AIMC Topic: Machine Learning

Clear Filters Showing 26681 to 26690 of 34417 articles

Emotional states recognition, implementing a low computational complexity strategy.

Health informatics journal
This article describes a methodology to recognize emotional states through an electroencephalography signals analysis, developed with the premise of reducing the computational burden that is associated with it, implementing a strategy that reduces th...

TMSEG: Novel prediction of transmembrane helices.

Proteins
Transmembrane proteins (TMPs) are important drug targets because they are essential for signaling, regulation, and transport. Despite important breakthroughs, experimental structure determination remains challenging for TMPs. Various methods have bri...

Making adjustments to event annotations for improved biological event extraction.

Journal of biomedical semantics
BACKGROUND: Current state-of-the-art approaches to biological event extraction train statistical models in a supervised manner on corpora annotated with event triggers and event-argument relations. Inspecting such corpora, we observe that there is am...

Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells.

PloS one
Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood...

Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions accurately, sinc...

Machine learning approaches to personalize early prediction of asthma exacerbations.

Annals of the New York Academy of Sciences
Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been system...

Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

Annals of the New York Academy of Sciences
Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuo...

Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.

ACS combinatorial science
The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gaps of 622 computationally optimized graphene nanoflakes were mapped to topological autocorrelation ve...

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients.

Sensors (Basel, Switzerland)
We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed...

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

PloS one
Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that ...