cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.668x2n
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Isachne bsipiana Veldkamp"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035662-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hn8oyz
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pongo Lacépède, 1799",
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 189 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007838-160118175350007/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7m9jh
title: Occurrence Download

description: A dataset containing 1265 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Lonchura spectabilis (P.L.Sclater, 1879)"
 ]
}

 The dataset includes 1265 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051432-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.77gdcf
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Isachne fera Veldkamp"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035753-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uzftjo
title: Occurrence Download

description: A dataset containing 196 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oecomys bicolor (Tomes, 1860)",
 "Country is Brazil"
 ]
}

 The dataset includes 196 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007838-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fytxdn
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Liuixalus hainanus (Liu & Wu, 2004)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042272-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6cjfep
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Knema poilanei W.J.de Wilde"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035686-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zqbv8t
title: Occurrence Download

description: A dataset containing 1185 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leptocybe invasa Fisher & La Salle, 2004"
 ]
}

 The dataset includes 1185 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039335-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j7qxff
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Parashorea chinensis WangHsie"
 ]
}

 The dataset includes 16 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007838-160623103540712/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mp25z4
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Jumellea arborescens H.Perrier"
 ]
}

 The dataset includes 108 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035694-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xavt79
title: Occurrence Download

description: A dataset containing 216174 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ixobrychus exilis (Gmelin, 1789)"
 ]
}

 The dataset includes 216174 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042274-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kcxv5t
title: Occurrence Download

description: A dataset containing 10816 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myocastor coypus (Molina, 1782)"
 ]
}

 The dataset includes 10816 records from 135 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007838-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xzx4sh
title: Occurrence Download

description: A dataset containing 4539 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Buteo albigula Philippi, 1899"
 ]
}

 The dataset includes 4539 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051456-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hk99nr
title: Occurrence Download

description: A dataset containing 2456 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Megaskepasma erythrochlamys Lindau"
 ]
}

 The dataset includes 2456 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010935-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b2bp9j
title: Occurrence Download

description: A dataset containing 314 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ateles chamek (Humboldt, 1812)"
 ]
}

 The dataset includes 314 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010936-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pknt45
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (90fd6680-349f-11d8-aa2d-b8a03c50a862, 33d232b5-5cc2-4521-8bd6-121c5bb41c32, 215fad6d-884d-4127-9635-fad209824cdf, 8483a1f0-1032-11db-ae00-b8a03c50a862, 8d861745-d64d-419e-a9ea-251f56a631a7, d0e01e0c-7e8c-49aa-aeed-15fab7dfee46)",
 "TaxonKey is Urtica dioica L."
 ]
}

 The dataset includes 18 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010937-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14457/tu.the.2024.89
title: Iris image segmentation for personal identification using unit gradient vectors and Iris-SAM

description: Iris recognition is one of a fundamental component of computer vision, extensively employed for authentication and identification within security and forensic contexts. Despite its broad utilization, the efficacy of current iris recognition systems is notably compromised under challenging conditions such as suboptimal lighting, partial iris visibility, and eye mis- alignment. These adverse circumstances frequently undermine the reliability of traditional iris recognition methodologies. Central to the effectiveness of iris recognition is the process of iris segmentation, which aims to precisely isolate the iris’s annular region from ocular images. Yet, the perfor- mance of conventional iris segmentation techniques typically deteriorates in diverse imaging environments. This thesis introduces an advanced approach to iris segmentation that inte- grates Unit Gradient Vectors (UGVs) with the Iris-Segment Anything Model (Iris-SAM) to enhance segmentation accuracy. This innovative methodology synergistically combines the Daugman algorithm with active contour methods, meticulously optimized using UGVs, to facilitate highly accurate initial segmentation. Further refinement is achieved through the implementation of Iris-SAM, which incorporates Focal Loss to address the significant class imbalance between iris and non-iris regions effectively. Comprehensive empirical evaluations conducted on the CASIA-Iris-Interval-v3 dataset affirm the superiority of this approach. The proposed model attains an average segmentation accuracy of 97.86%, surpassing the Iris-SAM benchmark of 96.94%. This improvement underscores the potential of combining traditional algorithms with modern machine learning techniques to overcome existing limitations and enhance the precision and robustness of iris recognition systems.
DOI: 10.14457/tu.the.2024.90
title: Deep learning-based microplastic identification using FTIR spectra

