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IEEE International Conference on Electronics, Circuits and Systems

作者： Matheus F. Stigger Victor H. S. Lima Leonardo B. Soares Claudio M. Diniz Sergio Bampi Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil Federal Institute of Rio Grande do Sul (IFRS) Rio Grande Brazil Graduate Program in Microelectronics (PGMicro) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Motion Estimation and mode decision are the most compute-intensive tasks in recent video encoders. Sum of Absolute Transformed Differences (SATD) is a distortion metric used inside ME and mode decision to decide the best block to be used as prediction for each block to be encoded. To reduce the complexity of SATD calculation, this work proposes an approximate SATD hardware accelerator based on 1D 4 × 4 Hadamard Transform, in contrast with the precise SATD hardware accelerator that contains a 2D 4 × 4 Hadamard Transform. The proposed approximate SATD accelerator reduces power dissipation in 66.86% compared to the precise SATD accelerator while increasing BD-BR only by less than 0.748%, which is a better result than SAD accelerator that increases BD-BR in up to 0.830% compared to precise SATD.

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The Effect of Labeling Duration and Temporal Resolution on Arterial Transit Time Estimation Accuracy in 4D ASL MRA Datasets - A Flow Phantom Study 1st

The Effect of Labeling Duration and Temporal Resolution on A...

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1st International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2019

作者： Phellan, Renzo Lindner, Thomas Helle, Michael Falcão, Alexandre X. Forkert, Nils D. Department of Radiology Hotchkiss Brain Institute and Biomedical Engineering Graduate Program University of Calgary CalgaryAB Canada Clinic for Radiology and Neuroradiology University Medical Center Schleswig-Holstein Kiel Germany Philips Technologie GmbH Innovative Technologies Hamburg Germany Laboratory of Image Data Science Institute of Computing University of Campinas CampinasSP Brazil

ISBN: (纸本)9783030333263

Medical imaging modalities, such as four-dimensional arterial spin label magnetic resonance angiography (4D ASL MRA), can acquire blood flow data of the cerebrovascular system. These datasets are useful to determine criteria of normality and diagnose, study, and follow-up on the treatment progress of cerebrovascular diseases. In particular, variations in the arterial transit time (ATT) are related to hemodynamic impairment as a consequence of vascular diseases. In order to obtain accurate ATT estimations, the acquisition parameters of the applied image modality need to be properly tuned. In case of 4D ASL MRA, two important acquisition parameters are the blood labeling duration and the temporal resolution. This paper evaluates the effect of different settings for the two mentioned parameters on the accuracy of the ATT estimation in 4D ASL MRA datasets. Six 4D ASL MRA datasets of a pipe containing a mixture of glycerine and water, circulated with constant flow rate using a pump, are acquired with different labeling duration and temporal resolution. A mathematical model is then fitted to the observed signal in order to estimate the ATT. The results indicate that the lowest average absolute error between the ground-truth and estimated ATT is achieved when the longest labeling duration of 1000 ms and the highest temporal resolution of 60 ms are used. The insight obtained from the experiments using a flow phantom, under controlled conditions, can be extended to tune acquisition parameters of 4D ASL MRA datasets of human subjects. © 2019, Springer Nature Switzerland AG.

关键词： Hemodynamics

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Skin cancer detection based on deep learning and entropy to detect outlier samples

arXiv

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arXiv 2019年

作者： Pacheco, Andre G. C. Ali, Abder-Rahman Trappenberg, Thomas Graduate Program in Computer Science Federal University of Espírito Santo Faculty of Computer Science Dalhousie University Faculty of Natural Sciences Computing Science and Mathematics University of Stirling

We describe our methods to address both tasks of the ISIC 2019 challenge. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it. To tackle the challenge, we apply an ensemble of classifiers, which has 13 convolutional neural networks (CNN), we develop two approaches to handle the outlier class and we propose a straightforward method to use the meta-data along with the images. Throughout this report, we detail each methodology and parameters to make it easy to replicate our work. The results obtained are in accordance with the previous challenges and the approaches to detect the outlier class and to address the meta-data seem to be work properly. Copyright © 2019, The Authors. All rights reserved.

关键词： Dermatology

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Improving the Vocabulary Learning by Personalized Proficiency

Improving the Vocabulary Learning by Personalized Proficienc...

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IEEE International Conference on Ubi-Media computing

作者： Yi-Zhen Lin Kai-Hsiang Chen Jen-Wei Huang Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

ISBN: (数字)9781728128207

ISBN: (纸本)9781728128214

To improve the vocabulary ability is very important in language learning. Thus, if we can learn and remember a word very effectively, then we will be able to master a language more quickly. Therefore, many scholars began to propose related research. Due to the learning mechanism of human brain, sometimes when people learn a new knowledge they may forgot at a short time. In order to make the consideration more complete, after analyzing Hermann Ebbinghaus's forgetting curve experiment, we added two variables, one is the acceptance of each word by the same person, and the other is the ability of different people to remember vocabulary. With the above two parameters, we want to design a system to help user to review the vocabulary which may be forgetting before. The forgetting curve can be personalized, and it is more accurate to calculate the best time for each user to review the vocabulary.

