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

作者： Julian, Kelvin Nugroho, Kuncahyo Setyo Nirwantono, Rudi Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies. © 2024 IEEE.

关键词： Lung cancer

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Rahmania, Rissa Heryadi, Yaya Lukas, Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products. © 2023 IEEE.

关键词： Embeddings

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Hidayaturrahman Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusatara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies. © 2023 IEEE.

关键词： Adversarial Domain Adaptation Domain Shifting Problem MNIST Transfer Learning

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Downsizing and soft X-ray tomography for cellular uptake of interpenetrated metal-organic frameworks

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Journal of Materials Chemistry B 2024年第25期12卷 6079-6090页

作者： Yu, Yu-Sheng Liang, Yung-Yi Hsieh, Chia-Chun Lin, Zi-Jing Cheng, Po-Hsiu Cheng, Chih-Chan Chen, Shu-Ping Lai, Lee-Jene Wu, Kevin C.-W. National Taiwan University Department of Chemical Engineering Taipei Taiwan National Health Research Institute Institute of Biomedical Engineering and Nanomedicine Miaoli Taiwan Hsinchu Taiwan International Graduate Program of Molecular Science and Technology Taiwan International Graduate Program Academia Sinica Taipei Taiwan National Taiwan University Taipei Taiwan

Metal-organic frameworks (MOFs) are porous materials with potential in biomedical applications such as sensing, drug delivery, and radiosensitization. However, how to tune the properties of the MOFs for such applications remains challenging. Herein, we synthesized two MOFs, Zr-PEB and Hf-PEB. Zr-PEB can be classified as porous interpenetrated zirconium frameworks (PIZOFs) and Hf-PEB is its analogue. We controlled their sizes while maintaining their crystal structure by employing a coordination modulation strategy. They were designed to serve as sensitizer for X-ray therapy and as potential drug carriers. Comprehensive characterizations of the MOFs' properties have been conducted, and the in vitro biological impacts have been studied. Since viability assay showed that Hf-PEB was more biocompatible compared to Zr-PEB, the cellular uptake of Hf-PEB by cells was evaluated using both fluorescence microscopy and soft X-ray tomography (SXT), and the three-dimensional structure of Hf-PEB in cells was observed. The results revealed the potential of Zr-PEB and Hf-PEB as nanomaterials for biomedical applications and demonstrated that SXT is an effective tool to assist the development of such materials. © 2024 The Royal Society of Chemistry.

关键词： Organometallics

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AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying Datasets, Labelling, and Alertness Level Classification 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Rahutomo, Reza Elwirehardja, Gregorius Natanael Asadi, Faisal Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-intrusive method and can offer high accuracy for the countermeasure development. Many Artificial Intelligence (AI) techniques have been developed to aid in video analysis for fatigue detection. However, the issues in AI-based video analysis for fatigue countermeasure development are rarely discussed. This paper reviews current video-based countermeasure development in the literature and highlights some reliability issues of AI-based fatigue countermeasure development in terms of underlying dataset, labelling method, and alertness level classification. The result serves as insights and consideration to academics and industrialists in developing a high reliable AI-based video analysis for driver fatigue countermeasure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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BERT and RoBERTa Models for Enhanced Detection of Depression in Social Media Text 9th

BERT and RoBERTa Models for Enhanced Detection of Depression...

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

作者： Kurniadi, Felix Indra Paramita, Ni Luh Putu Satyaning Pradnya Sihotang, Erna Fransisca Angela Anggreainy, Maria Susan Zhang, Rongfang Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Statistic Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11530 Indonesia Department of Rehabilitation and Health Service University of North Texas United States

Depression is a disease that affects everyone, both young and old. This mental illness not only affects the surrounding environment but everyone. Depression is characterized by deep sadness, behavioral changes and many other actions that are risky for people. In this research we try to solve the problem of detecting depression using Natural Language Processing (NLP) approaches, these two methods are Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa), where these two methods are used to detect posts made in reddit. The dataset is taken from Kaggle. The results obtained found that the average use of BERT and RoBERTa resulted in a high accuracy value of around 98% and with a well balanced precision, recall and F1-Score ratio. This research shows that there is a possibility of using BERT and RoBERTa in depression detection. © 2024 The Authors.

关键词： BERT depression natural language processing RoBERTa

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A hybrid machine learning model to estimate self-compacting concrete compressive strength

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Frontiers of Structural and Civil engineering 2022年第8期16卷 990-1002页

作者： Hai-Bang LY Thuy-Anh NGUYEN Binh Thai PHAM May Huu NGUYEN Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

This study examined the feasibility of using the grey wolf optimizer(GWO)and artificial neural network(ANN)to predict the compressive strength(CS)of self-compacting concrete(SCC).The ANN-GWO model was created using 115 samples from different sources,taking into account nine key SCC *** validation of the proposed model was evaluated via six indices,including correlation coefficient(R),mean squared error,mean absolute error(MAE),IA,Slope,and mean absolute percentage *** addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence *** results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s *** that,an examination of the parameters impacting the CS of SCC was provided.

