AIMC Topic: Algorithms

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Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion.

Sensors (Basel, Switzerland)
head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed...

An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition.

Medical engineering & physics
Emotional human-computer interaction (HCI) has become an important research area in the fields of artificial intelligence and cognitive science, owing to the requirement for active emotion perception. To enhance the performance of electroencephalogra...

Graph transform learning.

Neural networks : the official journal of the International Neural Network Society
Transform learning is a new representation learning framework where we learn an operator/transform that analyses the data to generate the coefficient/representation. We propose a variant of it called the graph transform learning; in this we explicitl...

Supervised machine learning for coronary artery lumen segmentation in intravascular ultrasound images.

International journal for numerical methods in biomedical engineering
Intravascular ultrasound (IVUS) has been widely used to capture cross sectional lumen frames of inner wall of coronary arteries. This kind of medical imaging modalities is capable of providing detailed and significant information of lumen contour sha...

Moth-Flame Optimization-Bat Optimization: Map-Reduce Framework for Big Data Clustering Using the Moth-Flame Bat Optimization and Sparse Fuzzy C-Means.

Big data
The technical advancements in big data have become popular and most desirable among users for storing, processing, and handling huge data sets. However, clustering using these big data sets has become a major challenge in big data analysis. The conve...

Real-Time Human Action Recognition with a Low-Cost RGB Camera and Mobile Robot Platform.

Sensors (Basel, Switzerland)
Human action recognition is an important research area in the field of computer vision that can be applied in surveillance, assisted living, and robotic systems interacting with people. Although various approaches have been widely used, recent studie...

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Sensors (Basel, Switzerland)
Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust train...

Transfer Learning: Making Retrosynthetic Predictions Based on a Small Chemical Reaction Dataset Scale to a New Level.

Molecules (Basel, Switzerland)
Effective computational prediction of complex or novel molecule syntheses can greatly help organic and medicinal chemistry. Retrosynthetic analysis is a method employed by chemists to predict synthetic routes to target compounds. The target compounds...

Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform.

BMC bioinformatics
BACKGROUND: The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to reveal the genome characteristics for downstream analysis for the purpose of understanding the mech...

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection.

Translational psychiatry
To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion...