AIMC Topic: Machine Learning

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Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method.

NeuroImage
Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identi...

A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods.

Annals of nuclear medicine
OBJECTIVE: This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to pre...

Camera-LiDAR Multi-Level Sensor Fusion for Target Detection at the Network Edge.

Sensors (Basel, Switzerland)
There have been significant advances regarding target detection in the autonomous vehicle context. To develop more robust systems that can overcome weather hazards as well as sensor problems, the sensor fusion approach is taking the lead in this cont...

Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column.

Sensors (Basel, Switzerland)
Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the...

Forecasting Air Temperature on Edge Devices with Embedded AI.

Sensors (Basel, Switzerland)
With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network...

XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes.

Scientific reports
This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adults (age: 63-89 years). Gait tests (20 m walkway) included speed modification (slower, preferred, and...

Personalized machine learning of depressed mood using wearables.

Translational psychiatry
Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use...

Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination Techniques.

Journal of healthcare engineering
Chronic kidney disease (CKD) is among the top 20 causes of death worldwide and affects approximately 10% of the world adult population. CKD is a disorder that disrupts normal kidney function. Due to the increasing number of people with CKD, effective...

A machine learning approach to predicting risk of myelodysplastic syndrome.

Leukemia research
BACKGROUND: Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from othe...

Measuring and modeling the motor system with machine learning.

Current opinion in neurobiology
The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experime...