2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)最新文献_第4页

Energy Distribution Image Processing of Stroke EEG Signal using Gray Level Co-occurrence Matrix 基于灰度共生矩阵的脑电信号能量分布图像处理

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887511

Safira Amalia, Koredianto Usman, Hilman Fauzi

{"title":"Energy Distribution Image Processing of Stroke EEG Signal using Gray Level Co-occurrence Matrix","authors":"Safira Amalia, Koredianto Usman, Hilman Fauzi","doi":"10.1109/IAICT55358.2022.9887511","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887511","url":null,"abstract":"In this study, we propose the effect of Electroencephalography (EEG) stroke signal processing into energy distribution images using energy distribution for stroke conditions. The EEG signals are used as an alternative method to help the improvement of Brain Computer Interface (BCI) to detect stroke conditions. The energy distribution clarifies the relationship for each channel while converting the EEG signal into an energy distribution image. The Gray-Level Co-Occurrence Matrix (GLCM) with Genetic Algorithm (GA) and ANN-BP are used as a method for image feature values to get the optimal system feature after brain mapping using Power Spectrum Density (PSD). We evaluate the system performances via a series of computer simulations. We investigate the feature combination using GLCM by taking the best 11 features, i.e., contrast, correlation, variance, entropy, homogeneity, energy, sum variance, sum entropy, difference variance, difference entropy and inverse difference momentum with an accuracy equal to 61.25%. Thus, the GA uses to select the feature on GLCM in order to find the best combination for the BCI system in this study. We found the accuracy value of GA-GLCM equals 72.5% with features, i.e., contrast, correlation, homogeneity, energy, sum variance, and different variance, while the EEG signal is tested with accuracy equals 59%. The result shows that the BCI system can be optimized using the converted EEG signal into energy distribution images. The results are expected to contribute to the future of biomedical development.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128461648","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

Classification of Foot Kicks in Taekwondo Using SVM (Support Vector Machine) and KNN (K-Nearest Neighbors) Algorithms 基于支持向量机和KNN算法的跆拳道踢腿分类

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887475

Rianta Athallah Dharmmesta, I. Jaya, Achmad Rizal, Istiqomah

引用次数: 1

A Stochastic Asynchronous Gradient Descent Algorithm with Delay Compensation Mechanism 一种具有延迟补偿机制的随机异步梯度下降算法

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887513

Tianyu Zhang, Tianhan Gao, Qingwei Mi

引用次数: 0

Performance Comparison of Open Speech-To-Text Engines using Sentence Transformer Similarity Check with the Korean Language by Foreigners 使用句子转换器与外国人韩语相似度检查的开放语音转文本引擎的性能比较

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887500

A. B. Wahyutama, Mintae Hwang

引用次数: 1

Systematic Literature Review on Process Mining in Learning Management System 学习管理系统中流程挖掘的系统文献综述

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887428

Fatharani Wafda, T. Usagawa, ER Mahendrawathi

{"title":"Systematic Literature Review on Process Mining in Learning Management System","authors":"Fatharani Wafda, T. Usagawa, ER Mahendrawathi","doi":"10.1109/IAICT55358.2022.9887428","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887428","url":null,"abstract":"In the era of Industry 4.0, information systems record a huge amount of event logs. Process Mining (PM) techniques can evaluate a Learning Management System (LMS) usage based on actual learner’s activity as recorded in the event logs. Similar systematic review research has highlighted the use of PM in LMS datasets and essential issues for future research. Our research is motivated by the fact that no literature reviews consider the process mining application in the learning process design. This paper aims to present the use of PM in LMS related to the deviation between learning design and actual execution. This literature study selects 20 out of 52 published articles from 2017 until 2021 based on the criteria and quality assessment. These articles were analyzed based on the research objectives, PM technique, and the tools used. The results show that student behavior is heavily related to the deviation between learning design and actual execution. Three perspectives are found in approaching student behavior: comparison of student behavior, performance prediction based on student behavior, and learning strategy evaluation. This structured literature review may help LMS learning designers improve learning design to suit student behavior.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132486446","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: 4

A Procedural Generation Method of Urban Roads Based on OSM 基于OSM的城市道路程序化生成方法

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887441

Tianhan Gao, Qi Gao, Tingting Yu

{"title":"A Procedural Generation Method of Urban Roads Based on OSM","authors":"Tianhan Gao, Qi Gao, Tingting Yu","doi":"10.1109/IAICT55358.2022.9887441","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887441","url":null,"abstract":"The cost of manually creating large-scale urban road 3D assets for a virtual city is prohibitively expensive, and there is no road environment that corresponds to real geographic space. Thus, the approach of procedural modelling has received a lot of attention. In this paper, we propose a procedural generation method for urban roads. By preprocessing the OpenStreetMap (OSM) data, we construct a network of urban roads with topological relations, and instantiate the road 3D models with procedural syntax. Furthermore, we use several procedural generation methods that have been proposed to create street furniture. The method proposed in this paper produces a coherent and robust generation framework for urban roads that contains traffic information, road surfaces, and street components. The experiment demonstrates that this method is able to create realistic cityscapes based on real world locations. Moreover, the form of 3D assets created by the method can be easily modified through parameters, making it possible to create endless variations. Compared with the previous approach, our urban road generation framework can greatly enhance modelling efficiency.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128193418","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

Leaf Disease image classification method based on improved convolutional neural network 基于改进卷积神经网络的叶片病害图像分类方法

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887392

Wei Wenxuan, Wang Qianshu, Hao Chaofan, Sun Xizhe, Bao Ruiming, Teoh Teik Toe

引用次数: 1

A passive Wi-Fi based monitoring system for urban flows detection 一种基于无源Wi-Fi的城市流量监测系统

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887478

M. Bertolusso, G. Pettorru, M. Spanu, M. Fadda, M. Sole, M. Anedda, D. Giusto

引用次数: 0

A Robust Offline Reinforcement Learning Algorithm Based on Behavior Regularization Methods 基于行为正则化方法的鲁棒离线强化学习算法

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887435

Yan Zhang, Tianhan Gao, Qingwei Mi

引用次数: 0

Human Activity Recognition from Skeletal Data using Covariance Descriptor and Temporal Subspace Clustering 基于协方差描述子和时间子空间聚类的骨骼数据人体活动识别

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887486

Guntru Prasanth Kumar, M. S. Subodh Raj, S. N. George

引用次数: 0