2020 2nd International Conference on Computer and Information Sciences (ICCIS)最新文献_第2页

Automatic Appliance Identification Based on Consumption Time Series and Event-Driven Processing 基于消费时间序列和事件驱动处理的设备自动识别

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257631

S. Qaisar

引用次数: 0

A Hybrid Deep Stacked LSTM and GRU for Water Price Prediction 用于水价预测的混合深度堆叠LSTM和GRU

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257651

A. Muhammad, Adamu Sani Yahaya, S. M. Kamal, Jibril Muhammad Adam, Wada Idris Muhammad, Abubakar Elsafi

{"title":"A Hybrid Deep Stacked LSTM and GRU for Water Price Prediction","authors":"A. Muhammad, Adamu Sani Yahaya, S. M. Kamal, Jibril Muhammad Adam, Wada Idris Muhammad, Abubakar Elsafi","doi":"10.1109/ICCIS49240.2020.9257651","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257651","url":null,"abstract":"Water pricing and freshwater scarcity is an emerging global issue, a topic of debate among researchers, households and water utility managers. This is due to the fact that, the process can provide early warning signs as well as assisting water utility managers to make proper decisions on control and management of the scarce water resources through implementing water pricing policies, ensuring proper water allocation, water-use restriction as well as water production. In this paper, we presented a two-step methodology coupled stacked LSTM+GRU models while analyzing their relative performance to our reference models i.e. stacked LSTM and GRU for long term water price Prediction. It is thought that, the coupled Stacked LSTM and GRU models to exploit building of higher level of representation of the input sequence data while creating a higher level of abstraction on the final results. The GRU on the other hand assists in solving the vanishing gradient problems. The experimental results obtained from this research work indicates our coupled (Stacked LSTM+GRU) with supervised learning to significantly outperform our reference models for water price Prediction.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122690125","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: 8

Utilizing the eSIM for Public Key Cryptography: a Network Security Solution for 6G 利用eSIM进行公钥加密:6G网络安全解决方案

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257601

Ali M Al Mousa, Mohammad Al Qomri, Salman Al Hajri, Rachid Zagrouba

引用次数: 4

Higher Organization Committee 高级组织委员会

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/iccis49240.2020.9257598

引用次数: 0

Classification of unknown Internet traffic applications using Multiple Neural Network algorithm 基于多元神经网络算法的未知互联网流量应用分类

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257715

Abdelbasst Abbas Mohamed, A. H. Osman, Abdelwahed Motwakel

{"title":"Classification of unknown Internet traffic applications using Multiple Neural Network algorithm","authors":"Abdelbasst Abbas Mohamed, A. H. Osman, Abdelwahed Motwakel","doi":"10.1109/ICCIS49240.2020.9257715","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257715","url":null,"abstract":"Traffic classification software is an important tool in complex environments like a cloud-based environment for network and device safety. The new methods of traffic classification attempt to benefit from numerical flow characteristics and computer teaching techniques, but minimal supervised knowledge and uncertain applications seriously affect classification efficiency. We propose a new way of dealing with an unknown application issue in the critical situation of a limited supervised training set to achieve an efficient network classification. The proposed model applied the multiple neural network algorithms to predict the unknown application that run through organization internet network. The advantage of the suggested approach is to filter and exclude the unknown internet applications that can be affecting into internet network performance. By Appling proposed method, the internet performance can be improved and the internet traffic and delay of transferred data can be reduced. The proposed method compared with other based line method in term of predication precision accuracy measure.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126946589","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: 4

Multi-Class Classification based on Relative Distribution of Class 基于类相对分布的多类分类

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257679

Seong-O Shim

引用次数: 0

Comparative Analysis of Classical and Post-quantum Digital Signature Algorithms used in Bitcoin Transactions 比特币交易中经典和后量子数字签名算法的比较分析

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257656

Moses Dogonyaro Noel, Onomza Victor Waziri, M. Abdulhamid, Adebayo Joseph Ojeniyi, Malvis Ugonna Okoro

