AIMC Topic: Stochastic Processes

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An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks.

Computational and mathematical methods in medicine Sep 20, 2021
There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in ...
Radiographic Image Interpretation, Computer-Assisted Syndrome Radiography, Thoracic Stochastic Processes Machine Learning Thoracic Diseases Humans Databases, Factual Neural Networks, Computer Computational Biology
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A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection.

Computational and mathematical methods in medicine Sep 13, 2021
Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous...
Diagnosis, Computer-Assisted Computational Biology Humans Neural Networks, Computer Bayes Theorem Alzheimer Disease Stochastic Processes Artificial Intelligence Nonlinear Dynamics Machine Learning Image Interpretation, Computer-Assisted
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Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction.

IEEE transactions on neural networks and learning systems Aug 31, 2021
Electronic health records (EHRs) are characterized as nonstationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missi...
Neural Networks, Computer Algorithms Electronic Health Records Humans Predictive Value of Tests Stochastic Processes Deep Learning Uncertainty Hospital Mortality Computational Biology
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Dendritic normalisation improves learning in sparsely connected artificial neural networks.

PLoS computational biology Aug 9, 2021
Artificial neural networks, taking inspiration from biological neurons, have become an invaluable tool for machine learning applications. Recent studies have developed techniques to effectively tune the connectivity of sparsely-connected artificial n...
Humans Neural Networks, Computer Neurons Nerve Net Stochastic Processes Dendrites Computational Biology Models, Neurological Machine Learning Action Potentials Animals Deep Learning
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A stochastic modeling approach for analyzing water resources systems.

Journal of contaminant hydrology Aug 8, 2021
Many uncertain factors exist in the water resource systems, leading to dynamic characteristics of the water distribution process. Especially for the watershed including irrigation area with multiple water sources and water users, it is complicated th...
Models, Theoretical Fuzzy Logic Probability Water Resources Stochastic Processes Water Supply Uncertainty
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Deep Semisupervised Multitask Learning Model and Its Interpretability for Survival Analysis.

IEEE journal of biomedical and health informatics Aug 5, 2021
Survival analysis is a commonly used method in the medical field to analyze and predict the time of events. In medicine, this approach plays a key role in determining the course of treatment, developing new drugs, and improving hospital procedures. M...
Survival Analysis Stochastic Processes Humans Supervised Machine Learning
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A Survey of Stochastic Computing Neural Networks for Machine Learning Applications.

IEEE transactions on neural networks and learning systems Jul 6, 2021
Neural networks (NNs) are effective machine learning models that require significant hardware and energy consumption in their computing process. To implement NNs, stochastic computing (SC) has been proposed to achieve a tradeoff between hardware effi...
Algorithms Neurons Stochastic Processes Humans Neural Networks, Computer Machine Learning
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Neural network aided approximation and parameter inference of non-Markovian models of gene expression.

Nature communications May 11, 2021
Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parame...
Computer Simulation Models, Genetic Kinetics Gene Expression Regulation Transcription, Genetic Stochastic Processes Feedback, Physiological Neural Networks, Computer
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Domain adaptation and self-supervised learning for surgical margin detection.

International journal of computer assisted radiology and surgery May 6, 2021
PURPOSE: One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in s...
Stochastic Processes Margins of Excision Mastectomy, Segmental Supervised Machine Learning Calibration Operating Rooms Breast Mastectomy Carcinoma, Basal Cell Reproducibility of Results Sensitivity and Specificity Breast Neoplasms Humans Algorithms Area Under Curve Machine Learning Female Skin Skin Neoplasms
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Block-cyclic stochastic coordinate descent for deep neural networks.

Neural networks : the official journal of the International Neural Network Society Apr 19, 2021
We present a stochastic first-order optimization algorithm, named block-cyclic stochastic coordinate descent (BCSC), that adds a cyclic constraint to stochastic block-coordinate descent in the selection of both data and parameters. It uses different ...
Classification Stochastic Processes Neural Networks, Computer Datasets as Topic Image Processing, Computer-Assisted Pattern Recognition, Automated
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