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Researchers Develop Algorithm to Predict an Actor’s Career

Hence, an actor’s success could be down to their circumstances rather than the acting ability

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Mathematics. Source: Pixabay

Researchers have developed an algorithm that predicts whether an actor’s career has peaked and also predicts their most successful days in future, with an accuracy of 85 per cent.

The research team discovered that the most productive years for actors, defined as the year with the largest number of credited jobs, are towards the beginning of their careers.

The study, published in the journal Nature Communications shows how around 70 per cent actors and actresses have careers that only last for one year.

“Our results shed light on the underlying social dynamics taking place in show business and raise questions about the fairness of the system. Our predictive model for actors is also far from the randomness that is displayed for scientists and artists,” study author from the Queen Mary University of London Oliver Williams said.

For the study, the researchers used data of Internet Movie Database (IMDb) and looked at the careers of over 2.4 million actors from around the world from 1888 to 2016 to analyse and predict success on the silver screen.

They found that careers are clustered into ‘hot’ and ‘cold’ streaks, as individuals do not tend to work at a steady rate in a business where unemployment rates hover at around 90 per cent.

New Algorithm That may Predict Your Intelligence
New Algorithm that may Predict actor’s career. (IANS)

There is also huge evidence of gender biases in the industry, as most of the patterns were observed different for actors and actresses, the study said.

According to the outcomes, the total number of jobs in a career is underpinned by the rich-get-richer phenomenon.

What is interesting about this observation is that the rich-get-richer effects are well known to develop out of arbitrary and unpredictable random events that get amplified.

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Hence, an actor’s success could be down to their circumstances rather than the acting ability.

“We think the approach and methods developed in this paper could be of interest to the film industry: for example, they could provide complementary data analytics to IMDb. This does also bring with it a number of open questions,” said Lucas Lacasa from the Queen Mary University of London. (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.

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“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)