AIMC Topic: Support Vector Machine

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A new backpropagation neural network classification model for prediction of incidence of malaria.

Frontiers in bioscience (Landmark edition) Jan 1, 2020
Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classifie...
Humans Neural Networks, Computer Algorithms Temperature Reproducibility of Results Malaria Seasons Humidity Geography Rain Incidence India Models, Theoretical Support Vector Machine
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Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments.

Sensors (Basel, Switzerland) Dec 30, 2019
In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists fo...
Humans Neural Networks, Computer Models, Theoretical Support Vector Machine Databases as Topic Pattern Recognition, Automated Human Activities Environment
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Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.

International journal of medical informatics Dec 28, 2019
BACKGROUND: The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for experienced multidisciplinary cent...
Tongue Neoplasms Aged Middle Aged Humans Chemoradiotherapy Cohort Studies Aged, 80 and over Brazil Algorithms Finland Child Young Adult Adolescent Mouth Neoplasms Prognosis Support Vector Machine Adult Incidence Female Neoplasm Recurrence, Local Male Decision Trees Combined Modality Therapy Bayes Theorem Supervised Machine Learning
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Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques.

Computer methods and programs in biomedicine Dec 27, 2019
BACKGROUND: The study compared the predictive outcomes of artificial neural network, support vector machine and random forest on the occurrence of anti-tuberculosis drug-induced hepatotoxicity.
Tuberculosis Humans Gene Frequency Neural Networks, Computer Genotype Polymorphism, Genetic Aged Support Vector Machine Chemical and Drug Induced Liver Injury Machine Learning Middle Aged Aged, 80 and over Liver Female Adult Drug Discovery Taiwan Reproducibility of Results Sensitivity and Specificity ROC Curve Antitubercular Agents Risk Factors Male Genomics
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Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Dec 27, 2019
This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the en...
Discriminant Analysis Signal Processing, Computer-Assisted Machine Learning Cerebral Cortex Psychomotor Performance Algorithms Neural Networks, Computer Macaca fascicularis Brain-Computer Interfaces Animals Male Support Vector Machine
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Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter.

BMC genomics Dec 27, 2019
BACKGROUND: Identification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical protein...
Area Under Curve Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins Support Vector Machine Proteins Humans Databases, Protein ROC Curve Principal Component Analysis Protein Interaction Maps
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Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

Physiological measurement Dec 27, 2019
OBJECTIVE: Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as ...
Support Vector Machine Signal Processing, Computer-Assisted Atrial Fibrillation Plethysmography Humans Female Aged Aged, 80 and over Young Adult Adult Male Middle Aged Deep Learning
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A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Dec 25, 2019
An important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, ...
Transfer, Psychology Healthy Volunteers Pattern Recognition, Automated Calibration Movement Female Adult Male Deep Learning Humans Support Vector Machine Neural Networks, Computer Wrist Electromyography Artificial Limbs Electrodes
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Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress.

Nutrients Dec 23, 2019
The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects ( = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine in...
Humans Machine Learning Young Adult Adult Support Vector Machine Heart Rate Dehydration Galvanic Skin Response Stroop Test Sensitivity and Specificity Male Stress, Psychological Cognition Autonomic Nervous System
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Comparing different supervised machine learning algorithms for disease prediction.

BMC medical informatics and decision making Dec 21, 2019
BACKGROUND: Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key tren...
Risk Factors Support Vector Machine Humans Bayes Theorem Machine Learning Data Mining Algorithms Clinical Decision Rules
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