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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Pratama, Galih Dea Soeparno, Haryono Arifin, Yulyani Gaol, Ford Lumban Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Mental health is one of the states of well-being which enables people to cope with various things in everyday life. Despite that, mental health is often overlooked, driving the emergence of numerous mental disorders on people. Along the presence of technological advancements, there is serious game which is utilized to entertain and achieve non-game goals related to real-life issues. Due to that, serious game can be leveraged as a media put into health sectors, particularly in presenting fun therapy to people diagnosed with mental disorders. This research presents systematic literature review on publications discussing the utilization of serious game in providing fun therapy for people with mental disorders, focusing on matters like the mental disorders emphasized within, the game elements presented in each publication, and ways to evaluate the serious game. The result indicates overall well-received responses regarding the usage of serious game in serving the purpose, albeit with different ways on evaluation process based on the mental disorders each publication addressed for. © 2024 IEEE.

关键词： Serious games

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Analyzing Patterns of Violence Against Women in Pernambuco Using Self-Organizing Maps and Apriori Algorithm

Analyzing Patterns of Violence Against Women in Pernambuco U...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Da Silva, Brenda Barros Alves Pereira, Rodolfo Moreira De Oliveira Rodrigues, Cleyton M. De Araujo Fagundes, Roberta Andrade Polytechnic School of Pernambuco POLI/UPE Graduate Program in Computer Engineering Recife Brazil

ISBN: (纸本)9798350374575

This study investigates significant patterns of violence against women in Pernambuco using Self-Organizing Maps (SOM) and the Apriori algorithm. The methodology includes trend analysis, clustering, and association rule mining, utilizing data from the Ministério da Mulher covering 2015 to 2021. The exploratory data analysis identified demographic, temporal, and spatial trends in violence. SOM was employed for dimensionality reduction and clustering, revealing three clusters representing 5.24%, 19.68%, and 75.08% of the dataset, respectively. The Apriori algorithm was applied to yearly subsets of each cluster to uncover relevant association patterns. Our findings highlight key characteristics and temporal dynamics of violence, supporting the development of targeted interventions and support systems. © 2024 IEEE.

关键词： Self organizing maps

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Sexism Identification in Tweets using BERT and XLM - Roberta 25

Sexism Identification in Tweets using BERT and XLM - Roberta

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Usmani, Maha Siddiqui, Rania Rizwan, Samin Khan, Faryal Alvi, Faisal Samad, Abdul Computer Science Program Dhanani School of Science and Engineering Habib University Karachi Pakistan

The rapid growth of social media platforms has led to an increase in offensive content, often targeting specific demographic groups. This paper focuses on identifying and categorizing sexism in tweets collected from various social media platforms. We address three tasks from the EXIST 2024 lab, involving the classification of tweets in English and Spanish. These tasks include binary classification for sexism identification, source intention categorization of sexist tweets, and multi-label classification for different facets of sexism. Our approach employs BERT multilingual and XLM-RoBERTa models, along with an ensemble technique to enhance prediction accuracy. We evaluate the models using both hard labels, determined by majority vote, and soft labels, based on class probabilities. © 2022 Copyright for this paper by its authors.

关键词： Tweets

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Enhancing Language Learning with Real-Time Sign Language Recognition and Feedback

Enhancing Language Learning with Real-Time Sign Language Rec...

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2024 IEEE MIT Undergraduate Research Technology Conference, URTC 2024

作者： Daniel, Clapp A. Liam, Nasr M. Habchi, Yasmina Basnet, Saurav Wentworth Institute of Technology Program of Electrical and Computer Engineering BostonMA United States

ISBN: (纸本)9798331531003

Despite the significance of Sign Language education, access to resources with immediate feedback remains a challenge. This study aims to assess the effectiveness of an online learning tool offering real-time video feedback, accessible via basic laptops. Leveraging the capabilities of Google's Media Pipe software and the project's robust logic, the prototype aims to provide learners with immediate and accurate feedback, addressing the challenge of access to effective resources for American Sign Language (ASL) education. Focusing on the ASL alphabet, computer-vision based learning tools for static and dynamic signs will be analyzed to explore the tool's capabilities and limitations. © 2024 IEEE.

