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To avoid colliding with Cows on road, Indian Engineers develop real-time Automatic Obstacle Detection and Alert system

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Cows in India, Wikimedia

Ahmedabad, April 8, 2017: Indian engineers have developed a real-time automatic obstacle detection and alert system to help cars avoid colliding with cows on the road, a common sight in this part of the world.

The system uses a dashboard camera and an algorithm that can determine whether an object near the vehicle is an on-road cow and whether or not its movements represent a risk to the vehicle.

A timely audio or visual indicator can then be triggered to nudge the driver to apply the brakes whether or not they have seen the animal.

“The proposed system has achieved an overall efficiency of 80 per cent in terms of cow detection,” the researchers said in a study published in the Indonesian Journal of Electrical Engineering and Computer Science.

According to researchers Sachin Sharma and Dharmesh Shah of the Department of Electronics & Communication, at Gujarat Technological University in Ahmedabad, the proposed system is a low-cost, highly reliable system which can easily be implemented in automobiles for detection of cow or any other animal after proper training and testing on the highway.

The algorithm requires optimisation and the issue of night-time driving is yet to be addressed, the team said in an article in International Journal of Vehicle Autonomous Systems. (IANS)

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AI-based Algorithm to Help Doctors Treat Traumatic Brain Injury

AI-based algorithm to treat brain injury developed

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Artificial Intelligence brain
An AI-based algorithm will help doctors treat patients with severe traumatic brain injury (TBI). Pixabay

Researchers, including one of Indian-origin, have developed an artificial intelligence (AI) based algorithm that could help doctors treat patients with severe traumatic brain injury (TBI).

The algorithms can predict the probability of the patient dying within 30-days with an accuracy of 80-85 per cent, said the study published in the journal Scientific Reports.

“A dynamic prognostic model like this has not been presented before. Although this is a proof-of-concept and it will still take some time before we can implement algorithms like this into daily clinical practice, our study reflects how and into what direction modern intensive care is evolving”, said Indian-origin researcher and study author Rahul Raj from Helsinki University Hospital in the Finland.

Traumatic brain injury is a significant global cause of mortality and morbidity with an increasing incidence, especially in low-and-middle income countries.

The most severe TBIs are treated in intensive care units (ICU), but in spite of the proper and high-quality care, about one in three patients dies.

Brain Injury
Traumatic brain injury is a significant global cause of mortality and morbidity. Pixabay

This is why researchers at Helsinki University Hospital (HUS) started to develop an artificial intelligence (AI) based algorithm that could help doctors treat patients with severe TBI.

At its best, such an algorithm could predict the outcome of the individual patient and give objective data regarding the condition and prognosis of the patient and how it changes during treatment.

“We have developed two separate algorithms. The first algorithm is simpler and is based only upon objective monitor data. The second algorithm is slightly more complex and includes data regarding the level of consciousness, measured by the widely used Glasgow Coma Scale score,” said study researcher Eetu Pursiainen.

As expected, the accuracy of the more complex algorithm is slightly better than for the simpler algorithm.

Also Read- Air Pollution Identified as a Life-threatening Illness: Study

“Still, the accuracy of both algorithms is surprisingly good, considering that the simpler model is based upon only three main variables and the more complex upon five main variables”, Pursiainen said.

In the future, the algorithms still have to be validated in national and international external datasets, the researchers concluded. (IANS)