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2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献

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Human Sentiments and Associated Physical Actions Detection in Disasters with Deep Learning. 基于深度学习的灾难中人类情感和相关物理行为检测。
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415229
Muhammad Sadiq Amin, Huynsik Ahn, Young Bok Choi
{"title":"Human Sentiments and Associated Physical Actions Detection in Disasters with Deep Learning.","authors":"Muhammad Sadiq Amin, Huynsik Ahn, Young Bok Choi","doi":"10.1109/ICAIIC51459.2021.9415229","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415229","url":null,"abstract":"In the study of emotions and activities, the increasingly growing development of social networks and the tendency of users to share their physical activities, opinions, expressions and perspectives in text, visual and audio content have opened up new possibilities and challenges. While the literature has widely examined sentiment and action interpretation of text streams, it is comparatively recent but challenging to evaluate sentiment and physical actions from visuals such as photos and videos together. This paper focuses human emotion with connected physical activity analysis in a socially critical area, namely social media disaster/catastrophe analysis. For disaster-related videos and photos in occluded areas, we propose multi-tagging sentiment and associated action analysis. We believe that the proposed approach will lead to more viable communities by benefiting multiple stakeholders, such as news broadcasters, emergency relief services, and the public in general.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132814637","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
Dynamic Channel Bonding Using Laser Chaos Decision Maker in WLANs 基于激光混沌决策器的无线局域网动态信道绑定
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415227
Hiroshi Kanemasa, Aohan Li, M. Naruse, N. Chauvet, M. Hasegawa
{"title":"Dynamic Channel Bonding Using Laser Chaos Decision Maker in WLANs","authors":"Hiroshi Kanemasa, Aohan Li, M. Naruse, N. Chauvet, M. Hasegawa","doi":"10.1109/ICAIIC51459.2021.9415227","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415227","url":null,"abstract":"Channel bonding (CB) is one of the most important techniques to optimize the spectrum efficiency in wireless local area networks (WLANs). IEEE 802.11ac as the most widely used standard of WLANs extends the number of basic channels of CB to 8, which can support the maximum channel widths of 160MHz. In order to avoid collisions, improve throughput while reducing communication latency, we propose an ultra-high-speed dynamic CB method using laser decision based MAB algorithm in IEEE 802.11ac network. Our proposed method can make the CB decision within the order of GHz. Besides, most existing works studying the CB protocol are based on analytical model or simulations. Since analytical model or simulations cannot accurately predict the network performance in practical communication scenarios, we evaluate our proposed algorithm by experiments in this paper. Experimental results show that our proposed method can make better CB decisions than other MAB algorithms termed as $varepsilon$-greedy policy and UCB1-tuned.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122339191","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
An Efficient Deep Neural Network Binary Classifier for Alzheimer’s Disease Classification 一种用于阿尔茨海默病分类的高效深度神经网络二值分类器
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415212
Rukesh Prajapati, Uttam Khatri, G. Kwon
{"title":"An Efficient Deep Neural Network Binary Classifier for Alzheimer’s Disease Classification","authors":"Rukesh Prajapati, Uttam Khatri, G. Kwon","doi":"10.1109/ICAIIC51459.2021.9415212","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415212","url":null,"abstract":"In recent research, deep neural networks have better classification results in the medical research fields. In this paper, a deep neural network with fully connected layers is designed to perform binary classification. Three different types of activation functions are used for the hidden layers. After performing k-folds validation with different activation function combinations, a model with the best performance is used. We used feature features extracted from the ADNI image for classification. To determine the best model, an experiment is performed for the classification of two groups: Alzheimer’s Disease (AD) and Cognitively Normal (CN). The proposed DNN with the best validation accuracy score obtained 85.19%, 76.93%, and 72.73% accuracy on the test data for AD vs. CN, Mild Cognitive Impairment (MCI) vs. CN, and AD vs. MCI classifications, respectively. This accuracy score is higher in comparison with other traditional machine learning algorithms.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121428302","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: 12
