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9th International Conference on Computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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THE EFFECT OF VISUAL APPEAL, SOCIAL INTERACTION, ENJOYMENT, AND COMPETITION ON MOBILE ESPORTS ACCEPTANCE BY URBAN CITIZENS

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Interdisciplinary Journal of Information, Knowledge, and Management 2022年 17卷 601-624页

作者： Wilis, Nufri Manik, Lindung Parningotan Computer Science Graduate Program University of Nusa Mandiri Jakarta Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

Aim/Purpose This study investigated a model of mobile esports acceptance among urban citizens based on an extended Technology Acceptance Model (TAM). Background Currently, esports are increasingly popular and in demand by the public. Supported by the widespread development of mobile devices, it has become an interactive market trend to play games in a new model, mobile esports. Methodology This study collected data from 400 respondents and analyzed it using partial least squares-structural equation modeling (PLS-SEM). Contribution This study addresses two research gaps. The first gap is limited esports information systems studies, particularly in mobile esports acceptance studies. The second gap is limited exploration of external variables in online gaming acceptance studies. Thus, this study proposed a TAM extended model by integrating the TAM native variables with other external variables such as visual appeal, enjoyment, social interaction, and competition to explore mobile esports acceptance by urban citizens. Findings Nine hypotheses were accepted, and four were rejected. The visual appeal did not affect the acceptance. Meanwhile, social interaction and enjoyment significantly affected both perceived ease of use and usefulness. However, perceived ease of use surprisingly had an insignificant effect on attitude toward using mobile esports. Moreover, competition significantly affected the acceptance, particularly on perceived usefulness. Recommendations Fresh and innovative features, such as new game items or themes, should be frefor Practitioners quently introduced to enhance players’ continued enjoyment. Moreover, mobile esports providers should offer a solid platform to excite players’ interactions to increase the likelihood that users feel content. On the other hand, the national sports ministry/agency or responsible authorities should organize many esports competitions, big or small, to search for new talents. Recommendations Visual appeal in this study did

关键词： Social networking (online)

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A Systematic Literature Review of CNN Approaches in Classifying Brain Tumor 6

A Systematic Literature Review of CNN Approaches in Classify...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Paulindino, Aedentrisa Yasmanda Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

The ability of Convolutional Neural Networks (CNNs) to accurately discriminate between normal and tumorous brain tissues has been promising. The review focuses on the different CNN models, pre-processing methods, data augmentation, and Transfer Learning (TL) strategies used in this research. This Systematic Literature Review (SLR) collected the data from Google Scholar. The results of this study indicate that open-source datasets from Kaggle and Brain MRI Images for Brain Tumor Detection are the most used datasets. However, limited data and imbalanced class problems remain common challenges across various datasets. To overcome those challenges, using a larger dataset, oversampling, Generative Adversarial Network (GAN), federated learning, and Self-Supervised Learning (SSL) to handle the imbalance are the potential solution. Additionally, popular CNN architectures for brain tumor classification extensively use pre-trained models such as VGG16, VGG19, DenseNet121, DenseNet201, GoogleNet, ResNet-50, and Inception-v3. TL strategies are preferred, allowing CNNs to leverage knowledge from large datasets, improving generalization even with limited labeled data. © 2023 IEEE.

关键词： brain tumor classification cnn mri pre-trained transfer learning

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A Systematic Literature Review of Deep Learning Application in Multiclass Anomaly Detection for Chest Medical Imaging 6

A Systematic Literature Review of Deep Learning Application ...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Philips Pardamean, Bens Elwirehardja, Gregorius Natanael Isnan, Mahmud Bina Nusantara University Binus Graduate Program - Master of Compuer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Medical imaging abnormality detection is challenging, but deep learning approaches have shown promise. This paper reviews the current state of the art in deep learning approaches for detecting abnormalities in chest medical imaging. To discover the trends, opportunities, and challenges associated with this field, 18 studies were selected from Google Scholar based on their titles, abstracts, and contents for extensive review to answer two research questions. The study found that the National Institutes of Health (NIH) Chest X-ray 14 dataset is the most used dataset for this task. Most research uses a single-modal approach, considering only image data as input, with X-ray being the more popular instrument. There are 8 out of 18 studies leverage the transfer learning approach, with ResN et50 being the most popular network. MobileNetV2 has demonstrated competitive results compared to more robust networks. Preprocessing techniques such as image enhancement and data augmentation are leveraged by 61.1 % of the reviewed studies and are shown to improve model performance. © 2023 IEEE.

