specific to the context – Lossless compression is used for full image screening, while lossy compression and the partial cutoff is used in the newsfeed images. The same goes for the video. However, in this case, users can customize the quality of streaming on their own.
• Despite all its faults, Tumblr contains some of the most progressive data compression algorithms in the social media industry. Similar to Facebook, Tumblr’s data compression system adapts to the platform on which the application is running. However, Tumblr is using solely lossless compression for the media content regardless of whether it is mobile or desktop.
• Youtube is an interesting beast in terms of data compression. Back in the day, the streaming platform used a custom compressing algorithm on all uploaded videos. The quality was so-so, and so in late 00s-early 10s, Youtube implemented streaming encoding. Instead of playing already compressed video – the system is adapting the quality with lossy compression of the video on the go according to the set preferences.
• Spotify’s sound compression algorithm is based on Ogg Vorbis (which was initially developed as a leaner and more optimized alternative for MP3). One of the benefits of Ogg file compression is extended metadata that simplifies the tagging system and consequently eases the search and discovery of the content. Spotify’s top priority is convenient playback.
• Netflix is at the forefront of streaming video compression. Just like Spotify, Netflix aims at a consistent experience and smooth playback. Because of that, their algorithm is more closely tied with the user. There is the initial setting of image quality, and then there is the quality of connection that regulates the compression methodology. By 2020 they are going to adopt the new standard – Versatile Video Coding (VVC) which expands its feature to 360 video and virtual reality environments.

Neural Networks for Data Encryption – Data Security / Data Loss Protection

Data encryption is a variation of data compression. The difference is that while data compression is designed to retain the original shape of data, encryption is doing the opposite – it conceals the content of data and makes it incomprehensible in the encoded form.

Multilayer perceptron neural networks are commonly used by different organizations to encode databases, points of entry, monitor access data, and routinely check the consistency of the database security.

These days, encryption is one of the major requirements for the majority of products and services that operate with sensitive user data. In addition to that, in 2018, the European Union had adopted the GDPR doctrine that imposes encryption and data loss prevention software as an absolute must upon dealing with personal data.

Overall, there are three informal categories for sensitive information:

• Personal information (name, biometric details, email, etc.)
• Data transmissions within a platform (for example, chat messages and related media content)
• Log information (IP-address, email, password, settings, etc.)

Today, the most prominent software applications of this category are BitLocker, LastPass password manager, and DiskCryptor.

Data Visualization – Data Analytics for Business

Presenting data in an accessible form is as important as understanding the insights behind it. Because of that, data visualization is one of the most viable tools in depicting the state of things and explaining complex data in simple terms.

This looks like a job for multilayer perceptron.

Data Visualization is a case of classification, clustering, and dimensionality reduction machine learning algorithms.

Since data is already processed – the major algorithm at play here is dimensionality reduction. Neural networks for classification and clustering are used to analyze the information that needs to be visualized. They identify and prioritize the data that is subsequently processed through a dimensionality reduction algorithms. The data is smoothened into a more accessible form.

Visualization is a transformation of data from one form to another while retaining its content. Think about it as a translation of a notation sheet into a MIDI file.

These days, the most commonly used library of visualizations is D3 (aka Data-driven documents). It is a multi-purpose library that can visualize streaming data, interpret documents through graphs and charts, and also simplify the data analysis by reiterating data into a more accessible form.

Autonomous Driving – Image Recognition, Object detection, Route Adjustment

Drones of all forms are slowly, but surely establishing themselves are viable multi-purpose tools. After all, if you can train a robotic assembly line to construct cars with laser-focused precision – why can’t try to teach artificial intelligence to drive it.

The groundwork of the autonomous driving framework consists of multilayer perceptrons that connect the eyes of the system (aka video feed) and the vehicular component (aka steering wheel).

The basic operation behind autonomous driving looks like this:

• The algorithm is trained on the data generated by the human driver (usually, it is a combination of vehicle log, stats, and video feed). There are both supervised and unsupervised machine learning algorithms at work.
• The driving session usually starts with planning the route on the map with the location of the vehicle and the place of the destination.
• The route is the approximate plan of the movement. It is adjusted on the go through the input video feed.
• Video feed covers the entire view around the car – from sharp left to sharp right and also on the side and the back.

The video feed is used to:

1. Detect objects in the way of the vehicle and nearby;
2. Predict the object’s direction to avoid a collision;
3. Adjust the direction of movement towards the goal;

Tesla self-driving vehicles use this type of deep neural networks for object detection and autonomous driving. As a service, self-driving cars are live tested in a taxi business by Uber.

Customer Ranking – User Profiling – CRM

Customer engagement is a high priority for any company that is interested in a continuous and consistent relationship with their customers. The key is in the value proposition design that is relevant to the target segments and appropriate calls to action that motivate customers to proceed.

The question is how to determine which users go to which category to adjust the value proposition and present an appropriate call to action.

Enter multi-layer perceptron.

The benefits of using neural networks for customer ranking are apparent. Given the fact that every service with an active user base generates a lot of data – there is enough information that can characterize the user. This factor can be beneficial to business operations.

The primary function of MLP is to classify and cluster information with multiple factors taken into consideration. These features are precisely what you need for user profiling.

Here’s how it works:

• User data is processed and analyzed for such metrics as session time, actions/conversions, form filling, signing in, and so on.
• The sum of metrics determines what kind of user it is.
• Then comes clustering. The clusters may be predetermined (with clearly defined thresholds) or organic (based on data itself)
• The results of the calculation from each user profile are compiled and clustered by similarity.

This approach is an efficient and simple way of figuring out what messages to transmit to specific subcategories of the target audience. In addition to being a time-saving and cost-effective measure, it also provides a ton of insights regarding the use of the service or product. In the long run, this information contributes to the improvement of the service.

These days, such algorithms are used by business CRM platforms like Salesforce and Hubspot and also partially by analytics tools like Google Analytics.

In Conclusion

One of the many neural network advantages is that it gives us more solid grounds for decision-making and makes us capable of foreseeing different possibilities from the data point of view.

In one way or another, the application of neural networks in various fields gives us a better understanding of how things are organized and the way they function. MultiLayer Perceptrons presents a simple and effective way of extracting value out of information.