2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)最新文献_第7页

Fuzzy Logic Control Strategy for Axial Flux Permanent Magnet Synchronous Generator in WHM 1.5KW WHM 1.5KW轴向磁通永磁同步发电机模糊控制策略

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865461

Demas Yangindrajat, H. Guntur

引用次数: 1

Comparison of Support Vector Machine and Neural Network Algorithm in Drone Detection System 支持向量机与神经网络算法在无人机检测系统中的比较

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865628

Risa Farrid Christianti, Hanin Latif Fuadi, M. Afandi, Azhari S.N., Andi Dharmawan

引用次数: 0

A study of machine translation for Vietnamese and Korean on the TED Talks 2020 corpus 2020年TED演讲语料库上越南语和韩语的机器翻译研究

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865594

Binh Van Duong, Kim Chi T. Phan, Chien Nhu Ha, Phat Cao Tran, Trong-Hop Do

引用次数: 0

Credit card fraud detection system using machine learning technique 信用卡诈骗检测系统采用机器学习技术

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865466

Ayushi Maurya, Arun C. S. Kumar

引用次数: 1

Performance Comparison of AODV, AODV-ETX and Modified AODV-ETX in VANET using NS3 基于NS3的AODV、AODV- etx和改进型AODV- etx在VANET中的性能比较

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865467

Bayu Ardianto, Hery Sapto Dwi Nurcahyo, Hasan Muftic, R. Harwahyu, R. F. Sari

引用次数: 0

Gas Turbine Anomaly Prediction using Hybrid Convolutional Neural Network with LSTM in Power Plant 基于混合卷积神经网络和LSTM的电厂燃气轮机异常预测

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865487

F. Zhultriza, Aries Subiantoro

{"title":"Gas Turbine Anomaly Prediction using Hybrid Convolutional Neural Network with LSTM in Power Plant","authors":"F. Zhultriza, Aries Subiantoro","doi":"10.1109/CyberneticsCom55287.2022.9865487","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865487","url":null,"abstract":"The fault and anomaly of real-time performance gas turbine data are difficult to predict because of the complexity of feature data and dynamically time series. In the case of real performance gas turbine, the complexity of the physical model is hard to interpret. In deep learning, the Convolutional Neural Network (CNN) is used to perform the identification of data with great feature extraction. But, since CNN is poorly accurate for time-series data, the prediction for gas turbine anomaly could be hardly optimized. Another neural network method that can interact with time-series data is Recurrent Neural Network (RNN), especially, the Long Short-Term Memory (LSTM) that can deal with the vanishing gradient problem in traditional RNN. This paper aims to develop hybrid CNN-LSTM as a proposed method to predict gas turbine anomaly more accurately than single CNN. The accuracy of the single CNN method is 81.33%. With the addition of LSTM in the same CNN architecture, the accuracy of hybrid CNN-LSTM is 91.79%. The accuracy of model data is significantly increased by adding LSTM layer after the convolutional and pooling layer.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114867931","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

Designing TehNusa Mobile Based Application Using Design Sprint Method 利用设计冲刺方法设计TehNusa移动应用程序

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865534

Dinda Maylan Setianti, Daffa Raihan Zaki, Gita Fadila Fitriana, Aditya Ammar Pradana

{"title":"Designing TehNusa Mobile Based Application Using Design Sprint Method","authors":"Dinda Maylan Setianti, Daffa Raihan Zaki, Gita Fadila Fitriana, Aditya Ammar Pradana","doi":"10.1109/CyberneticsCom55287.2022.9865534","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865534","url":null,"abstract":"Indonesia is a country where most of the population work in the agricultural and plantation sectors. Both sectors are significant for the economy of Indonesia because it contributes to the increase in Gross Domestic Product (GDP). Such as the tea plantation sub-sector, which is one of the export commodities in Indonesia. However, several problems occur to the tea farmers in Indonesia. These problems influence decreasing in tea quality and farmers' welfare in Indonesia. For that reason, TehNusa is created to help solve the issues. TehNusa can be utilized to buy and sell harvested or processed tea. Another hand, TehNusa also has other functions to develop the quality and ability of the community in caring for and managing processed tea. Another side, BUMDes will play a role by managing and developing village prospects and tea farmers to minimize gaps. The design of the TehNusa application is created using the design sprint methodology and for the design validation, we're using the system usability scale methodology. The design of the TehNusa application can be made within 40 working hours, and the test result of design validation gets a conclusion of 81 points or equivalent to grade B. The TehNusa application is rated by users as being able to help make it easier to solve problems. In addition, further research needs to be carried out to determine the implementation of making applications and building business cooperation with BUMDes in Indonesian villages that have skilled farmers and have the potential to become processed tea producers.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128670523","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

