Advanced Deep Learning-Based Bubbly Flow Image Generator under Different Superficial Gas Velocities

Perkembangan untuk memberikan caption pada suatu gambar merupakan suatu ranah perkembangan baru dalam bidang intelejensia buatan. Image captioning merupakan penggabungan dari beberapa bidang seperti computer vision, natural language, dan pembelajaran mesin. Aspek yang menjadi perhatian dalam bidang image captioning ini adalah ketepatan arsitektur neural network yang dimodelkan untuk mendapatkan hasil yang sedekat mungkin dengan ground-thruth yang disampaikan oleh person. Beberapa kajian yang sudah diteliti masih mendapatkan kalimat yang masih jauh dari ground-thruth tersebut. Permasalahan yang dibahas pada umumnya mengenai image captioning adalah image generator dan text generator yaitu penggunaan deep learning seperti CNN dan LSTM untuk menyelesaikan masalah captioning. Hal ini menjadi dasar permasalahan untuk memberikan kontribusi baru dalam bidang image captioning yang meliputi image extractor, text generator, dan evaluator yang bisa digunakan pada model yang diusulkan. Perspektif Kuhn dan Popper dalam hal image captioning, diperoleh bahwa caption dalam bidang geologi sangat diperlukan dan mencapai tahap krisis. Perlu adanya metode usulan baru untuk menyajikan caption untuk citra geologi.

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Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

International Journal of Multiphase Flow ◽

10.1016/j.ijmultiphaseflow.2020.103378 ◽

2020 ◽

Vol 131 ◽

pp. 103378 ◽

Cited By ~ 1

Author(s):

Han Bao ◽

Jinyong Feng ◽

Nam Dinh ◽

Hongbin Zhang

Keyword(s):

Deep Learning ◽

Bubbly Flow ◽

Computationally Efficient

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Single Volume Image Generator and Deep Learning-based ASD Classification

10.1101/763292 ◽

2019 ◽

Author(s):

Md Rishad Ahmed ◽

Yuan Zhang ◽

Omer T. Inan ◽

Hongen Liao

Keyword(s):

Deep Learning ◽

Large Scale ◽

Autism Spectrum ◽

Classification Performance ◽

Repetitive Behaviors ◽

Biomedical Data ◽

Learning Approaches ◽

Brain Disorder ◽

Brain Images ◽

Image Generator

AbstractAutism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by social deficits and repetitive behaviors. Associated ASD biomarkers can be supportive of apprehending the underlying roots of the disease and lead the targeted diagnosis as well as treatment. Although deep learning approaches have been applied in functional magnetic resonance imaging (fMRI) based clinical or behavioral identification of ASD, most erstwhile models are inadequate in their capacity to exploit the data richness. Classification techniques often solely rely on region-based summary and/or functional connectivity analysis of one pipeline or unique site dataset. Besides these, biomedical data modeling to analyze big data related to ASD is still perplexing due to its complexity and heterogeneity. Single volume image consideration has not been previously investigated in classification purposes. By deeming these challenges, in this work, firstly, we design an image generator to generate single volume brain images from the whole-brain image of each subject. Secondly, the single volume images are analyzed by evaluating four deep learning approaches comprising one amended volume base Convolutional Neural Network framework to classify ASD and typical control participants. Thirdly, we propose a novel deep ensemble learning classifier using VGG16 as feature extractor to ensure further classification performance. Then, to evaluate the classifier performance across the inter sites, we apply the proposed method on each site individually and validate our findings by comparing literature reports. We showcase our approaches on large-scale multi-site brain imaging dataset (ABIDE) by considering four preprocessing pipelines, and the outcome demonstrates the state-of-the-art performance compared with the literature findings; hence, which are robust and consistent.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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