description: The escalating concern of microplastic pollution in the environment, particularly in aquatic ecosystems, calls for advanced and reliable detection methodologies. This thesis presents a comprehensive study in three key areas: comparing machine learning (ML) and deep learning (DL) techniques, optimizing model performance through linear dimensionality reduction and fusion methods, and developing a desktop application for real-time microplastic classification using Fourier-transform infrared (FTIR) spectroscopy. In the first part, the study compares various ML and DL models to ascertain their effectiveness in classifying microplastics amid spectral noise challenges, particularly those arising from membrane filters in FTIR spectra. A detailed evaluation of traditional ML algorithms such as Support Vector Classification (SVC) and K-Nearest Neighbors (KNN), alongside Convolutional Neural Networks (CNNs) like LeNet5, is conducted. The analysis reveals that while ML models showcase high accuracy, the DL model LeNet5 outperforms them, achieving an accuracy of 96.93\%, thereby highlighting the advanced pattern recognition capabilities of CNNs in this domain. The second part delves into optimizing the performance of DL models for microplastics spectral classification by implementing linear dimensionality reduction and fusion techniques. Five linear dimensionality reduction methods, including Principal Component Analysis (PCA), Factor Analysis (FA), Fast Independent Component Analysis (Fast ICA), Non-negative Matrix Factorization (NMF), and Truncated Singular Value Decomposition (Truncated SVD), are explored for their efficacy in conjunction with CNN architectures. Furthermore, the study examines the impact of four fusion strategies: Shallow Feature Fusion (Data Fusion), Deep Feature Fusion, Hard Decision Fusion, and Soft Decision Fusion. The findings indicate that Soft Decision Fusion markedly enhances classification accuracy, emphasizing its potential in improving microplastics classification accuracy. In the final part, this research culminates in the development of a desktop application that integrates the optimized DL models and five linear dimensionality reduction techniques. This application offers a practical, real-time solution for microplastics classification, making it accessible to a wider range of users in environmental monitoring. It serves as a valuable tool for deploying developed models in real-world scenarios to detect and classify microplastics efficiently. This thesis demonstrates the potential of AI-driven models for efficient microplastics classification using FTIR spectroscopy, providing a scalable solution for environmental monitoring. The findings advocate for further refinement and adoption of these technologies to support global efforts in microplastics pollution mitigation.
DOI: 10.14457/tu.the.2024.91
title: Finetuning transformer-based MT using syntactic guides

description: Incorporating syntactic information into machine translation (MT) systems can enhance their performance by providing richer linguistic context. This paper investigates the impact of integrating dependency informations into the fine-tuning process of Transformer-based MT models. We evaluate two translation scenarios: multilingual source-to-English and English-to-multilingual targets. Our experiments demonstrate that leveraging dependency tags in the target language improves translation quality significantly, achieving up to a 50% increase in validation scores compared to models without such enhancements. Additionally, dependency informations consistently boost performance across diverse language pairs, except in cases where datasets lacking such annotations. These results underscore the effectiveness of dependency tags integration to enhance translation accuracy, minimize reliance on large training datasets, and improve the overall efficiency of modern MT systems.
DOI: 10.14457/tu.the.2024.92
title: Iterative nick thresholding region growing for hemorrhage and exudate detection and segmentation to prescreen diabetic retinopathy

description: Diabetic retinopathy (DR) is a prevalent eye condition affecting approximately one-third of individuals with diabetes, leading to vision loss in both working-age adults and the elderly. Early detection and intervention are crucial for improving patient outcomes and reducing the strain on healthcare systems. Developing advanced computational techniques enables the creation of automated systems for screening and managing DR. This study focuses on detecting and segmenting exudates and hemorrhages in retinal fundus images. We utilized the iterative nick thresholding region growing (INRG) method as the foundation of our approach. To enhance performance across different applications, we integrated the watershed separation (WS) algorithm and the Chi2 feature selection method into expanded feature sets. These enhancements were combined with the INRG method to effectively segment hemorrhages and exudates. The segmented results were then used to identify these features and ultimately detect diabetic retinopathy.Our method was evaluated by comparing its performance against two traditional approaches and two state-of-the-art techniques, including the original INRG-HSV model. For hemorrhage segmentation, the INRG method combined with WS (INRG-WS) achieved the best F-measure of 64.76%, surpassing all other methods. For exudate segmentation, the INRG-WS-Chi2 model, which combined the INRG method with WS and Chi2 ranking on expanded feature sets, delivered superior results. In hemorrhage detection, the INRG method without WS but utilizing hue, saturation, and brightness (INRG-HSV) achieved the highest accuracy of 90.27% with the lowest false negative rate (FNR) of 9.39%. For exudate detection, the INRG-WS-HSV model, which combined the INRG method with WS and HSV features, demonstrated the best accuracy of 88.14% and the lowest FNR of 8.75%.For diabetic retinopathy detection, the best-performing hemorrhage (INRG-HSV) and exudate (INRG-WS-HSV) models were compared against a state-of-the-art approach. Our models outperformed the benchmark, achieving an impressive accuracy of 89.89% and an FNR of 3.66%.
DOI: 10.14457/tu.the.2024.93
title: Energy management and optimization in smart grid

