AIMC Topic: Machine Learning

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Predicting Potential SARS-COV-2 Drugs-In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking.

International journal of molecular sciences
Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as we...

On the Impact of Biceps Muscle Fatigue in Human Activity Recognition.

Sensors (Basel, Switzerland)
Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems' performance...

Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning.

Scientific reports
Pain is an undesirable sensory experience that can induce depression and limit individuals' activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective an...

Classification of paediatric brain tumours by diffusion weighted imaging and machine learning.

Scientific reports
To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocyt...

Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review.

Journal of medical Internet research
BACKGROUND: Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs-based, aggregate-weighted early warning systems are com...

Creating artificial human genomes using generative neural networks.

PLoS genetics
Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create re...

Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas.

PLoS computational biology
The visual cortex of the mouse brain can be divided into ten or more areas that each contain complete or partial retinotopic maps of the contralateral visual field. It is generally assumed that these areas represent discrete processing regions. In co...

Task Similarity Estimation Through Adversarial Multitask Neural Network.

IEEE transactions on neural networks and learning systems
Multitask learning (MTL) aims at solving the related tasks simultaneously by exploiting shared knowledge to improve performance on individual tasks. Though numerous empirical results supported the notion that such shared knowledge among tasks plays a...

Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping.

IEEE transactions on neural networks and learning systems
The estimation of multitype cardiac indices from cardiac magnetic resonance imaging (MRI) and computed tomography (CT) images attracts great attention because of its clinical potential for comprehensive function assessment. However, the most exiting ...

Quaternion Projection Rule for Rotor Hopfield Neural Networks.

IEEE transactions on neural networks and learning systems
A rotor Hopfield neural network (RHNN) is an extension of a complex-valued Hopfield neural network (CHNN) and has excellent noise tolerance. The RHNN decomposition theorem says that an RHNN decomposes into a CHNN and a symmetric CHNN. For a large num...