AIMC Topic: Algorithms

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Brain CT registration using hybrid supervised convolutional neural network.

Biomedical engineering online
BACKGROUND: Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly r...

A Data-Driven Iterative Approach for Semi-automatically Assessing the Correctness of Medication Value Sets: A Proof of Concept Based on Opioids.

Methods of information in medicine
BACKGROUND: Value sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are ...

The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life.

PLoS computational biology
The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-s...

Federated Learning for 5G Radio Spectrum Sensing.

Sensors (Basel, Switzerland)
Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum w...

Fault Diagnosis of Rotating Machinery Based on Improved Self-Supervised Learning Method and Very Few Labeled Samples.

Sensors (Basel, Switzerland)
Convolution neural network (CNN)-based fault diagnosis methods have been widely adopted to obtain representative features and used to classify fault modes due to their prominent feature extraction capability. However, a large number of labeled sample...

Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures.

Sensors (Basel, Switzerland)
Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficien...

Measuring context dependency in birdsong using artificial neural networks.

PLoS computational biology
Context dependency is a key feature in sequential structures of human language, which requires reference between words far apart in the produced sequence. Assessing how long the past context has an effect on the current status provides crucial inform...

Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism.

Computational intelligence and neuroscience
There are some problems in the process of video intelligent description and analysis of volleyball, such as poor effective information extraction rate and poor dynamic tracking effect. Based on this, combined with long-term and short-term memory netw...

Software Defect Prediction Based on Hybrid Swarm Intelligence and Deep Learning.

Computational intelligence and neuroscience
In order to improve software quality and testing efficiency, this paper implements the prediction of software defects based on deep learning. According to the respective advantages and disadvantages of the particle swarm algorithm and the wolf swarm ...

Multipath Cross Graph Convolution for Knowledge Representation Learning.

Computational intelligence and neuroscience
In the past, most of the entity prediction methods based on embedding lacked the training of local core relationships, resulting in a deficiency in the end-to-end training. Aiming at this problem, we propose an end-to-end knowledge graph embedding re...