Recognition of Traffic Signs with Artificial Neural Networks: A Novel Dataset and Algorithm 基于人工神经网络的交通标志识别:一种新的数据集和算法
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415238
Abdulrahman Kerim, M. Efe
{"title":"Recognition of Traffic Signs with Artificial Neural Networks: A Novel Dataset and Algorithm","authors":"Abdulrahman Kerim, M. Efe","doi":"10.1109/ICAIIC51459.2021.9415238","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415238","url":null,"abstract":"Traffic sign classification is a prime issue for autonomous platform industries such as autonomous cars. Towards the goal of recognition, most recent classification methods deploy Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs). In this work, we provide a novel dataset and a hybrid ANN that achieves accurate results that are very close to the state-of-the-art ones. When training and testing on German Traffic Sign Recognition Benchmarks (GTSRB) a top-5 classification accuracy of 80% was achieved for 43 classes. On the other hand, a top-2 classification accuracy of 95% was reached on our novel dataset for 10 classes. This accomplishment can be linked to the fact that the proposed hybrid ANN combines 9 different models trained on color intensity, HOG (Histograms of Oriented Gradients) and LBP (Local Binary Pattern) features.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122488228","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
Determining factors contributing to the psychological impact of the COVID-19 Pandemic using machine learning 使用机器学习确定导致COVID-19大流行心理影响的因素
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415276
Carlos Alfonso V. Palattao, Geoffrey A. Solano, C. Tee, M. Tee
{"title":"Determining factors contributing to the psychological impact of the COVID-19 Pandemic using machine learning","authors":"Carlos Alfonso V. Palattao, Geoffrey A. Solano, C. Tee, M. Tee","doi":"10.1109/ICAIIC51459.2021.9415276","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415276","url":null,"abstract":"The COVID-19 pandemic has created a massive impact in the economy, healthcare, education and other aspects of society in each and every respect. Also greatly affected is the mental health of individuals. This study aims to determine the possible contributing factors to stress, anxiety, depression, and adverse psychological impact on the general population of the Philippines using machine learning approaches. The data gathered from 2119 participants who answered an online survey was analyzed using feature selection methods and machine learning classifiers to determine contributing factors to the aforementioned mental health issues. The results show that longer hours at home, on social media, age, how people rate their own health, pre-existence of a neuropsychiatric condition; wanting information on availability and effectiveness of a medicine or vaccine, being concerned for their family, feeling discriminated; and symptoms of body pain, difficulty breathing, and cough were good predictors of individuals being adversely impacted psychologically by the pandemic and others having elevated levels of stress, anxiety, depression.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125732430","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
Sparse Autoencoder for Sparse Code Multiple Access 稀疏码多址稀疏自编码器
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415203
Medini Singh, Deepak Mishra, M. Vanidevi
{"title":"Sparse Autoencoder for Sparse Code Multiple Access","authors":"Medini Singh, Deepak Mishra, M. Vanidevi","doi":"10.1109/ICAIIC51459.2021.9415203","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415203","url":null,"abstract":"In the forthcoming 5G technology, Sparse Code Multiple Access (SCMA) is the most promising scheme that aims at improving spectral efficiency further and providing massive connectivity. The challenge behind implementing SCMA scheme is: constructing optimized codebooks in order to obtain minimum BER while keeping the receiver complexity minimum. To address this problem, we resort to the usage of an efficient deep learning technique, autoencoders, that club the encoder and the decoder part and automatically learn the most optimum codeword that could give the least BER. In this paper, SCMA sparse autoencoder, which is a variant of the autoencoder, is proposed, that has better BER performance than a conventional autoencoder, without paying in terms of computational complexity.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134532989","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
Strabismus Classification using Convolutional Neural Networks 使用卷积神经网络进行斜视分类
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415280
Donghwan Kim, Jaehan Joo, Guohua Zhu, Jeongbin Seo, Jaeseung Ha, S. Kim
