Natural language processing examples

Best Natural Language Processing Examples in 2020

Best Natural Language Processing Examples in 2020

Muhammad Imran

Author 

September 27, 2019

Natural language processing examples

In today’s IT centred business environment, companies receive almost 95% of their customer data in the form of unstructured text. Sources include emails, surveys, online reviews, social media posts and comments on different forums.

Natural Language Processing (NLP), Cognitive services and AI an increasingly popular topic in business and, at this point, seems all but necessary for successful companies. NLP holds power to automate support, analyse feedback and enhance customer experiences. Although implementing AI technology might sound intimidating, NLP is a relatively pure form of AI to understand and implement and can propel your business significantly. This article will cover some of the common Natural Language Processing examples in the industry today.

Converse Smartly® is an advanced speech recognition application for the web developed by Folio3. It is a strong contender in the use and application of Machine Learning, Artificial Intelligence and NLP. It enables organisations to work smarter, faster and with greater accuracy. The advanced features of the app can analyse speech from dialogue, team meetings, interviews, conferences and more.

Natural Language Processing Definition, and What Is it?

In dictionary terms, Natural Language Processing (NLP) is “the application of computational techniques to the analysis and synthesis of natural language and speech”. What this jargon means is that NLP uses machine learning and artificial intelligence to analyse text using contextual cues. In doing so, the algorithm can identify, differentiate between and hence categorise words and phrases and therefore develop an appropriate response. Some of the most common NLP examples include Spell Check, Autocomplete, Voice-to-Text services as well as the automatic replies system offered by Gmail.

Natural Language Processing Uses in Businesses

Given that communication with the customer is the foundation upon which most companies thrive, communicating effectively and efficiently is critical. Regardless of whether it is a traditional, physical brick-and-mortar setup or an online, digital marketing agency, the company needs to communicate with the customer before, during and after a sale. The use of NLP, in this regard, is focused on automating the tracking, facilitating, and analysis of thousands of daily customer interactions to improve service delivery and customer satisfaction.

Improve user experience

A website integrated with NLP can provide more user-friendly interactions with the customer. Features such as spell check, autocorrect/correct make it easier for users to search through the website, especially if they are unclear of what they want. Most people search using general terms or part-phrases based on what they can remember. Enabling visitor in their search stops them from navigating away from the page in favour of the competition.

Automate support

Providing adequate support can be tedious and labour intensive. To improve communication efficiency, companies often have to either outsource to 3rd-party service providers or use large in-house teams. AI without NLP, cannot cope with the dynamic nature of human interaction on its own. With NLP, live agents become unnecessary as the primary Point of Contact (POC). Chatbots can effectively help users navigate to support articles, order products and services, or even manage their accounts.

Monitor and Analyse Feedback

Feedback comes in from many different channels with the highest volume in social media and then reviews, forms and support pages, among others. NLP can aggregate and help make sense of all the incoming information from feedback, and transform it into actionable insight.

Improve Internal Communication

One of the most monotonous and time-consuming aspects of any internal communication is record keeping. Minutes and transcriptions can take hours, but with NLP, interviews, meetings, seminars, conferences can all be converted to text quickly.

Make Sense of Unstructured data

There are a large number of information sources that form naturally in doing business. These can sometimes overwhelm human resources in converting it to data, analyzing it and then inferring meaning from it. NLP automates the process of coding, sorting and sifting of this text and transforming it to quantitative data which can be used to make insightful decisions.

Folio3’s NLP and Cognitive Services

Folio3 is a California based company that offers robust cognitive services through its NLP services and applications built using superior algorithms. The company provides tailored machine learning applications that enable extraction of the best value from your data with easy-to-use solutions geared towards analysing sophisticated text and speech. Their NLP apps can process unstructured data using both linguistic and statistical algorithms.

Natural Language Processing Examples in 2020

Below are some of the common real-world Natural Language Processing Examples. Most of these examples are ways in which NLP is useful is in business situations, but some are about IT companies that offer exceptional NLP services.

1) Search Autocorrect

Making mistakes when typing, AKA’ typos‘ are easy to make and often tricky to spot, especially when in a hurry. If the website visitor is unaware that they are mistyping keywords, and the search engine does not prompt corrections, the search is likely to return null. In which case, the potential customer may very well switch to a competitor. Therefore, companies like HubSpot reduce the chances of this happening by equipping their search engine with an autocorrect feature. The system automatically catches errors and alerts the user much like Google search bars.

2) Search Autocomplete

Autocomplete services in online search help users by suggesting the rest of the keywords after entering a few or a partial word. Historical data for time, location and search history, among other things becoming the basis. Autocomplete features have no become commonplace due to the efforts of Google and other reliable search engines.

Salesforce is an example of a software that offers this autocomplete feature in their search engine. As mentioned earlier, people wanting to know more about salesforce may not remember the exact phrase and only just a part of it.

