scholarly journals Leveraging Known Data for Missing Label Prediction in Cultural Heritage Context

2018 ◽  
Vol 8 (10) ◽  
pp. 1768 ◽  
Author(s):  
Abdelhak Belhi ◽  
Abdelaziz Bouras ◽  
Sebti Foufou
Keyword(s):  
Neural Network ◽  
Cultural Heritage ◽  
Feature Learning ◽  
Image Data ◽  
Heritage Sites ◽  
Multimodal Classification ◽  
Label Prediction ◽  
Missing Labels ◽  
Traditional Approaches ◽  
Better Than

Cultural heritage represents a reliable medium for history and knowledge transfer. Cultural heritage assets are often exhibited in museums and heritage sites all over the world. However, many assets are poorly labeled, which decreases their historical value. If an asset’s history is lost, its historical value is also lost. The classification and annotation of overlooked or incomplete cultural assets increase their historical value and allows the discovery of various types of historical links. In this paper, we tackle the challenge of automatically classifying and annotating cultural heritage assets using their visual features as well as the metadata available at hand. Traditional approaches mainly rely only on image data and machine-learning-based techniques to predict missing labels. Often, visual data are not the only information available at hand. In this paper, we present a novel multimodal classification approach for cultural heritage assets that relies on a multitask neural network where a convolutional neural network (CNN) is designed for visual feature learning and a regular neural network is used for textual feature learning. These networks are merged and trained using a shared loss. The combined networks rely on both image and textual features to achieve better asset classification. Initial tests related to painting assets showed that our approach performs better than traditional CNNs that only rely on images as input.

scholarly journals Neural Network for Aerosol Retrieval from Hyperspectral Imagery

2019 ◽  
Author(s):  
Steffen Mauceri ◽  
Bruce Kindel ◽  
Steven Massie ◽  
Peter Pilewskie
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Surface Albedo ◽  
Hyperspectral Image ◽  
A Priori ◽  
Image Data ◽  
Aerosol Retrieval ◽  
Hyperspectral Image Data ◽  
Priori Information ◽  
Better Than

Abstract. We retrieve aerosol optical thickness (AOT) independently for brown carbon-, dust and sulfate from hyperspectral image data. The model, a neural network, is trained on atmospheric radiative transfer calculations from MODTRAN 6.0 with varying aerosol- concentration and type, surface albedo, water vapor and viewing geometries. From a set of test radiative transfer calculations, we are able to retrieve AOT with a standard error of better than ±0.05. No a priori information of the surface albedo or atmospheric state is necessary for our model. We apply the model to AVIRIS-NG imagery from a recent campaign over India and demonstrate its performance under high and low aerosol loadings and different aerosol types.

scholarly journals Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 29 ◽  
Author(s):  
Mohamed Loey ◽  
Mukdad Naman ◽  
Hala Zayed
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Blood Cells ◽  
Classification Accuracy ◽  
Automated Classification ◽  
Classification Models ◽  
Fatal Disease ◽  
Microscopic Images ◽  
Traditional Approaches ◽  
Better Than

Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy.

scholarly journals Neural network for aerosol retrieval from hyperspectral imagery

2019 ◽  
Vol 12 (11) ◽  
pp. 6017-6036 ◽  
Author(s):  
Steffen Mauceri ◽  
Bruce Kindel ◽  
Steven Massie ◽  
Peter Pilewskie
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Surface Albedo ◽  
Hyperspectral Image ◽  
A Priori ◽  
Image Data ◽  
Aerosol Retrieval ◽  
Hyperspectral Image Data ◽  
Priori Information ◽  
Better Than

Abstract. We retrieve aerosol optical thickness (AOT) independently for brown carbon, dust and sulfate from hyperspectral image data. The model, a neural network, is trained on atmospheric radiative transfer calculations from MODTRAN 6.0 with varying aerosol concentration and type, surface albedo, water vapor, and viewing geometries. From a set of test radiative transfer calculations, we are able to retrieve AOT with a standard error of better than ±0.05. No a priori information on the surface albedo or atmospheric state is necessary for our model. We apply the model to AVIRIS-NG imagery from a recent campaign over India and demonstrate its performance under high and low aerosol loadings and different aerosol types.

