AIMC Topic: Reproducibility of Results

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Multi-Pesticide Residue Analysis Method Designed for the Robot Experimenters.

Journal of agricultural and food chemistry
Robots replacing humans as the executioners is crucial work for intelligent multi-pesticide residue analysis to maximize reproducibility and throughput while minimizing the expertise required to perform the entire process. Traditional analysis method...

Comparing different robots available in the European market for the preparation of injectable chemotherapy and recommendations to users.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
INTRODUCTION: Recent advances in technology have made it possible to develop robots for preparing injectable anticancer drugs. This study aims to compare characteristics between robots available in the European market in 2022 and to help future pharm...

Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine.

Academic radiology
RATIONALE AND OBJECTIVES: Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the ...

Early stopping by correlating online indicators in neural networks.

Neural networks : the official journal of the International Neural Network Society
In order to minimize the generalization error in neural networks, a novel technique to identify overfitting phenomena when training the learner is formally introduced. This enables support of a reliable and trustworthy early stopping condition, thus ...

EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human-Computer Interactions.

Sensors (Basel, Switzerland)
The parking problem, which is caused by a low parking space utilization ratio, has always plagued drivers. In this work, we proposed an intelligent detection method based on deep learning technology. First, we constructed a TensorFlow deep learning p...

Minimizing the effect of white matter lesions on deep learning based tissue segmentation for brain volumetry.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Automated methods for segmentation-based brain volumetry may be confounded by the presence of white matter (WM) lesions, which introduce abnormal intensities that can alter the classification of not only neighboring but also distant brain tissue. The...

Structure and Base Analysis of Receptive Field Neural Networks in a Character Recognition Task.

Sensors (Basel, Switzerland)
This paper explores extensions and restrictions of shallow convolutional neural networks with fixed kernels trained with a limited number of training samples. We extend the work recently done in research on Receptive Field Neural Networks (RFNN) and ...

Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis.

Sensors (Basel, Switzerland)
This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced prop...

Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Surface-enhanced Raman spectroscopy (SERS) is an improved Raman spectroscopy technique to identify the analyte under study uniquely. At the laboratory scale, SERS has realised a huge potential to detect trace analytes with promising applications acro...

Deriving a robust deep-learning model for subcortical brain segmentation by using a large-scale database: Preprocessing, reproducibility, and accuracy of volume estimation.

NMR in biomedicine
Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U-Net) and a large-...