scholarly journals MODELS AND METHODS OF CLASSIFICATION OF TEXT REQUESTS IN THE SYSTEM OF PATTERN IDENTIFICATION OF POLYLINGUAL TEXTS

2020 ◽  
Vol 1 ◽  
pp. 130-134
Author(s):  
Vladimir P. Kulikov ◽  
Valentina P. Kulikova ◽  
Elena M. Krylova ◽  
Gulnur T. Yerkebulan
Keyword(s):  
Quality Assessment ◽  
Classification Scheme ◽  
Pattern Identification ◽  
Classification Methods ◽  
Text Documents ◽  
And Training ◽  
Selection Of

A classification scheme for text documents consisting of five steps is described: pre-processing, indexing, selection of features, construction and training of a classifier, quality assessment. Two comparative analyzes by classification methods are considered. Conclusions are drawn about models and classification methods regarding implementation efficiency.

scholarly journals Efisiensi Big Data Menggunakan Improved Nearest Neighbor

2019 ◽  
Vol 6 (6) ◽  
pp. 665
Author(s):  
Aditya Hari Bawono ◽  
Ahmad Afif Supianto
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Efficient Method ◽  
Nearest Neighbor ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Knn Classification ◽  
Degree Of Similarity ◽  
Selection Of

<p>Klasifikasi adalah salah satu metode penting dalam kajian data mining. Salah satu metode klasifikasi yang populer dan mendasar adalah k<em>-nearest neighbor</em> (kNN). Pada kNN, hubungan antar sampel diukur berdasarkan tingkat kesamaan yang direpresentasikan sebagai jarak. Pada kasus mayoritas terutama pada data berukuran besar, akan terdapat beberapa sampel yang memiliki jarak yang sama namun amat mungkin tidak terpilih menjadi tetangga, maka pemilihan parameter k akan sangat mempengaruhi hasil klasifikasi kNN. Selain itu, pengurutan pada kNN menjadi masalah komputasi ketika dilakukan pada data berukuran besar. Dalam usaha mengatasi klasifikasi data berukuran besar dibutuhkan metode yang lebih akurat dan efisien. <em>Dependent Nearest Neighbor</em> (dNN) sebagai metode yang diajukan dalam penelitian ini tidak menggunakan parameter k dan tidak ada proses pengurutan sampel. Hasil percobaan menunjukkan bahwa dNN dapat menghasilkan efisiensi waktu sebesar 3 kali lipat lebih cepat daripada kNN. Perbandingan akurasi dNN adalah 13% lebih baik daripada kNN.</p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Classification is one of the important methods of data mining. One of the most popular and basic classification methods is k-nearest neighbor (kNN). In kNN, the relationships between samples are measured by the degree of similarity represented as distance. In major cases, especially on big data, there will be some samples that have the same distance but may not be selected as neighbors, then the selection of k parameters will greatly affect the results of kNN classification. Sorting phase of kNN becomes a computation problem when it is done on big data. In the effort to overcome the classification of big data a more accurate and efficient method is required. Dependent Nearest Neighbor (dNN) as method proposed in this study did not use the k parameters and no sample at the sorting phase. The proposed method resulted in 3 times faster than kNN. The accuracy of the proposed method is13% better results than kNN.</em></p><p class="Judul2" align="left"><em> </em></p>

scholarly journals Classification of surface water objects in visible spectrum images

2020 ◽  
pp. 87-95
Author(s):  
A. A. Artemyev ◽  
E. A. Kazachkov ◽  
S. N. Matyugin ◽  
V. V. Sharonov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surface Water ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Visible Spectrum ◽  
Training Dataset ◽  
And Training ◽  
Selection Of

This paper considers the problem of classifying surface water objects, e.g. ships of different classes, in visible spectrum images using convolutional neural networks. A technique for forming a database of images of surface water objects and a special training dataset for creating a classification are presented. A method for forming and training of a convolutional neural network is described. The dependence of the probability of correct recognition on the number and variants of the selection of specific classes of surface water objects is analysed. The results of recognizing different sets of classes are presented.

