scholarly journals Spherical Visualization Database Prototype for Education and Scientific Research: Designing and Managing

2020 ◽  
pp. paper12-1-paper12-15
Author(s):  
Sergey Presnyakov ◽  
Grigory Boyarshinov ◽  
Anastasia Odintsova ◽  
Alena Rybkina
Keyword(s):  
Data Model ◽  
Science Research ◽  
Educational Process ◽  
Database Management System ◽  
Hierarchical Data ◽  
Proposed Model ◽  
Basic Functions ◽  
Virtual Globes ◽  
High Degree

The article describes methods for spherical visualizing of data as global geophysical, environmental, atmospheric processes on the surface of planets and different kinds of processes on the surface of spherical bodies. Such data can be demonstrated through virtual globes and spherical screens. Volumetric visualization significantly increases the degree of visibility, comprehension and assimilation of the demonstrated content and may be used in the scientific and educational process. The main goal of this research is to develop database requirements for spherical visualization. The requirements are based on the modern educational and science research representation approaches. The following tasks have been formed and accomplished: a strict classification of data; a convenient way to interact at the stage of adding data to the database and analyzing; their representativeness; introduction of additional data classifications; the possibility of individual user navigation and a high degree of inter-activity. For the proposed model, the basic functions of the database management system are described. Requirements to a basic hierarchical data model were substantiated. The main object of hierarchical data model is a spherical Slide representing a separate topic of particular discipline. The superstructure is used above the hierarchical model in the form of an individual route map representing a directory with links to the main database.

scholarly journals Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

Hard and light gamification in education: Which one to choose?

2020 ◽  
Vol 1 (1) ◽  
pp. 20-27
Author(s):  
E. V. Karmanova ◽  
V. A. Shelemetyeva
Keyword(s):  
Computer Games ◽  
Educational Process ◽  
Course Development ◽  
Learning Technology ◽  
Training Course ◽  
Resource Requirements ◽  
E Learning ◽  
Characteristic Features ◽  
Game Elements

The article is devoted to the implementation of gamification methods in the educational process. The characteristic features of light and hard gamification are presented. The appropriateness of using gamification when applying e-learning technology is considered. Classification of courses based on hard gamification taking into account the technological features of development is proposed: courses-presentations, courses — computer games, VR/AR courses. The article also illustrates the use of various game elements of easy gamification using the example of the module “Level up! — Gamification” of the Moodle LMS. The capabilities of this module can be used in an electronic course by any teacher who has the skills of working with the Moodle.The authors present the analysis of the development of a training course in sales techniques using hard and light gamification technologies, where the course development was assessed for its complexity, manufacturability, and resource requirements. The results of the analysis showed that the development of courses using hard gamification requires much more financial and time-consuming than the development of courses using light gamification.The article evaluates the results of the educational intensiveness intense “Island 10–22”, held in July 2019 in Skolkovo, in which 100 university teams, teams of research and educational centers, teams of schoolchildren — winners of competitions, olympiads, hackathons (“Young Talents”) participated. The results of the intense confirmed the effectiveness of the use of light gamification methods in adult training. Thus, the conclusions presented in the article reveal a number of advantages that light gamification has in comparison with hard gamification.

VISUAL REPETITION OF EDUCATIONAL INFORMATION AT SCHOOL AND UNIVERSITY

2019 ◽  
pp. 41-50
Author(s):  
О. V. Ivanova
Keyword(s):  
Undergraduate Students ◽  
Educational Material ◽  
Educational Process ◽  
Mathematical Statistics ◽  
Crossword Puzzle ◽  
Conceptual Apparatus ◽  
Knowledge Types ◽  
Educational Information

The article discusses one of the stages of the educational process with the use of modular visualization that is systematization and synthesis of educational material. Various forms of visual repetition when studying the discipline “Theory of Probability and Mathematical Statistics” for undergraduate students who study non-mathematical profiles are presented. The concept of modular visualization is revealed, all types of each of the presented forms of visual repetition are described: through the conceptual apparatus (types: crossword puzzle, mathematical dictation, work with definitions, classification of concepts), transformation of knowledge (types: reference summary, proof of theorems, work with formulas, dictionary knowledge), by means of large-modular supports (types: table, flowchart, graph-diagram). Examples of each type of visual repetition of educational information on the discipline “Theory of Probability and Mathematical Statistics” developed by SMART Notebook and HTML are given. The technology of constructing various forms of visual repetition is presented schematically.

THE DEVELOPMENT OF THE MATHEMATICAL MODEL OF PREDICTING THE INDICATORS OF ACCREDITATION OF TECHNICAL UNIVERSITIES IN THE RUSSIAN FEDERATION

2017 ◽  
pp. 27-38
Author(s):  
Olga Mikhaylovna Tikhonova ◽  
Alexander Fedorovich Rezchikov ◽  
Vladimir Andreevich Ivashchenko ◽  
Vadim Alekseevich Kushnikov
Keyword(s):  
Mathematical Model ◽  
System Dynamics ◽  
Regression Model ◽  
Oriented Graph ◽  
Real Data ◽  
Educational Process ◽  
Proposed Model ◽  
Linear Differential ◽  
The University ◽  
The Mathematical Model

