Neuton Use Cases
Churn prevention
Challenge:
In the data-driven world, predictive churn management is a trending topic full of new possibilities. Effective churn prevention based on data mining and machine learning is typically dependent on a tremendous amount of existing data as well as new data.
Solution:
AI is a great solution for predictive churn that can help achieve these goals:
understand the key factors of client attrition
identify the clients most at risk of leaving
provide targeted insights on which retention actions should be implemented
Why Neuton:
Neuton excels and operates optimally, often requiring less than 10% of the data set needed for neural network training by other solutions, therefore allowing customers to achieve the same results, if not often times better, with less data.
Smart targeting
Challenge:
Direct marketing campaigns are expensive. Wrong targeting can damage your brand. Recipients are tired of getting too much spam and might opt out. Traditional techniques are not effective, resulting in lower response rates, which leads to higher acquisition costs.
Solution:
Artificial intelligence solutions can dramatically boost accuracy for identifying prospects with the highest probabilities for conversion for a certain marketing campaign, so that only those with a high likelihood of response will be contacted.
Why Neuton:
Neuton does not require any special knowledge or training, therefore it brings the tremendous power of artificial intelligence to the fingertips of each and every marketer. Furthermore, Neuton is as effective with small sets of data as it is with large datasets, making it available for any Small and Medium size businesses who might otherwise have less access to big data and less historical data from which to otherwise derive value out of their statistics.
Personalized offers
Challenge:
Customers increasingly expect more personalized engagement. They also demand engagement at the right time, on the right device, with the right message. An email with a generic message has a much lower click rate than a personalized offering. However, for B2C businesses, it is not possible for human teams to personalize every offer to adapt it to the individual needs and preferences of millions of customers.
Solution:
Today it is very easy to understand consumers and create 1:1 personalized offers with AI. Machine learning can help marketers personalize messaging and engagement to reach audiences of one more effectively. Neural network models can use a various set of parameters such as prior purchases, preferences, and interests to generate a relevant and compelling offer.
Why Neuton:
Neuton democratizes AI by making it available to companies of all sizes regardless of the amount of statistical data they have on their customers. Neuton works with small sets of data as well as with large datasets. Neuton also generates models that predict much faster than those built with other AI solutions, thus making it possible to deliver personalized offers and customer experience on the fly. Furthermore, thanks to its simple and user-friendly interface, working with Neuton is so straightforward that any marketer can handle it.
Sales forecasting
Challenge:
Accurate sales forecasts enable companies to make informed business decisions and predict short-term and long-term performance. In doing so, they also give insight into how a company should manage its workforce, cash flow, and resources. The challenge is that the majority of all AI machine learning algorithms excel only when the training data set contains significant amounts of data, which can be difficult to acquire.
Solution:
Neuton gathers data about past deals, both won and lost. It looks at data signals, such as emails, meetings, even phone calls, and analyzes how they relate to sales outcomes. Insights from the data are applied to the current pipeline, and the software then rates deals, provides visibility into them, and even advises sales reps on the next actions to take.
Why Neuton:
Neuton excels with both large sets of data well as small sets of data, therefore enabling effective neural network learning in either circumstance to meet business objectives. This enables Small and Medium size businesses with little historical data to do more with fewer statistics.
Pricing Optimization
Challenge:
Setting prices can be a challenge, in particular in industries like travel and transportation where prices may change almost hourly, constantly adapting to changing market conditions. The amount of data is so impressive that it is becoming impossible for managers to make efficient pricing decisions using traditional methods. Even a slightly suboptimal decision-making process inevitably leads to tangible losses.
Solution:
AI allows companies to make the best price decisions every time for every product and via every channel. Machine learning solutions automatically determine your best prices taking into account all relevant context conditions including seasonality, price elasticity, demand, inventory levels and competitive products and prices.
Why Neuton:
Neuton is so easy to use that any retailer can take advantage of it. Its elegant simplicity and ease of use makes artificial intelligence available to all users irrespective of their technical and scientific background.
