2020 International Conference on Computational Intelligence (ICCI)最新文献

Efficient and Flexible Checkpoint/Restore of Split-memory Virtual Machines 高效和灵活的检查点/恢复分裂内存虚拟机

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247679

Tokito Murata, Kenichi Kourai

{"title":"Efficient and Flexible Checkpoint/Restore of Split-memory Virtual Machines","authors":"Tokito Murata, Kenichi Kourai","doi":"10.1109/ICCI51257.2020.9247679","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247679","url":null,"abstract":"Recently, clouds provide virtual machines (VMs) with a large amount of memory for big data analysis. For easier migration of such VMs, split migration divides the memory of a VM into several pieces and transfers them to multiple hosts. Since the migrated VM called a split-memory VM needs to exchange memory data between the hosts, it is inherently subject to host and network failures. As a countermeasure, a checkpoint/restore mechanism has been used to periodically save the state of a VM, but the traditional mechanism is not suitable for split-memory VMs. It has to move a large amount of memory data between hosts during checkpointing and can just restores a normal VM on one host. This paper proposes D-CRES for efficient and flexible checkpoint/restore of split-memory VMs. D-CRES achieves fast checkpointing by saving the memory of a VM in parallel at all the hosts without moving memory data. For live checkpointing, it consistently saves the memory of a running VM by considering memory data exchanged by the VM itself. In addition, it enables a split-memory VM to be restored in parallel at multiple hosts. We have implemented checkpoint/restore of D-CRES in KVM and showed that the performance was up to 5.4 times higher than that of using the traditional mechanism.","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":"114674878","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

Missing Values Imputation Using Fuzzy C Means Based On Correlation of Variable 基于变量相关性的模糊C均值缺失值推定

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247675

Farahida Hanim Mausor, J. Jaafar, S. Taib

引用次数: 0

Random Search One Dimensional CNN for Human Activity Recognition 随机搜索一维CNN用于人类活动识别

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247810

M. G. Ragab, S. J. Abdulkadir, Norshakirah Aziz

{"title":"Random Search One Dimensional CNN for Human Activity Recognition","authors":"M. G. Ragab, S. J. Abdulkadir, Norshakirah Aziz","doi":"10.1109/ICCI51257.2020.9247810","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247810","url":null,"abstract":"Due to its wide application, human activity recognition (HAR) has become a common subject for research specially with the development of deep learning. Many researchers believe that deep convolutional neural networks (DCNN) are ideal for feature extraction from signal inputs. This has gained widespread interest in using these methods to identify human actions on the mobile phone in real time. A deep network architecture using random search one dimensional convolutional neural network (RS-1D-CNN) is proposed to find best networks connections and hyper-parameters to enhance model performance. Batch normalization (BN) layer was added to speed up the convergence. Moreover, we have applied a global average pooling (GAP) for dimensionality reduction and to reduce model hyper-parameters, followed two dense connected layers. The final dense layer has a softmax activation function and a node for each potential object category. Public UCI-HAR dataset was used to evaluate model performance. Random search has been utilized to perform hyper parameter tuning to determine the optimal model parameters. Proposed model will automatically extract and classify human behaviours. Daily human activities that provided by UCI-HAR include (walking, jogging, sitting, standing, upstairs and downstairs). Results has shown that our approach outperforms both CNN, LSTM method and other state-of-the-art approaches.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"42 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":"117095670","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: 13

ICCI 2020 TOC

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/icci51257.2020.9247812

引用次数: 0

Simulation of Hydrogen Sulfide Generation in Oil and Gas Geological Formations 油气地质构造中硫化氢生成模拟

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247695

Haithm Salah Hagar, Jalal Forooezsh, D. Zivar, Sunil Kumar, H. Abdulelah, I. Dzulkarnain

