AIMC Topic: Machine Learning

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Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients' diagnoses.

Artificial intelligence in medicine
BACKGROUND: Currently, the healthcare sector strives to improve the quality of patient care management and to enhance/increase its economic performance/efficiency (e.g., cost-effectiveness) by healthcare providers. The data stored in electronic healt...

A New Belief-Based Bidirectional Transfer Classification Method.

IEEE transactions on cybernetics
In pattern classification, we may have a few labeled data points in the target domain, but a number of labeled samples are available in another related domain (called the source domain). Transfer learning can solve such classification problems via th...

Active Domain Adaptation With Application to Intelligent Logging Lithology Identification.

IEEE transactions on cybernetics
Lithology identification plays an essential role in formation characterization and reservoir exploration. As an emerging technology, intelligent logging lithology identification has received great attention recently, which aims to infer the lithology...

Investigating the Bilateral Connections in Generative Zero-Shot Learning.

IEEE transactions on cybernetics
Zero-shot learning (ZSL) is a pretty intriguing topic in the computer vision community since it handles novel instances and unseen categories. In a typical ZSL setting, there is a main visual space and an auxiliary semantic space. Most existing ZSL m...

Frame-Correlation Transfers Trigger Economical Attacks on Deep Reinforcement Learning Policies.

IEEE transactions on cybernetics
Adversarial attack can be deemed as a necessary prerequisite evaluation procedure before the deployment of any reinforcement learning (RL) policy. Most existing approaches for generating adversarial attacks are gradient based and are extensive, viz.,...

Dual-Representation-Based Autoencoder for Domain Adaptation.

IEEE transactions on cybernetics
Domain adaptation aims to facilitate the learning task in an unlabeled target domain by leveraging the auxiliary knowledge in a well-labeled source domain from a different distribution. Almost existing autoencoder-based domain adaptation approaches f...

Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning.

Sensors (Basel, Switzerland)
Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion,...

Deep Learning Empowered Structural Health Monitoring and Damage Diagnostics for Structures with Weldment via Decoding Ultrasonic Guided Wave.

Sensors (Basel, Switzerland)
Welding is widely used in the connection of metallic structures, including welded joints in oil/gas metallic pipelines and other structures. The welding process is vulnerable to the inclusion of different types of welding defects, such as lack of pen...

Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN).

Sensors (Basel, Switzerland)
Starting from the concept of the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved i...

Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease.

PLoS neglected tropical diseases
BACKGROUND: Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. I...