The Reconstruction Project of Illumination Devices at the Krasnoselskaya Station of the Moscow Metro

2019 ◽  
pp. 59-66
Author(s):  
Ksenia I. Nechaeva
Keyword(s):  
Cultural Heritage ◽  
Historical Analysis ◽  
Metro Station ◽  
Heritage Sites ◽  
Passenger Transportation ◽  
Fluorescent Lamps ◽  
Current State ◽  
Heritage Site ◽  
Lighting Environment ◽  
Incandescent Lamps

The current state of the Moscow Metro station of the first priority that became operational in 1935 does not allow it to be called a cultural heritage site. This is due to the fact that lighting modernisation carried out by the Moscow Metro was based on fluorescent lamps. Such lamps are more energy efficient compared to incandescent lamps, which were used in original lighting devices specified in the Station Lighting Project developed by architects and designers. However, they significantly changed the station appearance, transforming the originally designed station with entire well visible architectural tectonics?1 from the standpoint of lighting into a simple, flat, unremarkable, and little loaded station of the Moscow Metro./br> This paper describes a method of lighting reconstruction at Krasnoselskaya station by means of original lighting devices that meet modern standards and requirements for cultural heritage sites. The historical analysis on the development of the station lighting environment was conducted during its operation in order to understand what kind of station was conceived by its architects, what changes occurred with its lighting over time, and how it influenced the station appearance and safety of passenger transportation.

Food, Museums, and Cultural Heritage Sites

2019 ◽  
Vol 49 (3) ◽  
pp. 372-381
Author(s):  
Tanfer Emin Tunc
Keyword(s):  
Cultural Heritage ◽  
Heritage Sites

Does historical nostalgia predict tourists' delight and their destination loyalty intention for the world cultural heritage site of Mahabalipuram, India?

2020 ◽  
Author(s):  
Anil Verma ◽  
G. Rajendran
Keyword(s):  
Cultural Heritage ◽  
Structural Equation ◽  
Destination Loyalty ◽  
Heritage Sites ◽  
Heritage Site ◽  
Loyalty Intention ◽  
Tourism Studies ◽  
The World ◽  
World Cultural Heritage ◽  
The Impact

Delighting consumers has been one of the most important goals for marketing stakeholders but the effect of historical nostalgia on tourists delight at the world cultural heritage sites has rarely been examined. This study examines the impact of historical nostalgia on the heritage tourists' delight, their satisfaction and destination loyalty intention. The survey for the study was conducted at the world cultural heritage site of Mahabalipuram, India. The hypotheses were tested through the structural equation modelling technique. The results indicated positive and significant effect of historical nostalgia on tourists' delight, satisfaction and destination loyalty intention. The study makes contribution to the tourism studies by examining the role of historical nostalgia in delighting the tourists at the cultural heritage sites and instructs the managers to evoke such experiences to keep the heritage tourists delighted and thereby enhance their loyalty.

scholarly journals CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chuandong Song ◽  
Haifeng Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Classification Model ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Post Translational Modification ◽  
Fuzzy Neural ◽  
Human Complex ◽  
Better Than

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

scholarly journals Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

2021 ◽  
Vol 11 (2) ◽  
pp. 870
Author(s):  
Galena Pisoni ◽  
Natalia Díaz-Rodríguez ◽  
Hannie Gijlers ◽  
Linda Tonolli
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Cultural Heritage ◽  
Conceptual Framework ◽  
Interaction Design ◽  
Participatory Design ◽  
Future Directions ◽  
Heritage Sites ◽  
Review Analysis

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.

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