关键词： artificial neural network grey wolf optimize algorithm compressive strength self-compacting concrete

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Improving free space optics performance using QC-LDPC codes and simplified bit flipping decoding in low visibility conditions

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Optical and Quantum Electronics 2025年第1期57卷 1-18页

作者： Marlina, Lina Pamukti, Brian Liaw, Shien-Kuei Lee, Pei-Jun Kishikawa, Hiroki Graduate Institute of Electro-Optical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Institute of Post-LED Photonics Tokushima University Tokushima Japan

Currently, free space optics (FSO) is the most promising technology for achieving high data transfer over short to medium or even long distances. Thus, FSO provides wireless line-of-sight (LOS) connectivity in the unlicensed spectrum and can be a high-bandwidth wireless alternative in contrast to fiber optic cabling. Moreover, the FSO offers rapid assembly times and significant cost savings. However, atmospheric weather (e.g., fog, rain, and snow) can degrade the FSO performance, especially in low visibility conditions. Therefore, this research aims to improve the FSO performance using quasi-cyclic low-density parity-check (QC-LDPC) codes in low visibility scenarios or thick turbulence conditions. In addition, a simplified bit-flipping (SBF) algorithm was proposed to decode the information by the receiver side to minimize complexity. Subsequently, the FSO system is simulated using a 1550 nm laser at different data rates of 1 to 20 Gbps data rate and 1-Watt transmitted power. This paper was analyzed using different empirical models of atmosphere attenuation, like the Kim, Kruse, and Naboulsi models. This simulation also uses modulation techniques such as on-off keying, phase shift keying, and quadrature amplitude modulation. Moreover, the looping iteration number of SBF could improve the system’s performance. We also consider using a low-cost PIN photodetector for practical implementation. Our simulation results show that QC-LDPC codes can achieve an average bit error rate of 10-9 in lower visibility than uncoded information. Our research has significant implications for enhancing fiber optic performance in adverse atmospheric phenomena. © The Author(s) 2025.

关键词： Quadrature amplitude modulation

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Question Classification of University Admission using Named-Entity Recognition (NER) 10

Question Classification of University Admission using Named-...

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10th International Conference on Information Technology, computer, and Electrical engineering, ICITACEE 2023

作者： Yossy, Emny Harna Suhartono, Derwin Trisetyarso, Agung Budiharto, Widodo Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Mathematics Department School of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350322729

The university admission process can be overwhelming for applicants and admission officers, with many questions being asked and answered every year. The manual classification of these questions can be time-consuming and may lead to errors, making exploring automated solutions to this problem essential. In recent years, advances in natural language processing (NLP), including question classification. The research proposes an architecture for a question classification system using the Indonesian Language in closed-domain question answering. The purpose of this research is to find out the classification of questions. We created a system with the Named-Entity Recognition method to recognize the next word that will be used as a candidate type of question and candidate answer. The type of question used is a factoid question. Research makes patterns and rules for extracting important words as features to determine the taxonomy of question classification. Datasets are collected from one private university in Indonesia. Data sources from Binus Online Learning websites and data on questions frequently asked by users, especially prospective students. The results of the question classification are presented using descriptive statistics. The results show that the question classification with users' most frequent question type is 'What' by 63.3%, and the answer category is 'tuition fees' by 22.4%. It can be concluded that the most frequent question type asked by users is 'What', and the most frequent topic of interest is 'tuition fees'. Organizations can use this information to improve customer service by providing more relevant information and resources about tuition fees. © 2023 IEEE.

关键词： Natural language processing systems

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P2P-assisted distributed-server content delivery of 8K media : a DLT-based approach

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Multimedia Tools and Applications 2024年 1-38页

作者： Barlas, Gerassimos El-Fakih, Khaled Hiromori, Akihito Yamaguchi, Hirozumi Department of Electrical and Computer Engineering American University in Dubai Dubai Dubai United Arab Emirates Computer Science & amp Engineering Dept American University of Sharjah Sharjah Sharjah26666 United Arab Emirates Graduate School of Information Science and Technology Osaka University Osaka Japan

In this paper we study the problem of multi-source, P2P-assisted, multimedia content delivery of 8K content. Such media constitute a big challenge for contemporary VoD services, as their media rate can surpass the clients’ upload/download speeds. Our proposed methods tackle this shortcoming while also allowing a reduction in the server resource consumption. Two methods are presented and evaluated, that include global and local multi-installment schemes, that are shown to surpass server-only or non-DLT-based content distribution by a fair margin, in all the significant metrics. Our methods can either offer a significant improvement in access time or a boost in the number of clients served, while at the same time reducing the consumed server resources. The performance of the proposed methods is evaluated with a rigorous simulation study based on the current state of the Internet on two geographic locations that represent best-case and average-case scenarios. Our findings suggest that the methodology used to assign the content parts to individual sources, has a big influence of the consumption of server resources, the system’s scalability and service quality. One of the proposed methods (LMI) achieves a maximum of 70.5% improvement on the average client access time, while saving up to 23% of server resources. Based on the system settings the reduced access-time/faster access to content can be balanced against an increase in the served clients by up to 25%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Multimedia systems

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