3) Form Spell Check

Frequent flyers of the internet are well aware of one the purest forms of NLP, spell check. It is a simple, easy-to-use tool for improving the coherence of text and speech. Nobody has the time nor the linguistic know-how to compose a perfect sentence during a conversation between customer and sales agent or help desk. Grammarly provides excellent services in this department, even going as far to suggest better vocabulary and sentence structure depending on your preferences while you browse the web.

4) Smart Search

A smart-search feature offers the same autocomplete services as well as adding relevant synonyms in context to a catalogue to improve search results. Klevu is a company that provides smart search capability powered by NLP coupled with self-learning technology. Best suited for e-commerce portals, Klevu offers relevant search results and personalised search based on historical data on how a customer previously interacted with a product or service.

5) Messenger or chatbots

Many companies today use messenger apps coupled with social media, to deliver connect and interact with customers. Facebook Messenger is one of the more recent platforms used for this purpose. In this case, NLP enables expansion in the use of automatic reply systems so that they not only advertise a product or service but can also fully interact with customers. The more comfortable the service is, the more people are likely to use the app. Uber took advantage of this concept and developed a Facebook Messenger chatbot, thereby creating a new source of revenue for themselves.

6) Machine Translation

Translation of both text and speech is a must in today’s global economy. Regardless of the physical location of a company, customers can place orders from anywhere at any time. The trouble lies in the apparent language barrier. When communicating with customers and potential buyers from various countries. Lilt is a translation tool that seeks to make the process easier. It integrates with any third-party platform to make communication across language barriers smoother and cheaper than human translators.

7) Virtual Assistants

Mastercard launched its first chatbot in 2016 which was compatible with Facebook Messenger. Although, compared to Uber’s bot, this bot functions more like a virtual assistant. Having a bank teller in your pocket is the closest you can come to the experience of using the Mastercard bot. The assistant can complete several tasks and offers helpful information such as a dashboard of spending habits and alerts for new benefits and offers available.

8) Knowledge Base Support

An answer bot provides direction within a pre-existing knowledge base. For example, Zendesk offers answer bot software for businesses that uses NLP to answer the questions of potential buyers’. The bot points them in the right direction, i.e. articles that best answer their questions. If the answer bot is unsuccessful in providing support, it will generate a support ticket for the user to get them connected with a live agent.

9) Email filters

Email filters were one of the earliest applications of NLP online. It began with just spam filters based on previous interactions with certain types of emails by the mail clients user base. By uncovering certain words or phrases that signal a spam message the mail client immediately flags the email and moves it to spam. One of the more recent additions to NLP applications in email Gmail’s classification system. The system recognises if emails belong in one of three categories (primary, social, or promotions) based on their contents. For all Gmail users, this keeps your inbox to a manageable size with meaningful, relevant emails you wish to review and respond to quickly.

10) Survey Analytics

One of the best ways for NLP to improve insight and company experience is by analysing data for keyword frequency and trends, which tend to indicate overall customer sentiment about a brand. Even though the name, IBM SPSS Text Analytics for Surveys is one of the best software out there for analysing almost any free text, not just surveys. One reviewer tested the system by using his Twitter archive as an input. Although the user interface leaves something to be desired.

11) Social Media Monitoring

Monitoring and evaluation of what customers are saying about a brand on social media can help businesses decide whether to make changes in brand or continue as it is. NLP makes this process automatic, quicker and more accurate. Social media listening tool such as Sprout Social help monitor, evaluate and analyse social media activity concerning a particular brand. The services sports a user-friendly interface does not require a ton of input for it to run.

12) Marketing Strategy

Developing the right content marketing strategies is an excellent way to grow the business. MarketMuse is one such company that produces marketing content strategy tools powered by NLP and AI. Much like Grammarly, the software analyses text as it is written, thereby giving detailed instructions about the direction to ensure that the content of the highest quality. MarketMuse also analyses current affairs and recent news stories, thus providing users to create relevant content quickly.

13) Descriptive Analytics

Reviews increase the confidence in potential buyers for the product or service they wish to procure. Collecting reviews for products and services has many benefits and can be used to activate seller ratings on Google Ads. However, NLP-equipped tools such as Wonderflow’s Wonderboard can bring together customer feedback, analyse it and show the frequency of individual advantages and disadvantage mentions.

14) Natural Language Programming

Programming is a highly technical field which is practically gibberish to the average consumer. NLP can help bridge the gap between the programming language and natural language used by humans. In this way, the end-user can type out the recommended changes, and the computer system can read it, analyse it and make the appropriate changes.

15) Automatic Insights

Automatic insights are the next step in NLP applications. This feature does not merely analyse or identify patterns in a collection of free text but can also deliver insights about a product or service performance that mimics human speech. In other words, let us say someone has a question like “what is the most significant drawback of using freeware?”. In this case, the software will deliver an appropriate response based on data about how others have replied to a similar question.

Conclusion

As is evident from the long list of Natural Language Processing examples described above, there is an infinite number of possibilities for NLP application in business. Teaching AI to read, listen and speak as humans will lead to significant efficiency improvements in businesses operations. From sifting through incoming emails to generating automatic insight using computer vision, NLP can change the way people interact with technology altogether.

