2023 IEEE Conference on Computer Applications (ICCA)最新文献

Adult Income Classification using Machine Learning Techniques 使用机器学习技术的成人收入分类

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10181907

Ei Ei Moe, S. Win, Kyi Lai Lai Khine

引用次数: 0

An Algorithm for Myanmar Syllable Segmentation based on the Official Standard Myanmar Unicode Text 基于官方标准缅甸语Unicode文本的缅甸语音节分割算法

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10181391

Sun Thurain Moe, Than Than Nwe

引用次数: 0

Vehicle Detection using Upper Local Ternary Features with SVM Classification 基于SVM分类的上局部三元特征车辆检测

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10181882

Linn Linn Thike, Thin Lai Lai Thein

引用次数: 0

Detecting Malicious Users on Twitter Using Topic Modeling 使用主题建模检测Twitter上的恶意用户

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10181604

M. Swe, Nyein Nyein Myo

引用次数: 0

Churn Prediction Models Using Gradient Boosted Tree and Random Forest Classifiers 使用梯度提升树和随机森林分类器的客户流失预测模型

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10181933

Yu Yu Win, Cing Gel Vung

引用次数: 0

IoT Network Intrusion Detection Using Long Short-Term Memory Recurrent Neural Network 基于长短期记忆递归神经网络的物联网网络入侵检测

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10182005

Yee Mon Thant, Mie Mie Su Thwin, Chaw Su Htwe

{"title":"IoT Network Intrusion Detection Using Long Short-Term Memory Recurrent Neural Network","authors":"Yee Mon Thant, Mie Mie Su Thwin, Chaw Su Htwe","doi":"10.1109/ICCA51723.2023.10182005","DOIUrl":"https://doi.org/10.1109/ICCA51723.2023.10182005","url":null,"abstract":"The Internet of Things (IoT) is the latest technologies for everyday physical devices which the digitally connected to the internet or each other. With the rapidly development of IoT platform, IoT have been encountered many malicious activities. IoT security is one of the most critical issues in developing and implementing of IoT platform. Intrusion Detection System (IDS) play an important role for security solution in IoT network. In our paper, we proposed an effective IDS model for intrusion detection in IoT network by using Long Short-Term Memory Recurrent Neural Network (LSTM RNN). Performance of proposed model to identify correctly the normal and attack has been evaluated on the benchmark intrusion dataset, UNSW-NB15 dataset which applied in the most of IoT intrusion detection research. Moreover, in this survey, we studied the performance of the proposed LSTM RNN IDS model by contrasting the Recurrent Neural Network (simple RNN) algorithm. The experimental results show the efficiency of proposed model with accuracy, precision, recall and F1 score. Our proposed LSTM RNN model outperformed than the simple RNN model to build a highly effective intrusion detection model with accuracy over 99% for IoT network attacks.","PeriodicalId":110447,"journal":{"name":"2023 IEEE Conference on Computer Applications (ICCA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127804679","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

Retail Demand Forecasting Using Sequence to Sequence Long Short-Term Memory Networks 基于序列对序列长短期记忆网络的零售需求预测

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10181450

Mon Myat Phyu, M. Khine

{"title":"Retail Demand Forecasting Using Sequence to Sequence Long Short-Term Memory Networks","authors":"Mon Myat Phyu, M. Khine","doi":"10.1109/ICCA51723.2023.10181450","DOIUrl":"https://doi.org/10.1109/ICCA51723.2023.10181450","url":null,"abstract":"Demand forecasting is crucial for a retail business as it can greatly affect everything ranging from promotion, pricing, product assortment and inventory. Building a reliable and useful demand forecasting model is still a challenging task. Machine learning techniques used for demand forecasting including Random Forest Regressor and Support Vector Regressor are inadequate when dealing with time series. Recent works show that Long Short-Term Memory (LSTM) networks can learn non-linear relationships and time-series specific information from retail time series data. In this paper, a methodology based on Sequence to Sequence Long Short-Term Memory (Seq2Seq LSTM) network is proposed to tackle short-term retail demand forecasting problem. The Seq2Seq architecture commonly used for language translation is adapted to retail demand forecasting to improve LSTM's ability of learning long-range temporal dependencies from retail time series data. Experiments are evaluated with different input sequence lengths on store item sales dataset with daily resolution data. Bayesian Optimization is conducted to tune models' hyperparameters and examine whether it could enhance the prediction accuracy of the models. In order to gauge the robustness of the proposed forecasting model, it is compared against Standard LSTM and Vanilla RNN.","PeriodicalId":110447,"journal":{"name":"2023 IEEE Conference on Computer Applications (ICCA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130735932","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

Machine Learning Based Ecommerce Model for Myanmar Hotel Industry 基于机器学习的缅甸酒店业电子商务模型

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10181748

Tin Nilar Win, Nang Khine Zar Lwin

引用次数: 0

A Conceptual Framework for ICT Policy Development in Myanmar Education Sector 缅甸教育部门信息通信技术政策发展的概念框架

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10181934

Htain Lynn Aung, Nang Saing Moon Kham

引用次数: 0

Actionable Static Analysis Code Warnings Identification with SMOTE-based Classification 基于smote分类的可操作静态分析代码警告识别

2023 IEEE Conference on Computer Applications (ICCA) Pub Date : 2023-02-27 DOI: 10.1109/ICCA51723.2023.10181854

Y. Win, Naw Lay Wah

{"title":"Actionable Static Analysis Code Warnings Identification with SMOTE-based Classification","authors":"Y. Win, Naw Lay Wah","doi":"10.1109/ICCA51723.2023.10181854","DOIUrl":"https://doi.org/10.1109/ICCA51723.2023.10181854","url":null,"abstract":"Automatic static code analysis warnings are produced by tools for static code analysis (like FindBugs), but they suffer from an excessive number of false alarms that are ignored by developers. Accurately identifying actionable static analysis code warnings helps developers be more efficient, and the static analysis tool can be made more useful. In reality, static analysis warning datasets have a class imbalance in actionable warning identification since there are a greater number of un-actionable (false alarms) than actionable alarms. Data sampling techniques address the issue of class imbalance in classification, which is a problem with the quality of the collected data. By producing synthetic instances that may be acted upon, the Synthetic Minority Oversampling Technique (SMOTE) and its variations assist in solving the problem of class imbalance. SVM, Random Forest, Naïve Bayes, and Decision Tree classifiers were created as classification models using the eight Java open-source software projects datasets (Cassandra, Jmeter, Commons.lang, Luence-solr, Ant, Tomcat, Maven, and Derby). The performance of the developed models is evaluated using precision, recall, f-measure, ROC and false positive rate. The analysis of the experimental findings revealed that the SMOTE not only addressed the issue of class imbalance but also enhanced the classification performance and reduced the false positive rate of the experimental models when it came to the identification of warnings.","PeriodicalId":110447,"journal":{"name":"2023 IEEE Conference on Computer Applications (ICCA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125261903","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