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arXiv 2022年

作者： Athalye, Chinmayee Arnaout, Rima Department of Medicine Division of Cardiology Bakar Computational Health Sciences Institute University of California San Francisco United States Department of Medicine Division of Cardiology Bakar Computational Health Sciences Institute Chan Zuckerberg Biohub Intercampus Research Award Investigator Biological and Medical Informatics Graduate Program University of California San Francisco United States

While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Here, we test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33 ± 0.24 vs 86.89 ± 0.60, p-value 0.0139;OB-125 F-score 74.60 ± 0.11 vs 72.43 ± 0.62, p-value 0.0039). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks. © 2022, CC BY-NC-ND.

关键词： Medical imaging

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia Computer Science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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Data Mining for Hospital Morbidity Forecasting

Data Mining for Hospital Morbidity Forecasting

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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： Leonardo Silva Vianna Raul Sidnei Wazlawick Graduate Program in Health Informatics Federal University of Santa Catarina Florianópolis Brazil Department of Informatics and Statistics Federal University of Santa Catarina Florianópolis Brazil

ISBN: (数字)9781728174150

ISBN: (纸本)9781728174167

Growing demand for hospital healthcare services has brought significant challenges for their managers. Variables with high uncertainty degree, such as the number of patients and the duration of their treatments, hinders the planning processes and make it difficult to properly comply with the established strategies. Controlling and identifying factors that affect the hospital management process depends on health database analysis. Therefore, it is important to consider the possibility of prospecting useful knowledge from the stored data. The objective of this research is to evaluate the hospital morbidity prediction through different data mining methods on ambulatory and hospital procedure records obtained from Brazilian public health databases. The research method consists of performing a predictive data mining by applying supervised learning algorithms on a regression problem. The highest Pearson correlation coefficient individually obtained in the three-month prediction time interval, through the data mining method that applied random forest associated with an attribute selection algorithm on the disease group of the ICD10 chapter XVI (Certain Conditions originating in the Perinatal Period), was 0.9682. Different results were achieved depending on the method applied, the group of diseases analyzed, and the proposed prediction time interval, which led to the conclusion that data mining on ambulatory and hospital records allowed the prediction of hospital morbidity. The hospital morbidity predictions obtained can minimize the undesired effect of the demand randomness for health services in the decision-making process.

关键词： Conferences Software architecture Data mining Hospitals Forecasting

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TOWARDS AUDITABILITY REQUIREMENTS SPECIFICATION USING AN AGENT-BASED APPROACH

arXiv

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arXiv 2020年

作者： de Albuquerque, Denis J.S. Nunes, Vanessa Tavares Cappelli, Claudia Ralha, Célia Ghedini Department of Computer Science University of Brasilia Brazil Informatics Graduate Program Federal University of Rio de Janeiro Brazil

Transparency is an important factor in democratic societies composed of characteristics such as accessibility, usability, informativeness, understandability and auditability. In this research we focus on auditability since it plays an important role for citizens that need to understand and audit public information. Although auditability has been a subject of discussion when designing systems, there is a lack of systematization in its specification. We propose an approach to systematically add auditability requirements specification during the goal-oriented agent-based Tropos methodology. We used the Transparency Softgoal Interdependency Graph that captures the different facets of transparency while considering their operationalization. An empirical evaluation was conducted through the design and implementation of LawDisTrA system that distributes lawsuits among judges in an appellate court. Experiments included the distribution of over 300,000 lawsuits at the Brazilian Superior Labor Court. We theorize that the presented approach for auditability provides adequate techniques to address the cross-organizational nature of transparency. Copyright © 2020, The Authors. All rights reserved.

关键词： Transparency

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A multicriteria approach to support task allocation in projects of distributed software development

A multicriteria approach to support task allocation in proje...

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作者： Simão Filho, Marum Pinheiro, Plácido R. Albuquerque, Adriano B. Simão, Régis P. S. Azevedo, Raimundo S. N. Nunes, Luciano C. Graduate Program in Applied Informatics University of Fortaleza Brazil Information Systems 7 de Setembro University Center Brazil

Organizations are increasingly investing in Distributed Software Development (DSD) over the years. A typical decision-making problem in the distributed scenario consists of deciding which team should be allocated each task. That decision takes into account a relative degree of subjectivity. That setting is suitable for applying Verbal Decision Analysis (VDA). This paper introduces an approach to support the allocation of tasks to distributed units in DSD projects, structured on the hybridisation of methods of Verbal Decision Analysis for classification and rank ordering applied to influencing factors and executing units. Firstly, a review of the literature was conducted aiming to identify the approaches to support the allocation of tasks in DSD contexts. Then, an approach was developed by applying VDA-based methods for classification and ordering. Bibliographic research and the application of surveys with professionals allowed identifying and characterising the main elements that influence task assignment in DSD projects. Afterwards, experiences were carried out in five real-world companies. In the end, the proposed approach has been submitted to the evaluation by the professionals of the participating companies and by some project management experts. The proposed approach comprises a workflow containing responsible actors and descriptions of the activities. Automated tools are also employed in automating the implementation of the approach. After applying the approach in five companies, task assignment recommendations are presented in groups for each company, according to the task type, i.e., requirements, architecture, coding, and testing, ranging from the most to the least preferable office. Results of the experiences and evaluations held during this work present evidence that the proposed approach is flexible, adaptable, and easy to understand and to use. Moreover, it helps to reduce decision subjectivity and to think of new aspects, supporting the task allocatio