Start Gowing with Folio3 AI Today.

We are the Pioneers in the Cognitive Arena – Do you want to become a pioneer yourself ?
Get In Touch

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Deep learning, Computer Vision, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@folio3.ai

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Predictive analytics examples

Predictive Analytics Examples and its Uses

Predictive Analytics Examples in 2019

Muhammad Imran

Author 

September 25, 2019

Predictive analytics examples

Effective utilisation of big data and predictive analytics has become critical in today’s data-driven world for businesses to achieve their goals. In conjunction with machine learning and artificial intelligence, we have many real-world predictive analytics applications. Governments, research and academic institutions and commercial businesses can all use predictive analytics to make informed and decisions that take into consideration historical data trends. Here in this article, we discuss how predictive analytics works and some of the most common predictive analytics examples that exist today.

What Is Predictive Analytics?

Predictive Analytics is the systematic process of using historical data (last month, last year) to establish connections and analyse possible patterns within the data. These relationships and trends, when used on current or future data (tomorrow, next month, next year), can help predict future outcomes.

The Artificial Intelligence (AI) uses a combination of machine learning applications and statistical techniques to determine the presence and magnitude of changes in one element caused by changes in another within the same data set. After establishing this causality, the AI can apply the same models to new data sets and predict whether there is a change in the second element. For example, you can use historical marketing and sales data to see how significantly specific marketing techniques impact sales.

Why Is Predictive Analytics the Talk of the Town?

In fairness, predictive analytics is nothing new. However, it used to be available only to companies that could afford the resources necessary to carry it out. Only recently has the use of predictive analytics become affordable, easy to use and therefore, popular. It is used increasingly by different organisations in various industries to improve efficiency in everyday operations and achieve that elusive competitive edge.

Send Marketing Material to Customers That Are Most Likely to Make a Purchase

Developing a marketing campaign is both a labour and capital intensive task. Successful marketing needs to be pertinent and engaging at all levels in your target audience. Therefore, you need precisely who is most likely to buy your product and what are the commonalities between such individuals (or groups). For example, let us say that your business has a $5,000 budget for a marketing campaign, and you have 100,000 customers. In this case, you clearly cannot give them all a 10% discount. Business intelligence, coupled with predictive analytics, can help forecast the customers that exhibit the most significant probability of buying your product. You can then focus your discounts/coupon books to only those that generate the most revenue.

Identify Customers that May Abandon a Service or Product

Consider, for example, a Gym that has implemented a predictive analytics model. Keep in mind that fitness centres usually have a high attrition rate with many customers dropping the subscription reasonably quickly. In this case, let us assume that ‘Jack’ is one such customer that the system identified will not continue the subscription. The basis of judgement is historical data gathered from the Gym as well as the data available about Jack. The next time that Jack comes into the Gym, the staff will be prepared to offer incentives and discuss the continuation of the relationship between the Gym and Jack.

A prominent predictive analysis example comes in the form of Folio3. It is a company that has developed a solution specifically designed for this problem. Their Customer Churn Prediction system offers advances customer segmentation, predictive attrition, statistical analysis and customer retention strategies tailored to the potential churners.

Increase customer service quality through precise planning

Knowing when customers are most likely to arrive and what they will need is the ideal situation. You can use this information to improve service quality and the overall efficiency of your organisation. Through predictive analytics, businesses can forecast demand better by using advanced models and business intelligence. Consider a hotel chain, for example, that wants to know the number of customers that are most likely to visit and stay at specific locations during the Halloween weekend. The hotel can then ensure that they have enough resources and the staff necessary to cater to the flow of customers.

Similarly, a fast-food chain can use predictive analytics to improve drive-thrus and reduce customer wait times. Folio3 successfully developed one such system for its client. Their Automated Authentication fro Drive-Thrus. ‘Dashcode’, the company that wanted the automated system wished to increase the workflow efficiency in Drive-Thrus by breaking down each activity and applying deep learning. In doing so, the system would be able to predict what customers are likely to order and recommend it first to save on order time.

Best Predictive Analytics Examples

No sector is precisely the same and therefore is likely to use predictive analytics in different ways. Here are the top seven industry predictive analytics examples.

1) Sports

One of the most recent additions to predictive analytics in sports is the Microsoft Sports Performance Platform. It is a tool that comes up with data-driven decisions for athletes and teams for almost any aspect of the game, including everything from training schemes for each player to the final composition of the team. The algorithms even help prevent injuries and also predict the total recovery time by taking into account factors such as distance sprinted and temperature.

A small Danish football club, FC Midtjylland is an excellent predictive analysis example. It now analyses each player twice a month and tailors individual training plans for every player. Analytical models are used to gain insights during the game to make changes to the game plan in half-time and also to suggest new players.

In tennis, IBM introduced a tool named ‘SlamTracker’ which predicts the winner of a match based on a player’s pattern of play, the propensity of forehand use and willingness to volley. Coupled with computer vision and live game footage, this data can then predict the winner.