Personal recommendations
Challenge:
Online service users expect more personalized recommendations be it music, movies, books, travel tours, or people. For companies that have thousands of products and millions of customers, it is impossible for human teams to make all recommendations manually.
Solution:
Recommendation engines based on neural network frameworks make recommendations that would benefit the customer most, thus increasing the possibility of a conversion. Some of the examples are YouTube “Recommended Videos” or Netflix “Other Movies You May Enjoy”. It is applicable to other industries such as travel, social networks, dating websites, mass media, etc.
Why Neuton:
Neuton generates models that make predictions much faster than those built with other AI solutions, thus making it possible to deliver personalized recommendations on the fly. In addition, it is so easy to use that any marketer can handle it.
Personalized Content
Challenge:
In social networks, it is difficult to see the information you really need. The flow of news from friends and companies is very large. In this vein, marketing should use personalized content for its audience.
Solution:
With the help of artificial intelligence, you can make your advertising campaigns as personalized as possible, attracting only the audience that is important for the promotion of a product or service. Since AI gives accurate forecasts, working with algorithms is very profitable.
Why Neuton:
Neuton is able to choose the right audience to target your new service or product, even if your social network group does not have many subscribers. Neuton is as effective working with large datasets as with small ones. Furthermore, its models work very fast enabling marketers to deliver personalized messages to their user feeds on the fly.
Personalized offers
Challenge:
Customers increasingly expect more personalized engagement. They also demand engagement at the right time, on the right device, with the right message. An email with a generic message has a much lower click rate than a personalized offering. However, for B2C businesses, it is not possible for human teams to personalize every offer to adapt it to the individual needs and preferences of millions of customers.
Solution:
Today it is very easy to understand consumers and create 1:1 personalized offers with AI. Machine learning can help marketers personalize messaging and engagement to reach audiences of one more effectively. Neural network models can use a various set of parameters such as prior purchases, preferences, and interests to generate a relevant and compelling offer.
Why Neuton:
Neuton democratizes AI by making it available to companies of all sizes regardless of the amount of statistical data they have on their customers. Neuton works with small sets of data as well as with large datasets. Neuton also generates models that predict much faster than those built with other AI solutions, thus making it possible to deliver personalized offers and customer experience on the fly. Furthermore, thanks to its simple and user-friendly interface, working with Neuton is so straightforward that any marketer can handle it.
Sales forecasting
Challenge:
Accurate sales forecasts enable companies to make informed business decisions and predict short-term and long-term performance. In doing so, they also give insight into how a company should manage its workforce, cash flow, and resources. The challenge is that the majority of all AI machine learning algorithms excel only when the training data set contains significant amounts of data, which can be difficult to acquire.
Solution:
Neuton gathers data about past deals, both won and lost. It looks at data signals, such as emails, meetings, even phone calls, and analyzes how they relate to sales outcomes. Insights from the data are applied to the current pipeline, and the software then rates deals, provides visibility into them, and even advises sales reps on the next actions to take.
Why Neuton:
Neuton excels with both large sets of data well as small sets of data, therefore enabling effective neural network learning in either circumstance to meet business objectives. This enables Small and Medium size businesses with little historical data to do more with fewer statistics.
Pricing Optimization
Challenge:
Setting prices can be a challenge, in particular in industries like travel and transportation where prices may change almost hourly, constantly adapting to changing market conditions. The amount of data is so impressive that it is becoming impossible for managers to make efficient pricing decisions using traditional methods. Even a slightly suboptimal decision-making process inevitably leads to tangible losses.
Solution:
AI allows companies to make the best price decisions every time for every product and via every channel. Machine learning solutions automatically determine your best prices taking into account all relevant context conditions including seasonality, price elasticity, demand, inventory levels and competitive products and prices.
Why Neuton:
Neuton is so easy to use that any retailer can take advantage of it. Its elegant simplicity and ease of use makes artificial intelligence available to all users irrespective of their technical and scientific background.
