2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)最新文献

Anomaly Detection in Cyber-Physical Systems based on BiGRU-VAE 基于BiGRU-VAE的信息物理系统异常检测

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013581

R. Alguliyev, L. Sukhostat, Aykhan Mammadov

引用次数: 0

Improving BERT Classification Performance on Short Queries About UNIX Commands Using an Additional Round of Fine-Tuning on Related Data 通过对相关数据进行一轮额外的微调，提高UNIX命令短查询的BERT分类性能

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013532

Grady McPeak

{"title":"Improving BERT Classification Performance on Short Queries About UNIX Commands Using an Additional Round of Fine-Tuning on Related Data","authors":"Grady McPeak","doi":"10.1109/AICT55583.2022.10013532","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013532","url":null,"abstract":"One of the great advantages of machine learning as a whole is its ability to assist a human with digesting extremely large sets of data, and helping them to learn useful information that otherwise would have been significantly more difficult to piece together, and improvements to ML models often can result in improvements in this ability. To that end, this paper presents an evaluation of the relative performances of differing versions of Bidirectional Encoder Representations from Transformers (BERT) on the task of classifying a dataset of titles from posts scraped from two UNIX-related Q&A forum websites into classes based on what command each post is most likely about. The differing versions of BERT were each first fine-tuned on a different dataset from the post titles in order to try to improve the accuracy and precision of the model’s classification abilities through the introduction of relevant yet longer, more detailed, and more information-rich information. Additionally, the performances of these models are compared to that of the Heterogeneous Graph Attention Network (HGAT). The novel contributions of this paper are a real-world-use comparison between HGAT and BERT, the production of a novel dataset, and the presentation of supporting evidence for the value of relevance and length of text in pretraining for short-text classification.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127522217","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

Automated detection of myocardial infarction using ECG-based artificial intelligence models: a systematic review 使用基于心电图的人工智能模型自动检测心肌梗死:系统综述

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013571

Pedro A Segura-Saldaña, Frank Britto-Bisso, D. Pacheco, M. Álvarez-Vargas, A. L. Manrique, Gisella M. Bejarano Nicho

{"title":"Automated detection of myocardial infarction using ECG-based artificial intelligence models: a systematic review","authors":"Pedro A Segura-Saldaña, Frank Britto-Bisso, D. Pacheco, M. Álvarez-Vargas, A. L. Manrique, Gisella M. Bejarano Nicho","doi":"10.1109/AICT55583.2022.10013571","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013571","url":null,"abstract":"Clinical decision making in the emergency room needs to be fast and accurate, especially for myocardial infarction (MI) cases. The best way to address data-based decisions is through artificial intelligence techniques (AI), which haven’t been systematize for MI detection. Thereby, we performed a systematic review (PROSPERO: CRD42021229084). The literature search from Pubmed, Web of Science, Scopus, IEEE Xplore and Embase resulted in n = 48 included articles. 71% of those articles implemented deep-learning models, while the other 29% developed machine-learning models, from which Convolutional Neural Networks and Support Vector Machines were the most common architectures. Data pre-processing methods, ECG-derived features with their corresponding feature extraction techniques, dimensionality reduction and redundancy evaluation algorithms and classifier are discussed in the present work. Furthermore, public and private datasets are analyzed, and class balance is addressed. To the extent of our knowledge, the present work is one of the most comprehensive reviews that addressed systematically the characteristics of artificial intelligence algorithms for the detection of MI based on ECG information.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115041856","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

An Ontological Approach to the Development of Analytical Platform Language Toolkits 分析平台语言工具箱开发的本体论方法

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013576

L. Lyadova, N. Suvorov, V. Zayakin, E. Zamyatina

{"title":"An Ontological Approach to the Development of Analytical Platform Language Toolkits","authors":"L. Lyadova, N. Suvorov, V. Zayakin, E. Zamyatina","doi":"10.1109/AICT55583.2022.10013576","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013576","url":null,"abstract":"The development of complex software systems is impossible without using modern modeling tools. At the design process, various models are developed: when solving each task, the attributes of processes and systems, which are significant for solving this task, are to be presented in the model. Developing analytical systems for data-intensive areas has specifics, which determine new requirements for the system functionality and architecture (interoperability, adaptability, etc.). These requirements can be implemented in the system based on a combination of two approaches: knowledge-driven development and model-driven development. This article presents an approach to creating a knowledge-driven analytical platforms, which integrate language toolkits allowing to create \"on the fly\" new domain-specific languages (DSLs). DSLs provide \"user interfaces\" which are customizable to the specifics of the tasks, solved by users with modeling tools, and to the corresponding users' domains. The architecture of the analytical platform, the graph model, and the metalanguage which are the basis of the language toolkits implementation are described in the paper. The multifaceted ontology, which is the core of the analytical platform, is presented too.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115093246","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

Performance analysis of transfer learning based deep neural networks in Alzheimer classification 基于迁移学习的深度神经网络在老年痴呆症分类中的性能分析