description: Advancements in technology, infrastructure, and global development have spurred a notable surge in energy consumption worldwide. Recent attention has been particularly directed toward residential areas, known for their substantial energy consumption. To address this, a Demand Side Management (DSM) system has been proposed as a solution. It involves the scheduling of household appliances, the integration of Renewable Energy Sources (RESs), and the incorporation of Battery Energy Storage Systems (BESS). However, these measures alone may not fully optimize the economic and environmental aspects. Another significant challenge lies in the growing prevalence of Electric Vehicles (EVs) and their potential to contribute to power management. Integrating EVs into existing Home Energy Management (HEM) systems within the Smart Grid (SG) presents an exciting avenue for future research. This thesis study introduces a unique HEM design that accommodates BESS, RESs, and EVs. A Mixed-Integer Linear Programming (MILP) approach is employed to minimize electricity costs. The optimization model factors in Real-Time Pricing (RTP) tariffs and delivers efficient scheduling of appliances, as well as optimal BESS and EV charging and discharging patterns. The simulation encompasses various scenarios, including individual and multiple Smart Home (SH), accounting for diverse user preferences, user consumption patterns, and EV driving activities. Both case studies showcase noteworthy reductions in electricity costs compared to a traditional SH scenario lacking PV, BESS, and EV integration. In the case of multiple SHs, a remarkable cost reduction of 46.38% is achieved compared to a traditional SH scenario. Moreover, the multiple SHs scenario as a centralized architecture shows a better result in total electricity cost reduction than the individual SH scenario in a distributed architecture, which is by 10.67% for all three SHs in the simulation and it ensures fairness in using a central BESS and PV for all users involved in the system.
DOI: 10.14457/tu.the.2024.94
title: AOD-YOLOv8: arbitrary-oriented object detection using CRIoU loss function

description: Detecting objects with specific orientations in aerial imagery is a complex task due to the random rotations and high density of objects within the images. Additionally, boundary discontinuity from regression parametrization poses a major challenge in designing loss functions for rotated object detection. Several bounding box representations and their associated loss functions are being gradually developed to address this challenge. In this study, we compare loss functions for oriented bounding boxes including Probabilistic IoU (ProbIoU), KFIoU and others. The evaluation uses a large scale aerial benchmark dataset DOTAv1.0, which includes 15 categories and DOTA-RBD, a subset of DOTAv1.0, for evaluation on square objects. We also introduce Complete Rotated IoU (CRIoU) loss which is improved over Rotated IoU (RIoU) loss. CRIoU and ProbIoU loss achieved mAP score of 64.3% and 63.4% on the test set while KFIoU and RIoU losses got 63.3% and 62.2% respectively. CRIoU demonstrates marginal improvement in oriented bounding box localization, supported by the brief experimental and visual analysis.
DOI: 10.15468/dl.d4ycce
title: Occurrence Download

description: A dataset containing 153 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "BasisOfRecord is one of (Specimen)",
 "TaxonKey is one of (Bothrocophias Gutberlet & Campbell, 2001)"
 ]
 }
 ]
}

 The dataset includes 153 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010938-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xcn8y8
title: Occurrence Download

description: A dataset containing 1939 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Glehnia littoralis (A.Gray) F.Schmidt ex Miq."
 ]
}

 The dataset includes 1939 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010940-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jcnbe5
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Peru",
 "OccurrenceStatus is Present",
 "TaxonKey is Vultur gryphus Linnaeus, 1758"
 ]
}

 The dataset includes 23 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010942-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wnvc7n
title: Occurrence Download

description: A dataset containing 347 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Colombia",
 "OccurrenceStatus is Present",
 "TaxonKey is Iguana iguana (Linnaeus, 1758)"
 ]
}

 The dataset includes 347 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010941-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28340411
title: KSTAR Networks v1.0

description: Networks and pre-generated files for the v1.0 release of KSTAR
DOI: 10.15468/dl.fnyngd
title: Occurrence Download

description: A dataset containing 347 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Colombia",
 "OccurrenceStatus is Present",
 "TaxonKey is Iguana iguana (Linnaeus, 1758)"
 ]
}

 The dataset includes 347 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010943-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jkwfeg
title: Occurrence Download

description: A dataset containing 347 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Colombia",
 "OccurrenceStatus is Present",
 "TaxonKey is Iguana iguana (Linnaeus, 1758)"
 ]
}

 The dataset includes 347 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010944-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.