Property value estimation
Challenge:
Estimating the value of property is not easy. There are a huge number of factors that affect the price of real estate. Commercial appraisers, who rely on a combination of intuition and statistical data, often give inaccurate valuations.
Solution:
AI can determine complex relationships among a huge number of factors, thus helping increase the accuracy of the estimates by 15%.
Why Neuton:
Many people assume that artificial intelligence is available only to large companies with a lot of data. Though this has been true in the past, with Neuton this is no longer the case. Neuton can easily be used by small companies that do not have a large database yet. Neuton’s unique neural network mechanism allows them to obtain accurate data and make a full assessment with small amounts of data.
Employee satisfaction estimation
Challenge:
There is never much information on employee satisfaction in any company. But if a company wants to maintain a healthy morale and keep its employees, it’s very important to identify people who are not satisfied with current conditions.
Solution:
Because neural network machine learning can recognize patterns and analyze data at light speed, it can help HR directors make decisions with greater confidence. As a result, personnel outflow in the company can be reduced, making the corporate atmosphere more friendly and thereby improving the quality of labor productivity.
Why Neuton:
Unlike other solutions, Neuton allows accurate predictions to be made based on a small training dataset, which makes AI available even to small companies with a relatively small number of employees. Furthermore, Neuton is so easy to use that any HR manager can handle it.
Recruiting
Challenge:
Time is money, and saving recruiters time by using AI to make their tasks more effective and efficient can definitely improve the bottom line. In addition, AI can impact workforce productivity by successfully sourcing, screening and identifying top-tier candidates.
Solution:
Data processing capabilities of machines augment the role of HR employees in numerous aspects of hiring including finding qualified candidates, interviewing with bots to understand their fit, and evaluating their assessment results to decide if they should receive an offer.
Why Neuton:
Using Neuton, it is possible to address multiple time-consuming steps in the HR hiring process and subsequent talent acquisition. Neuton’s neural network can help companies evaluate the likelihood of a candidate being successful from a large volume of hiring applications based on data from the company’s past experiences. However, unlike other solutions, Neuton doesn’t require large sets of data to make accurate analyses. Furthermore, Neuton is so easy to use that you do not need to have a data science team on staff to solve this problem. Instead, domain experts and analysts can solve it.
Prediction of yield
Challenge:
As the world population increases each year and land becomes more expensive, people need to find more efficient ways to farm, using less land to produce more crops, meanwhile boosting the productivity and yield.
Solution:
With the help of AI, farmers can now analyze a variety of variables in real time such as weather conditions, temperature, water usage or soil conditions collected from their farm to better inform their decisions. For example, AI technologies help farmers optimize planning to generate more bountiful yields by determining crop choices, the best hybrid seed choices and resource utilization.
Why Neuton:
There are a lot of factors that influence harvest, yet very little historical data, so traditional neural networks do not work. Neuton allows farmers to develop an accurate model to maximize crop based on a small training dataset. Predictions can also be used by crop managers to minimize losses when unfavorable conditions occur.
Hardware maintenance prediction
Challenge:
As various hardware elements continue to converge, the complexity around supporting environments and devices comes at a greater cost impact to the business.
Solution:
Predictive analytics can show when a particular piece of equipment may degrade or malfunction. It also allows scheduling of maintenance to prevent unexpected equipment failures. Other potential advantages include increased equipment lifetime, increased safety, fewer accidents, and optimized spare parts handling.
Why Neuton:
Neuton can immediately bring value to an organization, and is equally effective, whether the organization is looking to monitor existing equipment that has been in the landscape for an extended period of time, with adequate historical data, or with new equipment which when installed has minimal to no data. Neuton is able to bring value and operate effectively in both scenarios due to the fact that Neuton is not dependent on significant amounts of data as other solutions are. Furthermore, due to the compact size of Neuton models, they can be built into IoT devices to more accurately detect equipment anomalies.
