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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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Researchers Develop New Algorithm to Identify Cyber-bullies on Twitter

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn

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A man reads tweets on his phone in front of a displayed Twitter logo. VOA

Researchers have developed machine learning algorithms which can identify bullies and aggressors on Twitter with 90 per cent accuracy.

For the study published in the journal Transactions on the Web, the research team analysed the behavioural patterns exhibited by abusive Twitter users and their differences from other users.

“We built crawlers — programs that collect data from Twitter via variety of mechanisms,” said study researcher Jeremy Blackburn from Binghamton University in the US.

“We gathered tweets of Twitter users, their profiles, as well as (social) network-related things, like who they follow and who follows them,” Blackburn said.

The researchers then performed natural language processing and sentiment analysis on the tweets themselves, as well as a variety of social network analyses on the connections between users.

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Twitter is a social media app that encourages short tweets and brief conversations. Pixabay

They developed algorithms to automatically classify two specific types of offensive online behaviour, i.e. cyber-bullying and cyber-aggression.

The algorithms were able to identify abusive users — who engage in harassing behaviour like those who send death threats or make racist remarks — on Twitter with 90 per cent accuracy.

Also Read: Facebook Announces Some New Features to its Video Capabilities

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn.

“Our research indicates that machine learning can be used to automatically detect users that are cyber-bullies, and thus could help Twitter and other social media platforms remove problematic users,” Blackburn added. (IANS)