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Neuton Use Cases for Neural Network and Machine Learning

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.