AIMC Topic: Support Vector Machine

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EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

Schizophrenia bulletin
BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG rec...

Mental state classification based on electroencephalogram (EEG) using multiclass support vector machine.

The Medical journal of Malaysia
INTRODUCTION: Mental state refers to a person's state of mind from various perspectives, including consciousness, intention, and functionalism. Mental states closely related to everyday life include the concentrating state, neutral state, and relaxat...

Identifying the DNA methylation preference of transcription factors using ProtBERT and SVM.

PLoS computational biology
Transcription factors (TFs) can affect gene expression by binding to certain specific DNA sequences. This binding process of TFs may be modulated by DNA methylation. A subset of TFs that serve as methylation readers preferentially binds to certain me...

Enhancing mechanical ventilator reliability through machine learning based predictive maintenance.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundWith the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. ...

Advancing promiscuous aggregating inhibitor analysis with intelligent machine learning classification.

Briefings in bioinformatics
Small molecules have been playing a crucial role in drug discovery; however, some exhibit nonspecific inhibitory effects during hit screening due to the formation of colloidal aggregators. Such false positives often lead to significant research costs...

A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers.

BMC medical informatics and decision making
Recent advances in artificial intelligence-based audio and speech processing have increasingly focused on the binary and multi-class classification of voice disorders. Despite progress, achieving high accuracy in multi-class classification remains ch...

Automated Imaging Differentiation for Parkinsonism.

JAMA neurology
IMPORTANCE: Magnetic resonance imaging (MRI) paired with appropriate disease-specific machine learning holds promise for the clinical differentiation of Parkinson disease (PD), multiple system atrophy (MSA) parkinsonian variant, and progressive supra...

Evaluation of a BERT Natural Language Processing Model for Automating CT and MRI Triage and Protocol Selection.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
To evaluate the accuracy of a Bidirectional Encoder Representations for Transformers (BERT) Natural Language Processing (NLP) model for automating triage and protocol selection of cross-sectional image requisitions. A retrospective study was comple...

Detection of Neurogenic Voice Disorders Using the Fisher Vector Representation of Cepstral Features.

Journal of voice : official journal of the Voice Foundation
Neurogenic voice disorders (NVDs) are caused by damage or malfunction of the central or peripheral nervous system that controls vocal fold movement. In this paper, we investigate the potential of the Fisher vector (FV) encoding in automatic detection...

[Construction of recognition models for subthreshold depression based on multiple machine learning algorithms and vocal emotional characteristics].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVES: To construct vocal recognition classification models using 6 machine learning algorithms and vocal emotional characteristics of individuals with subthreshold depression to facilitate early identification of subthreshold depression.