2021 13th International Conference on Information & Communication Technology and System (ICTS)最新文献_第6页

Machine Learning Inspired Vision-based Drowsiness Detection using Eye and Body Motion Features 机器学习启发的基于视觉的困倦检测，使用眼睛和身体运动特征

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9608977

Ali Sheikh, J. Mir

{"title":"Machine Learning Inspired Vision-based Drowsiness Detection using Eye and Body Motion Features","authors":"Ali Sheikh, J. Mir","doi":"10.1109/ICTS52701.2021.9608977","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608977","url":null,"abstract":"Drowsiness-a state before the onset of sleep- resulting from insufficient s leep i s recognized a s a g lobal problem due to associated health and safety risks for the individuals involved in activities requiring constant attention. Therefore, several computer vision-based non-invasive techniques have been proposed for the timely detection of drowsiness. However, these methods are generally based on drowsy behavior indicators like yawning and excessive eye blinking. Moreover, the results are generally reported for databases with very few subjects or acted drowsy data. This paper proposes a drowsiness detection technique based on hybrid features using comprehensive and challenging real drowsy data. Primarily, eye state and body motion analysis is performed to determine drowsiness. Towards ameliorating this, the eye region is selected from each frame using facial landmarks and is described using a histogram of oriented gradients (HoG) descriptors. For body motion description, frame difference is computed and parameterized using HoG descriptors. Then, the hybrid feature set, i.e., the combination of eye and body motion features, is subjected to dimensionality reduction through principal component analysis. Finally, SVM is trained and tested on the hybrid feature set to detect drowsiness. The detection accuracy of 90% is achieved through our proposed technique.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"94 1","pages":"146-150"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73406413","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: 9

Human Activity Recognition in Smart Home using Deep Learning Techniques 使用深度学习技术的智能家居中的人类活动识别

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9609044

Ranjit P. Kolkar, V. Geetha

引用次数: 6

Topic Detection in Sentiment Analysis of Twitter Texts for Understanding The COVID-19 Effect in Local Economic Activities 推特文本情感分析中的话题检测，以了解COVID-19对当地经济活动的影响

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9608519

Apriantoni, Hazna At Thooriqoh, C. Fatichah, D. Purwitasari

{"title":"Topic Detection in Sentiment Analysis of Twitter Texts for Understanding The COVID-19 Effect in Local Economic Activities","authors":"Apriantoni, Hazna At Thooriqoh, C. Fatichah, D. Purwitasari","doi":"10.1109/ICTS52701.2021.9608519","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608519","url":null,"abstract":"During the COVID-19 situation, discussions about the effect of COVID-19 increase on Twitter. Not only affecting the health sector, but the COVID-19 pandemic has also affected other fields, such as economic activities. Issues related to the economy become an essential discussion on Twitter because this sector has close links with other sectors in public activities. It makes twitter relevant as a knowledge extraction medium to identify users' opini comparisons. The contribution of this research is to find the effect of the COVID-19 pandemic on the comparison of sentiment and emotion in three different locations in Surabaya. Based on the results of emotion detection, at the beginning of the COVID-19 pandemic, topics related to economic activities and personal activities were dominated by anger emotion in the ITS campus and the TP mall area. Then, despite the gradual decrease in the intensity of tweets, the dominance of anger emotion tends to be stable. On economics topics, 40% of tweets in the ITS campus area and 84% of tweets in the TP mall area were dominated by anger emotion. Then 37% of tweets in the ITS campus area and 32% tweets in the Tunjungan Plaza mall area based on personal activities were dominated by anger. The economics topic is related to buying-selling and shopping activities, while personal activity is related to lifestyle and daily activities. These results indicate that during the COVID-19 pandemic, anger became the most dominant sentiment related to local economic activity from Twitter users in Surabaya.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"635 1","pages":"354-359"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85550503","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