2) Retail

Retail is probably one of the most significant predictive analytics examples. Retailers are always in a search for ways to increase customer engagement, loyalty and retention. One such example is Amazon’s recommendations system. Whenever you make a purchase, the company offers a list of other similar items to the one you just bought. The AI supplies this list based on historical data of other customers that have been buying the same products.

There are many other benefits of predictive analytics in retail, including sales forecasts, market analysis, segmentation and managing inventory. You can also derive revisions to the business models and the best retail locations using predictive analysis. However, it also acts post-sale, acting to reduce returns, get the customer to come back and extend warranty sales.

3) Weather

Predictive analytics has become almost indispensable to weather forecasting today. Five-day forecast is now just as accurate as one-day forecasts from the 1980s. We can now accurately predict the occurrence of and movement of large weather systems including hurricanes, tornados and flood based on historical data. For example, recently, an extreme polar vortex brought frigid winds down to places like Wisconsin and Minnesota and dropped temperatures to -50 degrees Fahrenheit. The prediction arrived several days in advance, giving people time to prepare. All of this is possible thanks to satellite monitoring of the Earth and complete predictive models that represent how the Earth functions. The movie “Day after Tomorrow” is an excellent example of the use of predictive analytics in assessing the risk of global weather patterns.

4) Health

Predictive analytics in the health care sector is focused primarily on how likely is it that an individual will get better, or sicker. By analysing historical data, hospitals can predict which patients are likely to contract a chronic disease and are susceptible to Central-Line Associated Bloodstream (CLAB) infections. This cognitive services process can also help determine the risk of a patient that does not show up for scheduled appointments. Health Catalyst is one such company operating in Salt Lake City since 2008 that specialises in the focus areas mentioned above.

5) Insurance and Risk Assessment

Insurance firms managed to bring losses within risk tolerances levels despite recent financial disasters thanks to predictive analytics. The system ensures that firms set up competitive prices for underwriting, identify fraudulent claims, analyse and estimate future losses. Insurance firms can also use predictive analysis to develop marketing campaigns and generate better insights into risk selection and assessment.

6) Financial modelling

Predictive analytics for financial services helps fine-tune the overall business strategy, revenue generation strategy and resource optimisation. Automation of analytics in financial services enables firms to process thousands of models simultaneously and deliver quicker results than with traditional modelling based on manual human interpretation of the data. Human error can severely affect the process of analysing trends.

The predictive analysis systems can help financial institutions target specific customer segments based on profitability and risk level. By using historical data, the company can also forecast cash flows and predict demand patterns for specific financial services. Most importantly, the algorithms can alert the firm to customers that deviate from past payment patterns and therefore collect overdue payments much faster.

7) Energy

In the power generation sector, component failures are a real risk and can sometimes cause catastrophic results. The Chernobyl disaster is one such example. By using predictive analytics in power plants, the company can anticipate equipment failures and reduce sudden shutdowns. By predicting when a component will fail, the power plant can use preventive maintenance to address the issue in time and thus reduce maintenance costs and improve the availability of power.

Similarly, utility companies can predict when customers might get a high bill and then alert customers to spikes that occur at certain times of the day. Thereby, the power company can manage the load better and reduce customer complaints.

Predictive Analytics Vs Predictive Modelling

Predictive analytics and modelling serve the same purpose but differ in the method used. Both systems use historical data and statistical techniques to predict the occurrence of an unknown future event. Predictive modelling examples can be found most commonly in astrology and meteorology.

However, predictive modelling will only give you a probability based on a predetermined modelling framework. On the other hand, predictive analytics uses modelling that is driven by data mining and, therefore, yields more intuitive results. Predictive analytics can predict future events and suggest the best course of action to achieve the best results.

Conclusion

Predictive analytics is increasingly becoming a core function of business no matter the size. To succeed and grow the business, companies depend on predictive analytics to forecast cash flows, customer engagement, demand, risk and many more. Predictive analytics and predictive modelling examples are available throughout the business world, improving business insight and therefore increasing competency.

Start Gowing with Folio3 AI Today.

We are the Pioneers in the Cognitive Arena – Do you want to become a pioneer yourself ?
Get In Touch

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Deep learning, Computer Vision, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@folio3.ai

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Computer vision in manufacturing

Computer Vision in Manufacturing Along With Best Usages And Examples

Computer Vision in Manufacturing Along With Best Usages And Examples

Muhammad Imran

Author 

September 6, 2019

Computer vision in manufacturing

The concept of computer vision has existed for a long time. Automation has always held many applications for machine learning through computer vision. Whether it be processing social media traffic and trying to surface actionable insights or targeting consumers based on past purchases, automation through computer vision is a dream for any AI developer. In this piece, we deliver a brief introduction to computer vision as well as a breakdown of its history, best uses in manufacturing, and then some examples of computer vision in a building.

