scholarly journals Some approaches to classification of citation (on the archaeological materials)

Author(s):  
Yuri Kholushkin

An attempt is made to construct a system classification of differentiated quotations based on the theory of fractals. The essence of the method is the construction of fragments, each based on a universal classification model. It involves the use of dialectic laws to identify systemic links between concepts. In this case, the content of the model itself is revealed through six consecutive conceptual formations, starting with the basic concept and resulting in a five-element group. Such fragments have five system properties: unambiguousness, coordinate cartography, sub-fullness, systemic historicity and predictability. The most important advantage of this approach is that the quality of citation is in direct connection with the scientific level of the reference publication, which makes it possible to measure the quality and tightness of the links between scientific schools and the characteristics of the so-called “invisible” colleges.

Author(s):  
A. Vasantharaj ◽  
Pacha Shoba Rani ◽  
Sirajul Huque ◽  
K. S. Raghuram ◽  
R. Ganeshkumar ◽  
...  

Earlier identification of brain tumor (BT) is essential to increase the survival rate of the patients. The commonly used imaging technique for BT diagnosis is magnetic resonance imaging (MRI). Automated BT classification model is required for assisting the radiologists to save time and enhance efficiency. The classification of BT is difficult owing to the non-uniform shapes of tumors and location of tumors in the brain. Therefore, deep learning (DL) models can be employed for the effective identification, prediction, and diagnosis of diseases. In this view, this paper presents an automated BT diagnosis using rat swarm optimization (RSO) with deep learning based capsule network (DLCN) model, named RSO-DLCN model. The presented RSO-DLCN model involves bilateral filtering (BF) based preprocessing to enhance the quality of the MRI. Besides, non-iterative grabcut based segmentation (NIGCS) technique is applied to detect the affected tumor regions. In addition, DLCN model based feature extractor with RSO algorithm based parameter optimization processes takes place. Finally, extreme learning machine with stacked autoencoder (ELM-SA) based classifier is employed for the effective classification of BT. For validating the BT diagnostic performance of the presented RSO-DLCN model, an extensive set of simulations were carried out and the results are inspected under diverse dimensions. The simulation outcome demonstrated the promising results of the RSO-DLCN model on BT diagnosis with the sensitivity of 98.4%, specificity of 99%, and accuracy of 98.7%.


Author(s):  
Fatma Karem ◽  
Mounir Dhibi ◽  
Arnaud Martin ◽  
Med Salim Bouhlel

This paper reports on an investigation in classification technique employed to classify noised and uncertain data. However, classification is not an easy task. It is a significant challenge to discover knowledge from uncertain data. In fact, we can find many problems. More time we don't have a good or a big learning database for supervised classification. Also, when training data contains noise or missing values, classification accuracy will be affected dramatically. So to extract groups from  data is not easy to do. They are overlapped and not very separated from each other. Another problem which can be cited here is the uncertainty due to measuring devices. Consequentially classification model is not so robust and strong to classify new objects. In this work, we present a novel classification algorithm to cover these problems. We materialize our main idea by using belief function theory to do combination between classification and clustering. This theory treats very well imprecision and uncertainty linked to classification. Experimental results show that our approach has ability to significantly improve the quality of classification of generic database.


Author(s):  
Lyudmyla Matviychuk

Introduction. In today's market conditions, skillful and careful organization of the process of management and accounting in the agricultural sector of the economy becomes an essential factor in the efficient and rational use of fixed assets. Therefore, well-organized management and accounting of fixed assets at agricultural enterprises should be a holistic, unified system of interconnected, mutually agreed ways and methods. Methods. The methodological basis of scientific work is the following methods of cognition of economic phenomena. The following methods are applied, in particular: theoretical comparison – to build a facet-hierarchical classification model of fixed assets; tabular and graphic method – in order to visually display factual information; method of expert evaluations – in the development of methodological provisions for evaluating the choice and implementation of information technology for the management and accounting of fixed assets; abstract and logical, causal relations, description, concretization, formalization – for the formation of a system of management accounting and documenting the movement of fixed assets. Results. A comprehensive system of accounting for fixed assets at agricultural enterprises is proposed on the basis of generalized views of scientists on the organization of the management process, which provides maximum efficiency in the use of fixed assets with minimal costs for their maintenance and service. The key components and principles of the process of management and accounting of fixed assets, which became a prerequisite for building a classification of information on certain grounds, were determined. A faceted classification of fixed assets of an agricultural enterprise to assess the quality of their use is developed for this purpose. Discussion. The submitted proposals will improve the quality of management accounting of fixed assets through the development of its promising components, in particular, on the basis of facet-hierarchical classification of analytical display of information about their objects at all stages of the life cycle. An important achievement in this regard will be the creation of software products that allow you to quickly process information and respond instantly to the needs of management. Accordingly, work on their creation requires appropriate research in the future. Keywords: management, accounting, fixed assets, agricultural enterprises, organizational and information system, faceted classification.


