2019 International Conference on Advances in the Emerging Computing Technologies (AECT)最新文献_第5页

Random Bit Generator Mechanism Based on Elliptic Curves and Secure Hash Function 基于椭圆曲线和安全哈希函数的随机比特生成机制

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194180

O. Reyad, M. E. Karar, Kadry Hamed

{"title":"Random Bit Generator Mechanism Based on Elliptic Curves and Secure Hash Function","authors":"O. Reyad, M. E. Karar, Kadry Hamed","doi":"10.1109/AECT47998.2020.9194180","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194180","url":null,"abstract":"Pseudorandom bit generators (PRBG) can be designed to take the advantage of some hard number theoretic problems such as the discrete logarithm problem (DLP). Such type of generators will have good randomness and unpredictability properties as it is so difficult to find an easy solution to the regarding mathematical dilemma. Hash functions in turn play a remarkable role in many cryptographic tasks to achieve various security strengths. In this paper, a pseudorandom bit generator mechanism that is based mainly on the elliptic curve discrete logarithm problem (ECDLP) and hash derivation function is proposed. The cryptographic hash functions are used in consuming applications that require various security strengths. In a good hash function, finding whatever the input that can be mapped to any pre-specified output is considered computationally infeasible. The obtained pseudorandom bits are tested with NIST statistical tests and it also could fulfill the up-to-date standards. Moreover, a $256 times 256$ grayscale images are encrypted with the obtained pseudorandom bits following by necessary analysis of the cipher images for security prove.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116154051","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

Skin Lesions Identification Using Deep Convolutional Neural Network 基于深度卷积神经网络的皮肤病变识别

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194205

Tasneem Alkarakatly, Shatha Eidhah, Miaad Al-Sarawani, A. Al-Sobhi, M. Bilal

引用次数: 13

A Framework for Key Management Architecture for DTN (KMAD): Requirements and Design DTN (KMAD)密钥管理架构框架:需求与设计

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194164

Muhammad Nasir Mumtaz Bhutta, H. Cruickshank, A. Nadeem

引用次数: 2

Stealth assessment in serious games to improve OO learning outcomes 在严肃游戏中进行潜行评估，以提高OO学习成果

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194170

Suhni Abbasi, Hameedullah Kazi

引用次数: 2

Computerised Segmentation of Medical Images using Neural Networks and GLCM 基于神经网络和GLCM的医学图像计算机分割

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194196

Z. Khan, S. Alotaibi

{"title":"Computerised Segmentation of Medical Images using Neural Networks and GLCM","authors":"Z. Khan, S. Alotaibi","doi":"10.1109/AECT47998.2020.9194196","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194196","url":null,"abstract":"This article proposes a novel method combining the Neural Networks (NN) and the features of Gray Level Co-Occurrence Matrix (GLCM) for segmenting Region of Interest (ROI) of multiple medical images. The proposed methodology combines the texture of different pixels of medical images with the Radial Bias Function Neural Networks (RBFNN) in order to increase the performance of ROI segmentation and to obtain an optimal segmented region. The proposed approach works in two steps. Initially, the image borders are detected in order to separate the background skin and the ROI. This starts by extracting the GLCM features by the process of texture analysis which represents the ROI border clearly. GLCM features such as energy, homogeneity, contrast and the correlation are extracted. Secondly, the extracted features are passed towards the RBFNN for generating the ROI as segmented area. 870 scans of multiple medical images stored in a database are used for analysing the accuracy of the proposed methodology. Analysis of accuracy shows that the proposed methodology segments the ROIs of multiple medical images more accurately.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134247556","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

High Performance Computing for Detecting Complex Diseases using Deep Learning 基于深度学习的复杂疾病检测的高性能计算