Comparative Analysis of Hands-free Mouse Controlling based on Face Tracking 基于人脸跟踪的免提鼠标控制比较分析

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9608014

Salsabiil Hasanah, Aulia Teaku Nururrahmah, D. Herumurti

引用次数: 0

Hybrid Quantum Deep Learning with Differential Privacy for Botnet DGA Detection 基于差分隐私的混合量子深度学习用于僵尸网络DGA检测

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9608217

Hatma Suryotrisongko, Y. Musashi

{"title":"Hybrid Quantum Deep Learning with Differential Privacy for Botnet DGA Detection","authors":"Hatma Suryotrisongko, Y. Musashi","doi":"10.1109/ICTS52701.2021.9608217","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608217","url":null,"abstract":"In the DNS query-based botnet domain generation algorithm (DGA) detection, one might argue that domain names in DNS query data might disclose sensitive information related to browsing histories. User privacy preservation is important in the current personal data protection (PDP) era. This paper proposed implementing the differential privacy approach to the hybrid quantum deep learning model for botnet DGA detection. The proposed model consists of traditional deep learning layers and a quantum layer by combining angle embedding and random layer circuits from the Pennylane framework. We used ten botnet DGA datasets: Conficker, Cryptolocker, Goz, Matsnu, New_Goz, Pushdo, Ramdo, and Rovnix. We conducted experiments with considering noise models of eight IBM quantum devices: (ibmq_5_yorktown, ibmq_armonk, ibmq_athens, ibmq_belem, ibmq_lima, ibmq_quito, ibmq_santiago, and ibmqx2). We found that our proposed hybrid quantum model delivers a satisfactory performance (92.4% of maximum accuracy), superior to the classical deep learning counterpart. However, the hyperparameters of the differential privacy implementations (l2_norm_clip, noise_multiplier, microbatches, and learning_rate) still need to be tuned to improve the privacy guarantee of our proposed models.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"5 1","pages":"68-72"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78427216","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

Website, AR, VR: Comparison for Learning Motivation 网站、AR、VR:学习动机的比较

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9608469

Mikhael Ming Khosasih, D. Herumurti

引用次数: 1

AI Driven Solution for the Detection of COVID-19 Using X-ray images 利用x射线图像检测COVID-19的AI驱动解决方案

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9608932

Riya Singh, Shivani Wadkar, Semil Jain, Manisha Dodeja

{"title":"AI Driven Solution for the Detection of COVID-19 Using X-ray images","authors":"Riya Singh, Shivani Wadkar, Semil Jain, Manisha Dodeja","doi":"10.1109/ICTS52701.2021.9608932","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608932","url":null,"abstract":"COVID-19 is a contagious and highly infectious disease which has led to an ongoing pandemic. Researchers and scientists across the world, across various fields, are exploring new methods and approaches to fight against the disease since its outbreak. A study of the COVID-19 infected patients suggests that these patients are affected with the lung infection. In this paper, we have leveraged several deep learning models using the concept of transfer learning. We have also designed a custom convolutional neural network for the purpose of feature extraction and then for effective categorization into pneumonia, covid and normal classes, several classification methods from the machine learning domain such as SVM, Random Forest and softmax regression were utilised. The custom convolutional neural network with the final layer as the dense layer with three units employing softmax activation function achieved a significant accuracy of 94.6 % which was comparable to the accuracy achieved by the transfer learning models. In order to ensure the results are not biased in favour of one class we have utilized a balanced dataset containing 1345 X-ray images for each class - pneumonia, covid, normal in order to demonstrate these experiments.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"46 1","pages":"123-128"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82582926","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

Comparative Study of Single-task and Multi-task Learning on Research Protocol Document Classification 研究方案文件分类中单任务与多任务学习的比较研究

