2023 28th International Computer Conference, Computer Society of Iran (CSICC)最新文献_第2页

Balance Control of a Humanoid Robot Using DeepReinforcement Learning 基于深度强化学习的仿人机器人平衡控制

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105418

E. Kouchaki, M. Palhang

引用次数: 0

Improving Fault Tolerance of LoRaWAN With Predicting Packet Collision 基于碰撞预测的LoRaWAN容错性研究

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105385

Reza Rezazadeh, Yasser Sedaahat, Ismail Ghodsollahee, Farzad Azizi

引用次数: 0

Frost Prediction Using Machine Learning Methods in Fars Province 利用机器学习方法预测法尔斯省霜冻

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105391

Milad Barooni, K. Ziarati, Ali Barooni

{"title":"Frost Prediction Using Machine Learning Methods in Fars Province","authors":"Milad Barooni, K. Ziarati, Ali Barooni","doi":"10.1109/CSICC58665.2023.10105391","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105391","url":null,"abstract":"One of the common hazards and issues in meteorology and agriculture is the problem of frost, chilling or freezing. This event occurs when the minimum ambient temperature falls below a certain value. This phenomenon causes a lot of damage to the country, especially Fars province. Solving this problem requires that, in addition to predicting the minimum temperature, we can provide enough time to implement the necessary measures. Empirical methods have been provided by the Food and Agriculture Organization (FAO), which can predict the minimum temperature, but not in time. In addition to this, we can use machine learning methods to model the minimum temperature. In this study, we have used three methods Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN) as deep learning methods, and Gradient Boosting (XGBoost). A customized loss function designed for methods based on deep learning, which can be effective in reducing prediction errors. With methods based on deep learning models, not only do we observe a reduction in RMSE error compared to empirical methods but also have more time to predict minimum temperature. Thus, we can model the minimum temperature for the next 24 hours by having the current 24 hours. With the gradient boosting model (XGBoost) we can keep the prediction time as deep learning and RMSE error reduced. Finally, we experimentally concluded that machine learning methods work better than empirical methods and XGBoost model can have better performance in this problem among other implemented.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126812768","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

Leveraging Model Driven Techniques for Designing Web-GIS Systems 利用模型驱动技术设计Web-GIS系统

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105349

Zohreh Hashemian, B. Zamani, Mansour Adibi

引用次数: 0

Biological Signals for Diagnosing Sleep Stages Using Machine Learning Models 使用机器学习模型诊断睡眠阶段的生物信号

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105400

Sahar Hassanzadeh Mostafaei, J. Tanha, A. Sharafkhaneh, R. Agrawal, Zohair Hassanzadeh Mostafaei

{"title":"Biological Signals for Diagnosing Sleep Stages Using Machine Learning Models","authors":"Sahar Hassanzadeh Mostafaei, J. Tanha, A. Sharafkhaneh, R. Agrawal, Zohair Hassanzadeh Mostafaei","doi":"10.1109/CSICC58665.2023.10105400","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105400","url":null,"abstract":"Sleep quality is important for health and can prevent diseases. Polysomnography is a standard scientific and clinical tool to evaluate sleep. Sleep staging is one of the main tasks in the sleep study that characterizes sleep cycles. In recent years, many studies are conducted using machine learning approaches to classify sleep stages. These studies can improve the accuracy and speed of the classification task, but most of them have not considered the performance of minority sleep classes. Since the number of samples in the sleep classes varies greatly, it can be considered as imbalanced data. In this study, we propose an ensemble method to handle class imbalance problem in the sleep staging task. For this purpose, we select nine biomedical signals including two EEG channels, two EOG channels, EMG, ECG, Abdominal, Thorax, and Airflow from the Sleep Heart Health Study (SHHS1) dataset. Then we use an ensemble of data-level resampling methods to rebalance the data space of sleep classes. Finally, we employ different machine learning algorithms to classify sleep stages. To evaluate the performance of the proposed method, in addition to general metrics, we use various measures such as Geometric mean (G-mean) and Matthew's Correlation Coefficient (MCC), which are proper metrics for the imbalanced data classification. The results of the developed method show that it achieves high accuracies of 0.9727, 0.9410, 0.8816, and 0.8725 for two, three, five, and six sleep classes, respectively.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127905419","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 knowledge graph completion through time-aware relational message passing 通过时间感知的关系消息传递完成动态知识图谱

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105381

Amirhossein Baqinejadqazvini, Saedeh Tahery, Saeed Farzi

引用次数: 0

Latency-aware SDN-based Mobile Edge Computation Offloading in Industrial IoT 工业物联网中基于延迟感知sdn的移动边缘计算卸载

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105378

Seyyed Shayan Hoseini, Tebbo Beyer, Ali Ghaderi, Z. Movahedi

{"title":"Latency-aware SDN-based Mobile Edge Computation Offloading in Industrial IoT","authors":"Seyyed Shayan Hoseini, Tebbo Beyer, Ali Ghaderi, Z. Movahedi","doi":"10.1109/CSICC58665.2023.10105378","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105378","url":null,"abstract":"Industrial Internet of things (IIoT) is a promising architecture for cyber-physical systems. Although it brings a vast number of different advantages, it enables some severe challenges as well, such as energy consumption and delay management. Due to producing a big amount of raw sensing data and the demands for processing them, various computation offloading methods over different infrastructures have been proposed. Mobile Edge Computing (MEC) is one of those infrastructures being able to give the required execution power at a much closer distance from the end devices. Software Defined Networking (SDN) is set to provide a programmable interface that can be used to manage a network of MECs in order to choose the optimal MEC for the received offloading requests. In this paper, we proposed a latency-aware SDN-based computation offloading method with specific communication, computation, and energy consumption models which aim at optimizing the overall response time. Results show that with having a delay threshold, a significant number of resources will be freed and as a result, overall response time will be decreased.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116123379","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

An Approximate Method for Spatial Task Allocation in Partially Observable Environments 部分可观测环境下空间任务分配的近似方法

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105411

Sara Amini, M. Palhang, N. Mozayani

引用次数: 0

DeepSentiParsBERT: A Deep Learning Model for Persian Sentiment Analysis Using ParsBERT DeepSentiParsBERT:一个使用ParsBERT进行波斯语情感分析的深度学习模型

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105414

Omid Davar, Gholamreza Dar, Fahimeh Ghasemian

引用次数: 1

Predicting Users' Demographic Features Based on Searched Queries and Installed Apps and Games 基于搜索查询和安装的应用和游戏预测用户的人口特征

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105350

Ghazal Kalhor, B. Bahrak

引用次数: 0