Telfor Journal最新文献_第2页

Comparison of machine learning approaches to emotion recognition based on deap database physiological signals 基于深度数据库生理信号的情绪识别机器学习方法比较

Telfor Journal Pub Date : 2022-01-01 DOI: 10.5937/telfor2202073s

Tamara Stajić, J. Jovanović, Nebojša Jovanović, M. Janković

{"title":"Comparison of machine learning approaches to emotion recognition based on deap database physiological signals","authors":"Tamara Stajić, J. Jovanović, Nebojša Jovanović, M. Janković","doi":"10.5937/telfor2202073s","DOIUrl":"https://doi.org/10.5937/telfor2202073s","url":null,"abstract":"Recognizing and accurately classifying human emotion is a complex and challenging task. Recently, great attention has been paid to the emotion recognition methods using three different approaches: based on non-physiological signals (like speech and facial expression), based on physiological signals, or based on hybrid approaches. Non-physiological signals are easily controlled by the individual, so these approaches have downsides in real world applications. In this paper, an approach based on physiological signals which cannot be willingly influenced (electroencephalogram, heartrate, respiration, galvanic skin response, electromyography, body temperature) is presented. A publicly available DEAP database was used for the binary classification (high vs low for various threshold values) considering four frequently used emotional parameters (arousal, valence, liking and dominance). We have extracted 1490 features from the dataset, analyzed their predictive value for each emotion parameter and compared three different classification approaches - Support Vector Machine, Boosting algorithms and Artificial Neural Networks.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71141404","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: 2

Prototype wireless network for internet of things based on DECT standard 基于DECT标准的物联网无线网络原型

Telfor Journal Pub Date : 2022-01-01 DOI: 10.5937/telfor2201008s

Ivan V. Sinyavskiy, Igor M. Sorokin, A. Sukhov

引用次数: 0

Software optimization for fast encoding and decoding of Reed-Solomon codes 软件优化快速编码和解码里德-所罗门码

Telfor Journal Pub Date : 2022-01-01 DOI: 10.5937/telfor2202056s

Sergey Skorokhod, Andrey Barlit

引用次数: 0

System for 3D mapping using affordable LIDAR 系统3D地图使用实惠的激光雷达

Telfor Journal Pub Date : 2022-01-01 DOI: 10.5937/telfor2202067k

Veličko Krsmanović, M. Barjaktarović, A. Gavrovska

引用次数: 0

On guaranteed correction of error patterns with artificial neural networks 基于人工神经网络的误差模式保证校正

Telfor Journal Pub Date : 2022-01-01 DOI: 10.5937/telfor2202051b

Srdan Brkic, P. Ivaniš, B. Vasic

引用次数: 0

Event-based approach for analyzing and designing system: A case study of designing curriculum system 基于事件的系统分析与设计方法——以课程系统设计为例

Telfor Journal Pub Date : 2022-01-01 DOI: 10.5937/telfor2201033a

Yanti Andriyani, Al Aminuddin, Evfi Mahdiyah, N. Ario

引用次数: 0

A comparative study of deep learning and decision tree based ensemble learning algorithms for network traffic identification 深度学习与基于决策树的集成学习算法在网络流量识别中的比较研究

Telfor Journal Pub Date : 2022-01-01 DOI: 10.5937/telfor2202061n

Nedeljko Nikolić, S. Tomovic, I. Radusinović

{"title":"A comparative study of deep learning and decision tree based ensemble learning algorithms for network traffic identification","authors":"Nedeljko Nikolić, S. Tomovic, I. Radusinović","doi":"10.5937/telfor2202061n","DOIUrl":"https://doi.org/10.5937/telfor2202061n","url":null,"abstract":"In this paper, we apply Deep Learning (DL) and decision-tree-based ensemble learning algorithms to classify network traffic by application. Various Deep Learning (DL) models for network traffic identification have been presented, implemented and compared, including 1D convolutional, stacked autoencoder, multi-layer perceptron, and combination of the aforementioned. Then the results of DL models have been compared to those obtained with two popular ensemble learning models based on decision trees-Random Forest and XGBoost. To train and test the classification models, a dataset containing both encrypted and unencrypted traffic has been collected in a real network, under normal operating conditions, and pre-processed in a way that ensures non-biased results. The classification uncertainties of the models have been also quantified on publicly available ISCX VPN-nonVPN dataset. The models have been compared in terms of precision, recall, F1 score and accuracy, for different levels of complexity and training dataset sizes. The evaluation results indicate that the decision-tree ensemble learning algorithms provide more accurate results and outperform the DL algorithms. The performance gap reduces with the dataset complexity.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71140877","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

Role of sensors in the paradigm of industry 4.0 and IIoT 传感器在工业4.0和工业物联网范式中的作用

Telfor Journal Pub Date : 2022-01-01 DOI: 10.5937/telfor2202091p

A. Porokhnya, Ilia Yakimenko

{"title":"Role of sensors in the paradigm of industry 4.0 and IIoT","authors":"A. Porokhnya, Ilia Yakimenko","doi":"10.5937/telfor2202091p","DOIUrl":"https://doi.org/10.5937/telfor2202091p","url":null,"abstract":"The purpose of this article is to review new trends in monitoring the condition of oil on all factory area processes. New solutions are being introduced into this industry with new advantages in the development of artificial intelligence, as well as machine learning and sensor technologies, which are applicable for data-based maintenance. They are called predictive maintenance. This paradigm is going to replace the old one. It changes the traditional routine preventive maintenance scheme and provides a deep understanding of the equipment performance [1]. Monitoring and checkout of conditions are necessary to maintain in a real-time environment because on-line control of equipment status can put down an operating cost, by eliminating the need for equipment outage for everyday diagnostics. The analysis based on oil samples is an effective tribotechnical systems approach for early diagnosis of failures, as it contains valuable information about the process of degradation of oil and the state of tribotechnical pairs [2]. But there are some problems with this method. The first is the way of oil sampling. There are lots of mistakes that may be made during the oil sampling process, and they can affect the results. The second is a delivery to laboratory which complicates the diagnostic process. That's why we cannot say this approach is an on-line method of diagnostics. For the better prognosis of pending machinery failure one needs to know a real-time correlation between size, shapes, and concentration of wear debris parts [3].","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71141126","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 reactive service function path discovery in symmetrical environment 对称环境下响应式服务功能路径发现的评价

Telfor Journal Pub Date : 2022-01-01 DOI: 10.5937/telfor2201002m

M. Mihaeljans, A. Skrastins

引用次数: 0

Reliability testing, noise and error correction of real quantum computing devices 真实量子计算设备的可靠性测试、噪声和纠错

Telfor Journal Pub Date : 2021-01-01 DOI: 10.5937/telfor2101041g

P. I. Galanis, K. Savvas, V. A. Chernov, A. M. Butakova

引用次数: 3