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9608043

A. Abdillah, Mohammad Zaenuddin Hamidi, Ratih Nur Esti Anggraeni, R. Sarno

引用次数: 2

Machine Learning Approach for Stress Detection based on Alpha-Beta and Theta-Beta Ratios of EEG Signals 基于脑电信号Alpha-Beta和Theta-Beta比值的应力检测机器学习方法

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9608810

Hunain Altaf, S. Ibrahim, Nor F. M. Azmin, A. L. Asnawi, Balqis Hanisah Binti Walid, N.H. Harun

{"title":"Machine Learning Approach for Stress Detection based on Alpha-Beta and Theta-Beta Ratios of EEG Signals","authors":"Hunain Altaf, S. Ibrahim, Nor F. M. Azmin, A. L. Asnawi, Balqis Hanisah Binti Walid, N.H. Harun","doi":"10.1109/ICTS52701.2021.9608810","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608810","url":null,"abstract":"The contribution to stress detection and classification is far beyond demand as the statistics show that the health and mental illness of society have kept on deteriorating. Electroencephalogram (EEG) signals have the potential to detect stress levels reliably due to their high accuracy. Majority of studies of stress detection are based on alpha and beta waves and the corresponding ratio of the two waves and there are hardly any based-on theta waves. This work explores the impact of bandpower of alpha/beta and theta/beta ratios when combined with other features to classify two-levels of human stress based on EEG signals using five commonly used machine learning algorithms. A classification model is developed from the clustering model gained and Naïve Bayes shows the highest accuracy which is 95% in compared to the other four common machine learning algorithms (i.e., SVM, Logistic, IBk, and SGD) by using WEKA. The proposed framework recommends that both ratios are reliable features, and theta/beta appears to give a huge impact compared to alpha/beta. This study will ultimately contribute to society's development with improved robust machine learning algorithm for binary classification.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"2 1","pages":"201-206"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79409205","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

Mining Collaboration Business Process Containing Invisible Task by Using Modified Alpha 利用改进Alpha算法挖掘包含不可见任务的协同业务流程

2021 13th International Conference on Information & Communication Technology and System (ICTS) Pub Date : 2021-10-20 DOI: 10.1109/ICTS52701.2021.9608963

A. F. Septiyanto, R. Sarno, K. R. Sungkono

{"title":"Mining Collaboration Business Process Containing Invisible Task by Using Modified Alpha","authors":"A. F. Septiyanto, R. Sarno, K. R. Sungkono","doi":"10.1109/ICTS52701.2021.9608963","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608963","url":null,"abstract":"Business processes are experiencing increasingly complex developments; therefore, an extensive business process must cover all existing process flows. Applied business process collaboration between organizations can complete a complex business process. The additional information which shows the collaboration of activities is called messages. Process discovery is currently focused on a series of activities in a single process model, so the process discovery cannot depict the messages in the business process collaboration. In addition, there are several problems in describing the condition of activities, e.g., an Invisible Task. The Invisible Task is a condition of additional tasks that appear not in the event logs but in the process models. The Invisible Task must be described in the process model; therefore, it can be analyzed further. Several conditions which need the Invisible Task are redo, switch, and skip conditions. In this research, the proposed method is to obtain information about the event log of all activities of the business process collaboration and discover any Invisible Task to describe in the process model. The proposed method, named the Modified Alpha algorithm, builds several rules for adding messages and the Invisible Task in the event log before executing the Alpha algorithm. The results of this study indicate that the Modified Alpha algorithm can describe the collaboration process model. Based on the comparison results, the Modified Alpha algorithm gets the best results than other algorithms, namely Alpha# and Inductive Miner. Modified Alpha received 1.00, 1.00, 1.00, 0.82 for the fitness, precision, simplicity, and generalization. Alpha# Miner earned 0.74 and 0.70 for the simplicity and generalization, and Inductive Miner gained 0.55 simplicity value and 0.72 generalization value. Alpha# Miner and Inductive Miner got 0.00 for the fitness and the precision.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"31 1","pages":"90-95"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80353730","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