关键词： Project management

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm

Psychological Stress Classification Using Extreme Gradient B...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Maria Mahardini Sakanti Viacheslav Siniaev Aurelia Amaris Win-Jet Luo Suhartono C. Bambang Dwi Kuncoro Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taichung Taiwan Institute of Applied Computer Science ITMO University St. Petersburg Russian Federation Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taichung Taiwan Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers.

关键词： Accuracy Microcontrollers Psychology Human factors Predictive models Boosting Prediction algorithms Real-time systems Sensors Wearable devices

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Improving Land Cover Segmentation Using Multispectral Dataset

Improving Land Cover Segmentation Using Multispectral Datase...

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International Conference on informatics and Computing (ICIC)

作者： Herlawati Herlawati Rahmadya Trias Handayanto Yaya Heryadi Edi Abdurachman Haryono Soeparno Edy Irwansyah Eka Miranda Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Computer Engineering Universitas Islam 45 Bekasi Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Trisakti Institute of Transportation and Logistic Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

Computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia.

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Rahmad Kurniawan Iwan Iskandar Fitra Lestari Habibi Al Rasyid Harpizon Ilyas Husti Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Department of Informatics Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Industrial Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia’s top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube’s features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments.

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Currents Trends in Use of Collaborative Learning in Teaching of Robotics and programming – A Systematic Review of Literature

Currents Trends in Use of Collaborative Learning in Teaching...

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Frontiers in Education (FIE) Conference

作者： Joethe Moraes de Carvalho José Francisco de Magalhães Netto Post-Graduate Program in Informatics (PPGI) Institute of Computing (ICOMP) Federal University of Amazonas (UFAM) CMA-Federal Institute of Amazonas (IFAM) Manaus AM Brazil Post-Graduate Program in Informatics (PPGI) Institute of Computing (ICOMP) Federal University of Amazonas (UFAM) Manaus AM Brazil

ISBN: (数字)9781728189611

ISBN: (纸本)9781728189628

This Research Full paper presents a Systematic Mapping Study of actions that are being carried out in the academic environment, aiming at teaching robotics and programming through collaborative procedures at a distance. Recent studies point to an increasing utilization of collaborative activities in teaching-learning process of robotics and programming, favoring the STEM fields, with encouraging results regarding the improvement of student's skills and better use of educational institutions infrastructure. Our goal is to collect information about state of the art on collaborative learning, employed through Groupware. To carry out this investigation, goals were defined by the Systematic Mapping Process, with the purpose of providing a better process understanding. The Research Questions were stipulated to be answered after the results analysis and the Search Strings, which allowed to select publications that satisfy the objective of this research articles in main digital repositories, all carried out in the last 5 years. After applying filters, 22 articles were considered more relevant according to the research objective. The results show the activities, modalities, methodologies, accomplished pedagogical concepts and the conclusions obtained. This work aims to assist researchers who seek information on referred topic.

关键词： Libraries Collaboration Robots programming profession Collaborative work Collaborative software Systematics

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Digital art technology publication in indonesia: A bibliometric analysis (2011-2020) technology 6

Digital art technology publication in indonesia: A bibliomet...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Maulana, Fairuz Iqbal Audia Agustina, Ira Gasa, Frederik Masri Rahmadika, Sandi Ramdania, Diena Rauda Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Design Interior Design Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Economics Communication Communication Science Department Jakarta11480 Indonesia National University Busan Interdisciplinary Program of Information Security Graduate School Pukyong Korea Republic of UIN Sunan Gunung Djati Department of Informatics Bandung Indonesia

ISBN: (纸本)9781665449373

Digital art technology is a form of technology in the form of hardware or software that can produce artistic works or practices as part of the creative process. This research is an idea about the publication of digital art technology, which shows its widespread use based on data from publications in Indonesia. This study visually maps scientific publications in digital techniques indexed with bibliometric analysis at the Indonesian state level. This study collected data from the Scopus database, used the Scopus website analysis feature, and visualized the bibliometric network using Vosviewer. The method used consists of five phases: search keyword determination, initial search results, search results refinement, preliminary compilation and data analysis. 57 academic documents published in Indonesia between 2011 and 2020 were obtained in the search for data based on the Scopus database. The processed data show patterns and trends in the increasing number of international publications in digital art technology indexed by Scopus. This study proposes a classification of the convergence axis. © 2021 IEEE.

关键词： Arts computing

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