scholarly journals Machine Learning Classifiers Evaluation for Automatic Karyogram Generation from G-Banded Metaphase Images

2020 ◽  
Vol 10 (8) ◽  
pp. 2758
Author(s):  
Yahir Hernández-Mier ◽  
Marco Aurelio Nuño-Maganda ◽  
Said Polanco-Martagón ◽  
María del Refugio García-Chávez
Keyword(s):  
Machine Learning ◽  
Classification Scheme ◽  
Phase 1 ◽  
Staining Technique ◽  
Desktop Application ◽  
Stage Classification ◽  
Multiclass Classifier ◽  
Stage 1 ◽  
Selection Of

This work proposes the evaluation of a set of algorithms of machine learning and the selection of the most appropriate one for the classification of segmented chromosomes images acquired using the Giemsa staining technique (G-banding). The evaluation and selection of the best classification algorithms was carried out over a dataset of 119 Q-banding chromosomes images, and the obtained results were then applied to a dataset of 24 G-band chromosomes images, manually classified by an expert of the Laboratory of Cytogenetic of the Children’s Hospital of Tamaulipas. The results of evaluation of 51 classifiers yielded that the best classification accuracy for the selected features was obtained by a backpropagation neural network. One of the main contributions of this study is the proposal of a two-stage classification scheme based on the best classifier found by the initial evaluation. In stage 1, chromosome images are classified into three major groups. In stage 2, the output of phase 1 is used as the input of a multiclass classifier. Using this scheme, 82% of the IGB bank samples and 88% of the samples of a bank of images obtained with a Q-band available in the literature consisting of 119 chromosome studies were successfully classified. The proposed work is a part of an desktop application that allows cytogeneticist to automatically generate cytogenetic reports.

scholarly journals Efisiensi Big Data Menggunakan Improved Nearest Neighbor

2019 ◽  
Vol 6 (6) ◽  
pp. 665
Author(s):  
Aditya Hari Bawono ◽  
Ahmad Afif Supianto
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Efficient Method ◽  
Nearest Neighbor ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Knn Classification ◽  
Degree Of Similarity ◽  
Selection Of

<p>Klasifikasi adalah salah satu metode penting dalam kajian data mining. Salah satu metode klasifikasi yang populer dan mendasar adalah k<em>-nearest neighbor</em> (kNN). Pada kNN, hubungan antar sampel diukur berdasarkan tingkat kesamaan yang direpresentasikan sebagai jarak. Pada kasus mayoritas terutama pada data berukuran besar, akan terdapat beberapa sampel yang memiliki jarak yang sama namun amat mungkin tidak terpilih menjadi tetangga, maka pemilihan parameter k akan sangat mempengaruhi hasil klasifikasi kNN. Selain itu, pengurutan pada kNN menjadi masalah komputasi ketika dilakukan pada data berukuran besar. Dalam usaha mengatasi klasifikasi data berukuran besar dibutuhkan metode yang lebih akurat dan efisien. <em>Dependent Nearest Neighbor</em> (dNN) sebagai metode yang diajukan dalam penelitian ini tidak menggunakan parameter k dan tidak ada proses pengurutan sampel. Hasil percobaan menunjukkan bahwa dNN dapat menghasilkan efisiensi waktu sebesar 3 kali lipat lebih cepat daripada kNN. Perbandingan akurasi dNN adalah 13% lebih baik daripada kNN.</p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Classification is one of the important methods of data mining. One of the most popular and basic classification methods is k-nearest neighbor (kNN). In kNN, the relationships between samples are measured by the degree of similarity represented as distance. In major cases, especially on big data, there will be some samples that have the same distance but may not be selected as neighbors, then the selection of k parameters will greatly affect the results of kNN classification. Sorting phase of kNN becomes a computation problem when it is done on big data. In the effort to overcome the classification of big data a more accurate and efficient method is required. Dependent Nearest Neighbor (dNN) as method proposed in this study did not use the k parameters and no sample at the sorting phase. The proposed method resulted in 3 times faster than kNN. The accuracy of the proposed method is13% better results than kNN.</em></p><p class="Judul2" align="left"><em> </em></p>

scholarly journals Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ahmad Hasasneh ◽  
Nikolas Kampel ◽  
Praveen Sripad ◽  
N. Jon Shah ◽  
Jürgen Dammers
Keyword(s):  
Classification Scheme ◽  
Data Augmentation ◽  
High Reliability ◽  
Combined Model ◽  
Independent Components ◽  
Artifact Rejection ◽  
Auxiliary Signal ◽  
Electrocardiogram Ecg ◽  
And Training