The paper presents the system of predicting the indicators of accreditation of technical universities based on J. Forrester mechanism of system dynamics. According to analysis of cause-and-effect relationships between selected variables of the system (indicators of accreditation of the university) there was built the oriented graph. The complex of mathematical models developed to control the quality of training engineers in Russian higher educational institutions is based on this graph. The article presents an algorithm for constructing a model using one of the simulated variables as an example. The model is a system of non-linear differential equations, the modelling characteristics of the educational process being determined according to the solution of this system. The proposed algorithm for calculating these indicators is based on the system dynamics model and the regression model. The mathematical model is constructed on the basis of the model of system dynamics, which is further tested for compliance with real data using the regression model. The regression model is built on the available statistical data accumulated during the period of the university's work. The proposed approach is aimed at solving complex problems of managing the educational process in universities. The structure of the proposed model repeats the structure of cause-effect relationships in the system, and also provides the person responsible for managing quality control with the ability to quickly and adequately assess the performance of the system.

scholarly journals Perception of the Online Learning Environment of Nursing Students in Slovenia: Validation of the DREEM Questionnaire

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 998
Author(s):  
Lucija Gosak ◽  
Nino Fijačko ◽  
Carolina Chabrera ◽  
Esther Cabrera ◽  
Gregor Štiglic
Keyword(s):  
Nursing Students ◽  
Nursing Student ◽  
Student Perception ◽  
Psychometric Testing ◽  
Educational Environment ◽  
Educational Process ◽  
Survey Questionnaire ◽  
Self Perception ◽  
The Mean ◽  
High Degree

At the time of the outbreak of the coronavirus pandemic, several measures were in place to limit the spread of the virus, such as lockdown and restriction of social contacts. Many colleges thus had to shift their education from personal to online form overnight. The educational environment itself has a significant influence on students’ learning outcomes, knowledge, and satisfaction. This study aims to validate the tool for assessing the educational environment in the Slovenian nursing student population. To assess the educational environment, we used the DREEM tool distributed among nursing students using an online platform. First, we translated the survey questionnaire from English into Slovenian using the reverse translation technique. We also validated the DREEM survey questionnaire. We performed psychometric testing and content validation. I-CVI and S-CVI are at an acceptable level. A high degree of internal consistency was present, as Cronbach’s alpha was 0.951. The questionnaire was completed by 174 participants, of whom 30 were men and 143 were women. One person did not define gender. The mean age of students was 21.1 years (SD = 3.96). The mean DREEM score was 122.2. The mean grade of student perception of learning was 58.54%, student perception of teachers was 65.68%, student academic self-perception was 61.88%, student perception of the atmosphere was 60.63%, and social self-perception of students was 58.93%. Although coronavirus has affected the educational process, students still perceive the educational environment as positive. Nevertheless, there is still room for improvement in all assessed areas.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

scholarly journals Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jianfang Cao ◽  
Minmin Yan ◽  
Yiming Jia ◽  
Xiaodong Tian ◽  
Zibang Zhang
Keyword(s):  
Transfer Learning ◽  
Adaptive Learning ◽  
Deep Level ◽  
Image Resolution ◽  
Learning Rate ◽  
Stochastic Gradient Descent ◽  
Natural Image ◽  
Historical Period ◽  
Proposed Model

AbstractIt is difficult to identify the historical period in which some ancient murals were created because of damage due to artificial and/or natural factors; similarities in content, style, and color among murals; low image resolution; and other reasons. This study proposed a transfer learning-fused Inception-v3 model for dynasty-based classification. First, the model adopted Inception-v3 with frozen fully connected and softmax layers for pretraining over ImageNet. Second, the model fused Inception-v3 with transfer learning for parameter readjustment over small datasets. Third, the corresponding bottleneck files of the mural images were generated, and the deep-level features of the images were extracted. Fourth, the cross-entropy loss function was employed to calculate the loss value at each step of the training, and an algorithm for the adaptive learning rate on the stochastic gradient descent was applied to unify the learning rate. Finally, the updated softmax classifier was utilized for the dynasty-based classification of the images. On the constructed small datasets, the accuracy rate, recall rate, and F1 value of the proposed model were 88.4%, 88.36%, and 88.32%, respectively, which exhibited noticeable increases compared with those of typical deep learning models and modified convolutional neural networks. Comparisons of the classification outcomes for the mural dataset with those for other painting datasets and natural image datasets showed that the proposed model achieved stable classification outcomes with a powerful generalization capacity. The training time of the proposed model was only 0.7 s, and overfitting seldom occurred.

Immune-Based Systems on Network Security Situation Awareness and Risk Prediction Model

2014 ◽  
Vol 643 ◽  
pp. 99-104
Author(s):  
Jin Yang ◽  
Yun Jie Li ◽  
Qin Li
Keyword(s):  
Situation Awareness ◽  
Artificial Immune Systems ◽  
Epidemic Spreading ◽  
State Spaces ◽  
Real Time Processing ◽  
Network Intrusion ◽  
Markov Chain Theory ◽  
Proposed Model ◽  
Network Risk

In this paper, the process of the developments and changes of the network intrusion behaviors were analyzed. An improved epidemic spreading model was proposed to study the mechanisms of aggressive behaviors spreading, to predict the future course of an outbreak and to evaluate strategies to control a network epidemic. Based on Artificial Immune Systems, the concepts and formal definitions of immune cells were given. And in this paper, the forecasting algorithm based on Markov chain theory was proposed to improve the precision of network risk forecasting. The data of the Memory cells were analyzed directly and kinds of state-spaces were formed, which can be used to predict the risk of network situation by analyzing the cells status and the classification of optimal state. Experimental results show that the proposed model has the features of real-time processing for network situation awareness.

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