The machine learning approach is used for drowsiness detection of drivers to reduce the number of road accidents per year. Integration of machine learning algorithms into computer vision can help to detect whether drivers are feeling drowsy through video streams and facial recognition. IIT Ropar has built an algorithm that can extract facial features of drowsiness like eyes and mouths to effectively detect the real-time feeling of a driver. This is expected to reduce road accidents in a country by alerting the drivers on time.

There are three techniques that the team of IIT Ropar developed— driver’s operational behavior can be tracked with the understanding of the steering wheel, accelerator or brake patterns and speed; physiological features of a driver like heart rate, head posture or pulse rate and computer vision system to recognize facial expressions. Machine learning can detect driver’s drowsiness accurately in multiple vehicle models.

The tech companies and institutes have realized the utmost need for machine learning algorithms in drowsiness detection. Scientists have developed this alert system with the help of Video Stream Processing that analyses an eye blink through an Eye Aspect Ratio (EAR) as well as the Euclidean distance of an eye. IoT can send a warning message with a degree of collision along with real-time location data. The Raspberry Pi, OpenCV or Python monitoring system will help in issuing this crucial message on the spot.

EAR includes a simple calculation that is based on the ratio of distances between the lengths and width of the eyes. The eye aspect is very crucial in detecting drowsiness. Thus, EAR can be plotted for multiple frames of a video sequence through computer vision. There are three command lines to order the detector to use— shape-predictor, alarm, and webcam. If the EAR for a driver starts to decline over multiple frames, the machine learning algorithms can detect that the driver is drowsy. There is also a presence of Mouth Aspect Ratio (MAR)— the ratio of distances between the length and width of the mouth of a driver. This will detect when the driver will yawn and lose control over the mouth. There is a significant emphasis on the pupil of the eye known as Pupil Circularity. It helps to detect whether the eyes are half-open or almost closed during driving.

Thus, the advancement in cutting-edge technology is utilized in reducing road accidents per year with the help of machine learning algorithms. It is a natural feeling to be drowsy on roads for numerous causes. Thus, it is the work of machine learning algorithms to protect drivers and their families from incurring a massive loss.

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Researchers at Texas A&M can predict whether metallic nanoparticles in soil are likely to be absorbed by plants, which could cause toxicity.

While crop yield has achieved a substantial boost from nanotechnology in recent years, alarms over the health risks posed by nanoparticles within fresh produce and grains have also increased. In particular, nanoparticles entering the soil through irrigation, fertilizers and other sources have raised concerns about whether plants absorb these minute particles enough to cause toxicity.

In a new study published online in the journal Environmental Science and Technology, researchers at Texas A&M University have used machine learning to evaluate the salient properties of metallic nanoparticles that make them more susceptible for plant uptake. The researchers said their algorithm could indicate how much plants accumulate nanoparticles in their roots and shoots.

Nanoparticles are a burgeoning trend in several fields, including medicine, consumer products and agriculture. Depending on the type of nanoparticle, some have favorable surface properties, charge and magnetism, among other features. These qualities make them ideal for a number of applications. For example, in agriculture, nanoparticles may be used as antimicrobials to protect plants from pathogens. Alternatively, they can be used to bind to fertilizers or insecticides and then programmed for slow release to increase plant absorption.

These agricultural practices and others, like irrigation, can cause nanoparticles to accumulate in the soil. However, with the different types of nanoparticles that could exist in the ground and a staggeringly large number of terrestrial plant species, including food crops, it is not clearly known if certain properties of nanoparticles make them more likely to be absorbed by some plant species than others.

“As you can imagine, if we have to test the presence of each nanoparticle for every plant species, it is a huge number of experiments, which is very time-consuming and expensive,” said Xingmao “Samuel” Ma, associate professor in the Zachry Department of Civil and Environmental Engineering. “To give you an idea, silver nanoparticles alone can have hundreds of different sizes, shapes and surface coatings, and so, experimentally testing each one, even for a single plant species, is impractical.”

Instead, for their study, the researchers chose two different machine learning algorithms, an artificial neural network and gene-expression programming. They first trained these algorithms on a database created from past research on different metallic nanoparticles and the specific plants in which they accumulated. In particular, their database contained the size, shape and other characteristics of different nanoparticles, along with information on how much of these particles were absorbed from soil or nutrient-enriched water into the plant body.

Once trained, their machine learning algorithms could correctly predict the likelihood of a given metallic nanoparticle to accumulate in a plant species. Also, their algorithms revealed that when plants are in a nutrient-enriched or hydroponic solution, the chemical makeup of the metallic nanoparticle determines the propensity of accumulation in the roots and shoots. But if plants are grown in soil, the contents of organic matter and the clay in soil are key to nanoparticle uptake.

Ma said that while the machine learning algorithms could make predictions for most food crops and terrestrial plants, they might not yet be ready for aquatic plants. He also noted that the next step in his research would be to investigate if the machine learning algorithms could predict nanoparticle uptake from leaves rather than through the roots.

“It is quite understandable that people are concerned about the presence of nanoparticles in their fruits, vegetables and grains,” said Ma. “But instead of not using nanotechnology altogether, we would like farmers to reap the many benefits provided by this technology but avoid the potential food safety concerns.”

Other contributors include Xiaoxuan Wang, Liwei Liu and Weilan Zhang from the civil and environmental engineering department.

This research is partly funded by the National Science Foundation and the Ministry of Science and Technology, Taiwan under the Graduate Students Study Abroad Program.

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