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Using Artificial Neural Networks to Estimate the Equivalent Resistivity from typical Transmission Line Towers Grounding Arrangement in a Two-Layer Soil

Using Artificial Neural Networks to Estimate the Equivalent ...

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International Symposium on Lightning Protection (XII SIPDA)

作者： Raphael Batista João R. Souza Graduate Program in Automation and Control Engineering IFMG Betim MG Brazil Dept. of Electroeletronics and Computing CEFET-MG Alameda das Perdizes 61 Contagem MG Brazil

This paper presents a procedure to estimate a homogeneous equivalent resistivity for typical transmission line tower ground arrangements that are immersed in a stratified soil model. This technique is based on artificial neural network training due to a dataset developed in a numerical routine to compute grounding resistance. The results obtained from Levemberg-Marquadt algorithm and Bayesian regularization show good accuracy compared to the applied dataset and to the measurement data from a 230 kV transmission line, a practical case analyzed. We note medians lower than 5% and acceptable values of standard deviation for practical cases, which suggests that the proposed procedure can be used for real-world conditions.

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Long Short-Term Memory for Human Fall Detection Based Gamification on Unconstraint Smartphone Position

Long Short-Term Memory for Human Fall Detection Based Gamifi...

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International Congress on Applied Information Technology

作者： Maria Seraphina Astriani Yaya Heryadi Gede Putra Kusuma Edi Abdurachman Faculty of Computing and Media Bina Nusantara University Jakarta Indonesia Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Binus Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728158624

ISBN: (纸本)9781728158631

Fall incident can caused health problem in elderly and people with special treatments. Fall detection method is needed to minimized the problem when human fallen and smartphone can be used as the device to detect it. Usually people carry smartphone in any positions and can make fall detection method difficult to detect when fall occurs. Long Short-Term Memory (LSTM) combined with Sigmoid helps to answer the challenge to handle smartphone accelerometer and gyroscope data in many smartphone orientation position. LSTM method on this experiment can achieved 91.67% for the accuracy result. Since the fall data occurs rarely and there may be insufficient data available, the gamification prospect implemented in fall detection application especially on “Bug” bounty can help researcher to enhance the accuracy result to make a better human fall detection method.

关键词： Sensors Accelerometers Gyroscopes Senior citizens Computer bugs Androids Humanoid robots

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An Analysis of Machine Learning Classifiers in Breast Cancer Diagnosis

An Analysis of Machine Learning Classifiers in Breast Cancer...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Fabiano Teixeira João Luis Zeni Montenegro Cristiano André da Costa Rodrigo da Rosa Righi Software Innovation Laboratory - SOFTWARELAB Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos - UNISINOS Av Unisinos 950 Sao Leopoldo 93022-750 Brazil

ISBN: (数字)9781728155746

ISBN: (纸本)9781728155753

In the field of assisted cancer diagnosis, it is expected that the involvement of machine learning in diseases will give doctors a second opinion and help them to make a faster / better determination. There are a huge number of studies in this area using traditional machine learning methods and in other cases, using deep learning for this purpose. This article aims to evaluate the predictive models of machine learning classification regarding the accuracy, objectivity, and reproducible of the diagnosis of malignant neoplasm with fine needle aspiration. Also, we seek to add one more class for testing in this database as recommended in previous studies. We present six different classification methods: Multilayer Perceptron, Decision Tree, Random Forest, Support Vector Machine and Deep Neural Network for evaluation. For this work, we used at University of Wisconsin Hospital database which is composed of thirty values which characterize the properties of the nucleus of the breast mass. As we showed in result sections, DNN classifier has a great performance in accuracy level (92%), indicating better results in relation to traditional models. Random forest 50 and 100 presented the best results for the ROC curve metric, considered an excellent prediction when compared to other previous studies published.

关键词： Machine learning Breast cancer Support vector machines Neural networks Forestry

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Markerless tracking enables distinction between strategic compensation and functional recovery after spinal cord injury

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Experimental Neurology 2022年 354卷 114085页

作者： Mah, Kar Men Badillo-Martinez, Abdiel Jann, Victor Bixby, John L. Lemmon, Vance P. Miami Project to Cure Paralysis University of Miami Miller School of Medicine Miami 33136 FL United States Dept. of Neurological Surgery University of Miami Miller School of Medicine Miami 33136 FL United States Institute for Data Science and Computing University of Miami Miller School of Medicine Miami 33136 FL United States Dept. of Molecular and Cellular Pharmacology University of Miami Miller School of Medicine Miami 33136 FL United States Sylvester Comprehensive Cancer Center University of Miami Miller School of Medicine Miami 33136 FL United States Neuroscience Graduate Program University of Miami Miller School of Medicine Miami 33136 FL United States University of California Berkeley United States

