Quality Classification of Scientific Publications Using Hybrid Summarization Model

2018 ◽  
pp. 61-67
Author(s):  
Hafiz Ahmad Awais Chaudhary ◽  
Saeed-Ul Hassan ◽  
Naif Radi Aljohani ◽  
Ali Daud
Keyword(s):  
Scientific Publications ◽  
Quality Classification

OPPORTUNITIES FOR THE DEVELOPMENT OF MILITARY TOURISM IN UKRAINE

2018 ◽  
Author(s):  
Mariya Lushchyk ◽  
◽  
Yaroslava Moskvyak ◽  
Keyword(s):  
Young People ◽  
Basic Research ◽  
Military History ◽  
Theoretical Studies ◽  
Scientific Publications ◽  
Military Life ◽  
Historical Reconstructions ◽  
Constant Interest

The constant interest of people in military history, historical battles, battles and other military heritage promotes military tourism. However, despite the presence of a certain array of research and publications on various aspects of the development of this type of tourism, basic research in terms of defining and classifying types of military tourism, the geography of military tourism in Ukraine is not identified. Therefore, theoretical studies of the essence of military tourism, as well as the potential and opportunities for the development of this type of tourism in Ukraine are timely and relevant. To achieve this goal, an analysis of the main theoretical provisions and principles of military tourism, covered in domestic and foreign scientific publications. It is proposed to understand military tourism as one of the types of tourism, which involves visiting historical and modern military sites / institutions / locations, attracting tourists to the realities of modern military life, in safe military events or their historical reconstructions, etc. Among the tasks of military trism, its importance is clarified, first of all, for the patriotic upbringing of young people, especially in modern conditions, when the heroism and love of freedom of ancestors comes to life in Ukrainian soldiers who defend the country from Russian aggression. The main motives of tourists for military travel are described. The typification of the main criteria and principles of classification of military tourism in tourist activity is given. The author's classification of types of military tourism is offered, according to which military-historical, military and military-event types of tourism are distinguished. «Military» places and locations of Ukraine were monitored. This allowed to identify potentially popular for tourists objects: ramparts, fortresses, castles, castles, bastions, forts, defensive monasteries, temples, cathedrals, fortresses, defensive lines, battlefields, battles and military glory, which are associated with feats of national heroes, museums, memorial complexes, monuments dedicated to military themes, dioramas, bunkers, bunkers, DOTs, trenches, remnants of equipment, etc., which can be used in the development of new tourist products of military tourism.

scholarly journals Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

2019 ◽  
Vol 11 (14) ◽  
pp. 1674 ◽  
Author(s):  
Fangling Pu ◽  
Chujiang Ding ◽  
Zeyi Chao ◽  
Yue Yu ◽  
Xin Xu
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Inland Lakes ◽  
Quality Classification ◽  
Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

scholarly journals Electronic nose sensor development using ANN backpropagation for Lombok Agarwood classification

2020 ◽  
Vol 66 (No. 3) ◽  
pp. 97-103
Author(s):  
Farel Ahadyatulakbar Aditama ◽  
Lalu Zulfikri ◽  
Laili Mardiana ◽  
Tri Mulyaningsih ◽  
Nurul Qomariyah ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Poor Quality ◽  
Backpropagation Algorithm ◽  
The Poor ◽  
Quality Classification ◽  
Electronic Nose System ◽  
System Prototype

The aim of the present study is the development of an electronic nose system prototype for the classification of Gyrinops versteegii agarwood. The prototype consists of three gas sensors, i.e., TGS822, TGS2620, and TGS2610. The data acquisition and quality classification of the nose system are controlled by the Artificial Neural Network backpropagation algorithm in the Arduino Mega2650 microcontroller module. The testing result shows that an electronic nose can distinguish the quality of Gyrinops versteegii agarwood. The good-quality agarwood has an output of [1 –1], while the poor-quality agarwood has an output of [–1 1].

scholarly journals Aquatic biomonitoring: Lessons from the past, challenges for the future

2017 ◽  
Vol 76 (s1) ◽  
Author(s):  
Rossano Bolpagni ◽  
Mariano Bresciani ◽  
Stefano Fenoglio
Keyword(s):  
Adaptive Responses ◽  
Special Issue ◽  
The Past ◽  
Quality Classification ◽  
The Future ◽  
The Subject ◽  
Passionate Debate ◽  
Main Message ◽  
Special Symposium

This special issue stems from an increasing awareness on the key contribution made by biometrics and biological indices in the quality classification of aquatic ecosystems. This theme has been the subject of passionate debate during the 13th European Ecological Federation (EEF) and 25th Italian Society of Ecology’s (S.It.E.) joined congresses held in Rome in September 2015. In this frame, on the margins of the special symposium named “Biomonitoring: Lessons from the past, challenges for the future”, it was launched the idea of a special issue of the Journal of Limnology on the “aquatic” contributions presented at the conference. The present volume mainly reports these studies, enriched by few invited papers. Among the other things, the main message is the need of a better integration between sector knowledges and legislative instruments. This is even truer given the on-going climate change, and the necessity to record rapid changes in ecosystems and to elaborate effective/adaptive responses to them. 

scholarly journals Capillary Sensor with Disposable Optrode for Diesel Fuel Quality Testing

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1980 ◽  
Author(s):  
Michal Borecki ◽  
Przemyslaw Prus ◽  
Michael L. Korwin-Pawlowski
Keyword(s):  
Signal Processing ◽  
Diesel Fuel ◽  
Optoelectronic Device ◽  
Data Series ◽  
Fuel Quality ◽  
Engine Operation ◽  
Quality Classification ◽  
Artificial Neural Network Classifier ◽  
Quality Type

Diesel fuel quality can be considered from many different points of view. Fuel producers, fuel consumers, and ecologists have their own ideas. In this paper, a sensor of diesel fuel quality type, and fuel condition that is oriented to the fuel’s consumers, is presented. The fuel quality types include premium, standard, and full bio-diesel classes. The fuel conditions include fuel fit for use and fuel degraded classes. The classes of fuel are connected with characteristics of engine operation. The presented sensor uses signal processing of an optoelectronic device monitoring fuel samples that are locally heated to the first step of boiling. Compared to previous works which consider diesel fuel quality sensing with disposable optrodes which use a more complex construction, the sensor now consists only of a capillary probe and advanced signal processing. The signal processing addresses automatic conversion of the data series to form a data pattern, estimates the measurement uncertainty, eliminates outlier data, and determines the fuel quality with an intelligent artificial neural network classifier. The sensor allows the quality classification of different unknown diesel fuel samples in less than a few minutes with the measurement costs of a single disposable capillary probe and two plugs.

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