المؤلفون: Vanesa Gómez-Martínez, David Chushig-Muzo, Marit B. Veierød, Conceição Granja, Cristina Soguero-Ruiz

المصدر: BioData Mining, Vol 17, Iss 1, Pp 1-30 (2024)

مصطلحات موضوعية: Melanoma classification, Skin lesion classification, Ensemble feature selection, Tabular generative adversarial networks, Class imbalance, Interpretability methods, Computer applications to medicine. Medical informatics, R858-859.7, Analysis, QA299.6-433

وصف الملف: electronic resource

Relation: https://doaj.org/toc/1756-0381

URL الوصول: https://doaj.org/article/50c8a3a4ed4c4155a53a138f8c5dda42

المؤلفون: Alam, Mahbub Ul, 1988, Hollmén, Jaakko, 1970, Baldvinsson, Jón Rúnar, Rahmani, Rahim, 1963

المصدر: Nordic Machine Intelligence. 3(1):27-47

مصطلحات موضوعية: Deep Learning, Interpretability Methods, LIME, SHAP, Grad-CAM, LRP, Chest X-ray, Heatmap Score Visualization, Clinical Decision Support System, data- och systemvetenskap, Computer and Systems Sciences

وصف الملف: electronic

URL الوصول: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-223825
https://doi.org/10.5617/nmi.10471
https://su.diva-portal.org/smash/get/diva2:1813030/FULLTEXT01.pdf

المؤلفون: Lahdoudi, Youssef, Ghazdali, Abdelghani, Khalfi, Hamza, Lamghari, Nidal

المصدر: Statistics, Optimization & Information Computing; Online First ; 2310-5070 ; 2311-004X

مصطلحات موضوعية: Breast Cancer, Machine learning in radiology, Medical image classification, Mammography, Radiology image classification, Medical Imaging, Deep Learning, Breast Cancer Detection, Radiologist Recommendations, Algorithmic Precision, Class Imbalance, Hybrid Sampling Techniques, Interpretability Methods

جغرافية الموضوع: The research investigates the application of deep learning algorithms

Time: The research investigates the application of deep learning algorithms, specifically YOLOv5 and Densenet121, in enhancing the accuracy and efficiency of mammography image classification for breast cancer detection. It encompasses the preprocessing of mammo

وصف الملف: application/pdf

Relation: http://www.iapress.org/index.php/soic/article/view/2014/1100; http://www.iapress.org/index.php/soic/article/view/2014

الاتاحة: http://www.iapress.org/index.php/soic/article/view/2014
https://doi.org/10.19139/soic-2310-5070-2014

المؤلفون: Stadlhofer, Anja, Mezhuyev, Vitaliy

مصطلحات موضوعية: machine learning, interpretability methods, LIME, SHAP, image classifcation, convolutional neural networks, multi-criteria analysis, industry

جغرافية الموضوع: IWI:329

وصف الملف: text/html

Relation: vignette : https://epub.fh-joanneum.at/titlepage/urn/urn:nbn:at:at-fhj:3-1147/128; urn:nbn:at:at-fhj:3-1147; https://resolver.obvsg.at/urn:nbn:at:at-fhj:3-1147; local:99147708331803331; system:AC16970610

الاتاحة: https://doi.org/10.1007/s44244-023-00009-z
https://epub.fh-joanneum.at/doi/10.1007/s44244-023-00009-z
https://resolver.obvsg.at/urn:nbn:at:at-fhj:3-1147

المؤلفون: Katsuya Mizusawa, Kei Miyamoto, Shota Tamura, Takahiro Tanaka, 宮本 慧, 水澤 克哉, 田中 貴宏, 田村 将太

المصدر: 都市計画論文集 / Journal of the City Planning Institute of Japan. 2022, 57(3):1377

المؤلفون: Kriechbaumer, Clemens

مصطلحات موضوعية: interpretability methods, TabNet, Machine learning, Pharmaforschung, Sparsemax, Interpretierbarkeitsmethoden, Maschinelles Lernen, drug discovery

وصف الملف: 68 Seiten; text/html; Illustrationen

URL الوصول: https://explore.openaire.eu/search/publication?articleId=od______3361::249338b4b047bc78fac1fde8dbfc02a0

المؤلفون: Domagoj Begusic, Luke Frederick Walker, Sanja Krznaric, Damir Pintar

مصطلحات موضوعية: network intrusion detection system, cybersecurity, data mining, interpretability methods, classification

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fe124ada88d651752928b9d130f5919
https://www.bib.irb.hr/1222732

المؤلفون: Türkan, Yasemin, Tek, Faik Boray

مصطلحات موضوعية: 3D Modeling, 3D Structural MRI brain scans, 3D Ultrametric contour map, 3D VGG model, 3D-Data interpretation capabilities, Activation analysis, Activation mapping, Alzheimer’s disease, Alzheimers disease, Attention, Attention mechanism, Biomedical MRI, Brain, Chemical activation, Classification (of information), Cognition, Contour map, Convolution, Convolutional attention network, Convolutional networks, Convolutional neural networks, Deep learning, Deep learning approaches, Deep neural networks, Different interpretability methods, Different network models, Diseases, Heating systems, High-dimensional neuroimaging data, Image classification