 

Definition

Computer vision is a branch of computer science that focuses on enabling computers to see and therefore, visually identify images to process them as text-based information. It functions in many ways the same as human vision and then provides an appropriate output based on the (visual) input. In short, you impart human intelligence and the ability to solve the problem in the context of an Artificial Intelligence (AI). The computer must understand what it sees and then complete the suitable analysis or required action accordingly.

 

As mentioned earlier, the goal of Computer Vision as a concept is to mimic human vision by putting digital images through three main processing components, executed one after the other:

1. Image acquisition

2. Image processing

3. Image analysis and understanding

 

History

In the late 1960s, the concept of computer vision started at the university level. The pioneers of AI envisioned a system that could mimic the human visual system and thereby grant robots one more tool in aiding their decision-making abilities.

What differentiates computer vision from the predominant field of digital image processing at that time was a need to extract 3D structure from an image aimed at comprehending the exact details within that photograph besides what is available in the foreground. By the 1970s, developers had begun to lay the foundations for many of the computer vision algorithms available today. Includes extracting parts of an image, line labels, non-polyhedral and polyhedral modeling, object representation as interconnections of smaller assemblies, optical flows, and motion estimations.

In more recent work, feature-based methods of computer vision have arisen coupled with machine learning applications and complex optimization frameworks. The growth of Deep Learning techniques has further advanced the field of computer vision.

 

Examples

Autonomous Vehicles

Self-driving cars as well as the regular variety use an array of visual input based sensors to understand the world around them and thereby make decisions for the car’s performance. 

Facial Recognition

Businesses and personal electronics all use facial recognition for security. Facial recognition is a classic example of how a computer learns o recognize visual cues.

Furthermore, facial recognition can also extend (in a manner of speaking) to animals. The Zoological Society of London (ZSL) uses AutoML to recognize known species of animals and identify new ones from a vast array of visual data from camera traps around the world and place estimates on population size.

Google Cloud is capable of identifying species from thousands of images quickly and trains new ML models using ZSL’s archival photos. In the future, it could even enable real-time tracking of the world’s biodiversity.

Image Search and Object Recognition

Several systems use the data vision theory to identify objects that exist within a digital image. It allows you to search through extensive catalogs of images, and also extract information out of the photos.

For example, the google translate app has done wonders for bridging the gap between people speaking different languages. Using computer vision, all you have to do is point your camera to the words and let the app work its magic. The app will tell you precisely what it means in your preferred language. The results are almost instant by using Optical Character Recognition (OCR) to see the image coupled with augmented reality to generate an accurate translation (with context). Most PDF readers come with this feature, as well.

Robotics

A lot of robotic machines, especially in manufacturing, need to see what’s around them to perform the task at hand. In production, robots are often used to inspect entire assembly tolerances by visually gauging them.

Health and Medicine

Folio3’s Breast Cancer HER2 Subtype Identification was developed to supply an automated pipeline for cell segmentation and spot counting through a Computer Vision-based analytical-aid for the Fluorescent In-Situ Hybridization test. The company developed a computer-guided assistance system that allows technicians to perform the analysis more quickly and accurately while enabling them to digitize and store the images for later usage.

 

How Computer Vision Can be Used in Manufacturing

Computer vision has many applications in manufacturing including, but not limited to, predictive analytics of machinery, quality control for products, health and safety, and accurate assembly of components. Major businesses in all locations have adopted production and management systems aided by computer vision. Tesla uses sensors to outfit its vehicles for the delf-driving function as well as accident prevention. Kawasaki Heavy Industries uses machine vision to accurately assemble each component of the hydraulic pumps used in heavy machinery. Mining companies use computer vision aided monitoring systems to closely monitor drilling equipment to identify defects and other damage before they can cause an accident.

One of the more recent fields to use computer vision is 3D printing. Computer vision additive manufacturing uses helps maintain the integrity of the build by analyzing visual data and correcting flaws in real-time. The list can go on an on, but a few specific examples are detailed in the following section:

 

7 Examples of Using Computer Vision in Manufacturing 

Additive Manufacturing

The process of manufacturing that builds 3D objects by adding layers of material is known as additive manufacturing regardless of the type of substance.

Computer vision additive manufacturing is the use of a computer, 3D modeling software (Computer-Aided Design or CAD) and machine vision to combine the output data from the layering machine with the visual input data to accurately reproduce the design.

Predictive Maintenance

Predictive Maintenance is critical for businesses that rely on machinery for assembling physical components and or providing services. If these machines were to fail mid-operation, it could spell disaster for the company’s reputation and therefore, cash flow. 

Therefore, computer vision in manufacturing (specifically predictive Maintenance) is the system by which machine learning and IoT devices monitor incoming data from machinery and sometimes individual components through sensors. The sensors identify signals that trigger alerts informing you to take corrective action before an asset is lost entirely, or there is an accident.

Package Inspection

It is sometimes imperative for pharmaceutical companies to tally the number of tablets and capsules that are going into any form of packaging. Computer vision for quality control in automated manufacturing systems can help significantly in this task by checking for broken or partially formed tablets. As the medication makes its way through the production line, photos are taken and transferred to a dedicated computer that processes the image using a set of established algorithms designed to check if the tablets have the right color, dimensions, and shape.

