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2021 International Conference on Computer & Information Sciences (ICCOINS)最新文献

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Parametric Evaluation of Improved Deep Learning Networks for Musculoskeletal Disorder (MSD) Classification 肌肉骨骼疾病(MSD)分类改进深度学习网络的参数评价
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497139
Sadia Nazim, Syed Sajjad Hussain, M. Moinuddin, Muhammad Zubair, Rizwan Tanweer
Over the past few decades, the major debate regarding healthcare throughout the world is the analysis, and findings of diseases by investigating the medical images. Musculoskeletal disorder classification from a massive radiological image archive has always been a tedious task for radiologists. In recent literature, deep learning paves its way towards biomedical image classification with maximum accuracy and efficiency. Besides, deep learning models have already outperformed in various medical applications. Specifically, Convolution Neural Network (CNN) and LSTM architecture have been widely used. In this paper, new variants of conventional deep learning models have been proposed. Subsequently, an exhaustive parametric comparison from the existing pre-trained model has been established to validate the improved efficacy and productivity.
在过去的几十年里,世界各地关于医疗保健的主要争论是通过调查医学图像来分析和发现疾病。从大量的放射影像档案中对肌肉骨骼疾病进行分类对放射科医生来说一直是一项繁琐的任务。在最近的文献中,深度学习以最大的准确性和效率为生物医学图像分类铺平了道路。此外,深度学习模型已经在各种医疗应用中表现出色。具体来说,卷积神经网络(CNN)和LSTM架构得到了广泛的应用。本文提出了传统深度学习模型的新变体。随后,与现有的预训练模型进行详尽的参数比较,以验证提高的效率和生产率。
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引用次数: 0
Superlinear Speedup on GPGPU Using Laplacian Algorithm with Convolution Filtering as A Case Study 基于拉普拉斯卷积滤波算法的GPGPU超线性加速研究
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497235
Mogana Vadiveloo, Mishal Almazrooie, R. Abdullah
In this paper, the main idea is to investigate the hypothesis that superlinear speedup occurs when the concurrent threads on General Purposes Graphic Processing Units (GPGPU) carry heavy workloads. In order to evaluate this hypothesis, Laplacian image edge detection algorithm with convolution filtering is chosen as a case study. In this work, local memories of GPGPU are utilized in order to achieve the superlinear speedup. The convolution filtering kernels of the Laplacian edge detection algorithm are invoked in these local memories. By this, the low latency of the GPGPGU local memory are deployed efficiently and this subsequently leads to a higher speedup. The results obtained presented that the superlinear speedup is achieved when the size of the convolution kernel is large. In this study, when the convolution kernel size is 7×7, superlinear speedup is observed for image dataset of sizes between 1KB-2500KB.
本文的主要思想是研究当通用图形处理单元(GPGPU)上的并发线程承载繁重工作负载时出现超线性加速的假设。为了验证这一假设,本文以卷积滤波拉普拉斯图像边缘检测算法为例进行了研究。在本工作中,利用GPGPU的局部存储器来实现超线性加速。在这些局部存储器中调用拉普拉斯边缘检测算法的卷积滤波核。通过这种方式,GPGPGU本地内存的低延迟得到了有效的部署,并随后导致更高的加速。结果表明,当卷积核的大小较大时,可以实现超线性加速。在本研究中,当卷积核大小为7×7时,对于1KB-2500KB之间的图像数据集,可以观察到超线性加速。
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引用次数: 0
Development of Blended Learning Media Using Character-Based Flipbook Smartphone 基于字符的Flipbook智能手机的混合学习媒体开发
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497135
I. R. Ermawati, Meyta Dwi Kurniasih, Sri Astuti, Onny Fitriana, Wan Fatimah Wan Achmad, Mohd Hilmi Hasan
This study aims to determine the design stages of developing blended learning smartphone media learning products and to determine the feasibility of integral learning media using the developed flipbook. This study uses a small-scale trial on FHIP UHAMKA while for large-scale trials conducted by Malaysian UTP with 29 respondents including Physics Education FKIP UHAMKA, Mathematics Education FKIP UHAMKA and also Malaysian UTP. The method used is Research and Development (R&D) using the Brog and Gall development procedure. The results of the media interface test obtained a percentage of 77.18%. This shows that the products developed included in the category are very feasible to use while for the effectiveness of students as teaching material in the learning process, the average of the effectiveness of students is 83.26%, the value obtained can be said to be very well seen from the Likert scale index used, then from the assessment of the effectiveness of media students it is feasible to be used in integral learning. Blended learning media with character given to one class in FKIP UHAMKA, obtained an average post-test score of 48.04. The data obtained, the character development of the character questionnaire was 75.04.
