2020 International Conference on Computational Intelligence (ICCI)最新文献_第6页

An Approach for SARS-CoV-2 Infected Cases Report Analysis SARS-CoV-2感染病例报告分析方法

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247702

D. Tran, H. Ha, Duc Long Nguyen, R. Ibrahim, Luyl-Da Quach

引用次数: 1

Real Time Vein Visualization using Near-Infrared Imaging 使用近红外成像的实时静脉可视化

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247732

Hia Yee May, F. Ernawan

{"title":"Real Time Vein Visualization using Near-Infrared Imaging","authors":"Hia Yee May, F. Ernawan","doi":"10.1109/ICCI51257.2020.9247732","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247732","url":null,"abstract":"Vein visualization is one of the most researched biomedical technique. Although the concept behind the technique is not complicated, the vein pattern acquisition method and the design and implementation of image processing algorithms become challenging. Nowadays, the major challenge faced by the medical practitioners is the difficulty in accessing subcutaneous veins for intra-venous injections due to various factors like low visibility of vein by naked eyes and patients with too narrow veins. Failure during venipuncture may lead to several problems like bruises, bleeding and rashes. Therefore, the real time vein visualization system is developed accordance with the objective of visualizing subcutaneous veins which is to assist medical practitioners by providing them visual guidance during venipuncture process. This system is developed based on near-infrared imaging and is connected to the monitor screen. The development stage includes edge detection, vein segmentation and vein visualization. Evolutionary prototyping method is used to develop the system and to ensure the quality of the final system through a few prototype refinement cycles. OpenCV library is also used for its real-time functionalities. The functionality of the system is evaluated through a series of planned system tests. The experimental results show that the proposed system is able to show the veins pattern.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"49 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":"132309947","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

Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning 基于生成对抗网络和迁移学习的有限医疗数据半监督学习

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247724

I. Amin, Saima Hassan, J. Jaafar

引用次数: 2

Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network 基于RNN-LSTM神经网络的原油价格预测分析

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247665

Norshakirah Aziz, Mohd Hafizul Afifi Abdullah, Ahmad Zaidi

{"title":"Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network","authors":"Norshakirah Aziz, Mohd Hafizul Afifi Abdullah, Ahmad Zaidi","doi":"10.1109/ICCI51257.2020.9247665","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247665","url":null,"abstract":"Prediction of future crude oil price is considered a significant challenge due to the extremely complex, chaotic, and dynamic nature of the market and stakeholder’s perception. The crude oil price changes every minute, and millions of shares ownerships are traded everyday. The market price for commodity such as crude oil is influenced by many factors including news, supply-and-demand gap, labour costs, amount of remaining resources, as well as stakeholders’ perception. Therefore, various indicators for technical analysis have been utilized for the purpose of predicting the future crude oil price. Recently, many researchers have turned to machine learning approached to cater to this problem. This study demonstrated the use of RNN-LSTM networks for predicting the crude oil price based on historical data alongside other technical analysis indicators. This study aims to certify the capability of a prediction model built based on the RNN-LSTM network to predict the future price of crude oil. The developed model is trained and evaluated against accuracy matrices to assess the capability of the network to provide an improvement of the accuracy of crude oil price prediction as compared to other strategies. The result obtained from the model shows a promising prediction capability of the RNN-LSTM algorithm for predicting crude oil price movement.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"390 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":"124595679","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

Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site 波浪能转换场风波参数机器预测的最优特征识别

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247677

Muhammad Umair, M. Hashmani, Horio Keiichi

{"title":"Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site","authors":"Muhammad Umair, M. Hashmani, Horio Keiichi","doi":"10.1109/ICCI51257.2020.9247677","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247677","url":null,"abstract":"The hike in fossil-fuel prices and increasing environmental damage due to the subsequent Carbon Monoxide (CO) emission from burning fossil-fuel is becoming a major concern for every nation. The possibility of generating power from natural sources such as solar, wind, and sea waves is thus considered as an alternative. In the case of the sea waves, the kinetic energy of surface waves can be converted into single direction motion which runs a turbine to generate electricity. A Wave Energy Converter (WEC) is such an installation that converts the wave energy into electrical energy. In this study, we have conducted a literature investigation to identify the significant meteorological and wind-wave data parameters which determine wave-energy potential at a wave energy converter site and then identified optimal feature sets from buoy data for machine prediction of those identified parameters. The authors hope that by suggesting optimal feature sets, the outcomes of this study will help in improving the computational efficiency of machine learning models specially designed for wave parameter prediction at WEC sites.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"21 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":"121084763","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

A Review of Weight Optimization Techniques in Recurrent Neural Networks 递归神经网络权值优化技术综述

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247757

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

{"title":"A Review of Weight Optimization Techniques in Recurrent Neural Networks","authors":"Alawi Alqushaibi, S. J. Abdulkadir, H. Rais, Qasem Al-Tashi","doi":"10.1109/ICCI51257.2020.9247757","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247757","url":null,"abstract":"Recurrent neural network (RNN) has gained much attention from researchers working in the domain of time series data processing and proved to be an ideal choice for processing such data. As a result, several studies have been conducted on analyzing the time series data and data processing through a variety of RNN techniques. However, every type of RNN has its own flaws. Simple Recurrent Neural Networks (SRNN) are computationally less complex than other types of RNN such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). However, SRNN has some drawbacks such as vanishing gradient problem that makes it difficult to train when dealing with long term dependencies. The vanishing gradient exists during the training process of SRNN due to the multiplication of the gradient with small value when using the most traditional optimization algorithm the Gradient Decent (GD). Therefore, researches intend to overcome such limitations by utilizing weight optimized techniques such as metaheuristic algorithms. The objective of this paper is to present an extensive review of the challenges and issues of RNN weight optimization techniques and critically analyses the existing proposed techniques. The authors believed that the conducted review would serve as a main source of the techniques and methods used to resolve the problem of RNN time series data and data processing. Furthermore, current challenges and issues are deliberated to find promising research domains for further study.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"39 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":"125874135","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

