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Bayes Theorem

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Predicting risk of sepsis, comparison between machine learning methods: a case study of a Virginia hospital.

European journal of medical research
Sepsis is an inflammation caused by the body's systemic response to an infection. The infection could be a result of many diseases, such as pneumonia, urinary tract infection, and other illnesses. Some of its symptoms are fever, tachycardia, tachypne...

Machine Learning Models Identify New Inhibitors for Human OATP1B1.

Molecular pharmaceutics
The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors o...

Detecting Reconnaissance and Discovery Tactics from the MITRE ATT&CK Framework in Zeek Conn Logs Using Spark's Machine Learning in the Big Data Framework.

Sensors (Basel, Switzerland)
While computer networks and the massive amount of communication taking place on these networks grow, the amount of damage that can be done by network intrusions grows in tandem. The need is for an effective and scalable intrusion detection system (ID...

Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques.

Scientific reports
The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM)...

Bayesian Pseudoinverse Learners: From Uncertainty to Deterministic Learning.

IEEE transactions on cybernetics
Pseudo-inverse learners (PILs) are a kind of feedforward neural network trained with the pseudoinverse learning algorithm, which can be traced back to 1995 originally. PIL is an approach for nongradient descent learning, and its main advantage is the...

Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks.

Nature communications
Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions...

Estimation of Parameters on Probability Density Function Using Enhanced GLUE Approach.

Computational intelligence and neuroscience
The most essential process in statistical image and signal processing is the parameter estimation of probability density functions (PDFs). The estimation of the probability density functions is a contentious issue in the domains of artificial intelli...

Multi-Task Assignment Method of the Cloud Computing Platform Based on Artificial Intelligence.

Computational intelligence and neuroscience
To realize load balancing of cloud computing platforms in big data processing, the method of finding the optimal load balancing physical host in the algorithm cycle is adopted at present. This optimal load balancing strategy that overly focuses on th...

Online eye-movement classification with temporal convolutional networks.

Behavior research methods
The simultaneous classification of the three most basic eye-movement patterns is known as the ternary eye-movement classification problem (3EMCP). Dynamic, interactive real-time applications that must instantly adjust or respond to certain eye behavi...

Trainable Quaternion Extended Kalman Filter with Multi-Head Attention for Dead Reckoning in Autonomous Ground Vehicles.

Sensors (Basel, Switzerland)
Extended Kalman filter (EKF) is one of the most widely used Bayesian estimation methods in the optimal control area. Recent works on mobile robot control and transportation systems have applied various EKF methods, especially for localization. Howeve...