1998 ◽  
Vol 2 ◽  
pp. 115-122
Author(s):  
Donatas Švitra ◽  
Jolanta Janutėnienė

In the practice of processing of metals by cutting it is necessary to overcome the vibration of the cutting tool, the processed detail and units of the machine tool. These vibrations in many cases are an obstacle to increase the productivity and quality of treatment of details on metal-cutting machine tools. Vibration at cutting of metals is a very diverse phenomenon due to both it’s nature and the form of oscillatory motion. The most general classification of vibrations at cutting is a division them into forced vibration and autovibrations. The most difficult to remove and poorly investigated are the autovibrations, i.e. vibrations arising at the absence of external periodic forces. The autovibrations, stipulated by the process of cutting on metalcutting machine are of two types: the low-frequency autovibrations and high-frequency autovibrations. When the low-frequency autovibration there appear, the cutting process ought to be terminated and the cause of the vibrations eliminated. Otherwise, there is a danger of a break of both machine and tool. In the case of high-frequency vibration the machine operates apparently quiently, but the processed surface feature small-sized roughness. The frequency of autovibrations can reach 5000 Hz and more.


2020 ◽  
Vol 45 (4) ◽  
pp. 794-801
Author(s):  
Caroline Oliveira Andrino ◽  
Marcelo Fragomeni Simon ◽  
Jair Eustáquio Quintino Faria ◽  
André Luiz da Costa Moreira ◽  
Paulo Takeo Sano

Abstract—We describe and illustrate Paepalanthus fabianeae, a new species of Eriocaulaceae from the central portion of the Espinhaço Range in Minas Gerais, Brazil. Previous phylogenetic evidence based on analyses of nuclear (ITS and ETS) and plastid (trnL-trnF and psba-trnH) sequences revealed P. fabianeae as belonging to a strongly supported and morphologically coherent clade containing five other species, all of them microendemic, restricted to the Espinhaço range. Due to the infrageneric classification of Paepalanthus being highly artificial, we preferred not assigning P. fabianeae to any infrageneric group. Paepalanthus fabianeae is known from two populations growing in campos rupestres (highland rocky fields) in the meridional Espinhaço Range. The species is characterized by pseudodichotomously branched stems, small, linear, recurved, and reflexed leaves, urceolate capitula, and bifid stigmas. Illustrations, photos, the phylogenetic position, and a detailed description, as well as comments on habitat, morphology, and affinities with similar species are provided. The restricted area of occurrence allied with threats to the quality of the habitat, mainly due to quartzite mining, justifies the preliminary classification of the new species in the Critically Endangered (CR) category using the guidelines and criteria of the IUCN Red List.


2020 ◽  
Vol 6 (3) ◽  
pp. 158-164
Author(s):  
Navruza Yakhyayeva ◽  

The quality and content of information in the article media text is based on scientific classification of linguistic features. The study of functional styles of speech, the identification of their linguistic signs, the discovery of the functional properties of linguistic units and their separation on the basis of linguistic facts is one of thetasks that modern linguistics is waiting for a solution. Text Linguistics, which deals with the creation, modeling of its structure and the study of the process of such activity, is of interest to journalists today as a science.


2019 ◽  
pp. 86-88
Author(s):  
R. H. Batirova ◽  
S. S. Tashpulatov ◽  
I. V. Cherunova ◽  
M. A. Mansurova ◽  
S. L. Matismailov
Keyword(s):  

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Aulia Dwi Oktavia ◽  
Aam Alamudi ◽  
Budi Susetyo

Unemployment is one of the economic problems in Indonesia. Judging from the level of education that was completed there were unemployment from the level of college graduates. This encourages the level of competition in getting jobs to be more stringent, so that college graduates (bachelor of Statistics in IPB) must have the preparation of various factors to maintain the quality of their graduates. The quality of college graduates can be seen from the length of time waiting to get a job. This study aims to determine the influential factors in getting a job for graduates of the IPB Statistics degree, so that the CHAID method can be used in this study. The results of CHAID's analysis in this study in the form of tree diagrams using α = 10% explained that the factors influencing the waiting period variables were sex, internship, and the ability to master statistical software, where the accuracy value generated by the classification model was 79.3 %.


2018 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Desy Damayanti ◽  
Adin Fauzi ◽  
Azizatul Mahfida Inayati

Among some components of effective language classroom, learning materials indisputably play a focal role. They improve the quality of language teaching; facilitate teachers in doing their duties, and lead students to a higher level of understanding in learning. This research aims to discuss the notion of materials in language teaching. It made use of works of literature to outline the importance of materials in language teaching, and to analyze kinds of materials, which are relevant to language teaching. The analysis resulted in the classification of materials into two broad categories namely (1) created materials, which include course book, audio materials, and video materials; and (2) authentic materials, which cover authentic texts, movie/film, radio broadcasting, television program, graphs, maps, tables, and charts. This paper serves as an invaluable resource to facilitate language teachers in selecting appropriate materials for effective language teaching.


2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%


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