2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)最新文献_第6页

Energy Harvesting For Efficient 5G Networks 高效5G网络的能量收集

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538376

Fadhil Mukhlif, Kamarul Ariffin Bin Nooridin, Y. Al-Gumaei, Abdu Saif AL-Rassas

引用次数: 17

An Association Rule Mining-Based Framework for Profiling Regularities in Tactics Techniques and Procedures of Cyber Threat Actors 基于关联规则挖掘的网络威胁行为者战术、技术和程序规律分析框架

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538379

Umara Noor, Z. Anwar, Umara Noor, Z. Anwar, Zahid Rashid

引用次数: 5

An Empirical Analysis of Cost Estimation Models on Undergraduate Projects Using COCOMO II 基于COCOMO的本科项目成本估算模型实证分析

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538361

Faiza Tahir, Mahum Adil

引用次数: 3

Comparison Power of Semiconductor Lasers at wavelength 1480nm using InGaAs InGaAsP Materials for EDFA Pumping Scheme 使用InGaAs InGaAsP材料进行EDFA泵浦方案的1480nm半导体激光器功率比较

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538404

Satyo Pradana, A. Syahriar, Ahmad H. Lubis, S. Rahardjo

{"title":"Comparison Power of Semiconductor Lasers at wavelength 1480nm using InGaAs InGaAsP Materials for EDFA Pumping Scheme","authors":"Satyo Pradana, A. Syahriar, Ahmad H. Lubis, S. Rahardjo","doi":"10.1109/ICSCEE.2018.8538404","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538404","url":null,"abstract":"Long distance Optical Communications are affected by many problems; loss of signal is one of them. Erbium Doped Fiber Amplifier (EDFA) is the key to solve it. By using Semiconductor Laser as pumping source for EDFA, the signal can be brought back to it is normal condition. EDFA has a good wavelength operation at 1480nm. To achieve selected wavelength, we must construct the Semiconductor Laser that suitable in 1480nm. In that case, InGaAs and InGaAsP are the base materials to construct the Semiconductor Laser. At wavelength of 1480nm the materials that used are InGaAs and InGaAsP. The reason is it is suitable for wavelength selection. By using selected wavelength and materials, the Semiconductor Laser can be produced properly. Also, determining the parameter is the important thing to construct the Laser. By using Rate Equation, comparison of output power for InGaAs and InGaAsP at wavelength of 1480nm based on rate equation and can obtained four result. Those are injection current vs voltage, carrier density, photon density and output power vs injection current.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121118474","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

Big Data Visualization: Allotting by R and Python with GUI Tools 大数据可视化:用R和Python与GUI工具分配

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538413

SK Ahammad Fahad, Abdulsamad E. Yahya

{"title":"Big Data Visualization: Allotting by R and Python with GUI Tools","authors":"SK Ahammad Fahad, Abdulsamad E. Yahya","doi":"10.1109/ICSCEE.2018.8538413","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538413","url":null,"abstract":"A tremendous amount of data comes with a vast amount of knowledge. Decent use of the persistent information can assist to overcome provocations and support to establish further sophisticated judgment. Data visualization techniques are authenticated scientifically as thousand times reliable rather than textual representation. The premature data visualization system met some difficulties and there has some solution for handle this kind of big quantity of data. Data science used two distinct languages Python and R to visualize big data undeviatingly. There also have a lot of tools in operating business. This paper is focused on the visualization technique of Python and R. R appears including the extraordinary visualization library alike ggplot2, leaflet, and lattice to defeat the provocation of the extensive volume. Python has several particular libraries for data visualization. Commonly they are Bokeh, Seaborn, Altair, ggplot and Pygal. Also, with most modern, secure and powerful zero coding GUI's accessories to describe big data visualization for genuine recognition with practical determination. Method and process of visual description of data are significant to recover specific knowledge from the large-scale dataset.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122334966","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: 17

MapReduce a Comprehensive Review MapReduce全面回顾

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538364

M. Al-Khasawneh, S. Shamsuddin, S. Hasan, A. Bakar

引用次数: 4

Obstacles to the application of electronic administration in the Yemeni universities (University of Aden as a model) and solutions 也门大学(以亚丁大学为例)应用电子行政的障碍及其解决办法