Reading Barcodes

It is difficult for humans alone to identify, understand, and process barcodes at the scale we use them in our daily lives. Every single trip to the grocery store adds to this count. The human eye cannot reach the level of accuracy that an automated system might achieve. For speed, you will have to use machine learning coupled with computer vision to analyze the barcodes.

For example, most handheld devices, such as cell phones and mobiles, are becoming smaller and smaller. Meaning that the parts required to assemble them are also becoming smaller. Manufacturers require Printed Circuit Boards (PCBs) that can fit inside these tiny bodies, but manufacturing them is difficult. “Panelization” is the process used to produce several identical circuit boards on one large panel after which a machine separates each circuit. The final testing phase uses a machine vision-based solution (e.g., groups) to read the barcodes on each item to look for defects and any problems in printing.

Product and Components Assembly

Manufacturing plants for high volume and precision products have to ensure that products and components that come off the assembly/production line conform to the strict quality and safety guidelines set by the regulatory authority. Computer Vision-based systems (e.g., Acquire Automation) help businesses guarantee that their products and components are assembled to the standard.

For example, Acquire Automation implements machine vision that permits manufacturers to inspect bottles in a complete 360-degree view to verify that products are placed in the correct packaging as well as other critical attributes such as:

· Cap closure/seal

· Position

· Label

· Print quality, and much more!

Defect Reduction

Companies, justifiably, want components that come off their production line to be error-free. Achieving this at any significant scale can bring several problems for manual efforts. Machine vision, on the other hand, is the ideal technology to help businesses automate a solution to this problem.

WebSPECTOR is a surface inspection system that can identify defects, store photos as well as any accompanying metadata related to the image. As items move along the production line, any errors are classified according to type and then a grade to gauge the severity of the problem.

With this kind of information, companies can differentiate between the many types of defect that occur and implement the correct procedures and policies. For example, manufacturers can follow a process that stops the production line at a certain number of identified effects.

Improving Safety

Machine vision is not restricted to just production lines in manufacturing plants. Machine vision is also used in more diverse settings such as mines and large scale construction. For example, Komatsu Ltd, a leading UK based manufacturer of construction and mining equipment, recently announced its intention to partner with NVIDIA and its “cloud to edge” tech.

A combination of real-time, live feed from cameras, and video analytics algorithms allow the equipment to operate with greater efficiency and with improved safety. The idea is to use deep learning-based AI to track the movement of people and predict where the machines are going to be to avoid dangerous interactions. With as much as 10,000 injuries occurring on construction sites annually in the USA alone, solutions like these are welcomed by all organizations.

Furthermore, Folio3 offers an AI-powered Road and Safety solution that allows the analysis of the road and traffic situation through deep learning machine vision. It is a must-have solution for most road and safety situations as it not only allows for identification of the class of vehicle but also the rate of travel and the level of congestion.

 

Conclusion

Computer vision is by no means a fledgling concept. The idea has tickled the minds of innovators since the advent of the first robotic technologies. Nonetheless, computer vision in manufacturing still has significant room to grow, and the number of possible applications will only grow in time.

The idea that AI has the same ability to make decisions based on visual input without the need for manual data entry is spectacular. But since the AI will perform according to the training data provided, it is best to connect yourself with a certified machine learning service provider to avoid any unnecessary hiccups.

Start Gowing with Folio3 AI Today.

We are the Pioneers in the Cognitive Arena – Do you want to become a pioneer yourself ?
Get In Touch

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Deep learning, Computer Vision, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@folio3.ai

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Machine Learning Applications in business

Machine Learning Applications in Businesses 2020

Machine Learning Applications in Businesses 2020

Muhammad Imran

Author 

September 3, 2019

Machine Learning Applications in business

Machine learning as a Service (MLaaS) in any setting helps extract meaningful insight from large volumes of raw data to identify trends and patterns that otherwise are not apparent. It is most commonly used to solve complex business problems that rely heavily on data. Machine learning applications in business use algorithms to learn from the data with every iteration. i.e., every time there is any data input, the system will automatically compare with previous data entries. This comparison will allow the system to draw valuable insight and find hidden links between seemingly random information. The algorithm will do all of this without being explicitly programmed to do so, and therein lies the advantage.

Why You Need Machine Learning Application in Businesses?

Applications of machine learning in business primarily include improving business scalability and developing business operations for companies all over the world and in any industry. Artificial intelligence tools and various machine learning algorithms have gained considerable fame in the business analytics community. Factors such as increasing volumes, easy availability of data, more affordable and quicker processing, and practical data storage techniques have led to a significant increase in the use of machine learning. Therefore, businesses can now profit by understanding how companies can make use of machine learning and implement the same in their processes.

