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Long short-term memory for hate speech and abusive language detection on Indonesian YouTube comment section 11

Long short-term memory for hate speech and abusive language ...

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2021 11th International Workshop on computer Science and Engineering, WCSE 2021

作者： Salim, Calvin Erico Rudy Suhartono, Derwin Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9789811817915

Hate speech is one of the most challenging problem internet is facing today. With increasing numbers of users online, hate speech also rise and takes time to be classified manually particularly in languages other than English. This research examines hate speech detection problem in form of Bahasa Indonesia. Millions of comments and text posts are added to various social media and discussion platforms. Manual classification in all of the internet as hate speech and offensive language is a near impossible and time-consuming task. This research uses Long Short-Term Memory (LSTM) and Bidirectional Long Short Term Memory (Bi-LSTM) for the method of classifying hate speech and abusive language. The final accuracy is 88,44% by using 200 neurons with Bi-LSTM method. Most common challenges are different languages, out of vocabulary words, long range dependencies, and sarcasm. © 2021 11th International Workshop on computer Science and Engineering, WCSE 2021. All Rights Reserved.

关键词： Long short-term memory

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E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast Healthcare Interoperability Resources (FHIR)

E-Government Interoperability: Provincial-Level Architecture...

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International Conference on Information Management and Technology (ICIMTech)

作者： Diana Wijayanti Satriawansyah Urbaya Taufiq Sitompul Verry Adrian Department of Information Systems BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Country Health Information Systems and Data Use (CHISU) Program USAID Indonesia Jakarta Indonesia Department of Informatics University of Oslo Oslo Norway Computer Science Department Binus Graduate Program – Doctor Computer Science Bina Nusantara University Jakarta Indonesia Center of Data and Information Technology Jakarta Provincial Health Office Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users.

关键词： Prototypes Medical services Data models Security Public healthcare Electronic medical records Interoperability Information exchange Standards Electronic government

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Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： computerized tomography

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Bringing More Girls to STEM: Initiatives within the Frontiers in Education Conference

Bringing More Girls to STEM: Initiatives within the Frontier...

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Frontiers in Education (FIE) Conference

作者： Isabela M. Montingelli Andreia Malucelli Sheila Reinehr Carlos N. Silla Graduate Program in Computer Science (PPGIa) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba PR Brazil

STEM (Science, Technology, Engineering, and Math) courses are male-dominated spaces where women are the minority most of the time. Even now, when women are the majority in universities, the computer science and engineering degrees are still uneven when it comes to gender. There have been interesting initiatives in the past Frontiers In Education (FIE) conferences about several of the issues related to bringing more girls into the computer science and engineering fields of study. Some of these works typically address one or more challenges associated with increasing the number of women in computer science and engineering by using different approaches and targeting different age groups. The majority of the studied works focus on raising awareness through the development of boot camps, workshops, programs and other initiatives. Other works investigate the attitude towards computing careers and the lack of interest in these areas by female students, regardless of age. There are also works that are focused on female student's retention and success in their academic degrees. All of the reviewed works have one major goal in common: bringing more girls into STEM fields, such as engineering and computer science. In order to achieve this goal, the existing initiatives employ disciplinary and interdisciplinary approaches. Although the majority of the work's main technical focus is on teaching programming, this is achieved by using a variety of approaches and resources. For example, there are works aimed at developing digital games, approaches that use robotics with Arduino and also approaches that integrate computer programming with musical instruments, among others. We did not find a study that aggregated and analyzed these initiatives. The main goal of this paper is to analyze past FIE editions to identify and aggregate studies related to attracting girls to STEM areas. The method was based on systematic review procedures considering FIE 2010 thru FIE 2022. We analyze and ev

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A Review of Techniques for Reducing Shortcomings in Classical Information Visualization Charts

A Review of Techniques for Reducing Shortcomings in Classica...

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International Conference on Information Visualisation (IV)

作者： Natã Ferreira Lobato Tiago Davi O. de Araujo Bianchi Serique Meiguins Carlos Gustavo Resque dos Santos Graduate Program in Computer Science Federal University of Pará - UFPA Belém PA Brazil IEETA - ESAN University of Aveiro Oliveira de Azeméis Portugal

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

This study aims to review academic research publications on techniques that deal with the shortcomings of classic information visualization charts. This review used snowballing to capture the academic papers and applied a filter to focus on articles that developed a new technique, resulting in 28 articles. We proposed some research questions and a taxonomy to classify the papers and conduct a detailed analysis of each study. In this way, the study identified one solution proposal per article that makes use of one or more of the ten (10) visual shortcomings mitigation techniques found for each of the five (5) problems related to Visual shortcomings that affect one of the five (5) information visualization techniques. As a result, we found solutions and mapped them to each of the identified visual shortcomings, including overlap, outliers, categorical occlusion, misuse of space, and non-perceptible elements. These solutions help to minimize the problem as a whole or in part, making it easier for the user to understand the information in the graphs. This article characterizes an initial effort to study academic papers that solved or mitigated shortcomings in classical information visualization charts.

