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Assessing High School Students Readiness for Mobile learning 4

Assessing High School Students Readiness for Mobile Learning

引用

4th international conference on Information Management and Technology, ICIMTech 2019

作者： Sukmaningsih, Dyah Wahyu Information Systems Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

learning activities has evolved into the next level. Using Mobile technology and its application, students can acquire knowledge and learn new skills. Mobile technology offers a different set of rules to learn and change how students can learn. Nevertheless, there also a challenge to utilize mobile learning. Some factor has implication for the effectiveness of learning using mobile learning. this paper will evaluate the readiness factor towards students' intention for using mobile learning. there are self-efficacy, computer playfulness, subjective Norm, perceived enjoyment and Content quality. the survey was conducted with respondents from high school students. PLS-SEM was used to analyze those factors. the result show that self-efficacy and perceived enjoyment are significant factor for readiness students to use mobile learning. © 2019 IEEE.

关键词： Students

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application of ELM in Key Impact Factors Identification of Rail Transit System Cost 19

Application of ELM in Key Impact Factors Identification of R...

引用

4th international conference on Big Data and Computing (ICBDC)

作者： Chen, Haolin Yang, Jihong Xu, Gang Gao, Guofei Yuan, Quan Li, Zhouxin Wang, Ziqi Li, Yanfei Liu, Hui Cent South Univ Sch Math & Stat Changsha Peoples R China CRRC Qingdao Sifang CoLtd Qingdao Peoples R China Beijing Urban Construct Design & Dev Grp Co Ltd Beijing Peoples R China Guangzhou Metro Design & Res Inst Co Ltd Guangzhou Peoples R China Cent South Univ Sch Traff & Transportat Engn IAIR Changsha Peoples R China

ISBN: (纸本)9781450362788

Rail transit system has characteristics of large investment, long cycle and numerous impact factors. Identifying the key impact factors of rail transit system cost plays a supporting role in rail transit cost optimization. In this paper, ELM (Extreme learning machine) model was established based on the data of 11 rail transit lines in China to obtain the significative degree of impact factors. Compared with BP (Back Propagation) neural network, ELMAN neural network and LSSVM (Least Squares Support Vector machines), the model has high accuracy and strong robustness. We use significative degree and cross-domain correlation sensitivity which got by fuzzy clustering analysis to identify the key impact factors of rail transit cost. this paper provides a theoretical basis for the planning and decision-making of rail transit system. It provides support for key factors identification of rail transit system life cycle cost.

关键词： Rail transit system extreme learning machine fuzzy clustering key impact factors

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Comparative Assessment of Pulsar Families using GMM and DPGMM 4

Comparative Assessment of Pulsar Families using GMM and DPGM...

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4th international conference on computer Science and Engineering (UBMK)

作者： Ay, Fahrettin Eksi, Yavuz K. Kamasak, Mustafa E. Ince, Gokhan Istanbul Tech Univ Comp Engn Dept Istanbul Turkey Istanbul Tech Univ Phys Engn Dept Istanbul Turkey

ISBN: (纸本)9781728139647

Mixture models are frequently employed in astronomical studies to model observed data and interpret results. Gaussian mixture model (GMM) is probably the most widely used one due to its simplicity. To illustrate, GMM had been applied to the pulsar data set in a previous study and discovered six clusters. On the other hand, there are more sophisticated mixture models e.g. Dirichlet process Gaussian mixture model (DPGMM). It is a Bayesian non-parametric model such that it includes prior distributions for model parameters and automatically explores the optimum number of clusters in a data set, in contrast to GMM. In this study, we repeated the application of GMM, and also tested DPGMM as a first time on a larger pulsar data set. It is revealed that there arc six clusters in the data set as presented in the former study, according to both GMM and DPGMM. However, the estimated parameters of both models differ from each other. We, then, compared the clustering performance of models with respect to silhouette coefficients. Accordingly, it is observed that DPGMM exhibits better clustering performance. As a further analysis, we compared the classification performance of models. Apparently, DPGMM performs, once again, better than GMM in discriminating selected pulsar families.

关键词： machine learning mixture models GMM DPGMM pulsars

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4th Annual international conference on Information System and Artificial Intelligence - Chapter 3: Integrated applications and Other Related Fields

4th Annual International Conference on Information System an...

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2019 4th Annual international conference on Information System and Artificial Intelligence, ISAI 2019

the proceedings contain 222 papers. the topics discussed include: research on enterprise risk identification and early warning system based on big data background;modeling and optimization of heterogeneous vehicle routing problem in emergency resource distribution after disaster;empirical study of the impact of QFII shareholdings on board governance and corporate performances - based on Chinese listed companies data;multi-person pose estimation via learning feature integration;fuzzy twin support vector machine based on intra-class hyperplane;the research on the simulation of VHF radio broadcasting loss;and saliency object detection based on regions merging and its application in image retrieval.