{"title":"Strabismus Classification using Convolutional Neural Networks","authors":"Donghwan Kim, Jaehan Joo, Guohua Zhu, Jeongbin Seo, Jaeseung Ha, S. Kim","doi":"10.1109/ICAIIC51459.2021.9415280","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415280","url":null,"abstract":"Early diagnosis and treatment of amblyopia, including strabismus, is important to prevent permanent blindness in infants and toddlers. In this paper, we design an convolutional neural networks that classifies exotropia, esotropia, and normal eyes. Designed model uses the front view of 9-photo as input data, and evaluates its performance using prediction accuracy according to the number of training epochs.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134542305","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
Integrated System of Vehicle and Passenger Manifests in Port Based on IoT 基于物联网的港口车客舱单集成系统
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415253
I. G. S. M. Diyasa, Akhmad Fauzi, M. Idhom, A. Setiawan, Tresna Maulana Fahrudin, Prismahardi Aji Riantoko
{"title":"Integrated System of Vehicle and Passenger Manifests in Port Based on IoT","authors":"I. G. S. M. Diyasa, Akhmad Fauzi, M. Idhom, A. Setiawan, Tresna Maulana Fahrudin, Prismahardi Aji Riantoko","doi":"10.1109/ICAIIC51459.2021.9415253","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415253","url":null,"abstract":"The speed and timeliness of ferry service users in the Ketapang-Gilimanuk crossing port is not optimal. This is due to the ineffectiveness of work productivity of management at the ferry port. Based on this, an Integrated Vehicle and Passenger Registration System (Manifest) was made in IoT-based with Android and Web systems. The android-based system will be used by passengers and dock operators to make arrangements for berths, while the web-based system is used by officers to process the issuance of the SAL “Approval Letter”. This system can reduce service processing time so that it is not difficult for the harbormaster officers to issue a “Sailing Approval Letter” (SPB) as well as vehicle loading/unloading arrangements and passenger boarding/unloading","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131400005","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
Subject Authentication using Time-Frequency Image Textural Features 基于时频图像纹理特征的主体认证
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415236
T. Alotaiby, S. Alshebeili, Gaseb Alotibi
{"title":"Subject Authentication using Time-Frequency Image Textural Features","authors":"T. Alotaiby, S. Alshebeili, Gaseb Alotibi","doi":"10.1109/ICAIIC51459.2021.9415236","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415236","url":null,"abstract":"The growing internet-based services such as banking and shopping have brought both ease to human’s lives and challenges in user identity authentication. Different methods have been investigated for user authentication such as retina, finger print, and face recognition. This study introduces a photoplethysmogram (PPG) based user identity authentication relying on textural features extracted from time-frequency image. The PPG signal is segmented into segments and each segment is transformed into time-frequency domain using continuous wavelet transform (CWT). Then, the textural features are extracted from the time-frequency images using Haralick’s method. Finally, a classifier is employed for identity authentication purposes. The proposed system achieved an average accuracy of 99.14% and 99.9% with segment lengths of one and tweeny seconds, respectively, using random forest classifier.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131888155","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 Deep Learning based Scene Recognition Algorithm for Indoor Localization 基于深度学习的室内定位场景识别算法
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415278
Boney A. Labinghisa, Dong Myung Lee
{"title":"A Deep Learning based Scene Recognition Algorithm for Indoor Localization","authors":"Boney A. Labinghisa, Dong Myung Lee","doi":"10.1109/ICAIIC51459.2021.9415278","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415278","url":null,"abstract":"In this paper, we make use of deep convolutional neural networks to fine tune ImageNet, as an object detection dataset to train a scene dataset that can recognize indoor environments within universities. To utilize the application of scene recognition in indoor environments, a high accuracy is needed, and the proposed scene recognition algorithm is tested with different models trained in Places365 to compare what works best for a new dataset specialized in indoor space. The proposed algorithm resulted in 96.43% accuracy in recognizing different indoor scenes, and it was able to achieve an average error distance of 1.64 meters in indoor localization.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"74 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130987697","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
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