{"title":"Comparative Analysis of Classical and Post-quantum Digital Signature Algorithms used in Bitcoin Transactions","authors":"Moses Dogonyaro Noel, Onomza Victor Waziri, M. Abdulhamid, Adebayo Joseph Ojeniyi, Malvis Ugonna Okoro","doi":"10.1109/ICCIS49240.2020.9257656","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257656","url":null,"abstract":"The use of public key cryptosystems ranges from securely encrypting bitcoin transactions and creating digital signatures for non-repudiation. The cryptographic systems security of public key depends on the complexity in solving mathematical problems. Quantum computers pose a threat to the current day algorithms used. This research presents analysis of two Hash-based Signature Schemes (MSS and W-OTS) and provides a comparative analysis of them. The comparisons are based on their efficiency as regards to their key generation, signature generation and verification time. These algorithms are compared with two classical algorithms (RSA and ECDSA) used in bitcoin transaction security. The results as shown in table II indicates that RSA key generation takes 0.2012s, signature generation takes 0.0778s and signature verification is 0.0040s. ECDSA key generation is 0.1378s, signature generation takes 0.0187s, and verification time for the signature is 0.0164s. The W-OTS key generation is 0.002s. To generate a signature in W-OTS, it takes 0.001s and verification time for the signature is 0.0002s. Lastly MSS Key generation, signature generation and verification has high values which are 16.290s, 17.474s, and 13.494s respectively. Based on the results, W-OTS is recommended for bitcoin transaction security because of its efficiency and ability to resist quantum computer attacks on the bitcoin network.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129075114","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

Building A Multi-class XGBoost Model for Arabic Figurative Language 构建阿拉伯语比喻语言的多类XGBoost模型

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257669

N. Elmitwally

引用次数: 5

MedAccess: A Scalable Architecture for Blockchain-based Health Record Management MedAccess:基于区块链的健康记录管理的可扩展架构

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257720

Mohammed Misbhauddin, Abdulaziz AlAbdulatheam, Mohammed Aloufi, Hussien Al-Hajji, Ahmad AlGhuwainem

{"title":"MedAccess: A Scalable Architecture for Blockchain-based Health Record Management","authors":"Mohammed Misbhauddin, Abdulaziz AlAbdulatheam, Mohammed Aloufi, Hussien Al-Hajji, Ahmad AlGhuwainem","doi":"10.1109/ICCIS49240.2020.9257720","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257720","url":null,"abstract":"Electronic Health Record is a digital version of a patient's medical history maintained by a healthcare provider over the course of their visits. Unfortunately, medical records of one patient is fragmented across many hospitals, private clinics, labs, pharmacies and personal health records from wearables. Healthcare providers hesitate to share “proprietary” data. This is where the blockchain technology comes in use. Moreover, blockchain opens the opportunity to record patients' records as blocks, encrypt and make it impossible (immutable) to proceed with any changes for the information stored. The main goal would be to take advantage of the blockchain technology provide immutability, data integrity where each record is privately encrypted and publicly viewed. Nevertheless, the overall cost to store information in a practical blockchain itself is very expensive. This is where a decentralized P2P network that can store the data off-the-chain would assist in storing the actual content of each medical record and only the identifier is sent and stored on the blockchain. Any individual would be able to publicly view the medical record, however, only patients possess the private key and hence share it with desired individuals e.g. physicians or any medical practitioner. In this paper, we propose an architecture that can be used to develop scalable blockchain applications using an off-chain solution that will allow physicians, lab technicians and patients to manage the medical records in a secure manner.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133060452","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: 10

Decision Trees for Very Early Prediction of Student's Achievement 早期预测学生成绩的决策树

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257646

Eyman A. Alyahyan, Dilek Düşteaör

{"title":"Decision Trees for Very Early Prediction of Student's Achievement","authors":"Eyman A. Alyahyan, Dilek Düşteaör","doi":"10.1109/ICCIS49240.2020.9257646","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257646","url":null,"abstract":"The prediction of students' academic achievement is crucial to be conducted in a university for early detection of students at risk. This paper aims to present data mining models using classification methods based on Decision Trees (DT) algorithms to predict students' academic achievement after preparatory year, and to identify the algorithm that yields best performance. The students' academic achievement is defined as High, Average, or Below Average based on graduation CGPA. Three classifiers (J48, Random Tree and REPTree) are applied on a newly created dataset consisting of 339 students and 15 features, at the College of Computer Science and Information Technology (CCSIT). The outcome showed the J48 algorithm had an overall superior performance compared to other algorithms. Feature selection algorithms were used to reduce the feature vectors from 15 to 4 resulting in improvements in performance and computational efficiency. Finally, the results obtained help to pinpoint the preparatory year courses that impact graduation CGPA. Timely warnings, and preemptive counseling towards improving academic achievement is possible now.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122879309","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