We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed method are as follows: (1) it is a fully automated and user independent workflow of artifact classification in MEG data; (2) once the model is trained there is no need for auxiliary signal recordings; (3) the flexibility in the model design and training allows for various modalities (MEG/EEG) and various sensor types.

Use of photodynamic therapy for photodamage skin (review of literature)

2019 ◽  
Vol 1 (7) ◽  
pp. 19-23
Author(s):  
S. I. Surkichin ◽  
N. V. Gryazeva ◽  
L. S. Kholupova ◽  
N. V. Bochkova
Keyword(s):  
Photodynamic Therapy ◽  
Comparative Evaluation ◽  
Clinical Manifestations ◽  
Light Sources ◽  
Review Of Literature ◽  
Photosensitizing Agents ◽  
Selection Of

The article provides an overview of the use of photodynamic therapy for photodamage of the skin. The causes, pathogenesis and clinical manifestations of skin photodamage are considered. The definition, principle of action of photodynamic therapy, including the sources of light used, the classification of photosensitizers and their main characteristics are given. Analyzed studies that show the effectiveness and comparative evaluation in the selection of various light sources and photosensitizing agents for photodynamic therapy in patients with clinical manifestations of photodamage.

Philosophcal review of h. white̕s tropological theory in the context of the new philosophy of history

2020 ◽  
Vol 3 (152) ◽  
pp. 92-99
Author(s):  
S. M. Geiko ◽  
◽  
O. D. Lauta
Keyword(s):  
Modern Science ◽  
Historical Knowledge ◽  
Text Organization ◽  
The Social ◽  
History Of ◽  
Actual Material ◽  
The Common ◽  
Historical Nature ◽  
Selection Of

The article provides a philosophical analysis of the tropological theory of the history of H. White. The researcher claims that history is a specific kind of literature, and the historical works is the connection of a certain set of research and narrative operations. The first type of operation answers the question of why the event happened this way and not the other. The second operation is the social description, the narrative of events, the intellectual act of organizing the actual material. According to H. White, this is where the set of ideas and preferences of the researcher begin to work, mainly of a literary and historical nature. Explanations are the main mechanism that becomes the common thread of the narrative. The are implemented through using plot (romantic, satire, comic and tragic) and trope systems – the main stylistic forms of text organization (metaphor, metonymy, synecdoche, irony). The latter decisively influenced for result of the work historians. Historiographical style follows the tropological model, the selection of which is determined by the historian’s individual language practice. When the choice is made, the imagination is ready to create a narrative. Therefore, the historical understanding, according to H. White, can only be tropological. H. White proposes a new methodology for historical research. During the discourse, adequate speech is created to analyze historical phenomena, which the philosopher defines as prefigurative tropological movement. This is how history is revealed through the art of anthropology. Thus, H. White’s tropical history theory offers modern science f meaningful and metatheoretically significant. The structure of concepts on which the classification of historiographical styles can be based and the predictive function of philosophy regarding historical knowledge can be refined.

Using clusterization principles for the selection of repair sections of main oil pipelines based on diagnostic data

2011 ◽  
Vol 8 (1) ◽  
pp. 201-210
Author(s):  
R.M. Bogdanov
Keyword(s):  
Cluster Analysis ◽  
Defect Density ◽  
Density Variation ◽  
Distance Functions ◽  
Oil Pipeline ◽  
Oil Pipelines ◽  
Classification Of Images ◽  
Main Oil Pipeline ◽  
Selection Of

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.

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