Machine learning in business applications helps in extracting meaningful information from massive raw data sets. If implemented correctly, Machine Learning can serve as a solution to a variety of business complexities problems and predict complex customer behaviors. Applications of machine learning in business are practically limitless but can (and do) include the following:

Customer Lifetime Value Prediction

Machine Learning and data mining can help companies to predict customer behavior and purchase patterns. The algorithms assist in sending the best possible offers to individual customers, based on their browsing and purchase histories.

Increasing Customer Satisfaction

Machine Learning can help in improving customer loyalty and also ensure the superior customer experience. Previous call records are analyzed to determine customer behavior and predict requirements, thereby assigning the most suitable customer service executive.

The machine learning algorithms use customer’s purchase history and match it with the extensive product inventory to identify hidden patterns and group together similar products. Product recommendations are then made using this information.

Eliminates Manual Data Entry

Machine learning programs discover data rather than requiring manual entry.

 

Detecting Spam

Spam filters in e-mail applications can now create new rules by using neural networks to identify spam and phishing messages.

 

Predictive Maintenance

Predictive maintenance helps in reducing the risks associated with unexpected failures and eliminates unnecessary expenses using historical data, workflow visualization tools, flexible analysis environment, and the feedback loop.

 

Financial Analysis

Machine Learning in finance helps in portfolio management, algorithmic trading, loan underwriting, and fraud detection. However, future applications of Machine Learning in finance will include Chatbots and other conversational interfaces for security, customer service, and sentiment analysis.

 

Image Recognition

Image recognition involves machine learning through the use of data mining, computer vision, pattern recognition, and database knowledge discovery to produce numeric and symbolic information from images and other multi-dimensional data.

 

Medical Diagnosis

Machine learning in healthcare can potentially make near perfect diagnosis because of the objectivity in data analysis. The systems can also predict readmissions, recommend the best medicines, and identify patients at high risk, mostly based on patient records.

 

Improving Cyber Security

Machine learning can be used to enhance cybersecurity, as well. The algorithms can quickly analyze the most likely vulnerabilities and potential malware and spyware applications based on user data.

 

7 Best Machine Learning Applications in Businesses

Hiring data scientists or ML experts is never easy nor cheap, but the rise of MLaaS suggests that once the service becomes the mainstream, efficiency in delivery will improve. As data gathering becomes cheaper and processing power grows more powerful, miracles of data science become possible for everyone.

MLaaS is a good halfway point for companies that want to experience the benefits of machine learning without diving in themselves. Having an established provider handle it for you in the initial stages of implementation is an excellent way to minimize any transitional issues. Not to mention you have grater surety the process is going according to plan.

Here we will discuss some of the best machine learning service providers and application availability, and then you can see which one works for you.

 

AWS Machine Learning

Amazon Web Services have widely revolutionized the SaaS field and become a dominant player in the MLaaS market. Amazon Machine Learning offers a service that is incredibly popular for its useful guidance in creating an ML model without needing to delve into the complex world of the algorithms themselves. Some of the more prominent applications offered by AWS include Amazon EC2, Amazon S3, Amazon Aurora, and Amazon DynamoDB.

Amazon Machine Learning is highly automated and therefore is the best choice for beginners. It can automatically receive data from multiple input types such as Amazon RDS, Amazon Redshift and CSV files, etc. Categorical and numerical does not need to identify manually as the algorithms will do that for you and then determine the appropriate method of data preprocessing.

Amazon ML pricing is on a pay-as-you-go model. You first have a flat $0.42/hour fee for data analysis and model building, with separate charges for every addon. i.e., batch predictions ($0.10 per 1,000 predictions, to the nearest 1,000) and real-time predictions ($0.0001 per prediction, to the nearest penny). If you want Data storage as well, billing is separate.

 

Microsoft Azure Machine Learning Studio

Microsoft Azure has a lot of services to offer, but their machine learning options are particularly useful. Not only is their machine learning scalable, but it is also suitable for both beginners as well as experts in AI. It hosts a wide range of tools that lean towards flexibility for out-of-the-box algorithms.

That said, ML Studio is still a little challenging to get used to since the operations have to be completed manually. This includes everything from data exploration, preprocessing, choosing appropriate methods, and validating modeling results. Although the browser-based environment is a simple, visual drag-and-drop mechanism, so no coding needed there.

ML Studio’s more popular option is the free workspace. It only requires a Microsoft account and includes unrestricted access to over 10GB of storage, R and Python support as well as predictive web services. The standard enterprise-grade workspace is a little pricier, at $9.90/month plus an Azure subscription. At the same time, though, it offers much more in terms of support and services.

 

IBM Watson Machine Learning (WML)

WML is a public service provider that runs on IBM’s Bluemix. It helps data scientists and developers work together to fast-track the process of moving to deployment and integrate AI into their applications. By streamlining, accelerating, and leading AI deployments, Watson Machine Learning helps enable organizations to harness machine learning, deep learning, and decision optimization to deliver business value.

Similar to the Google Prediction API, Watson Studio offers fully automated data processing and model building. It is an interface that needs very little training to start processing data and therefore reducing the time for preparing the models and eventual deployment. It can be said that WML’s USP is its cloud computing expertise and the distribution of resources. It offers a public cloud, private cloud as well as a dedicated accelerated environment.

