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View article: Deep Ensembles and Multisensor Data for Global LCZ Mapping: Insights from So2Sat LCZ42
Deep Ensembles and Multisensor Data for Global LCZ Mapping: Insights from So2Sat LCZ42 Open
Classifying multiband images acquired by advanced sensors, including those mounted on satellites, is a central task in remote sensing and environmental monitoring. These sensors generate high-dimensional outputs rich in spectral and spatia…
View article: SAM-Based Input Augmentations and Ensemble Strategies for Image Segmentation
SAM-Based Input Augmentations and Ensemble Strategies for Image Segmentation Open
Despite the remarkable progress of deep learning in image segmentation, models often struggle with generalization across diverse datasets. This study explores novel input augmentation techniques and ensemble strategies to improve image seg…
View article: Convolutional neural networks and vision transformers for Plankton Classification
Convolutional neural networks and vision transformers for Plankton Classification Open
In this paper, we present a study on plankton classification for automated underwater ecosystems monitoring. The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer archit…
View article: Deep Ensembling of Multiband Images for Earth Remote Sensing and Foramnifera Data
Deep Ensembling of Multiband Images for Earth Remote Sensing and Foramnifera Data Open
The classification of multiband images captured by advanced sensors, such as satellite-mounted imaging systems, is a critical task in remote sensing and environmental monitoring. These sensors provide high-dimensional data that encapsulate…
View article: Advancing Precision: A Comprehensive Review of MRI Segmentation Datasets from BraTS Challenges (2012–2025)
Advancing Precision: A Comprehensive Review of MRI Segmentation Datasets from BraTS Challenges (2012–2025) Open
Brain Tumor Segmentation (BraTS) challenges have significantly advanced research in brain tumor segmentation and related medical imaging tasks. This paper provides a comprehensive review of the BraTS datasets from 2012 to 2024, examining t…
View article: Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification
Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification Open
Traditionally, classifying species has required taxonomic experts to carefully examine unique physical characteristics, a time-intensive and complex process. Machine learning offers a promising alternative by utilizing computational power …
View article: ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images Open
The Enteric Nervous System (ENS) is a dynamic field of study where researchers devise sophisticated methodologies to comprehend the impact of chronic degenerative diseases on Enteric Neuron Cells (ENCs). These investigations demand labor-i…
View article: Advancing Precision: A Comprehensive Review of MRISegmentation Datasets from BraTS Challenges (2012–2024)
Advancing Precision: A Comprehensive Review of MRISegmentation Datasets from BraTS Challenges (2012–2024) Open
The Brain Tumor Segmentation (BraTS) challenges have significantly contributed to advance research in brain tumor segmentation and related medical imaging tasks. This paper provides a comprehensive review of the BraTS datasets from 2012 to…
View article: Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification
Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification Open
Traditionally, classifying species has required taxonomic experts to carefully examine unique physical characteristics, a time-intensive and complex process. Machine learning offers a promising alternative by utilizing computational power …
View article: Matrix-based vector representations in neural networks for classifying molecular biology data
Matrix-based vector representations in neural networks for classifying molecular biology data Open
Summary Selecting an appropriate classifier is essential for achieving accurate classification. In this study, we propose novel neural network (NNs)-based alternatives to standard classifiers as support vector machines. NNs, particularly c…
View article: Multiband Image Analysis Using Ensemble Neural Networks
Multiband Image Analysis Using Ensemble Neural Networks Open
The classification of multiband images captured by advanced sensors, such as satellite-mounted imaging systems, is a critical task in remote sensing and environmental monitoring. These sensors provide high-dimensional data that encapsulate…
View article: AI-Powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning
AI-Powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning Open
Only 1.2 million out of an estimated 8.7 million species on Earth have been fully classified through taxonomy. As biodiversity loss accelerates, ecologists are urgently revising conservation strategies, but the “taxonomic impediment” remai…
View article: AI-powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning
AI-powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning Open
As sequencing technologies advance, short DNA sequence fragments increasingly serve as DNA barcodes for species identification. Rapid acquisition of DNA sequences from diverse organisms is now possible, highlighting the increasing signific…
View article: Vector to matrix representation for CNN networks for classifying astronomical data
Vector to matrix representation for CNN networks for classifying astronomical data Open
Choosing the right classifier is crucial for effective classification in various astronomical datasets aimed at pattern recognition. While the literature offers numerous solutions, the support vector machine (SVM) continues to be a preferr…
View article: An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images
An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images Open
The analysis of road continuity in satellite images is a complex challenge. This is due to the difficulty in identifying the directional vector of road sections, especially when the satellite view of roads is obstructed by trees or other s…
View article: Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation
Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation Open
To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves ca…
View article: Improving Existing Segmentators Performance with Zero-Shot Segmentators
Improving Existing Segmentators Performance with Zero-Shot Segmentators Open
This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar obj…
View article: Comparison of Different Methods for Building Ensembles of Convolutional Neural Networks
Comparison of Different Methods for Building Ensembles of Convolutional Neural Networks Open
In computer vision and image analysis, Convolutional Neural Networks (CNNs) and other deep-learning models are at the forefront of research and development. These advanced models have proven to be highly effective in tasks related to compu…
View article: Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation
Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation Open
To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves ca…
View article: Heterogeneous Ensemble for Medical Data Classification
Heterogeneous Ensemble for Medical Data Classification Open
For robust classification, selecting a proper classifier is of primary importance. However, selecting the best classifiers depends on the problem, as some classifiers work better at some tasks than on others. Despite the many results colle…
View article: Comparison of Different Methods for Building Ensembles of Convolutional Neural Networks
Comparison of Different Methods for Building Ensembles of Convolutional Neural Networks Open
In computer vision and image analysis, Convolutional Neural Networks (CNNs) and other deep learning models are at the forefront of research and development. These advanced models have proven to be highly effective in tasks related to compu…
View article: Improving Existing Segmentators Performance with Zero-Shot Segmentators
Improving Existing Segmentators Performance with Zero-Shot Segmentators Open
This paper explores the potential of using the SAM segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminat…
View article: Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning
Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning Open
This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically convolutional neural networks (CNNs), to analyze digital im…
View article: Building Ensemble of Resnet for Dolphin Whistle Detection
Building Ensemble of Resnet for Dolphin Whistle Detection Open
Ecoacoustics is arguably the best method for monitoring marine environments, but analyzing and interpreting acoustic data has traditionally demanded substantial human supervision and resources. These bottlenecks can be addressed by harness…
View article: Deep-Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble
Deep-Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble Open
Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performin…
View article: Building Ensemble of Resnet for Dolphin Whistle Detection
Building Ensemble of Resnet for Dolphin Whistle Detection Open
To effectively preserve marine environments and manage endangered species, it is necessary to employ efficient, precise, and scalable solutions for environmental monitoring. Ecoacoustics provides several benefits as it enables non-intrusiv…
View article: Deep Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble
Deep Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble Open
Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performin…