2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献_第5页

Relay Nodes Selection Using Reinforcement Learning 使用强化学习的中继节点选择

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415208

Haesik Kim, T. Fujii, K. Umebayashi

引用次数: 1

An Approach to Run Pre-Trained Deep Learning Models on Grayscale Images 一种在灰度图像上运行预训练深度学习模型的方法

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415275

Ijaz Ahmad, Seokjoo Shin

{"title":"An Approach to Run Pre-Trained Deep Learning Models on Grayscale Images","authors":"Ijaz Ahmad, Seokjoo Shin","doi":"10.1109/ICAIIC51459.2021.9415275","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415275","url":null,"abstract":"Transfer learning helps the performance of a learning algorithm significantly when training deep learning models on challenging datasets. However, the pre-trained networks have certain constraints in terms of their architecture. For example, due to the wide availability of color images, state-of-the-art pre-trained networks expect an input image with three color channels. Grayscale images have small sizes as compared to color images and thus can enable real time computer vision applications in scenarios where there are constraints on device memory and bandwidth. Therefore, in this work we propose an approach to run pre-trained models on grayscale images for image classification tasks. We have used the VGG16 pre-trained model to classify Kaggle Dogs vs. Cats dataset. We have compared our results with VGG16 applied on color images. Our results have shown that when the weights for the first hidden layer are initialized as the mean of the pre-trained network weights then the classification accuracy with only 0.04% error can be achieved. Our analysis has shown that comparable benefits can be reaped when using grayscale images for deep learning based classification tasks with only one-third of the bandwidth and storage requirements.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"74 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":"124545952","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

Q-learning based Stepwise Routing Protocol for Multi-UAV Networks 基于q学习的多无人机网络分步路由协议

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415265

Jae-Won Lim, Young-Bae Ko

引用次数: 5

Gene Expression Based Tumor Purity Estimation and Individual-Specific Survival Tool in Skin Cutaneous Melanoma 皮肤黑色素瘤中基于基因表达的肿瘤纯度评估和个体特异性生存工具

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415254

Marc Jermaine Pontiveros, J. Diaz, Geoffrey A. Solano

{"title":"Gene Expression Based Tumor Purity Estimation and Individual-Specific Survival Tool in Skin Cutaneous Melanoma","authors":"Marc Jermaine Pontiveros, J. Diaz, Geoffrey A. Solano","doi":"10.1109/ICAIIC51459.2021.9415254","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415254","url":null,"abstract":"Skin Cutaneous Melanoma (SKCM) is a type of cancer that arises from the occurrence of genetic mutations in melanocytes and is the most aggressive and fatal type of cancer affecting mostly the Caucasian population with increasing incidences in Asia. Tumors and lesions are highly heterogeneous comprised of cancerous and non-cancerous cells, and the admixture is thought to have an important role in tumor growth and progression of the disease. This study features a system capable of estimating tumor purity from RNA-Seq gene expression data using Gradient Boosting Machines and providing individual-specific survival prediction (death or progression of the disease) using a set of clinical features and the tumor purity estimate from the trained model. The performance of the models for tumor purity using the entire set of gene expression and selected features by importance scores were compared. The survival models have shown that the tumor purity estimate from the trained model provided additional prognostic information over established clinical features including age, tumor stage, and sex. Survival models using Cox Proportional Hazards are provided to allow users to evaluate and probe the models for further in-sights, whether with past historical cases, current or hypothetical patients. Future model improvements and prospective replication will be necessary to demonstrate true clinical utility.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"1 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":"115898244","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

Joint Communication and Computing Resource Allocation over Cell-Free Massive MIMO-enabled Mobile Edge Network: A Deep Reinforcement Learning-based Approach 无小区大规模mimo移动边缘网络联合通信和计算资源分配:一种基于深度强化学习的方法

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415215

Fitsum Debebe Tilahun, A. T. Abebe, C. Kang

引用次数: 2

Robust Shared Lateral Control for Autonomous Vehicles 自动驾驶汽车的鲁棒共享横向控制

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415260

S. Swain, Daijiry Narzary, J. Rath, K. Veluvolu

{"title":"Robust Shared Lateral Control for Autonomous Vehicles","authors":"S. Swain, Daijiry Narzary, J. Rath, K. Veluvolu","doi":"10.1109/ICAIIC51459.2021.9415260","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415260","url":null,"abstract":"The challenges existing under the category of fully autonomous systems call for a need of human automation interaction to ensure safety and trust. Motivated by the above, this paper deals with the design of a shared control framework that enables the interaction between the human driver and automation. Further, the potential of game theory in a cooperative framework is employed to model the strategic interaction between the human driver and automation. The lateral dynamics of the vehicle model is taken into consideration with an incomplete information of all states. Lateral displacement and Yaw angle are measured whereas lateral velocity and Yaw rate are the unavailable states. A higher Order sliding Mode (HOSM) observer is designed to estimate the unknown states. With the availability of the estimated states, the interaction between the human driver and automation is carried out to generate a shared control law based on cooperative game theory. Model predictive control (MPC) approach is employed to design the control action for the human driver and autonomous subsystem separately. Then, the proposed shared lateral control scheme is analyzed and examined through simulation to evaluate the driver performance in this cooperative game theoretic approach.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"21 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":"125251153","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

Intelligent Predictive Maintenance Model for Rolling Components of a Machine based on Speed and Vibration 基于转速和振动的机床滚动部件智能预测维修模型

