2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE)最新文献_第8页

ICITEE 2020 Committees

2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2020-10-06 DOI: 10.1109/icitee49829.2020.9271707

引用次数: 0

Accuracy Improvement of Object Selection in Gaze Gesture Application using Deep Learning 利用深度学习提高注视手势应用中对象选择的准确性

2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2020-10-06 DOI: 10.1109/ICITEE49829.2020.9271771

M. Alfaroby E., S. Wibirama, I. Ardiyanto

{"title":"Accuracy Improvement of Object Selection in Gaze Gesture Application using Deep Learning","authors":"M. Alfaroby E., S. Wibirama, I. Ardiyanto","doi":"10.1109/ICITEE49829.2020.9271771","DOIUrl":"https://doi.org/10.1109/ICITEE49829.2020.9271771","url":null,"abstract":"Gaze-based interaction is a crucial research area. Gaze gesture provides faster interaction between a user and a computer application because people naturally look at the object of interest before taking any other actions. Spontaneous gaze-gesture-based application uses gaze-gesture as an input modality without performing any calibration. The conventional eye tracking systems have a problem with low accuracy. In general, data captured by eye tracker contains errors and noise within gaze position signal. The errors and noise affect the performance of object selection in gaze gesture based application that controls digital contents on the display using smooth-pursuit eye movement. The conventional object selection method suffers from low accuracy (<80%). In this paper, we addressed this accuracy problem with a novel approach using deep learning. We exploited deep learning power to recognize the pattern of eye-gaze data. Long Short Term Memory (LSTM) is a deep learning architecture based on recurrent neural network (RNN). We used LSTM to perform object selection task. The dataset consisted of 34 participants taken from previous study of object selection technique of gaze gesture-based application. Our experimental results show that the proposed method achieved 96.17% of accuracy. In future, our result may be used as a guidance for developing gaze gesture application.","PeriodicalId":245013,"journal":{"name":"2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129500079","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: 5

Predicting Acute Aquatic Toxicity Towards Fathead Minnow (Pimephales Promelas) Using Neuro-Fuzzy Inference System (ANFIS) 基于神经模糊推理系统(ANFIS)的黑头鲦鱼急性水生毒性预测

2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2020-10-06 DOI: 10.1109/ICITEE49829.2020.9271739

Kate Michelle Y. Acosta, R. Baldovino

{"title":"Predicting Acute Aquatic Toxicity Towards Fathead Minnow (Pimephales Promelas) Using Neuro-Fuzzy Inference System (ANFIS)","authors":"Kate Michelle Y. Acosta, R. Baldovino","doi":"10.1109/ICITEE49829.2020.9271739","DOIUrl":"https://doi.org/10.1109/ICITEE49829.2020.9271739","url":null,"abstract":"The variety of chemicals used in everyday life tend to have a significant impact on the environment, only one of which is the negative impact on the earth’s bodies of water and its inhabitants. This paper aims to predict the acute aquatic toxicity rate of various chemicals towards the flathead minnow using a neuro-fuzzy approach given only six different molecular descriptors. Actual data parameters from a previously conducted research project on quantitative structure-activity relationship (QSAR) prediction models will be utilized as the training and testing data for the network. In testing the data, comparisons will be made between the various fuzzy inference system (FIS) models and their respective performances. Likewise, the generated fuzzy rules will be analyzed and assessed using a set of testing data to check for accuracy. Results show both training and testing errors to be at acceptable levels, thus, proving the feasibility of determining acute aquatic toxicity using adaptive neuro-fuzzy inference system (ANFIS) models.","PeriodicalId":245013,"journal":{"name":"2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115087370","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

ICITEE 2020 Content Announcement Page ICITEE 2020内容公告页面

2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2020-09-15 DOI: 10.1109/icvee50212.2020.9243227

引用次数: 0