Inventory & supply chain optimization
Challenge:
Any retail or manufacturing business is always faced with the problem of supply and storage of products. It can be difficult to keep track of change and carry out optimization of storage facilities. Furthermore, for manufacturers, moving and storing additional stocks is associated with significant costs.
Solution:
Neural machine learning can be used to effectively and dynamically manage uncertainty, and systematically reduce inventory levels across all locations. Artificial intelligence accumulates all historical data, recent trends, optimal costs and makes a forecast regarding the optimization of the supply chain. Automated machine learning also allows you to redirect inventory, increase production speed and reduce costs.
Why Neuton:
Neuton does not require any coding or special data science background, so any inventory manager can use it to make accurate predictions regarding how supplies and warehouse should work to increase operational efficiencies.
Credit Scoring
Challenge:
Banks and enterprise creditors use credit scores to better understand the risk associated with their potential borrowers. Traditional credit scoring takes into account such factors as credit history and salary, often rejecting borrowers who are credit-worthy, yet do not qualify, such as college graduates, immigrant entrepreneurs and other consumer groups that otherwise show high potential. On the other hand, banks might give credit to consumers that are not profitable.
Solution:
Machine learning solutions are a powerful tool for performing smarter scoring by taking a more individualized approach. A neural network model allows a more accurate assessment of each borrower based on large number of complex and in-depth rules and increases the pool of credit-worthy customers without taking too much risk. Furthermore, a self-learning model can continuously improve itself as new data is fed into the system.
Why Neuton:
Neuton builds models that execute significantly faster and more accurately than models produced by other solutions. This means it provides a faster and more accurate assessment of a potential borrower, and faster assessment means faster decisions and more issued credits with less risk, which translates into higher profitability.
Financial Trading
Challenge:
Predicting or timing the stock markets is an ongoing challenge for many individuals and companies. A stock market is considered as one of the most highly complex systems, consisting of many components whose prices move up and down without having a clear pattern.
Solution:
Machine Learning prediction algorithms are getting closer to solving this machine learning regression challenge all the time. Today, many prestigious trading firms use probability technology to predict and execute trades in higher volumes and speeds than a human could ever do.
Why Neuton:
In stock trading, sometimes the profitability of an operation depends on the millisecond difference in buying or selling. In this particular case, not only is the accuracy of the model essential, but the speed of execution of the prediction is equally critical. Contrary to most legacy solutions, Neuton develops models that are compact in size and executes significantly faster than other models produced.
Targeted Marketing
Challenge:
With a large variety of banking products to choose from and consumer expectations for personalized treatment, finding the right products to upsell is not a trivial task.
Solution:
AI can help define the best audience more quickly and accurately. This allows companies:
Make the upsell relevant
Prioritize which customers to target with which products
Adapt offers to the individual needs and preferences
Why Neuton:
Neuton models determine which products are the best fit for a given customer with far higher accuracy than those generated by other solutions. Furthermore, Neuton excels when working with either large or small sets of data, therefore enabling effective neural network learning in either circumstance to meet business objectives. Finally, it is so easy to use that any marketer can handle it.
Fraud insurance claim detection
Challenge:
Whether internal or external, there is a wide variety of threats posed to enterprises across multiple industries. The most difficult threat to diagnose and address, however, is fraud. Fraudulent activity is a high-cost threat that can compromise the integrity of your company as well as cripple your bottom line.
Solution:
The use of fraud detection solutions based on AI methodologies enable your organization to discover the triggers and situational interactions that are likely to produce fraudulent activity, thus empowering your company to prevent fraud while saving both time and money.
Why Neuton:
Typically, insurance claims require a significant amount of integrated data, including social media, cell phone, ATM usage, financial records, police records and hospital records, to effectively identify any hidden or potential fraudulent activity. In addition to the volume of data, it is critical to identify a threat quickly before it can cause harm. Neuton can easily handle such challenges because it does not require an exceptional amount of data in order to produce highly accurate and predictable results and its models work at nearly the speed of light.