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415249

Baseer Ahmad, B. Mishra, M. Ghufran, Zeeshan Pervez, N. Ramzan

{"title":"Intelligent Predictive Maintenance Model for Rolling Components of a Machine based on Speed and Vibration","authors":"Baseer Ahmad, B. Mishra, M. Ghufran, Zeeshan Pervez, N. Ramzan","doi":"10.1109/ICAIIC51459.2021.9415249","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415249","url":null,"abstract":"Machines have come a long way, from the industrial revolution to a modern-day industry 4.0. In this massive transition, one thing that has never changed within a machine is the moving part. Most industries use rotating machine with different load capacity and speed. These machines run at variable load and variable speed creating vibration bootstrap thus causing machine failure due to an increase in vibrations. Most of the researcher used vibration for fault detection in bearings but sometimes it caused by miss alignment in a shaft due to a fraction of overloading the machine. In this paper, we address it to solve those problems by using two parameters speed and vibration. To verify our approach, we use three different kinds of machine learning algorithms: Support Vector Machine (SVM), Naïve Bays, and Random Forest. By using these machine learning algorithms, we tried to find out the relationship between machine failure due to speed and vibration by predicting good and faulty bearings. After applying these models, we have seen that the SVM has 78% accuracy as compared to Naïve Bays, and Random Forest.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"155 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":"114419862","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: 6

Rain Attenuation Characterization for 6G Terahertz Wireless Communication 6G太赫兹无线通信的雨衰减特性

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415196

Mohammad Ehatasham Shawon, M. Z. Chowdhury, Md Biplob Hossen, Md. Faisal Ahmed, Y. Jang

{"title":"Rain Attenuation Characterization for 6G Terahertz Wireless Communication","authors":"Mohammad Ehatasham Shawon, M. Z. Chowdhury, Md Biplob Hossen, Md. Faisal Ahmed, Y. Jang","doi":"10.1109/ICAIIC51459.2021.9415196","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415196","url":null,"abstract":"High bandwidth, high speed data rate, and low latency are the demand of wireless connectivity over few decades. The demand is increasing and will be very high in near future. Terahertz (THz) frequency band (275 GHz to 3 THz) have some powerful characteristics to satisfy the demand through establishing ultra-high speed link. Existing millimeter wave (mm wave) has incapability of meeting high data rate with huge bandwidth for sixth generation (6G) communication systems. Though a terahertz link has a noticeable impact of weather condition, however, the powerful resources and minimization of attenuation by using high directional antenna opens the promising door for future use. In this paper, we show different losses, terahertz application, a brief comparison between mm wave and terahertz and we simulate the rain attenuation characteristics of terahertz link. To address this issue, this paper provides different field of application, its different losses and attenuation profile for rainy weather thus help to open the new door for the ultra-high speed world.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"209 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":"115929536","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: 7

A Method of Delivering Fuel to Telecommunication Exchange Buildings in Disaster Response 灾害响应中向电信交换大楼输送燃料的方法

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415246

Zhao Wang, Yuusuke Nakano, Keishiro Watanabe, K. Nishimatsu

{"title":"A Method of Delivering Fuel to Telecommunication Exchange Buildings in Disaster Response","authors":"Zhao Wang, Yuusuke Nakano, Keishiro Watanabe, K. Nishimatsu","doi":"10.1109/ICAIIC51459.2021.9415246","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415246","url":null,"abstract":"In this paper, we propose a data-driven and end-to- end deep reinforcement learning-based method for delivering fuel to telecommunication exchange buildings right after large-scale disasters in order to restore or continue their services. Specifically, we generate a fuel delivery plan for telecommunication exchange buildings by proposing a complex neural network model and optimize the model with an end-to-end and data-driven based Actor-Critic method. This method accepts inputs of all features of telecommunication exchange buildings required for the disaster response from one end and outputs an optimized fuel delivery plan at the other end. The experimental results show the effectiveness, robustness, and functionality of our method both on a simulated dataset and a real corporation dataset with the information of 200+ real telecommunication exchange buildings. To the best of our knowledge, our work is the first to not only show potential practical usage in the disaster response of telecommunication services but also leverage the lack of real data or work records of past disaster response because of our proposed deep reinforcement learning-based optimization mechanism.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"63 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":"123855621","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

Deep Learning Based Sentiment Analysis On COVID-19 Public Reviews 基于深度学习的COVID-19公众评论情感分析

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415191

Tajebe Tsega Mengistie, Deepak Kumar

{"title":"Deep Learning Based Sentiment Analysis On COVID-19 Public Reviews","authors":"Tajebe Tsega Mengistie, Deepak Kumar","doi":"10.1109/ICAIIC51459.2021.9415191","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415191","url":null,"abstract":"Sentiment Analysis is a classification task in order to identify public reviews about different issues like product reviews, movie reviews, restaurant reviews, political opinions, and other current issues by extracting the public reviews from Social Media, and other Micro blogging sites. As we all know Coronavirus Disease 2019 (COVID-19) is still a global issue for entire world and people are expressing their emotions, thoughts, and opinions about this issue with help of Twitter, Facebook, and other Media. In this paper we have collected public tweets from Twitter which are talked about the COVID-19 global pandemic and applied a Convolutional Neural Network with Bidirectional Long-Short Term Memory (CNN-Bi-LSTM) hybrid Deep Learning algorithm to detect the user’s outlook on this pandemic whether they have positive feelings, negative feelings, or neutral feelings. The proposed method used preprocessing techniques to clean the data and used a word embedding pre-trained model to extract word embedding for rare words in our corpus with the help of FastText and Globe pre-trained models. The CNN-Bi-LSTM hybrid model evaluated using accuracy, precision, recall, and f1 evaluation techniques. The experimental result has been shown 99.33% accuracy using CNN-Bi-LSTM with FastText pre-trained model, and 97.55% accuracy using CNN-Bi-LSTM with GloVe pre-trained model.","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":"123211206","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: 7