AIMC Topic: Machine Learning

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Machine Learning-Based Activity Pattern Classification Using Personal PM Exposure Information.

International journal of environmental research and public health
The activity pattern is a significant factor in identifying hotspots of personal exposure to air pollutants, such as PM. However, the recording process of an activity pattern can be annoying to study participants, because they are often asked to brin...

Energy Efficient Pupil Tracking Based on Rule Distillation of Cascade Regression Forest.

Sensors (Basel, Switzerland)
As the demand for human-friendly computing increases, research on pupil tracking to facilitate human-machine interactions (HCIs) is being actively conducted. Several successful pupil tracking approaches have been developed using images and a deep neu...

A Tactile Method for Rice Plant Recognition Based on Machine Learning.

Sensors (Basel, Switzerland)
Accurate and real-time recognition of rice plants is the premise underlying the implementation of precise weed control. However, achieving desired results in paddy fields using the traditional visual method is difficult due to the occlusion of rice l...

Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods.

Sensors (Basel, Switzerland)
Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. ...

Development of machine learning-based clinical decision support system for hepatocellular carcinoma.

Scientific reports
There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision suppor...

Prediction modeling-part 2: using machine learning strategies to improve transplantation outcomes.

Kidney international
Kidney transplant recipients and transplant physicians face important clinical questions where machine learning methods may help improve the decision-making process. This mini-review explores potential applications of machine learning methods to key ...

Radiomic Model for Distinguishing Dissecting Aneurysms from Complicated Saccular Aneurysms on high-Resolution Magnetic Resonance Imaging.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: To build radiomic model in differentiating dissecting aneurysm (DA) from complicated saccular aneurysm (SA) based on high-resolution magnetic resonance imaging (HR-MRI) through machine-learning algorithm.

Incomplete multi-view gene clustering with data regeneration using Shape Boltzmann Machine.

Computers in biology and medicine
Deciphering patterns in the structural and functional anatomy of genes can prove to be very helpful in understanding genetic biology and genomics. Also, the availability of the multiple omics data, along with the advent of machine learning techniques...

Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment.

Medicina (Kaunas, Lithuania)
Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed a...