AIMC Topic: Machine Learning

Clear Filters Showing 23941 to 23950 of 34417 articles

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype.

IEEE transactions on biomedical circuits and systems
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since b...

Classification and Recognition of Ovarian Cells Based on Two-Dimensional Light Scattering Technology.

Journal of medical systems
Ovarian cancer is a very insidious malignant tumor. In order to detect ovarian cancer cells early, the classification and recognition of ovarian cancer cells is mainly studied by two-dimensional light scattering technology. Firstly, a single-cell two...

Evaluating the evidence for biotypes of depression: Methodological replication and extension of.

NeuroImage. Clinical
BACKGROUND: Psychiatric disorders are highly heterogeneous, defined based on symptoms with little connection to potential underlying biological mechanisms. A possible approach to dissect biological heterogeneity is to look for biologically meaningful...

Machine learning in plant-pathogen interactions: empowering biological predictions from field scale to genome scale.

The New phytologist
Machine learning (ML) encompasses statistical methods that learn to identify patterns in complex datasets. Here, I review application areas in plant-pathogen interactions that have recently benefited from ML, such as disease monitoring, the discovery...

Predicting personalized process-outcome associations in psychotherapy using machine learning approaches-A demonstration.

Psychotherapy research : journal of the Society for Psychotherapy Research
Personalized treatment methods have shown great promise in efficacy studies across many fields of medicine and mental health. Little is known, however, about their utility in process-outcome research. This study is the first to apply personalized tr...

EEG characteristics of children with attention-deficit/hyperactivity disorder.

Neuroscience
The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-deficit/hyperactivity disorder (ADHD); one main goal is to characterize the EEG of children with ADHD. In this study, we employed the power spectrum, complexity...

Mass Surveilance of -Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection.

Sensors (Basel, Switzerland)
The nematode is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and r...

Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.

Scientific reports
Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine lea...

Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective.

PloS one
BACKGROUND & AIMS: Current non-invasive scores for the assessment of severity of non-alcoholic fatty liver disease (NAFLD) and identification of patients with non-alcoholic steatohepatitis (NASH) have insufficient performance to be included in clinic...

Using machine learning to translate applicant work history into predictors of performance and turnover.

The Journal of applied psychology
Work history information reflected in resumes and job application forms is commonly used to screen job applicants; however, there is little consensus as to how to systematically translate information about one's work-related past into predictors of f...