AIMC Topic: Algorithms

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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...

Automated Secchi disk depth measurement based on artificial intelligence object recognition.

Marine pollution bulletin
Water transparency affects the degree of sunlight penetration in water, which is important to many water quality processes. It can be visually measured by lowering a Secchi disk (SD) into water and recording its disappearance depth - the Secchi disk ...

Entropy and Variability: A Second Opinion by Deep Learning.

Biomolecules
BACKGROUND: Analysis of the distribution of amino acid types found at equivalent positions in multiple sequence alignments has found applications in human genetics, protein engineering, drug design, protein structure prediction, and many other fields...

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...

Coordinated Navigation of Two Agricultural Robots in a Vineyard: A Simulation Study.

Sensors (Basel, Switzerland)
The development of an effective agricultural robot presents various challenges in actuation, localization, navigation, sensing, etc., depending on the prescribed task. Moreover, when multiple robots are engaged in an agricultural task, this requires ...

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...

Finding the influential clinical traits that impact on the diagnosis of heart disease using statistical and machine-learning techniques.

Scientific reports
In recent years, the omnipresence of cardiac problems has been recognized as an epidemic. With the correct and quick diagnosis, both mortality and morbidity from cardiac disorders can be dramatically reduced. However, frequent medical check-ups are p...