本研究旨在确定开发混合学习智能手机媒体学习产品的设计阶段,并确定使用开发的flipbook集成学习媒体的可行性。本研究使用了FHIP UHAMKA的小规模试验,而马来西亚UTP进行了大规模试验,有29名受访者,包括物理教育FKIP UHAMKA,数学教育FKIP UHAMKA和马来西亚UTP。采用的方法是采用Brog和Gall开发程序的研究与开发(R&D)。介质界面试验结果达到77.18%。这表明,所开发的产品包含在类别中是非常可行的使用,而对于学生作为教材在学习过程中的有效性,学生的有效性的平均值为83.26%,从使用的李克特量表指数可以很好地看出所获得的价值,那么从对媒体学生有效性的评估来看,在整体学习中是可行的。在FKIP UHAMKA对一个班级进行性格混合学习媒体,平均后测成绩为48.04分。获得的数据显示,品格发展问卷的得分为75.04分。
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引用次数: 2
The Investment Opportunity, Information Technology and Financial Performance of SMEs 中小企业的投资机会、信息技术与财务绩效
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497182
T. Hastuti, Ridwan Sanjaya, Freddy Koeswoyo
Financial statements as a tool for show the company financial performance and can be used as a basis for making economic decisions. Understanding of accounting standards specifically for SMEs can help SMEs in making business decisions. Information technology support and investment opportunity in SMEs are important factors that can help improve the financial performance of SMEs. Investment opportunity can be seen from the development of market tastes and reflection on the development of SMEs businesses. Investment opportunity is also obtained by innovating in production and marketing. The development of the SMEs business is in line with increasing business age. This shows the ability to survive batik business in facing the times and business competition. This study examines the factors that influence the financial performance of SMEs. using a sample of batik craftsmen. Data analysis was performed using multiple regression program which are currently widely used by researchers to test the research model that they formulate. The result of this research were (1) for small and medium-sized enterprises (SMEs), the company's age and investments opportunity greatly affect the company's financial performance. (2) IT support and good financial management in small and medium enterprises (SMEs) do not affect the company's financial performance.
财务报表作为显示公司财务业绩的工具,可以作为制定经济决策的基础。了解专门针对中小企业的会计准则可以帮助中小企业做出商业决策。中小企业的信息技术支持和投资机会是提高中小企业财务绩效的重要因素。投资机会可以从市场的发展品味和对中小企业发展的反思中看出。通过生产和营销的创新也获得了投资机会。中小企业的发展顺应了商业时代的发展。这显示了蜡染企业在面对时代和商业竞争时的生存能力。本研究探讨影响中小企业财务绩效的因素。使用蜡染工匠的样本。数据分析使用多元回归程序进行,这是目前研究人员广泛使用的,以检验他们制定的研究模型。本研究的结果是:(1)对于中小企业(SMEs),公司的年龄和投资机会对公司的财务绩效影响很大。(2)中小企业的IT支持和良好的财务管理并不影响公司的财务绩效。
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引用次数: 2
Analysis User Interface: Mobile Application to Blended Learning Model 分析用户界面:混合学习模式的移动应用
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497142
Sri Astuti, Aisyah Fitriana, W. F. Wan Ahmad, Imas Ratna Ermawati, M. Hasan
This research is concerned with the use of development applications using mobile devices as a tool for blended learning models. However, in developing learning applications, one important concern is the user interface (UI). Therefore, the purpose of this research is to find out which UI design components are easy to use according to the user to be more user friendly, so that the purpose of using mobile applications for education can be realized. This study covers design principles that are appropriate for applications developed for platforms. Based on research, user interface testing has been carried out from 5 user interface principles and analysis using the System Usage Scale (SUS) given to 32 respondents. The results showed that the majority of respondents agreed that the mobile application developed had met the requirements of the user interface element.
