AIMC Topic: Machine Learning

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Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system.

PloS one
As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. Recently, there have been many studies ...

Enhancement of standardized precipitation evapotranspiration index predictions by machine learning based on regression and soft computing for Iran's arid and hyper-arid region.

PloS one
Drought is a climate risk that affects access to safe water, crop development, ecological stability, and food production. Therefore, developing drought prediction methods can lead to better management of surface and groundwater resources. Similarly, ...

A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing.

PloS one
Recent advancements in hardware technology have spurred a surge in the popularity and ubiquity of wearable sensors, opening up new applications within the medical domain. This proliferation has resulted in a notable increase in the availability of Ti...

Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach.

PloS one
Only about half of patients with social anxiety disorder (SAD) respond substantially to cognitive behavioral therapy (CBT). However, there has been little evidence available to clinicians or patients about whether any individual patient is more or le...

Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care.

PloS one
BACKGROUND: Venous thromboembolism (VTE) is a life-threatening complication commonly occurring after acute ischemic stroke (AIS), with an increased risk of mortality. Traditional risk assessment tools lack precision in predicting VTE in AIS patients ...

Machine learning for predicting metabolic-associated fatty liver disease including NHHR: a cross-sectional NHANES study.

PloS one
OBJECTIVE: Metabolic - associated fatty liver disease (MAFLD) is a common hepatic disorder with increasing prevalence, and early detection remains inadequately achieved. This study aims to explore the relationship between the non-high-density lipopro...

Human-in-the-Loop Myoelectric Pattern Recognition Control of an Arm-Support Robot to Improve Reaching in Stroke Survivors.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The objective of this study was to assess the feasibility and efficacy of using real-time human-in-the-loop pattern recognition-based myoelectric control to control vertical support force or vertical position to improve reach in individuals with chro...

Improving Acceptance to Sensory Substitution: A Study on the V2A-SS Learning Model Based on Information Processing Learning Theory.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The visual sensory organ (VSO) serves as the primary channel for transmitting external information to the brain; therefore, damage to the VSO can severely limit daily activities. Visual-to-Auditory Sensory Substitution (V2A-SS), an innovative approac...

Progress in the application of machine learning in CT diagnosis of acute appendicitis.

Abdominal radiology (New York)
Acute appendicitis represents a prevalent condition within the spectrum of acute abdominal pathologies, exhibiting a diverse clinical presentation. Computed tomography (CT) imaging has emerged as a prospective diagnostic modality for the identificati...