2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)最新文献_第4页

Artifact Detection in Chronically Recorded Local Field Potentials using Long-Short Term Memory Neural Network 基于长短期记忆神经网络的慢性记录局部场电位伪影检测

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/AICT50176.2020.9368638

Marcos I. Fabietti, M. Mahmud, Ahmad Lotfi, Alberto Averna, D. Guggenmos, R. Nudo, M. Chiappalone

引用次数: 9

Multimedia technologies to teach Sign Language in a written form 以书面形式教授手语的多媒体技术

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/AICT50176.2020.9368720

M. Myasoedova, Z. P. Myasoedova, M. Farkhadov

引用次数: 1

Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks 使用词嵌入和卷积神经网络的乌兹别克新闻分类

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/AICT50176.2020.9368822

I. Rabbimov, S. Kobilov, I. Mporas

引用次数: 3

Networking and Computing in Internet of Things and Cyber-Physical Systems 物联网与网络物理系统中的网络与计算

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/AICT50176.2020.9368793

Kh.E Khujamatov, E. Reypnazarov, D. Khasanov, Nurshod Akhmedov

引用次数: 12

Towards Decomposing Monolithic Applications into Microservices 将单片应用分解为微服务

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/AICT50176.2020.9368571

D. Kuryazov, Dilshod Jabborov, Bekmurod Khujamuratov

引用次数: 10

Parallel Algorithm For Constructing a Cubic Spline on Multi-Core Processors in a Cluster 在集群多核处理器上构造三次样条的并行算法

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/AICT50176.2020.9368680

Hakimjon Zaynidinov, O. Mallayev, Javohir Nurmurodov

{"title":"Parallel Algorithm For Constructing a Cubic Spline on Multi-Core Processors in a Cluster","authors":"Hakimjon Zaynidinov, O. Mallayev, Javohir Nurmurodov","doi":"10.1109/AICT50176.2020.9368680","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368680","url":null,"abstract":"The article explores the possibility of computing parallel data compression using cubic spline. For example, ways to parallel the process of digital processing of seismic signals have been considered. The main performance indicators of parallel algorithms have been compared with consecutive algorithms. Spline methods are a versatile signal processing tool. It is more accurate than other mathematical methods, information equality is faster, and maintenance costs are much lower. On the other hand, the equipment used in such systems must also meet high performance requirements. To achieve high speeds, parallel algorithms were developed using OpenMP and MPI technologies and implemented in the architecture of multi-core processors. A mathematical method for the parallel calculation of the coefficients of a cubic spline has been developed and a parallel signal processing algorithm has been developed on its basis. As an example, parallelization is a computation during seismic signal processing. The main indicators of efficiency and acceleration of the parallel algorithm were compared with the sequential algorithm. Explained the relevance of the use of parallel numerical systems, described the main approaches to the distribution of processes and methods of data processing, described the principles of parallel programming technology, studied the basic parameters of parallel algorithms for the initial calculation of the numerical value of cubic spline. The parallel algorithm considered for constructing the cubic spline of defect 1 as p - > n leads to the construction of a local cubic spline on each grid interval ω.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124632751","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: 3

AICT 2020 Conference Opening Speech AICT 2020会议开幕致辞

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/aict50176.2020.9368725

引用次数: 0

Implementation of the Reinforcement Learning Mechanism in the Random Access Channel Procedure 随机存取信道程序中强化学习机制的实现

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/AICT50176.2020.9368724

Amirsaidov U.B., Qodirov A.A.

引用次数: 2

Prediction of Student’s Academic Performance using Feedforward Neural Network Augmented with Stochastic Trainers 利用随机训练器增强的前馈神经网络预测学生学习成绩

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/AICT50176.2020.9368820

Thaer Thaher, Rashid Jayousi

{"title":"Prediction of Student’s Academic Performance using Feedforward Neural Network Augmented with Stochastic Trainers","authors":"Thaer Thaher, Rashid Jayousi","doi":"10.1109/AICT50176.2020.9368820","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368820","url":null,"abstract":"The academic performance of students is of great interest to tutors and decision-makers in educational institutions. The extensive use of information technology systems in education generates an enormous amount of data, which is challenging to analyze and extract valuable information. Therefore, Educational Data Mining (EDM) concept emerges to adapt Data Mining (DM) techniques to extract the hidden and valuable educational knowledge that improves the learning process. The primary purpose of this paper is to introduce an efficient student’s performance prediction model. For this purpose, a feed-forward Multi-Layer Perceptron approach boosted with stochastic training algorithms is proposed. The proposed model is benchmarked and assessed using three public educational datasets gathered from UCI and Kaggle repositories. Synthetic Minority Oversampling Technique (SMOTE) is utilized to handle the imbalanced data problem. The performance of the proposed model is evaluated by a set of classifiers, namely, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Logistic Regression, Linear Discriminant Analysis, and Random Forest. The comparative study revealed that the MLP achieved promising prediction quality on the majority of datasets compared to other traditional classifiers, as well as those in previous works.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121903613","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: 6

Financial Services Credit Scoring System Using Data Mining 基于数据挖掘的金融服务信用评分系统

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2020-10-07 DOI: 10.1109/AICT50176.2020.9368572

Adel Hassan, Rashid Jayousi

引用次数: 0