本研究关注的是使用移动设备作为混合学习模型工具的开发应用程序的使用。然而,在开发学习应用程序时,一个重要的关注点是用户界面(UI)。因此,本研究的目的是根据用户找出哪些UI设计组件易于使用,从而更加用户友好,从而实现使用移动应用进行教育的目的。本研究涵盖了适用于平台应用开发的设计原则。基于研究,用户界面测试从5个用户界面原则进行,并使用系统使用量表(SUS)对32名受访者进行分析。结果显示,大多数受访者认为开发的移动应用程序已经满足了用户界面元素的要求。
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引用次数: 4
Segmented Region Based Reconstruction of Magnetic Resonance Image 基于分割区域的磁共振图像重建
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497166
M. Faris, T. Javid, SS H. Rizvi, A. Aziz
Compressed Sensing theory promises to reconstruct the magnetic resonance images from partially sampled k-space data. Through this Compressed sensing - magnetic resonance imaging CS-MRI technique, we accelerate the reconstruction process but at the cost of high artifacts especially with the increase of high reduction factor and high reconstruction time. To minimize these artifacts, we proposed a segmented region based reconstruction technique to enhance the quality image without affecting much more the reconstruction time. In this algorithm, the partial k-space data segmented into two parts according to their frequencies. At central part which has lower frequency components selected and predicted by nuclear norm minimization. After that the part is fused with peripheral part of the k-space components and apply this recovery technique another time to reconstruct more accurate images in terms of conventional techniques. To analyze the performance of proposed algorithm, we compare the results for different data sets of brain with CS techniques. Better results in term of NMSE and time shows the effectiveness of proposed method with high reduction factor of data.
压缩感知理论有望从部分采样的k空间数据重建磁共振图像。通过压缩感知-磁共振成像技术,我们加快了重建过程,但代价是高伪影,特别是高还原系数和高重建时间的增加。为了最大限度地减少这些伪影,我们提出了一种基于分割区域的重建技术,在不影响重建时间的情况下提高图像质量。在该算法中,部分k空间数据根据频率被分割成两部分。在具有较低频率分量的中心部分,采用核范数最小化法进行选择和预测。之后,将该部分与k空间分量的外围部分融合,再次应用该恢复技术,根据常规技术重建更精确的图像。为了分析该算法的性能,我们比较了CS技术在不同大脑数据集上的结果。在NMSE和时间方面取得了较好的结果,表明该方法具有较高的数据缩减系数。
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引用次数: 2
Metocean Prediction using Hadoop, Spark & R metoocean预测使用Hadoop, Spark和R
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497204
Sumayema Kabir Ricky, L. Rahim
This project is the development of an analysis system for historical Metocean Data. It is a single page reactive web application with shiny web UI package of R containing forecasting model, ARIMA and two ML algorithms, Linear Regression and H2O AutoML developed with R for the variables of Metocean data stored in HDFS of a virtual Hadoop cluster and spark is integrated to make the computations happen in-memory. The predictions is compared to the actual data to see its correctness with RMSE. Performance difference of the application deployed on desktop and on the server is also discussed. The application performs better when running in the server than on desktop.
本项目是开发一个历史海洋气象数据分析系统。它是一个单页响应式web应用程序,具有闪亮的R web UI包,包含预测模型,ARIMA和两种ML算法,线性回归和H2O AutoML,用R开发,用于存储在虚拟Hadoop集群的HDFS中的Metocean数据的变量,并集成spark使计算发生在内存中。将预测与实际数据进行比较,以查看其与RMSE的正确性。还讨论了部署在桌面和服务器上的应用程序的性能差异。应用程序在服务器上运行时比在桌面上运行时性能更好。
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引用次数: 1
The Development of Digital Books of Islamic Science Integration on Respiratory System Materials 呼吸系统资料伊斯兰科学集成电子图书的开发
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497143
Maryanti, Jundiyah Rabbaniyah, Gufron Amirullah, Mayarni
The development of Science and Technology is very rapid in the era of globalization. It effects very extensive, especially in the field of education. Global demands demand the education sector to constantly adjust the development of Science and Technology. This study aims to produce a digital book of Integration of Islamic Science in Respiratory System material for teachers and to find out the quality of digital book products that have been produced so that it is suitable for use in learning biology. This study is R&D (Research and Development) using ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). However, this study only reached the Implementation stage. The research instrument was in the form of a quality assessment questionnaire sheet in the form of a checklist. The assessment was conducted by two media experts, one material integration expert, two material experts, and 17 teachers graduating from Biology Education FKIP UHAMKA Jakarta. Quality value data obtained were analyzed using descriptive, qualitative and quantitative analysis based on the ideal rating category. The results showed that the quality of digital books developed was included in the excellent category. According to media experts 98.33%; material integration expert 88.30%; and material experts 98.21%. The response of the teacher is in the excellent category with percentage of 90.69%. Based on this evaluation, it can be concluded that the digital book Integration of Islamic Science in Respiratory System material that has been developed is suitable for use as a learning resource.
