AIMC Topic: Neural Networks, Computer

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A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons.

Sensors (Basel, Switzerland)
The increasing penetration of renewable energy sources tends to redirect the power systems community's interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons...

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration.

Sensors (Basel, Switzerland)
In dense IoT deployments of wireless sensor networks (WSNs), sensor placement, coverage, connectivity, and energy constraints determine the overall network lifetime. In large-size WSNs, it is difficult to maintain a trade-off among these conflicting ...

Using deep learning-derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data.

European radiology
OBJECTIVES: Siamese neural networks (SNN) were used to classify the presence of radiopaque beads as part of a colonic transit time study (CTS). The SNN output was then used as a feature in a time series model to predict progression through a CTS.

Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers.

Computer methods and programs in biomedicine
UNLABELLED: Backgound and Objective: Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants w...

Approximation of classifiers by deep perceptron networks.

Neural networks : the official journal of the International Neural Network Society
We employ properties of high-dimensional geometry to obtain some insights into capabilities of deep perceptron networks to classify large data sets. We derive conditions on network depths, types of activation functions, and numbers of parameters that...

Collaborative neurodynamic optimization for solving nonlinear equations.

Neural networks : the official journal of the International Neural Network Society
A distributed optimization method for solving nonlinear equations with constraints is developed in this paper. The multiple constrained nonlinear equations are converted into an optimization problem and we solve it in a distributed manner. Due to the...

Towards global neural network abstractions with locally-exact reconstruction.

Neural networks : the official journal of the International Neural Network Society
Neural networks are a powerful class of non-linear functions. However, their black-box nature makes it difficult to explain their behaviour and certify their safety. Abstraction techniques address this challenge by transforming the neural network int...

A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas.

Journal of dentistry
OBJECTIVES: Dentists and oral surgeons often face difficulties distinguishing between radicular cysts and periapical granulomas on panoramic imaging. Radicular cysts require surgical removal while root canal treatment is the first-line treatment for ...

LGTRL-DE: Local and Global Temporal Representation Learning with Demographic Embedding for in-hospital mortality prediction.

Journal of biomedical informatics
Predicting the patient's in-hospital mortality from the historical Electronic Medical Records (EMRs) can assist physicians to make clinical decisions and assign medical resources. In recent years, researchers proposed many deep learning methods to pr...

Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study.

Sensors (Basel, Switzerland)
Subsurface inclusions are one of the most common defects that affect the inner quality of continuous casting slabs. This increases the defects in the final products and increases the complexity of the hot charge rolling process and may even cause bre...