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Development of Web application based on ITIL - Incident Management Framework in computer Laboratory 4

Development of Web Application based on ITIL - Incident Mana...

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4th international conference on Information Management and Technology, ICIMTech 2019

作者： Richard Gaol, Ford Lumban Warnars, Harco Leslie Hendric Spits Abdurachman, Edi Soewito, Benfano Information Systems Department School of Information Systems Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

While Information Technology becomes the interesting topic in education, computer Laboratory as a learning center becomes an important supporting facilities in Higher Education. Whereas, current awareness level of service quality for higher education management is quite lower than the enterprise management. In the daily operation of IT Service Management (ITSM) in computer laboratory, service activities in computer laboratory mostly uncontrolled and not standardized. By maximizing the service standard level in computer laboratory, university could maintain customer satisfaction subsequently. this research will focus in creating an IT Service Model, Service Standard Process, and the web application by referring to ITIL-Incident Management Framework as a best practice of ITSM framework. the design process use UML diagram as the tools in drawing the process flow of system. © 2019 IEEE.

关键词： Customer satisfaction

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application of Euler Elastica Regularized Logistic Regression on Resting-state fMRI for Identification of Alzheimer's Disease 19

Application of Euler Elastica Regularized Logistic Regressio...

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4th international conference on Biomedical Signal and Image Processing

作者： Guo, Weiping Yao, Li Long, Zhiying Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China

ISBN: (纸本)9781450372244

Many machine-learning methods have been widely applied to predict Alzheimer's disease based on functional magnetic resonance imaging (fMRI) data. In our previous study, we proposed the Euler Elastica Regularized Logistic Regression (EELR) method and demonstrated its advantages over the other classifiers. In this study, we applied EELR to resting-state fMRI (RS-fMRI) data of 24 healthy aged subjects and 22 Alzheimer's disease (AD) patients for the identification of Alzheimer's disease. Moreover, in order to reveal the neural discriminative pattern, permutation test was performed to test the differences of EELR weight between AD and healthy aged subject. the results showed that EELR classifier could successfully classify AD and healthy aged subject. Moreover, EELR revealed that the amplitude of low-frequency fluctuations (ALFF) of posterior cingulate cortex, prefrontal cortex and hippocampus are the important biomarkers for distinguishing AD and healthy aged subject.

关键词： Alzheimer's disease Resting-state functional magnetic resonance imaging (RS-fMRI) Amplitude of low-frequency fluctuations Euler elastica regularized Logistic regression

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4th international conference on Soft Computing in Data Science, SCDS 2018

4th International Conference on Soft Computing in Data Scien...

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4th international conference on Soft Computing in Data Science, SCDS 2018

ISBN: (纸本)9789811334405

the proceedings contain 30 papers. the special focus in this conference is on Soft Computing in Data Science. the topics include: Exploratory analysis of MNIST handwritten digit for machine learning modelling;Improved conditional value-at-risk (CVaR) based method for diversified bond portfolio optimization;ranking by fuzzy weak autocatalytic set;fortified offspring fuzzy neural networks algorithm;forecasting value at risk of foreign exchange rate by integrating geometric brownian motion;fog of search resolver for minimum remaining values strategic colouring of graph;incremental software development model for solving exam scheduling problems;visualization of frequently changed patterns based on the behaviour of dung beetles;applications of machine learning techniques for software engineering learning and early prediction of students’ performance;scalable single-source shortest path algorithms on distributed memory systems;opinion mining for skin care products on twitter;tweet hybrid recommendation based on latent dirichlet allocation;assessing structured examination question using automated keyword expansion approach;improving topical social media sentiment analysis by correcting unknown words automatically;big data security in the web-based cloud storage system using 3d-aes block cipher cryptography algorithm;an empirical study of classifier behavior in rattle tool;clutter-reduction technique of parallel coordinates plot for photovoltaic solar data;data visualization of violent crime hotspots in Malaysia;malaysia election data visualization using hexagon tile grid map;a computerized tool based on cellular automata and modified game of life for urban growth region analysis;simulation study of feature selection on survival least square support vector machines with application to health data.

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1st international Workshop on Smart Ultrasound Imaging, SUSI 2019, and the 4th international Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2019, held in conjunction with the 22nd international conference on Medical Imaging and computer-Assisted Intervention, MICCAI 2019

1st International Workshop on Smart Ultrasound Imaging, SUSI...