{"title":"Simulation of Hydrogen Sulfide Generation in Oil and Gas Geological Formations","authors":"Haithm Salah Hagar, Jalal Forooezsh, D. Zivar, Sunil Kumar, H. Abdulelah, I. Dzulkarnain","doi":"10.1109/ICCI51257.2020.9247695","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247695","url":null,"abstract":"Hydrogen sulphide generation in subsurface formation–of ten dubbed as souring--is a phenomenon that happens as a result of in-situ biodegradation reactions during and after the water-flooded reservoir. This phenomenon is caused by sulfate-reducing microorganisms, which a group composed of sulfate-reducing bacteria and sulfate-reducing archaea. Sulfate-reducing bacteria, by oxidizing a carbon source, sulfate ions can be turned into hydrogen Sulfide. Furthermore, Water cut, temperature, pressure, and fluid chemistry can affect the concentration observed. This paper introduced a simulation model that describes We simulated H2 S generation (souring) at subsurface formation utilizing a 2D model. The conditions that are favorable for souring are met in the constructed model. We chose STARS- CMG--an advanced Process Thermal Compositional Simulator –to simulate the aftermath of geochemical and chemical reactions. The bacterial-induced souring. The results suggest that bacterial activity has consumed the sulfate in the aqueous phase. Such consumption was seen as the SO4 concentration dropped from 1.8e-05-6.0e-06mo1/L. The consumed SO4 was converted into H2 S or caused water souring. The souring occurrence was inferred by the sharp increase in H2 S concentration that reached a maximum of $sim$0.0006mo1/L. The introduced simulation approach could serve as a way of predicting the aftermath of biodegradation reactions that causes H2 S generation in the subsurface.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"67 2 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":"128658454","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

Human Action Recognition using ConvLSTM with GAN and transfer learning 结合GAN和迁移学习的ConvLSTM人体动作识别

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247670

Mohsin Raza Siyal, Mansoor Ebrahim, Syed Hasan Adil, Kamran Raza

{"title":"Human Action Recognition using ConvLSTM with GAN and transfer learning","authors":"Mohsin Raza Siyal, Mansoor Ebrahim, Syed Hasan Adil, Kamran Raza","doi":"10.1109/ICCI51257.2020.9247670","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247670","url":null,"abstract":"Human Action Recognition (HAR)is a challenging time series classification problem that has received significant attention from computer vision researchers. In this paper, different techniques used for human activities are investigated, and a human action recognition approach using a Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) and generative adversarial network is proposed. The proposed research evaluates the performance of cross-entropy and adversarial loss function for HAR analysis. Two different datasets UFC101 and the classic KTH dataset, are used for experimental purposes. The UFC101 dataset contains 13k videos in which 101 human actions are included i.e., playing instrument, makeup, etc. In contrast, KTH dataset contains 600 videos containing six human activities, including walking, running, jogging, hand clapping and hand waving, performed by 25 different persons. Also, demonstrates the process of HAR by mixing both datasets and evaluate the performance. The GAN enhances the model robustness by applying adversarial training which fully discovers the underlying connections in both intra-view and cross-view aspects.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"80 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":"131389131","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

Feature Selection Based on Grey Wolf Optimizer for Oil & Gas Reservoir Classification 基于灰狼优化的油气储层分类特征选择

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247827

Qasem Al-Tashi, H. Rais, S. J. Abdulkadir, S. Mirjalili

{"title":"Feature Selection Based on Grey Wolf Optimizer for Oil & Gas Reservoir Classification","authors":"Qasem Al-Tashi, H. Rais, S. J. Abdulkadir, S. Mirjalili","doi":"10.1109/ICCI51257.2020.9247827","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247827","url":null,"abstract":"The classification of the hydrocarbon reserve is a significant challenge for both oil and gas producing firms. The factor of reservoir recovery contributes to the proven reservoir growth potential which leads to a good preparation of field development and production. However, the high dimensionality or irrelevant measurements/features of the reservoir data leads to less classification accuracy of the factor reservoir recovery. Therefore, feature selection techniques become a necessity to eliminate the said irrelevant measurements/ features. In this paper, a wrapper-based feature selection method is proposed to select the optimal feature subset. A Binary Grey Wolf Optimization (BGWO) is applied to find the best features/measurements from big reservoir data obtained from U.S.A. oil & gas fields. To our knowledge, this is the first time applying the Grey Wolf Optimizer (GWO) as a search technique to search for the most important measurements to achieve high classification accuracy for reservoir recovery factor. The wrapper K-Nearest Neighbors (KNN) classifier is used to evaluate the selected features. In addition, to examine the efficiency of the proposed method, two recent algorithms namely: Whale Optimization algorithm (WAO) and Dragonfly Algorithm (DA) are implemented for comparison. The experimental results showed that, the proposed BGWO-KNN significantly outperforms benchmarking methods in terms of feature reduction as well as increasing the classification accuracy. The proposed method shows a great potential for solving the real oil & gas problems.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"127 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":"130258606","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: 15

