2023 IX International Conference on Information Technology and Nanotechnology (ITNT)最新文献_第3页

Analysis of the Influence of Psychological Characteristics and Their Combinations on the Students' Academic Performance 心理特征及其组合对学生学业成绩的影响分析

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/ITNT57377.2023.10138991

Natalya V. Pustovalova, T. Avdeenko

{"title":"Analysis of the Influence of Psychological Characteristics and Their Combinations on the Students' Academic Performance","authors":"Natalya V. Pustovalova, T. Avdeenko","doi":"10.1109/ITNT57377.2023.10138991","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10138991","url":null,"abstract":"The paper presents the results of regression analysis of specially designed dataset. The students from the second to fourth year of the Novosibirsk State Technical University have passed testing procedure, among them 123 men and 68 women at the age from 18 to 23 years. The presented dataset contains the results of eight different tests. We designed this set of psychometric tests for implementing \"Learner model\". The \"Learner model\" is an important component of personal educational environment of a university. For implementing the \"Learner model\", we preprocess testing data and create regression models. The method of regression analysis allows exploring the most significant predictors affecting academic performance. As a result, the most significant predictors are \"conscientiousness\" and \"behavior inhibition system\". The same predictors are significant for exploring interaction effects with categorical predictors \"modality\", \"style of reaction on changes\", \"gender\". We also explored combinations of psychometric characteristics for finding their influence on academic problems. For this reason, we divided students into two categories, considering their academic performance. Then, we build a logistic regression model.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124262894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Connected Vehicles Travel Time Prediction in a Scenario with Adaptive Traffic Light Control 基于自适应交通灯控制的互联车辆行驶时间预测

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/ITNT57377.2023.10139009

A. Agafonov, Evgeniya Efimenko

引用次数: 0

Analysis of sea surface temperature maps via topological machine learning 通过拓扑机器学习分析海洋表面温度图

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/ITNT57377.2023.10139044

Francesco Conti, O. Papini, D. Moroni, G. Pieri, M. Reggiannini, M. A. Pascali

引用次数: 0

Method for Frame Removal Detection in Static Camera Surveillance Video 静态摄像机监控视频中帧移除检测方法

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/ITNT57377.2023.10139053

A. Bavrina

引用次数: 0

Development of a Methodology for Estimating the Heat Loss of Buildings based on Neural Networks 基于神经网络的建筑热损失估算方法研究

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/ITNT57377.2023.10139124

K. Shoshina, I. Vasendina, A. Shoshin

引用次数: 0

Analysis of statistically significant indicators for the 4 types of surface electromyography 4种表面肌电图指标的统计学意义分析

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/ITNT57377.2023.10139181

Gleb O. Bondarenko

{"title":"Analysis of statistically significant indicators for the 4 types of surface electromyography","authors":"Gleb O. Bondarenko","doi":"10.1109/ITNT57377.2023.10139181","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139181","url":null,"abstract":"Intensive neuromonitoring at the bedside of patients with severe traumatic brain injury, cerebral stroke, and any acute cerebral insufficiency is crucial for preventing secondary ischemic and hypoxic damage. Multiple authors estimate that traumatic brain injury (TBI) is the most common cause of death and severe disability in people under the age of 35. In addition, men are 2 to 3 times more likely to suffer from TBI than women. TBI can lead to a process of secondary damage that causes long-term neurological and neuropsychiatric consequences, which is a significant public health issue globally. Some studies have demonstrated differences between normal and abnormal muscle electrical activity associated with Parkinson's disease (PD). Some methods have been developed to use electromyography (EMG) as a tool to diagnose motor symptoms associated with PD, such as stiffness, gait disturbance, and tremor. Due to changes in muscle activity caused by a lack of dopamine in the central nervous system, the EMG signal in PD patients shows a different pattern than in a normal person. The use of surface and stimulation EMG directly at the bedside in the intensive care unit during the treatment of acute (TBI, stroke, etc.) and chronic cerebral insufficiency (CCI) of various etiologies, as well as intraoperatively during neurosurgical, otolaryngological, and other interventions, is urgent but difficult due to the use of modern equipment and the length of the examination. The use of surface and stimulation EMG directly at the bedside of the ambulance team in the case of CCI of various etiologies during neurosurgical, otorhinolaryngological, and other interventions is also urgent but difficult due to the use of modern equipment and the length of the examination.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132056706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Neural Network for Automated Classification of Plant Component on Histological Section 植物组织切片自动分类的神经网络评价

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/ITNT57377.2023.10139262

M. Nikitina

{"title":"Evaluation of Neural Network for Automated Classification of Plant Component on Histological Section","authors":"M. Nikitina","doi":"10.1109/ITNT57377.2023.10139262","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139262","url":null,"abstract":"Classification of plant component on image histological sections is critical for determining non-compliance type of undeclared additiveand further action for technologist, or other responsible person. However, this task is often challenging due to the absence of professional histologists or non-compliance with the conditions of microstructural analysis and also the subjective criteria for evaluation. In this study, we propose a machine learning model that automatically classifies the plant component on images histological sections. Our model uses a convolutional neural network to identify regions of plant components, then aggregates those classifications to infer predominant and minor plant components on histological sections image. We evaluated our model on an independent set of 95 images histological sections. It achieved a kappa score of 0.525 and an agreement of 66.6% with three histologists for classifying the predominant plant component, slightly higher than the inter-histologists kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three histologists were within 95% confidence intervals of agreement.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116809161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ITNT 2023 Cover Page ITNT 2023封面

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/itnt57377.2023.10139106

引用次数: 0

An Intelligent Assistant for Decision Support in the Case of Aircraft Troubleshooting 飞机故障诊断中决策支持的智能助手

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/ITNT57377.2023.10139242

N. Dorodnykh, A. Stolbov, Olga O. Nikolaychuk, A. Yurin

引用次数: 0

Method for automatic cartoon colorization 自动卡通上色方法

2023 IX International Conference on Information Technology and Nanotechnology (ITNT) Pub Date : 2023-04-17 DOI: 10.1109/ITNT57377.2023.10139184

Vitaly Konovalov

{"title":"Method for automatic cartoon colorization","authors":"Vitaly Konovalov","doi":"10.1109/ITNT57377.2023.10139184","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139184","url":null,"abstract":"Colorization task consists of acquiring a full-color RGB image from grayscale image or a sketch. Article is concerned with the task of colorizing grayscale cartoon images and image sequences using neural networks. Efficiency of an existing prototype algorithm is reviewed with different modifications, as well as different combinations of loss functions. A new neural network loss function is proposed. It is based on a hypothesis that specifics of cartoons, such as clear object boundaries and color consistency within those boundaries can be used to improve colorization quality. Proposed loss function uses segmentation of cartoon images in the bilateral space, and minimizes difference between closest found segments and inside each segment, thus bringing closer predicted colors within the segment and between neighboring segments. Quantitative and qualitative experiments are conducted on efficiency as well as generalization ability of modified prototype algorithm with proposed loss function. Quantitative experiments consisted of measuring PSNR, LPIPS, MSE in Lab color space and CC, while qualitative focused on comparing temporal consistency, quality of colorization and quality of generalization.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125760127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0