COVID-19: A Novel Framework to Globally Track Coronavirus Infected Patients using Blockchain COVID-19:使用区块链全球跟踪冠状病毒感染患者的新框架

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247659

Mohamed Rimsan, A. Mahmood, Muhammad Umair, Farruk Hassan

{"title":"COVID-19: A Novel Framework to Globally Track Coronavirus Infected Patients using Blockchain","authors":"Mohamed Rimsan, A. Mahmood, Muhammad Umair, Farruk Hassan","doi":"10.1109/ICCI51257.2020.9247659","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247659","url":null,"abstract":"An outbreak of coronavirus caused by a novel virus called SARS-CoV-2 occurred at the end of 2019. The unexpected outbreak and unchecked global spread of COVID19 indicate that the current global healthcare networks have limitations in addressing the crises for public safety. The detection of infected or tested patients globally is challenging when patients travel abroad. As such, innovative technology such as blockchain has emerged as a potential approach for addressing coronavirus patient tracking. Blockchain technology can tackle pandemics by allowing early detection of outbreaks, protecting individual privacy while maintaining data security by using smart contracts. Motivated by these facts, we propose a novel blockchain-based framework that integrates intercountry for COVID-19 to track infected or tested patients globally using the methodology of design science research. The proposed framework could help governments, aviation industries, health authorities, and residents make essential decisions on infection identification, infection prediction, and infection prevention.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"30 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":"127752734","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 deep learning based neuro-fuzzy approach for solving classification problems 基于深度学习的神经模糊方法解决分类问题

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247639

Noureen Talpur, S. J. Abdulkadir, M. H. Hasan

{"title":"A deep learning based neuro-fuzzy approach for solving classification problems","authors":"Noureen Talpur, S. J. Abdulkadir, M. H. Hasan","doi":"10.1109/ICCI51257.2020.9247639","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247639","url":null,"abstract":"Techniques involved artificial intelligence and machine learning offers various classification methods in order to deal with daily life problems. Among these methods, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Deep Neural Network (DNN) are the most commonly used classifiers. Since ANFIS is not suitable for high-dimensional data, therefore DNN was introduced to overcome this problem faced by conventional methods. However, due to the optimization of millions of parameters in their deep architecture, the decision made by DNN faced the criticism of being non-transparent. To overcome this problem, recently, various researchers are coming up with the idea of using fuzzy logic with DNN. Therefore, this study also proposed a Deep Neuro-Fuzzy Classifier (DNFC) with a cooperative based structure for solving classification problems, particularly. The performance of the proposed DNFC was evaluated with ANFIS and DNN classifier, where overall results show that the performance of ANFIS classifier decreased when input size increased. While the performance of the proposed model demonstrated nearly similar or slightly higher accuracy as compared to DNN.","PeriodicalId":194158,"journal":{"name":"2020 International Conference on Computational Intelligence (ICCI)","volume":"84 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":"126247330","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

ICCI 2020 Breaker Page ICCI 2020断路器页面

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/icci51257.2020.9247746

引用次数: 0

Predicting potential development for land areas in Perak, Malaysia using spatial data technique 利用空间数据技术预测马来西亚霹雳州土地的潜在发展

2020 International Conference on Computational Intelligence (ICCI) Pub Date : 2020-10-08 DOI: 10.1109/ICCI51257.2020.9247764

N. Azmina, M. Zamani, Z. Ahmad, S. Ahmad, S. Hatim, S. Masrom, M. Surani

{"title":"Predicting potential development for land areas in Perak, Malaysia using spatial data technique","authors":"N. Azmina, M. Zamani, Z. Ahmad, S. Ahmad, S. Hatim, S. Masrom, M. Surani","doi":"10.1109/ICCI51257.2020.9247764","DOIUrl":"https://doi.org/10.1109/ICCI51257.2020.9247764","url":null,"abstract":"Predicted size and spatial distribution of future population are crucial drivers of development growth and critical determinants for the development type per se. Population data is a prime example of spatial demographic inputs that can be used to predict land areas development and also assists in effective rural or urban planning. Data can be collected by various individuals or different teams with a variety of technologies and assumptions over a period span. As a result, they may contain a great many redundancies, duplicates, and inconsistencies. By using Geographic Information System (GIS), data can be more organized and processed to produce a more desirable result. The spatial data technique will be applied in the system by plotting the geocoordinates on the map of Perak state according to the districts being analyzed. Every district contains information of the predicted potential development with the population data for the year 2020. The prediction will be based on an exponential model where population data is processed. This information is displayed in an informative way via visualization of data using the Data Driven Document (D3) tool. It gives users a dynamic display function to select the area that will show the relevant information. Therefore, it is expected that the development of rural areas can be planned more efficiently in the future.","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":"115393755","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