关键词： Visualization Reviews Prevention and mitigation Taxonomy Information filters Proposals

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An Accuracy Assessment for Active Data Physicalization

An Accuracy Assessment for Active Data Physicalization

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International Conference on Information Visualisation (IV)

作者： Cleyton Luiz Ramos Barbosa Thiago Augusto Soares de Sousa Walbert Cunha Monteiro Diego Hortencio Dos Santos Tiago Davi Oliveira de Araujo Bianchi Serique Meiguins Computer Science Graduate Program Federal University of Para Belem Para Brazil IEETA DETI University of Aveiro Aveiro Portugal

Recent literature reviews point out that active data physicalizations still lack evaluation scenarios. These evaluations mainly focus on the perception of visual and physical characteristics of physicalizations and users' performance of analysis tasks. This paper addresses a proposed accuracy assessment of data physicalization for data representation. The data physicalization used is a physical and dynamic bar chart using 3D printed models (3D bars) and LED strips inside the bars, both controlled by microcontrollers (Arduino), being possible to represent different datasets. The accuracy assessment of this physicalization is based on the height and color of the 3D bars. For this, distance sensors measure the actual height values of the bars, and a computer vision camera/algorithm collects and estimates the RGB value of the bars. From original and measured values datasets, bar charts images are built to perform a similarity comparison between the images. The similarity metric is used as an accuracy metric of the bar heights to represent data and obtained as initial results value higher than 96%. Furthermore, an evaluation scenario of the distance between the reference and measured colors 2000) classifies most of them as having proper perception. Additionally, the accuracy loss was evaluated on data physicalization in a scenario of continuous use (ten configurations without any calibration), and the variation of errors was around 2%.

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Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study

Pain Classification Using Statistical Feature Extraction Usi...

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IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

作者： Sudimanto Agung Trisetyarso Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Computer Engineering Department Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta Indonesia

Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people’s psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from ***. The *** data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from *** will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%.

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Automatic Multiple Choice Question Generation: A Systematic Literature Review

Automatic Multiple Choice Question Generation: A Systematic ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Matthew Martianus Henry Nur Adhianti Heryanto Mahmud Isnan Kuncahyo Setyo Nugroho Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Despite their drawbacks, multiple-choice questions (MCQ) have been widely used to assess the students' understanding of lectures through examinations. The development of automatic MCQ generation is beneficial, especially for educators. As a starting point for further development, a Systematic Literature Review (SLR) is conducted to uncover current trends, future challenges, and opportunities in automatic MCQ generation. Previously, an SLR was conducted, but it lacks coverage of the utilization of transformer-based models. This SLR covers the development of automatic MCQ generation using either traditional or advanced approaches such as Transformers. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to gather the data from Scopus, IEEE Xplore, SpringerLink, arXiv, and Semantic Scholar. The included articles must be open-access computer science conference papers or journal articles and written in English less than five years ago. Four independent reviewers analyzed the research workflow, evaluation metric, and dataset used in each study. There are 18 included studies, where 17% (n = 3) studies are from 2024, 33% (n = 6) studies are from 2023, 22% (n = 4) studies are from 2022, 11% (n = 2) studies are from 2021, and 17% (n = 3) studies are from 2020. There are 33% (n = 6) of the studies used the traditional feature-based engineering approach, 39% (n = 7) of the studies used the Transformer-based model fine-tuning approach, and the remaining used novel approaches. The study found that BERT variants are the most utilized Transformer-based model in automatic MCQ. The research notes some challenges, but also open various opportunities for further research, including Large Language Model (LLM) utilization for automatic MCQ generation, the utilization BERT-based models for standardized machine-learned evaluation metrics, and the initiative for the creation of an MCQ dataset benchmark.

关键词： Measurement Semantics Reinforcement learning Benchmark testing Ontologies Transformers Question generation Prompt engineering Standards Systematic literature review

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Influence of Feature Extraction Methods using Neighborhood Component Analysis (NCA) to Improve Classification Performance for Hepatitis C Disease

Influence of Feature Extraction Methods using Neighborhood C...

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International Conference on Informatics and Computing (ICIC)

作者： Christiella Abinosy Setiawan Maria Susan Anggreainy Nora Fitriawati Raymasterio Vera Lucky Renatha Dewi Sabrina Steven Matthew Vierdaria Wijayanti Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Research in the development of Hepatitis C disease prediction is increasingly developing, especially using machine learning models which is able to make predictions quickly and accurately. In this study, a comparison of several classification methods was carried out by also applying feature reduction, NCA. In this study, a comparison of performance was carried out if the data was entered into the NCA feature extraction method with KNearest Neighborhood (KNN) and Support Vector Machine (SVM) and a comparison of performance if the data did not use the NCA feature extraction method. The performance comparison metrics used in the study were accuracy, sensitivity, specification, Matthews Correlation Coefficient (MCC), and Kappa value. The highest accuracy (99.36%), sensitivity (91.94%), specification (99.67%), MCC $(0.895)$ and the best Kappa value $(0.889)$ were obtained in the KNN-NCA prediction method.

关键词： Support vector machines Accuracy Sensitivity Liver diseases Computational modeling Nearest neighbor methods Predictive models Feature extraction Diseases Testing

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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease

Comparative Analysis of CNN Models for Multiclass Detection ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Jason Sebastian Sulistyawan Kuncahyo Setyo Nugroho Mahmud Isnan Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Gastrointestinal diseases are significant health concerns that mostly affect the digestive and biliary tracts. These can only be observed internally through endoscopy, or a modern approach named Wireless Capsule Endoscopy. Though the approach has proven to work, the process relies heavily on medical experts, which is prone to error when given a considerably large load. The use of deep learning in detecting diseases with images has been proven to work in various cases and can be implemented for gastrointestinal diseases. This study compared four deep learning models based on Convolutional Neural Network architectures consisting of the VGG16, ResNet152V2, InceptionV3, and Xception for classifying colon disease images. The experiment showed that the ResNet152V2 performed the best, compared to the other three with a testing accuracy score of 0.9837. On the other hand, the VGG16 had the lowest performance with around accuracy score of 0.89 while the Xception and InceptionV3models yielded similar accuracy scores of 0.9587 and 0.9549, respectively. The results highlight the effectiveness of ResNet152V2 in handling the complexity of colon disease detection

关键词： Hands Deep learning Accuracy Endoscopes Real-time systems Gastrointestinal tract Data models Colon Medical diagnostic imaging Diseases

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