AIMC Topic: Neural Networks, Computer

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A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction.

Journal of environmental management
Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flo...

A singular Riemannian geometry approach to deep neural networks II. Reconstruction of 1-D equivalence classes.

Neural networks : the official journal of the International Neural Network Society
We proposed in a previous work a geometric framework to study a deep neural network, seen as sequence of maps between manifolds, employing singular Riemannian geometry. In this paper, we present an application of this framework, proposing a way to bu...

End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification.

ACS nano
The prediction of mechanical and dynamical properties of proteins is an important frontier, especially given the greater availability of proteins structures. Here we report a series of models that provide end-to-end predictions of nanodynamical prope...

Application of Transformer Models to Landslide Susceptibility Mapping.

Sensors (Basel, Switzerland)
Landslide susceptibility mapping (LSM) is of great significance for the identification and prevention of geological hazards. LSM is based on convolutional neural networks (CNNs); CNNs use fixed convolutional kernels, focus more on local information a...

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals.

Sensors (Basel, Switzerland)
The use of machine learning (ML) techniques in affective computing applications focuses on improving the user experience in emotion recognition. The collection of input data (e.g., physiological signals), together with expert annotations are part of ...

Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods.

Sensors (Basel, Switzerland)
Recent methods for automatic blood vessel segmentation from fundus images have been commonly implemented as convolutional neural networks. While these networks report high values for objective metrics, the clinical viability of recovered segmentation...

Remaining Useful Life Prediction Based on Adaptive SHRINKAGE Processing and Temporal Convolutional Network.

Sensors (Basel, Switzerland)
The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment. The traditional data-driven rolling bearing RUL prediction methods require a substantia...

Deep Learning Anomaly Classification Using Multi-Attention Residual Blocks for Industrial Control Systems.

Sensors (Basel, Switzerland)
This paper proposes a novel method monitoring network packets to classify anomalies in industrial control systems (ICSs). The proposed method combines different mechanisms. It is flow-based as it obtains new features through aggregating packets of th...

Vision-Based Detection and Classification of Used Electronic Parts.

Sensors (Basel, Switzerland)
Economic and environmental sustainability is becoming increasingly important in today's world. Electronic waste (e-waste) is on the rise and options to reuse parts should be explored. Hence, this paper presents the development of vision-based methods...

Influence of Insufficient Dataset Augmentation on IoU and Detection Threshold in CNN Training for Object Detection on Aerial Images.

Sensors (Basel, Switzerland)
The objects and events detection tasks are being performed progressively often by robotic systems like unmanned aerial vehicles (UAV) or unmanned surface vehicles (USV). Autonomous operations and intelligent sensing are becoming standard in numerous ...