AIMC Topic: Reproducibility of Results

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Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett's analysis: can we trust a bot to do that?

Dento maxillo facial radiology
OBJECTIVE: To assess the reliability of CEFBOT, an artificial intelligence (AI)-based cephalometry software, for cephalometric landmark annotation and linear and angular measurements according to Arnett's analysis.

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring.

Sensors (Basel, Switzerland)
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications...

Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives.

Progress in retinal and eye research
When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount...

TRACK: A New Method From a Re-Examination of Deep Architectures for Head Motion Prediction in 360 Videos.

IEEE transactions on pattern analysis and machine intelligence
We consider predicting the user's head motion in 360 videos, with 2 modalities only: the past user's positions and the video content (not knowing other users' traces). We make two main contributions. First, we re-examine existing deep-learning appro...

Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data.

Genomics, proteomics & bioinformatics
Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of the applicability of these model explainers to bio...

Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study.

Sensors (Basel, Switzerland)
As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electro...

A Questionnaire-Based Ensemble Learning Model to Predict the Diagnosis of Vertigo: Model Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Questionnaires have been used in the past 2 decades to predict the diagnosis of vertigo and assist clinical decision-making. A questionnaire-based machine learning model is expected to improve the efficiency of diagnosis of vestibular dis...

Scalable Inverse Reinforcement Learning Through Multifidelity Bayesian Optimization.

IEEE transactions on neural networks and learning systems
Data in many practical problems are acquired according to decisions or actions made by users or experts to achieve specific goals. For instance, policies in the mind of biologists during the intervention process in genomics and metagenomics are often...

Long Short-Term Memory Neural Network with Transfer Learning and Ensemble Learning for Remaining Useful Life Prediction.

Sensors (Basel, Switzerland)
Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain s...

Jellyfish Search-Optimized Deep Learning for Compressive Strength Prediction in Images of Ready-Mixed Concrete.

Computational intelligence and neuroscience
Most building structures that are built today are built from concrete, owing to its various favorable properties. Compressive strength is one of the mechanical properties of concrete that is directly related to the safety of the structures. Therefore...