引用

1st international Workshop on Smart Ultrasound Imaging, SUSI 2019, and the 4th international Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2019, held in conjunction with the 22nd international conference on Medical Imaging and computer-Assisted Intervention, MICCAI 2019

ISBN: (纸本)9783030328740

the proceedings contain 20 papers. the special focus in this conference is on international Workshop on Smart Ultrasound Imaging, and the international Workshop on Preterm, Perinatal and Paediatric Image Analysis, held in conjunction with the international conference on Medical Imaging and computer-Assisted Intervention. the topics include: Deep learning based minimum variance beamforming for ultrasound imaging;estimation of preterm birth markers with u-net segmentation network;investigating image registration impact on preterm birth classification: An interpretable deep learning approach;dual network generative adversarial networks for pediatric echocardiography segmentation;reproducibility of functional connectivity estimates in motion corrected fetal fmri;plug-and-play priors for reconstruction-based placental image registration;a longitudinal study of the evolution of the central sulcus’ shape in preterm infants using manifold learning;prediction of failure of induction of labor from ultrasound images using radiomic features;longitudinal analysis of fetal mri in patients with prenatal spina bifida repair;quantifying residual motion artifacts in fetal fmri data;registration of untracked 2d laparoscopic ultrasound liver images to ct using content-based retrieval and kinematic priors;topology-preserving augmentation for cnn-based segmentation of congenital heart defects from 3d paediatric cmr;direct detection and measurement of nuchal translucency with neural networks from ultrasound images;automated left ventricle dimension measurement in 2d cardiac ultrasound via an anatomically meaningful cnn approach;SPRNet: Automatic fetal standard plane recognition network for ultrasound images;representation disentanglement for multi-task learning with application to fetal ultrasound;adversarial learning for deformable image registration: application to 3d ultrasound image fusion;deep learning-based pneumothorax detection in ultrasound videos.

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AWGI: An Auto-weighted Framework Integrating Heterogeneous Networks for Gene Interaction Prediction 19

AWGI: An Auto-weighted Framework Integrating Heterogeneous N...

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4th international conference on Biomedical Signal and Image Processing

作者： Luo, Han Lei, Zhenfeng Ke, Hanping Xiamen Univ Sch Informat Xiamen Peoples R China Ningde Normal Univ Coll Informat & Mech & Elect Engn Ningde Peoples R China

ISBN: (纸本)9781450372244

Gene networks (GNs) capture the knowledge of diverse interactions among genes, which are conducive to discovering disease gene sets and locate the target of the drug. Discovering gene interactions via biological experiments is always exhaustive and expensive. the application of combining gene networks with integrated computing for effectively guiding experiments is widely used in gene interaction prediction. Some of work has been designed to integrate the neighbor information of Heterogeneous Data (HD) to predict gene interaction, but ignored the difference among gene networks and interaction types. Here, we proposed an auto-weighted framework integrating heterogeneous networks for gene interaction prediction (called AWGI), which evaluates and aggregates the neighbor information of five GNs to infer the associations among genes. In this paper, we compared the prediction ability of our method with other state-of-the-art methods. the experimental results show that AWGI can learn more informative gene representations from the HD and achieved a significant improvement in terms of the accuracy of interaction prediction.

关键词： Auto-weight Gene network machine learning Interaction prediction

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A Bayesian Information Criterion for Unsupervised learning Based on an Objective Prior 19th

A Bayesian Information Criterion for Unsupervised Learning B...

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19th international conference on Computational Science and Its applications (ICCSA)

作者： Baimuratov, Ildar Shichkina, Yulia Stankova, Elena Zhukova, Nataly Nguyen than ITMO Univ St Petersburg Russia St Petersburg State Electrotech Univ St Petersburg Russia St Petersburg State Univ St Petersburg Russia Russian Acad Sci St Petersburg Inst Informat & Automat St Petersburg Russia

ISBN: (纸本)9783030242893

Data processing techniques, such as mathematical formulas, statistical methods and machine learning algorithms, require a set of tools for evaluating knowledge extracted from data. In unsupervised learning it is impossible to use referential or predictive estimation. therefore, the only reliable way to evaluate results of unsupervised learning is information estimation. Unfortunately, information estimation suffer from underfitting and overfitting. We propose a new method for evaluating unsupervised learning results, which is based on the Bayesian criterion for optimal decision and an objective prior probability distribution of partitions. We illustrate the proposed method application on Fisher's iris data set by comparing original label distribution with results of clustering with different numbers of clusters. We show the method prevents underfitting and overfitting and verify it by comparing the recommended value with posterior distribution.

关键词： Unsupervised learning Information estimation Bayesian criterion Objective prior

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