2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)最新文献_第5页

Aquatic Iguana: A Floating Waste Collecting Robot with IoT Based Water Monitoring System 水生鬣蜥:带有物联网水监测系统的漂浮垃圾收集机器人

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251890

Mirza Turesinin, Abdullah Md Humayun Kabir, Tanzina Mollah, Sadvan Sarwar, M. S. Hosain

引用次数: 4

Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm 结合粒子群搜索方法和J48算法改进异常检测

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251872

Kurniabudi, A. Harris, Albertus Edward Mintaria, Darmawijoyo, D. Stiawan, Mohd Yazid Bin Idris, R. Budiarto

{"title":"Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm","authors":"Kurniabudi, A. Harris, Albertus Edward Mintaria, Darmawijoyo, D. Stiawan, Mohd Yazid Bin Idris, R. Budiarto","doi":"10.23919/EECSI50503.2020.9251872","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251872","url":null,"abstract":"The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"44 1","pages":"119-126"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73389994","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

A Machine Learning Model on Virtual University of Senegal's Educational Data Based On Lambda Architecture 基于Lambda架构的塞内加尔虚拟大学教育数据机器学习模型

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251903

S. M. Gueye, A. Diop, Amadou Dahirou Gueye

{"title":"A Machine Learning Model on Virtual University of Senegal's Educational Data Based On Lambda Architecture","authors":"S. M. Gueye, A. Diop, Amadou Dahirou Gueye","doi":"10.23919/EECSI50503.2020.9251903","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251903","url":null,"abstract":"Nowadays, a new form of learning has emerged in higher education. This is e-Learning. Lessons are taught on a Learning Content Management Systems (LCMS). These platforms generate a large variety of data at very high speed. This massive data comes from the interactions between the system and the users and between the users themselves (Learners, Tutors, Teachers, administrative Agents). Since 2013, UVS (Virtual University of Senegal), a digital university that offers distance learning through Moodle and Blackboard Collaborate platforms, has emerged. In terms of statistics, it has 29340 students, more than 400 active Tutors and 1000 courses. As a result, a large volume of data is generated on its learning platforms. In this article, we have set up an architecture allowing us to execute all types of queries on all data from platforms (historical data and real-time data) in order to set up intelligent systems capable of improving learning in this university. We then set up a machine learning model as a use case which is based on multiple regression in order to predict the most influential learning objects on the learners' final mark according to his learning activities.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"88 1","pages":"270-275"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80291753","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

Data Reduction Approach Based on Fog Computing in IoT Environment 物联网环境下基于雾计算的数据约简方法

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251894

Rawaa Majid Obaise, M. A. Salman, H. A. Lafta

引用次数: 2

Method Using IOT Low Earth Orbit Satellite to Monitor Forest Temperature in Indonesia 方法利用物联网低地球轨道卫星监测印度尼西亚森林温度

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251873

Ariesta Satryoko, A. Runturambi

{"title":"Method Using IOT Low Earth Orbit Satellite to Monitor Forest Temperature in Indonesia","authors":"Ariesta Satryoko, A. Runturambi","doi":"10.23919/EECSI50503.2020.9251873","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251873","url":null,"abstract":"The ultimate goal of this paper is to ensure the proper functioning of the Monitoring Forest Temperature program in Indonesia using the IoT Narrow-Band Low earth orbit Satellite. As a new technology for monitoring the temperature continue to expand, its implementation in developing countries particularly in Indonesia requires strategic guidance of how the whole process will be executed. Nevertheless, due to this, cross-sectoral partnership in technology, policy, budget, industry is essential to be addressed. The World Bank has recorded the loss from forest fire where 28 million people directly affected including 19 people who died and over 500 thousand people suffered from respiratory problems. Smokes from forest and land fires have also struck Malaysia, Singapore, and Brunei Darussalam respectively. To respond to this, the IoT (Internet of Things) now comes with an extensive feature, using the capability of satellite reach. The Narrow Band Low Earth Orbit Satellite has released a feature for IoT connect to Low Orbit Satellite and transmit the data from the sensor directly. Therefore, we argue that this technology is crucial and needs to be functioned immediately to monitor forest temperature in Indonesia.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"6 1","pages":"240-243"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91015065","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

Organizing Committee EECSI 2020 组委会EECSI 2020

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/eecsi50503.2020.9251879

引用次数: 0

Software Defect Prediction Using Neural Network Based SMOTE 基于SMOTE的神经网络软件缺陷预测

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251874

Rizal Broer Bahaweres, Fajar Agustian, I. Hermadi, A. Suroso, Y. Arkeman

{"title":"Software Defect Prediction Using Neural Network Based SMOTE","authors":"Rizal Broer Bahaweres, Fajar Agustian, I. Hermadi, A. Suroso, Y. Arkeman","doi":"10.23919/EECSI50503.2020.9251874","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251874","url":null,"abstract":"Software defect prediction is a practical approach to improve the quality and efficiency of time and costs for software testing by focusing on defect modules. The dataset of software defect prediction naturally has a class imbalance problem with very few defective modules compared to non-defective modules. This situation has a negative impact on the Neural Network, which can lead to overfitting and poor accuracy. Synthetic Minority Over-sampling Technique (SMOTE) is one of the popular techniques that can solve the problem of class imbalance. However, Neural Network and SMOTE both have hyperparameters which must be determined by the user before the modelling process. In this study, we applied the Neural Networks Based SMOTE, a combination of Neural Network and SMOTE with each hyperparameter of SMOTE and Neural Network that are optimized using random search to solve the class imbalance problem in the six NASA datasets. The results use a 5*5 cross-validation show that increases Bal by 25.48% and Recall by 45.99% compared to the original Neural Network. We also compare the performance of Neural Network-based SMOTE with “Traditional” Machine Learning-based SMOTE. The Neural Network-based SMOTE takes first place in the average rank.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"5 1","pages":"71-76"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82662590","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

Practical application of IOT and its implications on the existing software 物联网的实际应用及其对现有软件的影响

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251302

I. Al-Barazanchi, Zahraa A. Jaaz, H. Abbas, Haider Rasheed Abdulshaheed

引用次数: 9

Designing Shiyam Application: An Android-based Fasting Reminder 设计Shiyam应用程序:基于android的禁食提醒

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251891

D. Nurnaningsih, A. Permana, Salsabila Ramadhina, A. Rodoni

引用次数: 3

The Improvement Impact Performance of Face Detection Using YOLO Algorithm 使用YOLO算法改进人脸检测的影响性能

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251905

Rakha Asyrofi, Yoni Azhar Winata

引用次数: 2