2020 International Conference on Computational Intelligence (ICCI)最新文献_第5页

Collaborative Visualization Framework for Cross-field Working Group: A Qualitative Focus Group Study 跨领域工作组的协同可视化框架:定性焦点小组研究

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247663

Danial Ilman Muhammad Hasni, A. Sarlan, R. Ahmad

{"title":"Collaborative Visualization Framework for Cross-field Working Group: A Qualitative Focus Group Study","authors":"Danial Ilman Muhammad Hasni, A. Sarlan, R. Ahmad","doi":"10.1109/ICCI51257.2020.9247663","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247663","url":null,"abstract":"This study is conducted to develop a collaborative visualization framework in the cross-field working group. The ultimate goal of this project is to provide a proper framework that can be used to develop a platform to allow collaborative visualization to be implemented inter-disciplinary groups, in two different settings; university students and research groups in research and development companies and institutions. The study begins with preliminary works to define the collaborative visualization based on previous researches. This study will also focus on the factors to develop an effective collaborative working environment thru visualization and shared understanding among the staffs/users from inter-disciplinary backgrounds. In addition, this study will also investigate the interaction between human cognition and ICT attributes of visualization in developing an efficient working group to achieve a common goals and objectives. Towards the end of the study, the framework will be tested to validate its possible contributions to the targeted collaborative working groups.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"51 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114001037","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

An Improved Robust Watermarking Scheme using Flexible Scaling Factor 一种改进的灵活比例因子鲁棒水印方案

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247798

F. Ernawan, Dhani Ariatmanto, Z. Musa, Z. Mustaffa, J. Zain

引用次数: 7

Improving Classification Accuracy of Scikit-learn Classifiers with Discrete Fuzzy Interval Values 提高离散模糊区间值Scikit-learn分类器的分类精度

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247696

Muhammad Nur Fikri Hishamuddin, M. Hassan, D. Tran, A. Mokhtar

{"title":"Improving Classification Accuracy of Scikit-learn Classifiers with Discrete Fuzzy Interval Values","authors":"Muhammad Nur Fikri Hishamuddin, M. Hassan, D. Tran, A. Mokhtar","doi":"10.1109/ICCI51257.2020.9247696","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247696","url":null,"abstract":"Understanding machine learning (ML) algorithm from scratch is time consuming. Thus, many software and library packages such as Weka and Scikit-Learn have been introduced to help researchers run simulation on several amounts of well-known classifiers. In ML, different classifiers have different performance and this depends on factor such as type of data used as input for the classification phase. Thus, it is necessary to perform data discretization when dealing with continuous data for classifiers that perform better with discrete data. However, in data mining, depending solely on discretization is not enough as real-world data can be large, imprecise and noisy. In addition, knowledge representation is necessary to help researchers to understand better about the data during the discretization process. Thus, the objective of this study is to observe the effect of fuzzy elements inside the discretization phase on the classification accuracy of Scikit-learn classifiers. In this study, fuzzy logic has been proposed to assist the existing discretization technique through fuzzy membership graph, linguistic variables and discrete interval values. All classifiers in Scikit-learn packages were used during the classification phase through 10-fold cross validation. The simulation results showed that the presence of fuzzy in assisting the discretization process slightly improved the classification accuracy of ensemble type classifiers such as Random Forest and Naive Bayes while slightly degrading the performance of other classifiers.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"80 16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123421425","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

Motorcycle Plated Recognition Based on FPGA 基于FPGA的摩托车电镀识别

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247711

M. Nong, J. Yusof, R. Osman, R. Sidek, Suzila Sabil

引用次数: 0

Blockchain in Education – Track ability and Traceability 教育中的区块链——跟踪能力和可追溯性

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247760

M. Hashmani, Aisha Zahid Junejo, A. Alabdulatif, Syed Hasan Adil

{"title":"Blockchain in Education – Track ability and Traceability","authors":"M. Hashmani, Aisha Zahid Junejo, A. Alabdulatif, Syed Hasan Adil","doi":"10.1109/ICCI51257.2020.9247760","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247760","url":null,"abstract":"Block-chain is one of the latest trends of tech world. The technology was introduced with Bitcoin and is being highly experimented for various cryptocurrencies ever since. However, with recent advancements in the technology, the research community and industries have started focusing on its applicability in other various areas such as healthcare, business, logistics, education and many more. Consequently, the research carried out in this paper investigates the use of Block-chain technology in education sector. Over the years, the use of technology in education have helped in improving the accessibility and quality of education using touch screens, Artificial Intelligence, cloud computing, e-classrooms etc. However, there are certain areas in education that need further consideration such as keeping track of students and student related records and transcripts, reduction in manual and paperwork and more. This is where Block-chain is predicted to play a significant role. In this paper, the potential applications of Block-chain for tracing and tracking in education sector are discussed. The discussion is done by first presenting the existing issues in education sector. Further, using the tracking and tracing ability of the Block-chain, few solutions to the existing problems have been proposed. The proposal of solutions is followed by the challenges of integration of Block-chain technology in education sector. Finally, the paper concludes stating that Block-chain has a huge potential in revolutionizing the education sector if the existing challenges are overcome through research and experimentation.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117000166","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 Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems 面向AI监测与预测系统开发的梯度增强算法研究

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247843

Norshakirah Aziz, E. A. P. Akhir, I. Aziz, J. Jaafar, M. H. Hasan, Ahmad Naufal Che Abas

引用次数: 13

Fuzzy Type-1 Triangular Membership Function Approximation Using Fuzzy C-Means 模糊c均值模糊1型三角隶属函数逼近

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247773

Muhammad Hamza Azam, M. H. Hasan, Saima Hassan, S. J. Abdulkadir

引用次数: 10

Using SURF to Improve ResNet-50 Model for Poultry Disease Recognition Algorithm 基于SURF改进ResNet-50模型的家禽疾病识别算法

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247698

Luyl-Da Quach, Nghi Pham Quoc, Nhien Huynh Thi, D. Tran, M. Hassan

引用次数: 13

An Optimized Recurrent Neural Network for Metocean Forecasting 一种用于海洋气象预报的优化递归神经网络

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247681

Alawi Alqushaibi, S. J. Abdulkadir, H. Rais, Qasem Al-Tashi, Mohamed Ragab

{"title":"An Optimized Recurrent Neural Network for Metocean Forecasting","authors":"Alawi Alqushaibi, S. J. Abdulkadir, H. Rais, Qasem Al-Tashi, Mohamed Ragab","doi":"10.1109/ICCI51257.2020.9247681","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247681","url":null,"abstract":"Metocean data plays a crucial role in planning and constructing offshore projects. the success of many offshore projects depends on the accuracy of metocean data analyzing and forecasting. And analyzing metocean data requires a tremendous effort to validate the data and determine the transformation of the metocean data conditions. Hence the wind plays an important role in the climate changes, recurrent neural network approaches such as vanilla recurrent neural network (VRNN), long short-term memory (LSTM), and Gated recurrent units (GRU) are used and compared to yield an accurate wind speed forecasting. The highest wind speed forecasting accuracy contribute to the minimization of cost and helps avoiding the operational faulty risk. Different models for estimating the hourly wind speed one hour ahead and one day ahead has been developed according to literature. However, this research compares the mentioned Artificial Neural Networks and selects the outstanding performance model to process the metocean data. The training and validation data of this work has been collected from free oceanic websites.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125049681","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

Hybrid Parameterisation Model for Missing Datasets 缺失数据集的混合参数化模型

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247668

Masurah Mohamad, A. Selamat, S. Masrom, K. Salleh

引用次数: 0