关键词： Contrastive Learning

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Artificial Intelligence-Powered Intent Classification for Indonesian E-Wallet Customer Service Chatbots 4

Artificial Intelligence-Powered Intent Classification for In...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Owen, Christopher Suhartono, Derwin Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

The rapid expansion of e-wallet services in Indonesia has significantly heightened the need for efficient customer service solutions, making chatbots an essential tool for user support. However, many providers continue to rely on rule-based chatbots, resulting in rigid interactions that struggle to meet diverse user needs or handle the linguistic complexities of the Indonesian language. This study addresses these limitations by developing an AI-powered intent classification model specifically tailored for Indonesian e-wallet customer service, aimed at delivering a more accurate, adaptive, and user-centered experience. A custom dataset was created from user comments on social media associated with Indonesia's top e-wallet providers, followed by data pre-processing and clustering using the BERTopic model. To improve interpretability, OpenAI's GPT-4 was employed for label refinement, resulting in enhanced clarity. Various models were tested, including IndoBERT, RoBERTa, and Convolutional Neural Network (CNN) architectures in both 2D and 3D configurations. The highest-performing model combined IndoBERT embeddings with a 3D CNN classifier, achieving an accuracy of 84.30%, precision of 84.33%, recall of 84.30%, and an F1-score of 84.24%. This study contributes a unique Indonesian-specific dataset and demonstrates the potential of AI to transform customer service interactions in Indonesia's e-wallet sector, offering a clear advancement over traditional rule-based approaches. © 2024 IEEE.

关键词： Sales

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Imagery contents descriptions for People with visual impairments 22

Imagery contents descriptions for People with visual impairm...

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22nd Brazilian Symposium on Human Factorsin Computing Systems, IHC 2023

作者： Jandrey, Alessandra Helena Ruiz, Duncan Dubugras Alcoba Silveira, Milene Selbach School of Technology Graduate Program in Computer Science - PUCRS Brazil

ISBN: (纸本)9798400717154

Image descriptions are crucial in assisting individuals without eyesight by providing verbal representations of visual content. While manual and Artificial Intelligence (AI)-generated descriptions exist, automatic description generators have not fully met the needs of visually impaired People. In this study, we have examined the problems related to image descriptions reported in existing literature using the Snowballing technique. Through this method, we have identified thirteen issues, including ethical concerns surrounding physical appearance, gender and identity, race, and disability. Furthermore, we have identified five reasons why sighted Individuals often fail to provide descriptions for visual content, highlighting the necessity for accessibility campaigns that raise awareness about the social significance of descriptive sentences. We conducted interviews with eight low-vision volunteers, in which we analyzed the characteristics of descriptive sentences for 25 indoor images and gathered participants' expectations regarding image descriptions. As a result, we propose a set of Good Practices for writing descriptive sentences aimed to assist automatic tools and sighted Individuals in generating more satisfactory and high-quality image descriptions. We hope our results will emphasize the societal importance of imagery descriptions and inspire the community to pursue further interdisciplinary research to address the issues identified in our study. © 2023 ACM.

关键词： Image analysis

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Comparison of Spelling Error Correction Algorithms for the Indonesian Language 11

Comparison of Spelling Error Correction Algorithms for the I...

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11th International Conference on Information and Education Technology, ICIET 2023

作者： Yanfi, Yanfi Setiawan, Reina Soeparno, Haryono Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781665465489

Edit distance as a string measurement metric is often used to help detect misspellings in languages. This paper aims to compare two string spelling error correction algorithms for the Indonesian language. The N-gram, Jaro-Winkler distance, and Levenshtein distance algorithms are used to determine whether they can accurately correct typological errors in the Indonesian language. Moreover, this study utilized KNIME tools to process the data from beginning to end. The data was retrieved from news in Indonesia. After the experiment on N from 1 to 12, the results obtained for the comparative analysis proved that Jaro-Winkler distance performed better than Levenshtein distance for comparing smaller strings like words and names. However, Levenshtein distance performs as well as Jaro-Winkler distance started from four strings. Finally, both Jaro-Winkler distance and Levenshtein distance algorithm got the best performance accuracy for eight strings with an accuracy of 99.52 percent. The result of this study is also presented that both algorithms can support word error correction for the Indonesian language. © 2023 IEEE.