وصف الملف: application/pdf

Relation: 2021 6th International Conference on Computer Science and Engineering (UBMK); Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı; Türkan, Y. & Tek, F. B. (2021). Convolutional attention network for MRI-based Alzheimer's disease classification and its interpretability analysis. Paper presented at the 2021 6th International Conference on Computer Science and Engineering (UBMK), 151-156. doi:10.1109/UBMK52708.2021.9558882; https://hdl.handle.net/11729/4351; http://dx.doi.org/10.1109/UBMK52708.2021.9558882; 151; 156; 2-s2.0-85125875116; N/A

الاتاحة: https://hdl.handle.net/11729/4351
https://doi.org/10.1109/UBMK52708.2021.9558882

المؤلفون: F. Boray Tek, Yasemin Turkan

المساهمون: Işık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Işık University, Faculty of Engineering, Department of Computer Engineering, Türkan, Yasemin, Tek, Faik Boray

المصدر: 2021 6th International Conference on Computer Science and Engineering (UBMK).

مصطلحات موضوعية: Convolutional attention network, Learning (artificial intelligence), MRI-based Alzheimer, Neuroimaging techniques, Computer science, Neural nets, Diseases, Cognition, Biomedical MRI, 3D Modeling, Deep neural networks, Attention, Interpretability, Visualization, Network model, Classification (of information), Interpretability analysis, medicine.diagnostic_test, Occlusion, Activation mapping, Neurodegenerative diseases, Contour map, Brain, Shapley, Chemical activation, Different interpretability methods, Mild cognitive impairments, Mapping, Shapley additive explanation, Positron emission tomography, SHAP, Three-dimensional displays, Convolutional neural networks, Multimodal neuroimaging data, Alzheimer’s disease, Convolutional networks, MRI, Image classification, Attention mechanism, Neurophysiology, High-dimensional neuroimaging data, Neuroimaging, Deep learning approaches, Magnetic resonance imaging, Different network models, Ultrametrics, medicine, Medical image processing, 3D-Data interpretation capabilities, business.industry, Mechanism (biology), Activation analysis, Deep learning, 3D VGG model, Pattern recognition, Heating systems, Convolution, Alzheimers disease, 3D Structural MRI brain scans, 3D Ultrametric contour map, Solid modeling, Artificial intelligence, business

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14217ce3b28c562e1d630002d939b57c
https://doi.org/10.1109/ubmk52708.2021.9558882

المؤلفون: Ozan Ozyegen

مصطلحات موضوعية: Modelling and simulation in information systems, Interpretability, Local interpretability methods, Model explanations, Time series data, Temporal nature, High dimensionality, Evaluation metrics, SHAP method, Time series modeling, Financial forecasting, Sales forecasting, Inventory management, Time series clustering, Deep neural network, Interpretable models

Relation: https://figshare.com/articles/thesis/Local_Interpretability_Methods_for_Time_Series_Modeling/26052709

الاتاحة: https://doi.org/10.32920/26052709.v1
https://figshare.com/articles/thesis/Local_Interpretability_Methods_for_Time_Series_Modeling/26052709

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/figure/Comparison_of_different_models_of_the_approximate_data_distribution_for_multiple_binding_sites_of_RBFOX1_/14589862

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.s002

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/figure/Comparison_of_different_models_of_the_approximate_data_distribution_for_GC-bias_of_CG17838_/14589871

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.s005

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/figure/Investigation_of_a_ResidualBind_model_trained_on_VTS1_from_the_2009-RNAcompete_dataset_/14589883

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.g003

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/dataset/Performance_comparison_on_RNAcompete_/14589859

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.s001

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/figure/GC-bias_in_high_binding_score_sequences_/14589886

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.g004

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/figure/Comparison_of_different_models_of_the_approximate_data_distribution_for_secondary_structure_preferences_of_VTS1_from_the_2009-RNAcompete_dataset_/14589865

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.s003

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/figure/Investigation_of_a_ResidualBind_model_trained_on_RBFOX1_/14589880

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.g002

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/figure/Comparison_of_different_models_of_the_approximate_data_distribution_for_GC-bias_of_HuR_/14589874

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.s006

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/figure/Performance_comparison_on_the_2013-RNAcompete_dataset_/14589877

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.g001

المؤلفون: Peter K. Koo (819788), Antonio Majdandzic (10797625), Matthew Ploenzke (4108792), Praveen Anand (42856), Steffan B. Paul (10797628)

مصطلحات موضوعية: Biochemistry, Genetics, Evolutionary Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Global importance analysis, model interpretability method, case study, model interpretability methods, GIA, effect size, attribution methods, genomic features, pattern, networks Deep, generalizable trends, convolutional network, gain insights, test hypotheses, RNA-protein interactions, RNA-binding proteins, sequence motifs, interpretability method, DNA, DNN, sequence context, sequence specificities, importance analysis, position weight matrices

Relation: https://figshare.com/articles/figure/Comparison_of_different_models_of_the_approximate_data_distribution_for_GC-bias_of_SNF_/14589868

الاتاحة: https://doi.org/10.1371/journal.pcbi.1008925.s004