Best Offer
Challenge:
With a large variety of products on the market, the pressure to retain customers, and consumer expectations for personalized treatment, marketing and selling generic products is no longer an option. For insurance companies this means that finding the right products to cross-sell to customers is critical for success.
Solution:
With AI models, insurance companies can determine which products and policy options are the best fit for a given consumer. AI can also determine an individualized price based on consumer behavior and historical data.
Why Neuton:
Neuton excels with both large sets of data well as small sets of data, therefore enabling effective neural network learning in either circumstance to meet business objectives. This enables small insurance companies with little historical data to do more with fewer statistics. In addition, Neuton excels at producing models that execute significantly faster than those built by other AI solutions.
Customer sentiment analysis
Challenge:
With the increasing role of Internet services in the telecommunication industry’s everyday functions, it has become difficult to keep up with both problems and opportunities. A neural network can solve this challenge using a sophisticated customer sentiment analysis. However, accuracy depends on how closely the test data set resembles the dataset used to develop the dictionary or the machine learning model. Both can perform poorly if the datasets have little in common. Additionally, most algorithms are designed to handle large pieces of text like news articles, therefore might not work on shorter pieces like customer reviews.
Solution:
Neuton easily overcomes these challenges allowing companies to:
Assess its customers’ positive or negative reactions to their service or product quickly, even when the number of customers that reacted is not large
Reveal trends using aggregated data from text reviews and social media sources
React to customers’ sentiment quickly in order to prevent churning.
Why Neuton:
Neuton is not dependent on significant amounts of data to produce predictable and accurate results. Neuton excels at working with both large and small sets of data, therefore enabling effective customer sentiment analysis.
Customer requests prioritization
Challenge:
Handling an increasing number of customer requests can be resource intensive and costly to maintaining good service. One of the key factors to meeting customer expectations is to find a way to prioritize which support ticket should be addressed first, but it is difficult to prioritize while a continuous flow of requests are forever coming in to a help desk. There are also a lot of possible ways in which to classify each request. Moreover, combining and weighing such requests is nearly impossible for the staff to do manually. Prioritizing tickets should be automated by the system and be both scalable and team independent.
Solution:
AI takes the guesswork out of customer requests classification. All the requests pass through a neural network module that categorizes customer requests by assigning a category attribute that defines the processing path, through which company departments or experts the case should go to be resolved. AI-based algorithm removes the possibility of a human error and any form of subjectivity in reading a ticket.
Why Neuton:
Neuton is much more effective than any other neural network framework or non-neural algorithm available on the market. Higher accuracy translates to fewer errors resulting in faster problem resolution and enhanced customer service.
Fraud detection
Challenge:
Mobile communication fraud is a big issue to all telecommunication companies around the globe, and is a significant factor in their annual revenue losses. There are many types of fraud with subscription fraud taking the lead. Subscription fraud is characterized by a criminal using their own, or a stolen or fabricated identity, to get services with no intention to pay. The theft is hard to detect at the point of sale.
Solution:
It is possible to detect fraudulent behavior using machine learning. Neural network models can process a large volume of transactions and other activities to find fraud patterns and then use those patterns to identify fraud as it happens in real-time. When fraud is suspected, AI models can be used to flag these subscribers for investigation and can even score the likelihood of fraud.
Why Neuton:
Typically, a significant amount of data is required to effectively identify any hidden or potential fraudulent activity. Neuton easily handles such challenge because it doesn’t require an exceptional amount of data in order to produce highly accurate and predictable results.
Predictive maintenance and network optimization
Challenge:
Loyalty is no longer a matter of choice, but a necessity dictated by the growing expectations of customers. Improving customer experience at all stages of interaction is one of the key success factors for telecom companies. One of the most important ways to give customers what they want is to prevent outages.
Solution:
AI can help companies monitor equipment, learn from historical information, anticipate equipment failure, and proactively fix it. Another important aspect is network optimization. A Self Organizing Network (SON) powered by AI can help networks continually adapt and reconfigure based on current needs. It is also beneficial when designing new networks. Since AI-enabled networks can self-analyze and self-optimize, they are more efficient at providing consistent service.