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538419

Farid Saleh Amer, W. Yafooz, Ahmed Abdo Abdullah Al-Manakhi

{"title":"Obstacles to the application of electronic administration in the Yemeni universities (University of Aden as a model) and solutions","authors":"Farid Saleh Amer, W. Yafooz, Ahmed Abdo Abdullah Al-Manakhi","doi":"10.1109/ICSCEE.2018.8538419","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538419","url":null,"abstract":"This paper aims to identify the main obstacles to the application of electronic administration, ways and mechanisms to overcome the obstacles from the point of view of administrative and academic staff. The researcher used the descriptive (procedural) approach and the questionnaire was used as a tool for collecting information. The study community is composed of administrative and academic staff working at the University of Aden. The paper reached many results, the most important of which are the following. There is an agreement among the sample of the study on the obstacles to the application of electronic administration in the Yemeni universities (Aden University as a model). The most prominent of these constraints are inadequate funding and lack of financial incentives for workers in the field of electronic management, adherence to the centralization of the university administration and administrative burdens of some administrative leader. The members of the sample of the study agreed on some certain ways and mechanisms to overcome obstacles to the application of electronic administration at the University of Aden, and the most prominent of these mechanisms: Providing means of funding to activate electronic management, Training the administrative staff to deal with the applications of electronic management efficiently. Providing material incentives for employees in the field of electronic management and recruitment of specialists in the operation and maintenance of electronic management techniques, and in light of the results reached, the researcher recommends some procedures to overcome these obstacles. The results showed that the lack of financial incentives for employees in the field of electronic administration hindered the application of management away from centralization in the university administration and to delegate a part of the powers (responsibilities) to the employees.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527470","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

Deep Neural Classifiers For Eeg-Based Emotion Recognition In Immersive Environments 沉浸式环境中基于面部情绪识别的深度神经分类器

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538382

J. Teo, J. Chia

{"title":"Deep Neural Classifiers For Eeg-Based Emotion Recognition In Immersive Environments","authors":"J. Teo, J. Chia","doi":"10.1109/ICSCEE.2018.8538382","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538382","url":null,"abstract":"Emotion recognition has become a major endeavor in artificial general intelligence applications in recent years. Although significant progress has been made in emotion recognition for music, image and video stimuli, it remains largely unexplored for immersive virtual stimuli. Our main objective for this line of investigation is to enable consistently reliable emotion recognition for virtual reality stimuli using only cheap, commercial-off-the-shelf electroencephalography (EEG) headsets which have significantly less recording channels and far lower signal resolution commonly called “Wearable EEG” as opposed to medical-grade EEG headsets with the ultimate goal of applying EEG-based emotion prediction to procedurally-generated affective content such as immersive computer games and virtual learning environments through machine learning. Our prior preliminary study has found that the use of a 4-channel, 256-Hz was indeed able to perform the required emotion recognition tasks from VR stimuli albeit at classification rates of between 65-89% classification accuracy only using Support Vector Machines (SVMs) and K-Nearest Neighbor (KNN) classifiers. For this particular study, we attempt to improve the classification rates to above 95% by conducting a comprehensive investigation into the use of various deep neural-based learning architectures for this domain. By tuning the deep neural classifiers in terms of the number of hidden layers, number of hidden nodes and the nodal dropout ratio, the emotion prediction accuracy was able to be improved to over 96%. This shows the continued promise of the application of wearable EEG for emotion prediction as a cost-effective and userfriendly approach for consistent and reliable prediction deployment in virtual reality-related content and environments through deep learning approaches.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131888843","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

Feature Data Reduction of MFCC Using PCA and SVD in Speech Recognition System 语音识别系统中基于PCA和SVD的MFCC特征数据约简

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538414

A. Winursito, Risanuri Hidayat, Agus Bejo, Muhammad Nur Yasir Utomo

{"title":"Feature Data Reduction of MFCC Using PCA and SVD in Speech Recognition System","authors":"A. Winursito, Risanuri Hidayat, Agus Bejo, Muhammad Nur Yasir Utomo","doi":"10.1109/ICSCEE.2018.8538414","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538414","url":null,"abstract":"The development of the pattern recognition system has increased rapidly in this century. Many developments of methods have been done. Mel Frequency Cepstral Coefficients (MFCC) is a popular feature extraction method but still has many disadvantages, especially regarding the level of accuracy and the high dimensional feature of the extraction method. This paper presents the feature data reduction of MFCC using Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). Combining MFCC and data reduction methods, it is expected to improve the accuracy and increase the computational speed of the classification process by decreasing the dimensions of feature data. The result of extraction MFCC feature data plus the delta coefficient forms the matrix data which will be combined with the data reduction method. The data reduction process is designed into two versions. Then the results of data reduction are done classification process with Support Vector Machine (SVM) method. The dataset is composed of 140 recorded speech data from 28 speakers. The results showed that MFCC + PCA version 2 and MFCC + SVD version 1 were able to provide the maximum accuracy improvement with an increase of accuracy from conventional MFCC method from 83.57% to 90.71%. In addition, MFCC + PCA version 2 and MFCC + SVD version 1 method can accelerate the process of classification in speech recognition system from 7.819 seconds into for about 7.6 seconds by decreasing dimension of feature data from 26 into 10 for MFCC + PCA version 2 and decreasing dimension of feature data from 26 into 14 for MFCC + SVD version 1.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115349567","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: 18

Adaptive Shuffled Frog Leaping Algorithm For Optimal Power Rate Allocation: Power Line 最优功率分配的自适应洗牌青蛙跳跃算法:电力线

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) Pub Date : 2018-07-01 DOI: 10.1109/ICSCEE.2018.8538378

A. Altrad, Angela Amphwan, Shadi M. S. Hilles

引用次数: 3