Pricing for IBM WML ranges from the “basic” free version to the “professional” service charged at USD 1,000 per instance and USD 0.4 per 1,000 predictions and capacity unit hour.

 

Folio3 AI

Folio3 machine learning applications for business bring continuous, uninterrupted intelligence to your enterprise processes. By radically improving productivity and decreasing costs, you can boost your ROI.

The company offers dynamic deployment strategies that analyze your ML or DL needs, skilled resources to determine the ML/DL models, integrate and then design solutions based around offsite ML. The Folio3 AI automates your processes and prioritizes the more routine aspects of data management through advanced algorithms.

Folio3 is an expert in adapting machine learning application in business intelligence as well as understanding customer and therefore, encourages regular touchpoints with customers and relevant stakeholders. Furthermore, tailored AI and ML algorithms. These algorithms can be integrated with both image and video analytics and coupled with emerging technologies such as augmented virtual reality. As such, the company has extensive experience working in multiple industries with featured applications such as Converse Smartly (Natural Language Processing), Breast Cancer HER2 Subtype Identification (Computer Vision), Road Traffic Analysis (Deep Learning), ATM Cash Forecasting (Deep Learning), Automated Authentication for Drive Throughs (Deep Learning), Cognitive services and Facial Recognition System (Histogram of Oriented Gradients or “HOG”).

As far as pricing is concerned, given the high-level os customisability, the cost of service depends on your exact requirements. It is best to contact the company to see how best Folio3 can help you in the deployment of advanced machine learning systems.

 

Iflexion

Iflexion is a software development company that distributes AI and machine learning applications to more than 30 countries around the globe. For almost 20 years, Iflexion has cooperated with a high number of clients, including big companies such as Philips, Toyota, and Adidas.

Iflexion develops AI solutions-focused on natural language processing, marketing personalization, and predictive analytics (to name a few) built around some of the most efficient machine learning and deep learning algorithms available.

 

Google Cloud Machine Learning Engine

Google offers a top-class MLaaS platform as well. Across all their Cloud AI services, they offer a Machine Learning Engine, as well as services for natural language processing and APIs for video, and image recognition, speech, etc. 

However, it is the Google Cloud Machine Learning Engine that is most important. It offers a simple alternative for building ML models for any data type or size. The Google ML Engine is highly flexible and based on the ever-popular TensorFlow project. Of course, this integrated platform works with any Google service, but it is aimed primarily at tasks requiring a deep neural network.

As for the cost, if you’re interested in trying out Cloud ML Engine, you can sign up for a free trial with no initial charge, and it comes with a $300 credit. However, a subscription to the Google Cloud Platform isn’t cheap.

 

BigML

BigML is a little set apart from the rest. It is the only MLaaS provider on this list that isn’t backed by a major tech giant. Nonetheless, it is a worthy candidate for this list mostly because BigML is compatible with almost any platform and allows data imports from all significant sources such as AWS, MS Azure, Google Storage, Google Drive, Dropbox, etc. 

Although BigML’s focus on machine learning means that it has far more web UI integrated features available than some of its counterparts. The platform is relatively intuitive and therefore easy to use, with flexible deployment for any enterprise option. BigML also boasts an extensive gallery of free datasets and models for experimentation.

As far as pricing is concerned, BigML as many options. If you’re smart with your dataset sizes, you can perform unlimited tasks for datasets up to 16MB for free. Students and educators have discounts, as well. There’s even a pay-as-you-go option. For more private deployments, Big ML offers opportunities for companies with more stringent data security or privacy requirements.

 

Difference Between Supervised And Unsupervised Machine Learning Applications for Business

 

Supervised Machine Learning

Supervised learning is the act of Data mining by inferring meaning from labeled training data. The data consists of a set of examples intended to train the algorithms used by the AI. In supervised learning, each case consists of a data pair where one element is an input object (typically a vector) and the second element is the desired output value (also known as the supervisory signal). In this way, a supervised learning algorithm analyzes the data and produces an inferred function (in other words, a connection). The learned relationship between two elements of data can then be used for mapping new examples. In the best-case scenario, the training data will allow for the algorithm to accurately determine the class labels for data where the relationship is undefined. Machine learning application in business intelligence is most efficiently achieved through supervised machine learning.

 

Unsupervised Machine Learning

In the case of unsupervised learning, there is so established precedent for the AI algorithms to develop relationships between unlabelled data. Meaning that there is significant room for error since there is no error or reward signal for the machine to evaluate a potential data set correctly.

 

Conclusion

There are many MlaaS providers out there that efficiently implement machine learning applications in business some of the more notable ones we have mentioned here, but the fact of the matter is that it all depends on what you need and how you need it. If the solutions offered solves the problem, then that is the provider for you. The best course of action is to explore your options first thoroughly before making a decision. AI deployment, as mentioned at the beginning of this article, is not always cheap and easy. That is why it pays off to make the right decision the first time.

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