AIMC Topic: Neural Networks, Computer

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Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for Predicting Domestic Power Consumption in a Residential Building.

Computational intelligence and neuroscience
Buildings are considered to be one of the world's largest consumers of energy. The productive utilization of energy will spare the accessible energy assets for the following ages. In this paper, we analyze and predict the domestic electric power cons...

Optimization Method for Energy Saving of Rural Architectures in Hot Summer and Cold Winter Areas Based on Artificial Neural Network.

Computational intelligence and neuroscience
With the phased spatial planning of the rural revitalization strategy, the proportion of architecture energy consumption in the overall social energy consumption is also increasing year by year. Considering the hot summer and cold winter areas, the p...

Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source domains to an unlabeled target domain. MDA is a challenging task due to the severe domain shift, which not only exists between target and source but also exists amon...

Pediatric age estimation from radiographs of the knee using deep learning.

European radiology
OBJECTIVES: Age estimation, especially in pediatric patients, is regularly used in different contexts ranging from forensic over medicolegal to clinical applications. A deep neural network has been developed to automatically estimate chronological ag...

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.

NeuroImage
Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the acc...

A generic neural network model to estimate populational neural activity for robust neural decoding.

Computers in biology and medicine
BACKGROUND: Robust and continuous neural decoding is crucial for reliable and intuitive neural-machine interactions. This study developed a novel generic neural network model that can continuously predict finger forces based on decoded populational m...

Deep learning representations to support COVID-19 diagnosis on CT slices.

Biomedica : revista del Instituto Nacional de Salud
INTRODUCTION: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding chara...

Segmentation of CT Lung Images Using FCM with Active Contour and CNN Classifier.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: Lung cancer is one of the unsafe diseases for human which reduces the patient life time. Generally, most of the lung cancers are identified after it has been spread into the lung parts and moreover it is difficult to find the lung cancer a...

Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models.

Sensors (Basel, Switzerland)
This work addresses the challenge of building an accurate and generalizable periocular recognition model with a small number of learnable parameters. Deeper (larger) models are typically more capable of learning complex information. For this reason, ...

Identifying the Strength Level of Objects' Tactile Attributes Using a Multi-Scale Convolutional Neural Network.

Sensors (Basel, Switzerland)
In order to solve the problem in which most currently existing research focuses on the binary tactile attributes of objects and ignores identifying the strength level of tactile attributes, this paper establishes a tactile data set of the strength le...