关键词： abnormality detection attention mechanisms chest x-ray CT images deep learning transfer learning

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Smartphone-Based Orientation Estimation for Assisting the Visually Impaired in Lines 23

Smartphone-Based Orientation Estimation for Assisting the Vi...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Tokuda, Haruto Bilguunmaa, Myagmardulam Takagi, Noboru Fuse, Yotato Motoyoshi, Tatsuo Sawai, K.E.I. Masuta, Hiroyuki Graduate School of Intelligent Robotics Program Toyama Prefectural University Japan Toyama Prefectural University Department of Data Science Japan Toyama Prefectural University Department of Intelligent Robotics Japan

ISBN: (纸本)9798331528041

Standing in line poses a significant challenge for the visually impaired. Existing queue guidance systems, which recognize line locations and estimate personal orientation, require highspecification computers, making them impractical for everyday use. This study proposes a smartphone-based queue guidance system that leverages the device's camera and sensors to estimate personal orientation efficiently. By optimizing computationally efficient algorithms for smartphone hardware, we aim to create an accessible, cost-effective solution. This system will enhance the independence and mobility of visually impaired individuals, providing real-time guidance to navigate queues seamlessly. Initial testing indicates promising usability and accuracy, paving the way for broader implementation. © 2024 IEEE.

关键词： Smartphones

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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review 6

Pap Smear Medical Image Classification Using Deep Learning: ...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Selvano, Elvan Elwiehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability. © 2023 IEEE.

关键词： cervical cancer deep learning machine learning pap test

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression 6

Systematic Literature Review of Transformer Model Implementa...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Nanggala, Kenjovan Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities. © 2023 IEEE.

关键词： deep learning major depressive disorder multimodal transformer unimodal

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Sentiment Analysis: Indonesia Netflix User's Comment Using Multiple Lexicon-Based Dictionaries 4

Sentiment Analysis: Indonesia Netflix User's Comment Using M...

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

作者： Mola, Sebastianus Adi Santoso Widiastuti, Tiwuk Roma, Rosa V. K. Isabela Ohe Karnyoto, Andrea Stevens Pardamean, Bens University of Nusa Cendana Faculty of Science and Engineering Kupang Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

A subscription movie streaming service from the United States, Netflix, has been present in Indonesia since 2016 and provides a wide variety of films without showing any single advertisements that can be viewed from anywhere, as long as there is access to the internet. Despite these advantages, many users have complained through the Google Play Store's comments column. Some of the common complaints include frequent buffering and connectivity issues, dissatisfaction with the limited selection of Indonesian movies, lack of subtitles for specific languages, or pricing concerns have also been raised. In this study developed two combined scenario methods using the InSet and SentiStrength-id dictionary to obtain better performance and compare them against independent InSet and SentiStrength-id. This study collected users' comments for the Netflix app in the Google Play Store as a dataset using web scraping techniques through the Google Collaboration tool. The dataset contains 3250 rows spanning the period from January 22, 2024, through June 6, 2024. To ensure proper processing of the text, data cleaning, lowercasing, normalization, tokenization, stemming, and stopwords removal are conducted. The results show that most user opinions are negative. The InSet dictionary has an accuracy of 92%, SentiStrength-id 78%, Combined scenario 1 is 87%, and scenario 2 reaches the highest among others which is 92.52%. © 2024 IEEE.

关键词： Video streaming

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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Estimation Algorithms for MLE of Three-Mode GMANOVA Model with Kronecker Product Covariance Matrix 15th

Estimation Algorithms for MLE of Three-Mode GMANOVA Model ...

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15th International KES Conference on Intelligent Decision Technologies, KESIDT 2023

作者： Horikawa, Keito Nagai, Isamu Monden, Rei Yanagihara, Hirokazu Informatics and Data Science Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8521 Japan Faculty of Liberal Arts and Science Chukyo University Aichi Nagoya466-8666 Japan Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8526 Japan

ISBN: (纸本)9789819929689

Currently, there are a lot of measurement data on different items collected over time. The GMANOVA model is appropriate for analyzing the trends in such data, in order to analyze some longitudinal data collected on different items simultaneously, we need to create separate GMANOVA models, but separate GMANOVA models cannot reveal the relationships between items. Also, if we try to apply one model to all of these data, the problem arises that there are too many parameters and the covariance structure is too large. Therefore, we propose an extended GMANOVA model and two estimation algorithms. The proposed model’s covariance matrix has a Kronecker structure. Therefore, because the estimator cannot be uniquely determined using the log-likelihood, we propose two parameter estimation algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Covariance matrix

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