AIMC Topic: Machine Learning

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CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks.

Sensors (Basel, Switzerland)
Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs...

Automated prediction of mastitis infection patterns in dairy herds using machine learning.

Scientific reports
Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route...

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding.

Scientific reports
Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline dec...

Early prediction of circulatory failure in the intensive care unit using machine learning.

Nature medicine
Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monit...

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms.

Journal of healthcare engineering
Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped h...

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning.

Biomolecules
c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors available in the clinic. Antibodies blocking the interaction with its only known ligand, hepatocyte growth...

Addressing database variability in learning from medical data: An ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring.

Computers in biology and medicine
In this work we examine some of the problems associated with the development of machine learning models with the objective to achieve robust generalization capabilities on common-task multiple-database scenarios. Referred to as the "database variabil...

Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

NeuroImage. Clinical
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of ...

Learning the generative principles of a symbol system from limited examples.

Cognition
The processes and mechanisms of human learning are central to inquiries in a number of fields including psychology, cognitive science, development, education, and artificial intelligence. Arguments, debates, and controversies linger over the question...

Augmented patient-specific functional medical imaging by implicit manifold learning.

International journal for numerical methods in biomedical engineering
This paper uses machine learning to enrich magnetic resonance angiography and magnetic resonance imaging acquisitions. A convolutional neural network is built and trained over a synthetic database linking geometrical parameters and mechanical charact...