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Institution

Hellenic Military Academy

EducationVoula, Greece
About: Hellenic Military Academy is a education organization based out in Voula, Greece. It is known for research contribution in the topics: Complex space & Jacobi operator. The organization has 36 authors who have published 97 publications receiving 541 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the convergence of precision agriculture, in which farmers respond in real-time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production.
Abstract: Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.

86 citations

Journal ArticleDOI
TL;DR: Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures as mentioned in this paper, which has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural network, generative adversarial networks, and autoencoders.
Abstract: De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development

80 citations

Book ChapterDOI
11 Sep 2017
TL;DR: Current data quality indicators for geographic information as part of the ISO 19157 (2013) standard and how these have been used to evaluate the data quality of VGI in the past are reviewed.
Abstract: Uncertainty over the data quality of Volunteered Geographic Information (VGI) is the largest barrier to the use of this data source by National Mapping Agencies (NMAs) and other government bodies. A considerable body of literature exists that has examined the quality of VGI as well as proposed methods for quality assessment. The purpose of this chapter is to review current data quality indicators for geographic information as part of the ISO 19157 (2013) standard and how these have been used to evaluate the data quality of VGI in the past. These indicators include positional, thematic and temporal accuracy, completeness, logical consistency and usability. Additional indicators that have been proposed for VGI are then presented and discussed. In the final section of the chapter, the idea of integrated indicators and workflows of quality assurance that combine many assessment methods into a filtering system is highlighted as one way forward to improve confidence in VGI.

68 citations

Journal ArticleDOI
TL;DR: An inventory of the metadata that are collected with geo-tagged photographs is provided and what elements would be essential, desirable, or unnecessary for three use cases related to land cover: Calibration, validation and verification.
Abstract: Geo-tagged photographs are used increasingly as a source of Volunteered Geographic Information (VGI), which could potentially be used for land use and land cover applications. The purpose of this paper is to analyze the feasibility of using this source of spatial information for three use cases related to land cover: Calibration, validation and verification. We first provide an inventory of the metadata that are collected with geo-tagged photographs and then consider what elements would be essential, desirable, or unnecessary for the aforementioned use cases. Geo-tagged photographs were then extracted from Flickr, Panoramio and Geograph for an area of London, UK, and classified based on their usefulness for land cover mapping including an analysis of the accompanying metadata. Finally, we discuss protocols for geo-tagged photographs for use of VGI in relation to land cover applications.

66 citations

Journal ArticleDOI
TL;DR: In this paper, the notion of *-Ricci soliton is introduced and real hypersurfaces in non-flat complex space forms admitting a *-ricci s soliton with potential vector field being the structure vector field.

53 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20233
20227
202111
202012
20195
20186