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013501

Mohammad Jaber Hossain, Juan Luis Nieves

{"title":"Performance analysis of transfer learning based deep neural networks in Alzheimer classification","authors":"Mohammad Jaber Hossain, Juan Luis Nieves","doi":"10.1109/AICT55583.2022.10013501","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013501","url":null,"abstract":"Medical image analysis using deep learning techniques found good attention to diagnose critical diseases within a shorter time and recommendable performance in the identification of disease conditions. Early detection of this disease has a way of doing the treatment effectively if it is possible to identify it before the symptoms appear. In this study, different methods are being proposed with their performance analysis using deep neural networks to diagnose the different stages of Alzheimer’s disease. The dataset used in this study was collected from the kaggle repository and consists of 3 different classes of Alzheimer’s disease which include Very Mild Demented, Mild Demented and Non Demented. In this study, VGG19 and ResNet50 pre-trained models with fine-tuning were used to classify different stages of the disease, alongside other two deep neural networks used where these VGG19 and ResNet50 pre-trained models were used as a feature extractor. Finally, an AlzheimerNet proposed, which outperformed previously mentioned methods that achieved 96.41% accuracy, 97% precision, 96% recall and F1- score. The current findings of the study indicate deep learning-based method achieved significant improvement in classifying Alzheimer’s disease in its early stage.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129476231","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

A Business Recommender System Based on Zones and Commercial Data 基于区域和商业数据的商业推荐系统

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013597

Mai Abusair, Rania Dameh, Ruba Egbaria, Salsabeel Alzaqa

引用次数: 0

An Improved Hybrid LDPC Decoder over Rayleigh Fading Channel 一种基于瑞利衰落信道的改进混合LDPC解码器

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013650

Reza Biazaran, H. J. Helgert

{"title":"An Improved Hybrid LDPC Decoder over Rayleigh Fading Channel","authors":"Reza Biazaran, H. J. Helgert","doi":"10.1109/AICT55583.2022.10013650","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013650","url":null,"abstract":"Soft decision decoders applicable to low density parity check codes such as sum product algorithms provide excellent error performance, however, it comes at the expense of computational complexity. Additionally, many iterations may be required of these decoders to achieve desired error performance. The processing delay associated with too many iterations may be a drawback for cases where low latency is a critical requirement. Conversely, performance of hard decision decoders such as bit flipping decoder and its variants, while improved, generally are not on par with that of soft decision decoders. Such decoders also require many iterations to achieve a given error performance. We have proposed a two-stage hybrid decoder with a simplified sum product algorithm (SPA) in the first stage, and an improved noisy gradient decent bit flipping decoder in the second stage. We have shown that our proposed hybrid decoder outperforms the legacy individual decoders, studied in this paper, from error performance point of view as well as required number of iterations that will reduce the overall network latency.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133368907","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

A Syntax-based BSGCN Model for Chinese Implicit Sentiment Analysis with Multi-classification 基于句法的多分类汉语隐式情感分析BSGCN模型

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013562

Lifang Fu, Shuai Liu

{"title":"A Syntax-based BSGCN Model for Chinese Implicit Sentiment Analysis with Multi-classification","authors":"Lifang Fu, Shuai Liu","doi":"10.1109/AICT55583.2022.10013562","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013562","url":null,"abstract":"There are two types of sentiment analysis about Chinese sentences: explicit sentiment analysis and implicit sentiment analysis. Implicit sentiment, unlike explicit sentiment, lacks a well-defined sentiment vocabulary. Currently, the majority of relevant research has focused on extracting implicit emotion via word analysis, ignoring the role of syntactic structures and relationships between words in the analysis of implicit emotions in Chinese. In this paper, we use a graph convolutional neural network (GCN) to analyze the syntactic structure of implicit sentiment texts, then combine it with a Bidirectional Encoder Representations from Transformers (BERT) to extract contextual information to create the BSGCN, a multi-classification Chinese implicit sentiment analysis model, that can classify implicit sentiment Chinese sentences into five types: happiness, sadness, disgust, surprise, and neutral. In the experiment based on the dataset SMP-ECISA, the accuracy of the Chinese implicit sentiment analysis model proposed in this paper was 82.1%, which is a significant improvement over existing models.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131660670","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

Interference Resistant Position Awareness for Collision Avoidance in Dense Drones Swarming 密集无人机群防碰撞的抗干扰位置感知

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013534

Syed Hussain Ali Kazmi, Faizan Qamar, Rosilah Hassan, K. Nisar

{"title":"Interference Resistant Position Awareness for Collision Avoidance in Dense Drones Swarming","authors":"Syed Hussain Ali Kazmi, Faizan Qamar, Rosilah Hassan, K. Nisar","doi":"10.1109/AICT55583.2022.10013534","DOIUrl":"https://doi.org/10.1109/AICT55583.2022.10013534","url":null,"abstract":"Global interest in drones is prone to surface challenges related to collision avoidance in dense constellation. Existing collision avoidance mechanism, known as Automatic Dependent Surveillance-Broadcast (ADS-B), contains serious limitations of interference effects due to broadcasting. Therefore, we propose a novel Discrete Sequence Spread Spectrum (DSSS) enabled Minimum Shift Keying (MSK) modulation for Three Dimension (3D) position sharing in collision avoidance mechanism. Our proposed scheme avoids extra processing through physical layer addressing and provides convergence to further reduced broadcast rate. We analyzed the performance of the proposed mechanism in the spectrum completely covered with Gaussian noise. The MATLAB based analysis results indicate the proposed scheme as a potential solution to address the challenges faced in drone to drone communication for collision avoidance in dense swarms of drones or Unmanned Aerial Vehicles (UAV). Moreover, the proposed scheme outperforms the traditional demodulation approach compare to direct correlation without demodulation. Further, we discussed possible future research directions in subject solution.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115344942","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: 1

Formation of Unified Digital Health Information Space in Healthcare 4.0 Environment and interoperability issues 医疗4.0环境下统一数字医疗信息空间的形成与互操作性问题

2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2022-10-12 DOI: 10.1109/AICT55583.2022.10013605

M. Mammadova, A. Ahmadova

引用次数: 0