Drone Flight Logs Sequence Mining 无人机飞行日志序列挖掘

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865663

Swardiantara Silalahi, T. Ahmad, H. Studiawan

{"title":"Drone Flight Logs Sequence Mining","authors":"Swardiantara Silalahi, T. Ahmad, H. Studiawan","doi":"10.1109/CyberneticsCom55287.2022.9865663","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865663","url":null,"abstract":"Data mining techniques in analyzing log data can discover a useful pattern which then is used to infer knowledge. Interesting patterns in log data can help the stakeholder to take action to diagnose a problem or improve the running system. Drone as one loT device, which consists of subsystems working together, also implements a logging mechanism. While a drone is flying, event-related logs are written into specific log files. These files contain precious information in case of incident happens to the drone. Assuming that the integrity of the log files is guaranteed, the investigator can find useful patterns and help conclude the incidents. To this end, this paper studies the sequence mining approach to discover some pre-defined incident-related events. As this is an initial study, the main contribution of this paper is the domain adaptation and modeling of the flight logs into a sequence database. After experimenting, we conclude that the modeling procedure is an essential step in conducting sequence mining. Frequency-oriented techniques are not suitable for small sequence databases, as the found patterns tend to have less critical events. Finally, two potential future directions are elaborated.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131081683","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

Machine Learning Algorithms and Datasets for Modern IDS Design 现代IDS设计的机器学习算法和数据集

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865255

Inam Abdullah Abdulmajeed, I. Husien

{"title":"Machine Learning Algorithms and Datasets for Modern IDS Design","authors":"Inam Abdullah Abdulmajeed, I. Husien","doi":"10.1109/CyberneticsCom55287.2022.9865255","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865255","url":null,"abstract":"Intrusion Detection System (IDS) is a critical component in cyber security to capture and analyze the traffic and then differentiate between benignant and malicious traffic indicating the attack type. This review is aimed to investigate various Machine Learning (ML) algorithms utilized in IDS design; with particular focus on dataset used. The parameters used to compare the performance of each algorithm have been studied also. Dataset choice is exceptionally critical to guarantee that it is matching the IDS requirements. The dataset structure can influence in a great manner the selection of the of ML algorithm. Hence, metric will provide a numerical relation between ML algorithm against specific dataset. This review concluded that researches are liberating themselves from Supervised Learning and moving toward Clustering and other algorithms, which gives the hope that IDS in the future will be able to detect more unknown and zero-day attacks, also the percentage of utilizing hybrid algorithms has increased dramatically. On the other hand, recent ML researchers are depending more and more on modern datasets which contributes as a significant consideration in IDS design although some research articles are still seeing the KDDCup99 and its reduced variant as principal training dataset of IDSs, despite the fact that it is more than 20 years old, while cyber-threats keep rising together with adapting new technologies in the cyber world like cloud computing, IoT, and IPv6.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122566110","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

COVID-19 Disease Classification by Cough Records Analysis using Machine Learning 基于机器学习的咳嗽记录分析的COVID-19疾病分类

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865610

Kien Trang, Hoang An Nguyen, Long TonThat, Hung Ngoc Do, B. Vuong

{"title":"COVID-19 Disease Classification by Cough Records Analysis using Machine Learning","authors":"Kien Trang, Hoang An Nguyen, Long TonThat, Hung Ngoc Do, B. Vuong","doi":"10.1109/CyberneticsCom55287.2022.9865610","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865610","url":null,"abstract":"The rapid spreading rate of the Coronavirus disease 2019 (COVID-19) has resulted in more than 6.2 million deceased cases. Furthermore, the patients of the latest Omicron variation carry light to almost no symptoms of the disease themselves. Thus, the requirement for a new diagnosis method besides Reverse Transcription-Polymerase Chain Reaction (RT-PCR) becomes the most important step to successfully detect infected cases. In this research, the application of the KNN, Ensemble and SincNet models are implemented as the main models for classification diagnosis based on cough sound records of infected patients. After pre-processing steps for removing silence ranges in the audio scripts, the cough sounds are augmented, subsequently separated into single cough samples, then generated 3 testing scenarios for dealing with the imbalanced problem between the sample classes. Afterward, MelFrequency information and MelSprectrogram are extracted as main features for analysis in order to distinguish patients with COVID-19 disease and healthy cases. The AICV115M dataset consisting of two classes COVID-19 and NonCOVID-19 is implemented for performance evaluation. The recorded highest accuracy on the models KNN, Ensemble and SincNet are 92.49%, 90.1% and 85.15%, respectively.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116989694","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