在全球化时代,科学技术的发展非常迅速。它的影响非常广泛,特别是在教育领域。全球需求要求教育部门不断调整科学技术的发展。本研究的目的是为教师制作一本伊斯兰科学呼吸系统教材的电子书,并找出已经生产的电子书产品的质量,以便它适合用于学习生物学。本研究采用ADDIE (Analysis, Design, Development, Implementation, and Evaluation)模型进行R&D(研究与开发)。然而,本研究仅处于实施阶段。研究手段是以核对表形式的质量评价调查表的形式。评估由两名媒体专家、一名材料整合专家、两名材料专家和17名从雅加达生物教育学院毕业的教师进行。根据理想评定类别,采用描述性、定性和定量分析方法对所得质量值数据进行分析。结果显示,开发的数字图书质量被列入优秀类别。据媒体专家称98.33%;材料集成专家88.30%;材料专家98.21%。教师的回答为优秀,占90.69%。基于此评估,可以得出结论,已开发的数字图书“呼吸系统伊斯兰科学集成”材料适合作为学习资源使用。
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引用次数: 0
Fine-grained Emotion Classification: Class Imbalance Effects on Classifier Performance 细粒度情绪分类:类别不平衡对分类器性能的影响
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497181
Jasy Liew Suet Yan, Howard R. Turtle
We explore a set of machine learning experiments in fine-grained emotion classification to test different proportion of positive and negative samples in the training data with the goal to examine if class imbalance affects classifier performance. The class distribution in a tweet corpus (EmoTweet-28) labelled with 28 emotion categories varies significantly with the largest category (happiness) occurring 11.5% and the smallest category occurring only 0.2%. For each emotion category, there are far more negative examples than positive examples. Unlike conventional wisdom, downsampling the data in our skewed corpus did not improve classifier performance. However, we found that increasing the negative examples in the training data leads to lower recall but higher precision. Demonstrating how the ratio of positive and negative examples in the training data affect the performance of emotion classifiers is the main contribution of this study.
我们探索了一组细粒度情绪分类的机器学习实验,以测试训练数据中不同比例的正样本和负样本,目的是检验类别不平衡是否会影响分类器的性能。在tweet语料库(EmoTweet-28)中,标记有28种情绪类别的类别分布差异很大,最大类别(快乐)占11.5%,最小类别仅占0.2%。对于每一种情绪类别,消极的例子远远多于积极的例子。与传统智慧不同,在倾斜语料库中对数据进行降采样并没有提高分类器的性能。然而,我们发现在训练数据中增加负例会导致召回率降低,但准确率提高。本研究的主要贡献是展示了训练数据中积极和消极例子的比例如何影响情绪分类器的性能。
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引用次数: 0
Application of a Wavelet based Krylov Subspace Algorithm on Digital Signal Convergence 基于小波的Krylov子空间算法在数字信号收敛中的应用
Pub Date : 2021-07-13 DOI: 10.1109/ICCOINS49721.2021.9497212
Ahmed Sikander, Syed Sajjad Hussain Rizvi, R. Hussain, Jawwad Ahmed, Sheeraz Arif
In the World of Communication, digital transmission has its importance in sense of speed and accuracy which is the basic requirement of current time. Nowadays, successful as well as rapid communication is the main concern of the research. It can be achieved by using different technique and methods based on theories built upon the branches of applied sciences and engineering. In this research an algorithm is presented by sequentially combining two transforms, Wavelet and Krylov. The Algorithm was formerly developed by the same and was known as WK Algorithm. In this research the Algorithm is first studied for digital signal application and results are presented and concluded by applying also with other two methods in order to verify and validate the research.
在通信世界中,数字传输在速度和准确性方面具有重要意义,这是当今时代的基本要求。如今,成功和快速的通信是研究的主要关注点。它可以通过使用基于应用科学和工程分支理论的不同技术和方法来实现。本文提出了一种将小波变换和克雷洛夫变换序贯结合的算法。该算法以前是由该公司开发的,被称为WK算法。本研究首先对该算法在数字信号中的应用进行了研究,并将结果与其他两种方法结合使用进行了总结,以验证和验证研究结果。
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引用次数: 0
期刊
2021 International Conference on Computer & Information Sciences (ICCOINS)
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