AIMC Topic: Neural Networks, Computer

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Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest.

Sensors (Basel, Switzerland)
In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human-computer interaction), etc. Such emotions could be detected from various means, such as information integra...

A Novel Groundwater Burial Depth Prediction Model Based on Two-Stage Modal Decomposition and Deep Learning.

International journal of environmental research and public health
The variability of groundwater burial depths is critical to regional water management. In order to reduce the impact of high-frequency eigenmodal functions (IMF) generated by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN...

Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware.

Nature communications
Ever-growing demand for artificial intelligence has motivated research on unconventional computation based on physical devices. While such computation devices mimic brain-inspired analog information processing, the learning procedures still rely on m...

SuHAN: Substructural hierarchical attention network for molecular representation.

Journal of molecular graphics & modelling
Recently, molecular representation and property exploration, with the combination of neural network, play a critical role in the field of drug design and discovery for assisting in drug related research. However, previous research in molecular repres...

Intraoperative cytological diagnosis of brain tumours: A preliminary study using a deep learning model.

Cytopathology : official journal of the British Society for Clinical Cytology
BACKGROUND: Intraoperative pathological diagnosis of central nervous system (CNS) tumours is essential to planning patient management in neuro-oncology. Frozen section slides and cytological preparations provide architectural and cellular information...

An efficient deep learning framework for P300 evoked related potential detection in EEG signal.

Computer methods and programs in biomedicine
BACKGROUND: Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can...

Hourly Water Level Forecasting in an Hydroelectric Basin Using Spatial Interpolation and Artificial Intelligence.

Sensors (Basel, Switzerland)
In this work, a new hydroelectric basin modelling approach is described and applied to the Pontecosi basin, Italy. Several types of data sources were used to learn the model: a number of weather stations, satellite observations, the reanalysis datase...

Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.

Scientific reports
Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it ...

Tucker network: Expressive power and comparison.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks have achieved great success in solving many machine learning and computer vision problems. In this paper, we propose a deep neural network called the Tucker network derived from the Tucker format and analyze its expressive power....

Data Valuation Algorithm for Inertial Measurement Unit-Based Human Activity Recognition.

Sensors (Basel, Switzerland)
This paper proposes a data valuation algorithm for inertial measurement unit-based human activity recognition (IMU-based HAR) data based on meta reinforcement learning. Unlike previous studies that received feature-level input, the algorithm in this ...