AIMC Topic: Models, Statistical

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Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms.

Journal of environmental and public health
Applicability of statistical models in predicting chlorine decay remains minimally explored. This study predicted residual chlorine using six deep learning and nine machine learning techniques. Suitability of multimodel ensembles (MMEs) including ari...

Rotating Single-Antenna Spoofing Signal Detection Method Based on IPNN.

Sensors (Basel, Switzerland)
The traditional carrier-phase differential detection technology mainly relies on the spatial processing method, which uses antenna arrays or moving antennas to detect spoofing signals, but it cannot be applied to static single-antenna receivers. Aimi...

Developing a hybrid time-series artificial intelligence model to forecast energy use in buildings.

Scientific reports
The development of a reliable energy use prediction model is still difficult due to the inherent complex pattern of energy use data. There are few studies developing a prediction model for the one-day-ahead energy use prediction in buildings and opti...

A Concise Yet Effective Model for Non-Aligned Incomplete Multi-View and Missing Multi-Label Learning.

IEEE transactions on pattern analysis and machine intelligence
In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing labels, incomplete views, and non-aligned views. Existing methods mainly concern the first two and commonly need multiple assumptions to attack them,...

Prediction of placenta accreta spectrum by combining deep learning and radiomics using T2WI: a multicenter study.

Abdominal radiology (New York)
PURPOSE: To achieve prenatal prediction of placenta accreta spectrum (PAS) by combining clinical model, radiomics model, and deep learning model using T2-weighted images (T2WI), and to objectively evaluate the performance of the prediction through mu...

Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy.

PloS one
The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been ma...

Modeling biosurfactant production from agroindustrial residues by neural networks and polynomial models adjusted by particle swarm optimization.

Environmental science and pollution research international
Biosurfactants are molecules with wide application in several industrial processes. Their production is damaged due to inefficient bioprocessing and expensive substrates. The latest developments of strategies to improve and economize the biosurfactan...

Dissipativity-Based Intermittent Fault Detection and Tolerant Control for Multiple Delayed Uncertain Switched Fuzzy Stochastic Systems With Unmeasurable Premise Variables.

IEEE transactions on cybernetics
This study focuses on dissipativity-based fault detection for multiple delayed uncertain switched Takagi-Sugeno fuzzy stochastic systems with intermittent faults and unmeasurable premise variables. Nonlinear dynamics, exogenous disturbances, and meas...

Attention-based generative adversarial network in medical imaging: A narrative review.

Computers in biology and medicine
As a popular probabilistic generative model, generative adversarial network (GAN) has been successfully used not only in natural image processing, but also in medical image analysis and computer-aided diagnosis. Despite the various advantages, the ap...

Review and comparison of treatment effect estimators using propensity and prognostic scores.

The international journal of biostatistics
In finding effects of a binary treatment, practitioners use mostly either propensity score matching (PSM) or inverse probability weighting (IPW). However, many new treatment effect estimators are available now using propensity score and "prognostic s...