scholarly journals Application of Hyperspectral Imaging to Underwater Habitat Mapping, Southern Adriatic Sea

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2261 ◽  
Author(s):  
Federica Foglini ◽  
Valentina Grande ◽  
Fabio Marchese ◽  
Valentina A. Bracchi ◽  
Mariacristina Prampolini ◽  
...  
Keyword(s):  
Supervised Classification ◽  
Adriatic Sea ◽  
Cold Water ◽  
Habitat Mapping ◽  
Taxonomic Rank ◽  
Spectral Angle Mapper ◽  
Technical Problems ◽  
First Results ◽  
First Time

Hyperspectral imagers enable the collection of high-resolution spectral images exploitable for the supervised classification of habitats and objects of interest (OOI). Although this is a well-established technology for the study of subaerial environments, Ecotone AS has developed an underwater hyperspectral imager (UHI) system to explore the properties of the seafloor. The aim of the project is to evaluate the potential of this instrument for mapping and monitoring benthic habitats in shallow and deep-water environments. For the first time, we tested this system at two sites in the Southern Adriatic Sea (Mediterranean Sea): the cold-water coral (CWC) habitat in the Bari Canyon and the Coralligenous habitat off Brindisi. We created a spectral library for each site, considering the different substrates and the main OOI reaching, where possible, the lower taxonomic rank. We applied the spectral angle mapper (SAM) supervised classification to map the areal extent of the Coralligenous and to recognize the major CWC habitat-formers. Despite some technical problems, the first results demonstrate the suitability of the UHI camera for habitat mapping and seabed monitoring, through the achievement of quantifiable and repeatable classifications.

scholarly journals Forest vegetation assessment using geoinformation tools: a case of the Burla pine forest, Novosibirsk Region, Russia

2018 ◽  
Vol 11 ◽  
pp. 00008
Author(s):  
Tatiyana S. Chernikova ◽  
Yury S. Otmakhov ◽  
Daria D. Daibova
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Pine Forest ◽  
Spectral Angle Mapper ◽  
Sensing Data ◽  
Classification Technique ◽  
Spectral Angle ◽  
Birch Forests

The paper presents the vegetation thematic classification of the Burla banded pine forest carried on using "Canopus-V" remote sensing data and the supervised classification technique by a spectral angle mapper. Areas of selected elements have been assessed: 1. Pine forests, 2. Birch forests; 3. Meadows; 4. Anthropogenic objects (roads, etc.); 5. Agricultural lands; 6. Water objects. Sites of anthropogenic disturbed forests are identified according to remote sensing data. The results show that the data obtained in the classification by a spectral angle can be used to compile geobotanical maps, but due to low spectral resolution of Canopus-V satellite data, it is not always possible to classify individual objects validlys.

scholarly journals Selection of Suitable Supervised Classification Techniques for the Geographic Analysis of Land Using GIS Techniques

2018 ◽  
Vol 7 (3.27) ◽  
pp. 82
Author(s):  
S L. Senthil Lekha ◽  
S S.Kumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Kappa Coefficient ◽  
Landsat Image ◽  
Training Data ◽  
Spectral Angle Mapper ◽  
Gis Techniques ◽  
Spectral Angle

Nation has realised the changes in the land surface and the influence of this in the whole ecosystem. The activities of human on land is directly deteriorating the environment quality. This paper mainly focuses on the analysis of the destruction of land cover with the development of land use. The performance of five different Supervised Classification algorithms, which are Parallelepiped, Mahalanobis, Neurel Net, Adaptive Coherence and Spectral Angle Mapper  have been analysed in classifying the Landsat Image of kanyakumari district. Automatic classification of five classes using training data have been performed and the best suitable algorithm for the classification of each class have been analysed. Being a tourism centre with coastal areas on all three sides, the development and the deterioration of kanyakumari district have to be monitored constantly. The proposed system is an automatic approach which helps in the analysis of the patterns of land use and land cover which constantly changes and to map each class clearly and distinct from each other using GIS techniques. The system was evaluated using the performance measures like accuracy and  kappa coefficient using the tools Envi, ArcGIS and QGIS. From the performance analysis, the Spectral Angle Mapper with an overall accuracy  of 97% and kappa coefficient of 0.54 has been selected as the best suitable algorithm for the classification of landsat image of kanyakumari district. 

СОЦИАЛЬНАЯ КОНФЛИКТОЛОГИЯ: ТЕОРЕТИКО-ИСТОРИЧЕСКИЙ АСПЕКТ

2020 ◽  
Vol 15 (2) ◽  
pp. 68
Author(s):  
А. Н. Сухов
Keyword(s):  
Theory And Practice ◽  
Important Task ◽  
Social Conflicts ◽  
Psychological Approach ◽  
Structure Dynamics ◽  
History Of ◽  
The Subject ◽  
First Time

This given article reveals the topicality not only of destructive, but also of constructive, as well as hybrid conflicts. Practically it has been done for the first time. It also describes the history of the formation of both foreign and domestic social conflictology. At the same time, the chronology of the development of the latter is restored and presented objectively, in full, taking into account the contribution of those researchers who actually stood at its origins. The article deals with the essence of the socio-psychological approach to understanding conflicts. The subject of social conflictology includes the regularities of their occurrence and manifestation at various levels, spheres and conditions, including normal, complicated and extreme ones. Social conflictology includes the theory and practice of diagnosing, resolving, and resolving social conflicts. It analyzes the difficulties that occur in defining the concept, structure, dynamics, and classification of social conflicts. Therefore, it is no accident that the most important task is to create a full-fledged theory of social conflicts. Without this, it is impossible to talk about effective settlement and resolution of social conflicts. Social conflictology is an integral part of conflictology. There is still a lot of work to be done, both in theory and in application, for its complete design. At present, there is an urgent need to develop conflict-related competence not only of professionals, but also for various groups of the population.

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

Quantification and Classification of Beach Litter in Montenegro (South-East Adriatic Sea)

2021 ◽  
Author(s):  
Milica Mandić ◽  
Slađana Gvozdenović ◽  
Ines Peraš ◽  
Aleksandra Ivanović ◽  
Nemanja Malovrazić
Keyword(s):  
Adriatic Sea ◽  
Beach Litter

scholarly journals A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

2021 ◽  
Vol 9 (5) ◽  
pp. 1034
Author(s):  
Carlos Sabater ◽  
Lorena Ruiz ◽  
Abelardo Margolles
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Human Milk Oligosaccharides ◽  
Future Studies ◽  
High Fiber ◽  
Machine Learning Approach ◽  
Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

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