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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The rise of precision medicine is being augmented by greater use of deep learning technologies that provide predictive analytics for earlier diagnosis of a range of debilitating diseases.

The latest example comes from researchers at Michigan-based Beaumont Health who used deep learning to analyze genomic DNA. The resulting simple blood test could be used to detect earlier onset of Alzheimer’s disease.

In a study published this week in the peer-reviewed scientific journal PLOS ONE, the researchers said their analysis discovered 152 “significant” genetic differences among Alzheimer’s and healthy patients. Those biomarkers could be used to provide diagnoses before Alzheimer’s symptoms develop and a patient’s brain is irreversibly damaged.

“The holy grail is to identify patients in the pre-clinical stage so effective early interventions, including new medications, can be studied and ultimately used,” said Dr. Ray Bahado-Singh, a Beaumont Health geneticist who led the research.

The need to identify the early signs of Alzheimer’s disease grows as the global population ages. For example, the annual World Alzheimer Report estimates 75 million will be stricken by 2030. Researchers are working to prevent some of those predicted cases by leveraging new deep learning tools to accelerate the diagnoses of a disease that often goes undetected until it is too late to stop the damage.

The Beaumont researchers said they used deep learning and other machine learning platforms along with “genome-wide” DNA analysis of leukocytes, a type of blood cell manufactured in bone marrow and associated with the body’s immune system.

“We used and compared conventional machine learning and deep learning classification algorithms which typically begin with an established set of data … and a certain understanding of how that data is classified” as either Alzheimer’s or healthy patients, said co-investigator Buket Aydas, analytics manager at Blue Cross Blue Shield of Michigan.

“These algorithms are intended to find patterns in data that can be applied to an analytics process,” Aydas added in an email.

The researchers compared the performance of their deep learning framework with five other machine learning algorithms, including a prediction analysis tool. The six platforms scanned about 800,000 changes in the leukocytes genome.

The deep learning algorithm performed best.

“We also found out the important genetic features that contribute most to the [deep learning] prediction and were able to predict the absence or presence of Alzheimer’s by the help of these important genetic features,” Aydas said.

The genetic analysis ultimately predicted either the absence or presence of the disease, “allowing us to read what is going on in the brain through the blood,” Dr. Bahado-Singh said.

One problem encountered by the investigators was “overfitting,” which occurs when data sets fit a machine learning too precisely. Counterintuitively, the snug fit often produces unreliable results.

To avoid overfitting in the deep learning framework, the researchers said they employed standard parameters to tune models and overcome the overfitting problem.

The researchers said the next step is an expanded study over the next year designed to replicate the initial findings of the Alzheimer’s analysis. Advances in this branch of precision medicine could lead to development of targeted treatments to “interrupt the disease process,” according to Dr. Bahado-Singh.

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The federal government is working with a Canberra-based company to trial machine learning technology that aims to detect pests at Australian shipping ports.

The trial is being implemented in partnership with the Department of Industry, Science, Energy and Resources, the Department of Agriculture, Water and the Environment, Australian company Trellis Data, and global logistics company DP World, in Brisbane.

The technology will stop pests from breaching biosecurity processes at Australia’s container ports by detecting invasive species that are less than 10 millimetres in size, according to Trellis.

“The technology allows pests to be detected on the outside of every container that is transported from ship to shore in Australia. It also has the same detection capability for any internal container inspections required by biosecurity authorities,” the company told The Mandarin.

“The software works in all environments 24/7, identifying pests in real-time. Not only does it identify pests, it explains its reason for the identification and ensures all detections are correctly associated with the individual container ID.”

A recent CSIRO report found the amount of biosecurity risk materials intercepted in Australia increased by almost 50% in the five years to 2017. It warned that outbreaks across biosecurity sectors “are continuing to rise in volume and complexity”, and called for transformational changes to Australia’s biosecurity system.

The CSIRO noted that greater levels and speed of global trade would create new opportunities for pests and diseases to enter and spread across Australia.

Trellis said that initial progress has shown “very promising” results. The first official trial figures are expected to be released in May, with final evaluation to be completed by the end of the year.

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Dr. V.N. Manjunath Aradhya, Associate Professor and Head, Department of Computer Applications, JSS Science and Technology University, Mysuru, has developed a model for detecting COVID-19 from chest X-ray images.
This concept has an advantage of learning from a few samples. The model proposed is a multi-class classification model as it classifies images of four classes — pneumonia bacterial, pneumonia virus, normal, and COVID-19. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The proposed concept is the first-of-its-kind in the literature and expected to open up several new dimensions in the field of machine learning.

This research article was recently accepted in one of the top tier Journal, Cognitive Computation, Springer. This work is a combined effort with Prof. D. S. Guru of University of Mysore (UoM) and Prof. Mufti Mahmud of Nottingham Trent University, UK. Recently, Dr. Aradhya also published papers on understanding and analysis of COVID-19 which is co-authored with Prof. G. Hemantha Kumar, Vice-Chancellor, UoM, according to a press release from Dr. S. A. Dhanaraj, Registrar of the University.

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