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NITI Aayog to Partner with Leading Technology Company

The first phase of the project will focus on developing a model for 10 backward districts -- branded as aspirational districts by NITI Aayog -- across Assam, Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh.

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After signing a statement of intent with IBM, NITI Aayog CEO Amitabh Kant said bringing future technologies like AI into practical use will greatly benefit agriculture in the country by improving efficiency in resource-use, crop yields and promoting scientific farming.
NITI Aayog collaborates with IBM, Wikimedia commons

The government’s policy think-tank NITI Aayog on Friday decided to partner with leading technology company IBM to develop a crop yield prediction model using artificial intelligence (AI) to provide real time advisory to farmers in “aspirational districts”.

“The partnership aims to work together towards use of technology to provide insights to farmers to improve crop productivity, soil yield, control agricultural inputs with the overarching goal of improving farmers’ incomes,” an official statement said.

After signing a statement of intent with IBM, NITI Aayog CEO Amitabh Kant said bringing future technologies like AI into practical use will greatly benefit agriculture in the country by improving efficiency in resource-use, crop yields and promoting scientific farming.

After signing a statement of intent with IBM, NITI Aayog CEO Amitabh Kant said bringing future technologies like AI into practical use will greatly benefit agriculture in the country by improving efficiency in resource-use, crop yields and promoting scientific farming.
NITI Aayog’s CEO

The first phase of the project will focus on developing a model for 10 backward districts — branded as aspirational districts by NITI Aayog — across Assam, Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh.

Also read: Modi Urges Karnataka BJP Women to Popularise His Schemes .

“The scope of this project is to introduce and make available climate-aware cognitive farming techniques and identifying systems of crop monitoring, early warning on pest and disease outbreak based on advanced AI innovations,” the statement said.

“It also includes deployment of weather advisory, rich satellite and enhanced weather forecast information along with IT and mobile applications with a focus on improving the crop yield and cost savings through better farm management.” (IANS)

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Here’s how MRI may Predict Intelligence Level in Children

MRI may predict intelligence level in children

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Researchers have found that MRI scanning can help predict the intelligence level in children. Pixabay

Researchers have used ensemble methods based on deep learning 3D analysis networks to answer the global Magnetic Resonance Imaging (MRI) prevision challenge for children.

Importantly, they made predictions for both the fluid intelligence level and the target variable independent from age, gender, brain size or MRI scanner used.

MRI is a common technique used to obtain images of human internal organs and tissues. Scientists wondered whether the intelligence level can be predicted from an MRI brain image.

“Our team develops deep learning methods for computer vision tasks in MRI data analysis, amongst other things,” said study researcher Ekaterina Kondratyev from Skolkovo Institute of Science and Technology (Skoltech) in Russia.

“In this study, we applied ensembles of classifiers to 3D of super precision neural networks: with this approach, one can classify an image as it is, without first reducing its dimension and, therefore, without losing valuable information,” said Kondratyeva.

The US National Institutes of Health (NIH) database contains a total of over 11,000 structural and functional MRI images of children aged 9-10.

MRI
MRI is a common technique used to obtain images of human internal organs and tissues. Pixabay

In 2013, NIH launched the first grand-scale study of its kind in adolescent brain research, Adolescent Brain Cognitive Development, to see if and how teenagers’ hobbies and habits affect their further brain development.

NIH scientists launched an international competition, making the enormous NIH database available to a broad community for the first time ever.

The participants were given a task of building a predictive model based on brain images.

As part of the competition, the Skoltech team applied neural networks for MRI image processing.

To do this, they built a network architecture enabling several mathematical models to be applied to the same data in order to increase the prediction accuracy, and used a novel ensemble method to analyse the MRI data.

In their recent study, Skoltech researchers focused on predicting the intelligence level, or the so called “fluid intelligence”, which characterises the biological abilities of the nervous system and has little to do with acquired knowledge or skills.

Importantly, they made predictions for both the fluid intelligence level and the target variable independent from age, gender, brain size or MRI scanner used.

Also Read- Severity of Autism Spectrum Disorder Varies Among Twins: Study

The results of the study helped find the correlation between the child’s “fluid intelligence” and brain anatomy.

Although the prediction accuracy is less than perfect, the models produced during this competition will help shed light on various aspects of cognitive, social, emotional and physical development of adolescents. (IANS)