Injuries to the cervical spinal cord represent around 60% of all spinal cord injuries (SCIs). A major priority for patients with cervical SCIs is the recovery of any hand or arm function. The similarities between human and rodent “reach-to-eat movements” indicate that analyzing mouse forelimb reaching behavior may be a method of identifying clinically relevant treatments for people with cervical SCIs. One popular behavioral measure of forelimb functional recovery comprises the Single Pellet Retrieval Task (SPRT). The most common outcome measure for this task, however (percentage of pellets successfully retrieved), cannot readily distinguish between recovery of pre-injury motor patterns and strategic compensation. Our objective was to establish outcome measures for the SPRT that are readily adopted by different investigators and capable of measuring recovery of limb function after SCI. We used a simple semi-automated approach to high-speed tracking of mouse forepaw movements during pellet retrieval. DeepLabCut™, a machine learning based computer vision software package, was used to track individual features of the mouse forepaw, allowing a more detailed assessment of reaching behavior after SCI. Interestingly, kinematic analysis of movements pre- and post-injury illuminated persistent deficits in specific features of the reaching motor patterns, namely pronation and paw trajectory, that were poorly correlated with recovery of the ability to successfully retrieve pellets. Thus, we have developed an inexpensive method for detailed analysis of mouse reach-to-eat behavior following SCI. Further, our results suggest that binary success/fail outcome measures primarily assess an animal's ability to compensate rather than a restoration of normal function in the injured pathways and networks. © 2022 Elsevier Inc.

关键词： DeepLabCut Forelimb Machine learning Mouse kinematics Pronation Reaching Spinal cord injury

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Improving Students Skills to Solve Elementary Equations in K-12 programs Using an Intelligent Tutoring System

Improving Students Skills to Solve Elementary Equations in K...

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Frontiers in Education (FIE) Conference

作者： Arcanjo Miguel Mota Lopes José Francisco de Magalhães Netto Ricardo A. L. de Souza Andreza Bastos Mourão Thais Oliveira Almeida Dhanielly P. R. de Lima Post-Graduate Program in Informatics (PPGI) Institute of Computing (IComp) Federal University of Amazonas (UFAM) Manaus AM Brazil Institute of Exact Sciences (ICE) Federal University of Amazonas (UFAM) Manaus AM Brazil University of State of Amazonas (UEA) Post-Graduate Program in Informatics (PPGI) Institute of Computing (IComp) Federal University of Amazonas (UFAM) Manaus AM Brazil Department of Science Computer Federal University of Roraima Boa Vista RR Brazil

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This full paper of research to practice category describes an experiment using an Intelligent Tutoring System to support teaching and learning that evolve solutions of first-degree equations. The difficulties encountered in solving algebra problems can make students consider math and related science a difficult task. We propose a Tutor to promote the learning of this class of problems. In this article, we describe the functioning and architecture of the tutor composed by the Interface, Student Model, Tutorial Strategies and Domain Model. Also, a Virtual Assistant was included to motivate students in the learning process. The tutoring strategies that comprise the tutor are projected from models found in the literature. To approximate the interactions of the tutor with the actions of the teachers, Lev Vygotsky's Concept of Zone Proximal Development was used. The methodology used in this project was the case study applied in a real-life context, obtaining a combination of quantitative and qualitative evidence about technical and pedagogical usability that show the degree of satisfaction of the students regarding the proposed approach.

关键词： Education Mathematical model Informatics Abstract algebra Cognition

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Intelligent E-commerce for Special Needs

Intelligent E-commerce for Special Needs

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International Conference on Cyber and IT Service Management (CITSM)

作者： Diana Teresia Spits Warnars Leonardo Primadala Putra Logiansa Harco Leslie Hendric Spits Warnars Wiranto Herry Utomo School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Computing President University Bekasi Indonesia

Special need is a person who need assistance for their disabilities which include medical, physical, mental or psychological such as person with autism, down syndrome, dyslexia, Attention Deficit Hyperactivity Disorder (ADHD), multiple sclerosis, schizophrenia, cystic fibrosis, cerebral palsy, muscular dystrophy, blindness, deafness, epilepsy, chronic asthma and so on. In this proposed research idea about intelligent E-commerce for special needs, we limited to four types of difficulties such as hearing difficulty, vision difficulty, language difficulty, and learning difficulty. In this paper, we interest in those special needs persons who can use e-commerce and how to model a system that can help them to joy and easy to join with e-commerce. For some, this intelligent e-commerce is not only for shopping only but will become part of their healing for special needs in order to engage with daily human activities and increase their daily confidence in life. This paper also a preliminary investigation that can be used for Ph.D. or Doctoral research degree who is interested in building e-commerce framework for people with a special need or disability.

关键词： Companies Machine learning Speech recognition Tools Pricing

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