Why Neuton:
Neuton can immediately bring value to telecom companies, and is equally effective, whether the organization is looking to monitor existing equipment that has been in the landscape for an extended period of time, with adequate historical data, or with new equipment which when installed has minimal to no data. Neuton is able to bring value and operate effectively in both scenarios due to the fact that Neuton is not dependent on significant amounts of data as other solutions are.
Malicious Network Traffic Detection
Challenge:
Due to an increased awareness on the corporate level of the high potential for malicious attacks, including DDoS attacks to social networks and phishing attacks, organizations are more concerned about their network and data security then ever before.
Solution:
AI can both help detect malicious events and deal with the attack as quickly as possible when identified. Based on the metadata of encrypted network traffic packets, machine learning can help classify traffic from network devices and determine the likelihood of it potentially belonging to dangerous traffic. Data on traffic marked as potentially dangerous can then used by the information security service for further investigation.
Why Neuton:
Neuton builds models that work so much faster than other solutions that the time needed for traffic analysis for the first line of NOC (Network Operations Center) operators can be significantly reduced further, even if previously utilizing another AI product.
Spam Filters
Challenge:
Spam is the scourge of the modern world. In a spam stream, it is becoming extremely difficult to see a truly relevant message.
Solution:
Machine learning to fight spam is essential. Nobody can control the spam that exists now on the Web. Therefore, the use of machine learning algorithms is our best option in the fight against spam. To trick spammers who come up with new ways to cheat each time, AI classifies the mail flow using unique neural algorithms by scanning metadata such as sender’s location, keywords, etc.
Why Neuton:
Neuton builds models that are self-growing and learning. Its resulting models work with much higher accuracy in comparison to other algorithms, enabling detection of more spam more efficiently and eliminating false positives at the same time.
Home lab testing
Challenge:
Chronic disease patients need to regularly do blood tests. However, patients - especially those suffering from cardiac attacks or undergoing cancer therapy - may experience difficulty travelling and being exposed to costly and stressful healthcare environments.
Solution:
Home lab testing devices bring patients a quick and reliable way to test their blood levels from the comfort of their home and reduce unnecessary urgent care visits. Empowered with AI, these devices can go far beyond chronic disease monitoring, making predictions on cardiac and other events days before they may occur.
Why Neuton:
Unlike other solutions, Neuton generates models that are so tiny that they can easily be embedded into any devices, and even microcontrollers. Neuton models work extremely fast making near real-time predictions. This allows patients to potentially buy some time and get to a hospital before it is too late. Furthermore, the predictions are much more accurate compared to those made by competitors, thus reducing false alarms.
Eye Decease Classification
Challenge:
As population aging has become a major demographic trend around the world, the number of patients suffering from eye diseases is expected to increase steeply. Early detection and appropriate treatment of eye diseases are of great significance to prevent vision loss and maintain quality of life. Conventional diagnostic methods are tremendously dependent on physicians' professional experience and knowledge, which lead to high misdiagnosis rate and a huge waste of medical data.
Solution:
Deep integration of ophthalmology and AI has the potential to revolutionize current disease diagnose pattern and generate a significant clinical impact. Based on data collected from the results of eye tonometry, examinations using lensometers, doctor’s observations and other tests, models for diagnostic equipment that shows the probability of eye pathology development can be created.
Why Neuton:
Neuton builds models that provide much higher accuracy compared to the ones created with other AI solutions. Besides, Neuton does not require an extensive amount of data to build accurate models. This means eye pathology can be caught and treated early before it can impact patients’ quality of life.
Early diagnosis
Challenge:
The scourge of modern society is neglected diseases. Identifying some of them at an early stage is very difficult. Considering the significant amount of research papers and medical records available, we cannot expect a doctor to master every aspect of medical care.
Solution:
AI machine learning algorithms excel far beyond what human physicians are capable of. AI can help detect life-threatening conditions based on routine vital signs and metabolic levels from electronic medical records. It allows doctors to identify patients at risk before it is too late.