Application of Blockchain to Ensure Temper-Proof Data Availability for Energy Supply Chain 应用区块链确保能源供应链的数据可用性

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247768

Mohamed Rimsan, A. Mahmood

引用次数: 1

Student Absenteeism Monitoring System Using Bluetooth Smart Location-Based Technique 基于蓝牙智能定位技术的学生旷课监控系统

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247809

Siti Khadijah Baharin, Zalikha Zulkifli, Samsiah Ahmad

{"title":"Student Absenteeism Monitoring System Using Bluetooth Smart Location-Based Technique","authors":"Siti Khadijah Baharin, Zalikha Zulkifli, Samsiah Ahmad","doi":"10.1109/ICCI51257.2020.9247809","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247809","url":null,"abstract":"Conventional method of recording students’ attendance is still being used in Universiti Teknologi MARA (UiTM) Perak Tapah Campus. The method used is by recording students’ attendance in attendance sheets which is an inefficient way to monitoring students’ attendance. The absenteeism of students without valid excuses during lectures appears to be a serious problem as it falls under the term of truancy. Once the absenteeism percentage reaches 10%, the students will receive a notification letter issued by the Academic Affairs Division as the first warning. The last warning will be issued when the absenteeism percentage reaches 20% where the students might be barred from sitting the final examination. Therefore, the “Student Absenteeism Monitoring System Using Bluetooth Smart Location-Based Technique” is developed specifically for lecturers and students of the UiTM Tapah Campus to automatically monitor students’ attendance. The objective of this project is to determine the percentage of students’ absenteeism to prevent students from getting a ZZ status. ZZ status is a situation where a student is being barred from sitting the final examination. The system was evaluated based on functionality, usability efficiency, and user acceptance test. The result from evaluations indicates that most of the users have good experience in using the system. This system which was specifically developed for UiTM can also be enhanced and customized to meet the needs of learning institutions throughout Malaysia.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"1 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":"128923022","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

P-BBA: A Master/Slave Parallel Binary-based Algorithm for Mining Frequent Itemsets in Big Data P-BBA:基于主/从并行二进制的大数据频繁项集挖掘算法

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247689

Aliya Najiha Amir, H. Alhussian, S. Fageeri, R. Ahmad

{"title":"P-BBA: A Master/Slave Parallel Binary-based Algorithm for Mining Frequent Itemsets in Big Data","authors":"Aliya Najiha Amir, H. Alhussian, S. Fageeri, R. Ahmad","doi":"10.1109/ICCI51257.2020.9247689","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247689","url":null,"abstract":"Frequent itemsets mining is an effective but computational expensive technique especially when dealing with big datasets. Hence, the need for a customizable algorithm to work with big datasets in a reasonable time becomes a necessity. The Binary-based Technique Algorithm (BBT) used a binary representation of the database transactions as well as binary operations in order to simplify the process of identifying the frequent patterns as well as reduce the memory consumption. However, BBT algorithm still suffer the problem of low performance in terms of execution times when dealing with big data. This is due to the fact that the BBT algorithm was designed to run as a single thread of execution. Therefore, there is a need to improve the performance of the Binary-based Technique Algorithm (BBT). In this research, we proposed a Parallel Binary-Based Algorithm (P-BBA) towards solving the above mentioned problem. The objective of the proposed P-BBA is to process big datasets by developing collaborative threads that would work together concurrently and collaboratively and generates the list of frequent itemsets within an acceptable time frame. The algorithm is designed using a Master/Slave thread model to fits in Apache Spark distributed platform. The performance will be evaluated based on the total execution time.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"63 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":"124739438","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