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194158

Sahar I. Ghanem, A. A. Ghoneim, Nagia M. Ghanem, M. Ismail

{"title":"High Performance Computing for Detecting Complex Diseases using Deep Learning","authors":"Sahar I. Ghanem, A. A. Ghoneim, Nagia M. Ghanem, M. Ismail","doi":"10.1109/AECT47998.2020.9194158","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194158","url":null,"abstract":"The study of the Genome-wide association study (GWAS) and the complex diseases is of high importance nowadays. The epistasis describes the analysis of the single nucleotide polymorphisms (SNPs) interactions and their effects on the complex diseases. However, enormous number of SNPs interactions should be tested against the disease that is highly computational expensive. In this paper, High Performance Computing (HPC) is being applied on a supercomputer to reduce the processing time. Parallel Deep Learning (PDL) is applied and tested using different datasets. Simulated datasets of 12 different models and the real WTCCC Rheumatoid arthritis (RA) dataset are being tested. Results show the high accuracy, specificity and true positive rate values. Moreover, they show low values of the false discovery rate and the robustness of power through the different simulated models. When tested on the real RA dataset, our model shows the ability to detect the 2-way interaction SNPs with their promising related genes with high accuracy due to the parallel deep learning architecture.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116602539","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

A Transparent and Trusted Property Registration System on Permissioned Blockchain 基于许可区块链的透明可信的财产登记系统

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194222

Toqeer Ali, A. Nadeem, Ali Alzahrani, Salman Jan

{"title":"A Transparent and Trusted Property Registration System on Permissioned Blockchain","authors":"Toqeer Ali, A. Nadeem, Ali Alzahrani, Salman Jan","doi":"10.1109/AECT47998.2020.9194222","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194222","url":null,"abstract":"Performing various transactions on real estate records in the government organization is a cumbersome task and has many pitfalls. There are a number of problems with the existing land registry systems. First, the process of land registry and transferring of a land is currently manual. Even though, some of the processes and records are digitized, however, it still has many problems, such as, it is developed on a centralized system, it cannot maintain the complete history of the land and it does not have a built-in mechanism for record tempering detection. To overcome these problems there is no viable solution based on a centralized system. The decentralized systems, such as, Blockchain comes built-in with this feature. In this proposal, aforementioned problems are addressed. The lands are registered over Blockchain network by designing a smart contract. The proposed studies can provide several features to the stakeholders including efficiency, transparency, trustworthiness, integrity for various entities and processes involved in buying and selling a property. Essentially the framework offers services that will provide a detailed history and untampered information regarding a property to ensure that the record is not manipulated. There is an external link available via Restful to the traditional property dealing apps so they can extract the real-time record of the land, such as, dimension, location, price of that specific land. The proposed system will ultimately safeguard confidence in doing real estate business over the Internet.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128614065","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

Short Term Load Forecasting on PRECON Dataset 基于PRECON数据集的短期负荷预测

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194176

Ahmad Nadeem, N. Arshad

{"title":"Short Term Load Forecasting on PRECON Dataset","authors":"Ahmad Nadeem, N. Arshad","doi":"10.1109/AECT47998.2020.9194176","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194176","url":null,"abstract":"As the electricity market is growing, the need for accurate Short Term Load Forecasting (STLF) is increasing. Electrical grid operators require STLF to plan schedules for power generation plants. With the introduction of intermittent renewable resources, the stakes are now even higher. Developed countries have been fortunate in this regard as most of the research on STLF focused on these countries and developed highly accurate models. There is now a need to focus on developing countries as these are substantial energy markets with thriving economies and high population growth rates. With the 43rd largest economy by GDP and 6th largest nation by population, Pakistan is one such country. As the energy demand of Pakistan is increasing, there is a need to understand the energy demand patterns of its citizens better. PRECON is an electricity consumption dataset of residential buildings in Pakistan that can help in this regard. In this paper, we present preliminary results of applying STLF techniques on PRECON. These initial results show that Multiple Linear Regression and Support Vector Regression perform better than Artificial Neural Network ambient temperature and autoregressive attributes as input variables. The results also discuss various performance metrics, such as ME, RMSE, and MPE. The results show a unique phenomenon, load shedding, not experienced in developed countries.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124003716","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: 3

Structural Equation Modeling for Acceptance of Cloud Computing 云计算接受的结构方程建模

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194206

W. Ahmed, S. M. Hizam, Ilham Sentosa, Jawad Ali, Toqeer Ali

引用次数: 5

Medical Imaging for the Detection of Tuberculosis Using Chest Radio Graphs * 利用胸部x线图检测结核病的医学成像*

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI: 10.1109/AECT47998.2020.9194212

Abnash Zaman, S. Khattak, Zohaib Hassan

引用次数: 1