Why Neuton:
Most neural network algorithms perform well on large volumes of data, but when it comes to assessing small sets of data, they fail. Unlike other solutions, Neuton excels with both large and small sets of data, delivering similarly accurate predictions in both cases.
Fraud Prediction
Challenge:
Medical fraud represent a huge cost for the national healthcare system. CMS’s Medicare is estimated to dole out $6B due to fraud every year, representing about 10% of its $60B annual expenditures.
Solution:
Machine learning plays an innovative role in fraud detection, e.g. for predicting expected treatments, medications, quantities, volumes, and costs and comparing them with the actual transactions and requested payments. This allows the state insurer to decide to postpone or deny payments if transactions or combinations of transactions seem highly suspicious, and perform investigations before processing payments.
Why Neuton:
Usually, a significant amount of data is required to effectively identify any hidden or potential fraudulent activity. Neuton easily handles such a challenge because it doesn’t require an exceptional amount of data in order to produce highly accurate results.
Wearables
Challenge:
A lot of people are still out of shape, in spite of putting a lot of time, energy, and often money in to improving diets and increasing exercise. There is no generic recipe for how to get or stay fit, thanks to the uniqueness of our bodies and our lifestyles. Many different things affect our physique, and in order to really be healthy, we need to have custom-fit, tailor-made workout routines and diets.
Solution:
AI in the personal health and fitness industry aims to personalize the users’ experience to make more of an impact in a shorter amount of time. Through smart data and analytics, AI is transforming the way we see health and fitness. Automated Machine Learning enables wearables to perform recommendations on daily routines embedded in fitness bracelets and aims to change the way people think about exercise - to get people up and moving in a practical way.
Why Neuton:
Neuton is leading the charge to commoditize artificial intelligence and neural machine learning as Neuton specializes in producing resulting models that are far less in size and can easily be embedded into tiny computing devices and even microcontrollers. Furthermore, Neuton models execute significantly faster compared to those of other vendors, adjusting recommendations in real time. This enables people to get and stay fit more effectively.
Cardio Training Simulators
Challenge:
Most modern cardio training simulators are equipped with sensors in the handlebars for heart rate monitoring. Heart rate is important information to determine that you are training efficiently. You can get different fitness benefits by exercising in different heart rate (HR) zones, which are very individual for each person.
Solution:
AI can help you gear your cardio workout to the best intensity to get the results you want. Machine learning can advise on optimal heart rate zones, frequency, intensity, and duration of your training to be the most effective depending on your goals, gender, age, physique, and health.
Why Neuton:
Neuton excels in building models that are much smaller than those generated by other AI solutions. Neuton models can easily be built into microcontrollers inside handlebars on cardio training simulators, thus providing effective yet unobtrusive way to get the most out of your workout.
Drug discovery
Challenge:
There are many life-threatening diseases that we have yet to find a cure for, like Cancer, HIV, Alzheimer’s etc. It is very expensive and time consuming to synthesize and test all of the different permutations.
Solution:
AI-driven solutions based on previous data and medical intelligence can help facilitate the research process. This allows for drawing non-intuitive insights about drug candidates, or even attempting to model the whole biological systems to identify novel pathways, targets and biomarkers.
Why Neuton:
Unlike other solutions, based on only a small training set of biomedical and clinical data Neuton can (efficiently? or effectively?) help choose the best molecules to use, with the desired properties, for development of a particular drug or treatment.
Driver vital sign monitoring
Challenge:
Long distance truck or passenger vehicle drivers spend long hours behind the wheel, which can not only be dangerous to their health, but may also ultimately impact the safety of passengers, pedestrians, or other drivers on the road, and, of course, may potentially cause a significant amount of material (and financially costly) damage in case of an accident. Too many automobile crashes are caused due to driver drowsiness, and other predictable and preventable health concerns.
Solution:
AI can help in monitoring vital signs such as respiration rate and heart rate to determine the occurrence and level of driver drowsiness. Combined with a notification system, this can reduce the risk of a driver falling asleep, thus preventing numerous vehicle crashes.