关键词： Error correction

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Employee hypertension self-management support with microlearning and social learning

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International Journal of Networking and Virtual Organisations 2024年第4期30卷 350-365页

作者： Simons, Luuk P.A. Gerritsen, Bas Wielaard, Bas Neerincx, Mark A. Faculty of Computer Science Delft University of Technology Delft Netherlands Health Coach Program Laan van Waalhaven 442 The Hague2497 GR Netherlands

A majority of employees over the age of 40 have hypertension, impacting their health and performance. A two-week self-management support (SMS) intervention was tested, with daily feedback and microlearning cycles to improve health self-management competences. On average, participants (n = 8) reduced their blood pressure from 145/92 mmHg to 126/86 mmHg. User evaluation confirmed the importance of core SMS aspects: information transfer, daily monitoring, enhancing problem solving/decision making, self-treatment using a tailored action plan, coping skills, and skilful coach follow-up. Several lessons are drawn on microlearning, peer coaching, health results, intrinsic motivation, and social learning, which appear useful for other health improvement initiatives. © 2024 Inderscience Enterprises Ltd.

关键词： Blood pressure

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Implementation of Random Forest Model with Hyperparameter Tuning for Diabetes Mellitus Classification 7

Implementation of Random Forest Model with Hyperparameter Tu...

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Wulandari, Ajeng Anggreainy, Maria Susan School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia BINUS Graduiate Program Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798331505530

This research aims to classify Diabetes Mellitus (DM) using the Random Forest (RF) model by exploring feature selection techniques and hyperparameter tuning. DM is a metabolic disorder in the body due to bodily incompetence produces or delivers the finished hormone insulin causes blood sugar levels to rise above normal. High sugar levels in the bloodstream can cause heart disease, stroke, kidney disease, blindness, nerve damage in the feet, etc. So, diabetes must be detected as early as possible so that sufferers can receive early treatment. One of the machine learning models that can be used to create a diabetes detection model is the random forest (RF) model. The results from RF will be compared with other machine learning models such as K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost). The comparison results show that RF model is better and achieves the highest accuracy with an accuracy value of 97%. © 2024 IEEE.

关键词： Diseases

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Implementation of Sentiment Analysis of Shopee E-Commerce Reviews using Naïve Bayes, N-Gram, and Information Gain 8

Implementation of Sentiment Analysis of Shopee E-Commerce Re...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Sunjaya, Andriana Wijaya, Novresia Wong, Ng Poi Winardi, Sunaryo Universitas Mikroskil Undergraduate Program of Computer Science Medan Indonesia

ISBN: (纸本)9798350342604

One of the important parts of e-commerce like Shopee reviews. The reviews are part of user trust in conducting business transactions, so it becomes a challenge for sellers to evaluate their products and services. Evaluation can be done by looking at star ratings, but the discrepancy between the content of the review text and the star rating causes the information received to be biased and evaluations carried out by classifying reviews manually require a longer time so sentiment analysis is needed to speed up the process of classifying by objectively dividing product quality into several categories such as positive, neutral, and negative. This study uses the Naïve Bayes method combined with N-Gram feature extraction and Information Gain feature selection to determine positive, neutral, and negative sentiments toward buyer reviews. In its testing, this study used confusion matric to obtain accuracy, precision, recall, and f1-Score results. The best results show an accuracy value of 92%, precision of 56%, recall of 65%, and f1-score of 60%. © 2023 IEEE.

关键词： Sentiment analysis

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