Why Neuton:
Neuton builds compact models that can be embedded into a vehicle microcontroller inside the wheel. This allows for the vital signs of the driver to be monitored noninvasively, for example to detect the drowsiness of the driver, before he/she makes a mistake.
Flight arrival time prediction
Challenge:
When a plane arrives late at its destination, it does more than inconvenience passengers. In fact, it can have a severe operational impact on an airline. A delayed arrival may impact catering service, flow of flight crews between aircraft, gate availability, connecting flights for passengers, and more. This can add up to significant costs, especially in cases where a passenger misses a connecting flight.
Solution:
AI can help predict if a flight is likely to be delayed and by how much. This enables an airline to make adjustments to minimize costs – such as rescheduling catering, reassigning crew, changing gates to be closer to departure gates for connecting passengers, pre-ordering shuttles between gates for passengers, or proactively rebooking them on later flights.
Why Neuton:
Neuton build models that work more accurately than those created by other AI solutions. This means Neuton models provide a more accurate assessment of arrival time, and better arrival time prediction means faster decisions, which translates to better customer service and lower operating costs.
Vehicle arrival time prediction
Challenge:
The accuracy of the arrival of vehicles is very important for both freight and passenger transportation. On busy routes, with regular accidents on the roads, it can be difficult to predict when vehicles will arrive at their final destination. As a result, delivery contracts are broken and penalties arise.
Solution:
For more accurate forecasts, transport companies have already begun to use artificial intelligence. It helps to accurately analyze the situation (in real time?) and sends the received data to the drivers application, as well as to managers at the point of product reception or passengers station.
Why Neuton:
Neuton models make much more accurate predictions in comparison to its peers, Furthermore, the significant speed and efficiency of Neuton predictions allows drivers to adjust their route plans on the fly, empowering drivers to deliver their cargo on time more consistently.
Traffic optimization
Challenge:
Traffic jams are a plague in our lives. Many traffic lights still work with out-of-sync timers, preventing traffic from flowing efficiently.
Solution:
Instead of basing signal timings on traffic models, AI can help optimize signal timings based on the current traffic on the road. Machine learning algorithms can dynamically coordinate signals as traffic conditions change.
Why Neuton:
Neuton generates models that make predictions much faster than those built with other AI solutions, thus making it possible to deliver real-time data to traffic lights to optimize traffic in the city. This enables people to get to their destinations much faster, spend less time waiting at intersections, and make fewer stops along the way, thus producing fewer harmful emissions.
Malicious Network Traffic Detection
Challenge:
Due to an increased awareness on the corporate level of the high potential for malicious attacks, including DDoS attacks to social networks and phishing attacks, organizations are more concerned about their network and data security then ever before.
Solution:
AI can both help detect malicious events and deal with the attack as quickly as possible when identified. Based on the metadata of encrypted network traffic packets, machine learning can help classify traffic from network devices and determine the likelihood of it potentially belonging to dangerous traffic. Data on traffic marked as potentially dangerous can then used by the information security service for further investigation.
Why Neuton:
Neuton builds models that work so much faster than other solutions that the time needed for traffic analysis for the first line of NOC (Network Operations Center) operators can be significantly reduced further, even if previously utilizing another AI product.
Spam Filters
Challenge:
Spam is the scourge of the modern world. In a spam stream, it is becoming extremely difficult to see a truly relevant message.
Solution:
Machine learning to fight spam is essential. Nobody can control the spam that exists now on the Web. Therefore, the use of machine learning algorithms is our best option in the fight against spam. To trick spammers who come up with new ways to cheat each time, AI classifies the mail flow using unique neural algorithms by scanning metadata such as sender’s location, keywords, etc.
Why Neuton:
Neuton builds models that are self-growing and learning. Its resulting models work with much higher accuracy in comparison to other algorithms, enabling detection of more spam more efficiently and eliminating false positives at the same time.
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