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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Prasetyo, Simeon Yuda Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350365283

Breast cancer presents a significant health challenge globally, demanding effective early detection and diagnosis for improved patient outcomes. Leveraging machine learning techniques offers promising avenues for enhancing diagnostic accuracy, with methods like machine learning demonstrating remarkable performance. However, to further enhance precision in breast cancer diagnosis, this study explores the effectiveness of ensemble learning methodologies. Evaluating ensemble models comprising various classifiers, including Random Forest, AdaBoost, Bagging, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Hist Gradient Boosting, the research aims to identify optimal configurations for accurately distinguishing between malignant and benign breast lesions. The experimental results reveal XGBoost as the optimal ensemble technique, with an impressive accuracy of 94.737%. Notably, XGBoost's scalability, efficiency, and robustness, coupled with features like gradient boosting and regularization techniques, contribute to its exceptional predictive capability. This comprehensive analysis provides detailed insights into the specific strengths of ensemble techniques, highlighting their potential to significantly enhance breast cancer diagnosis and patient outcomes. The findings lay a solid foundation for better clinical decision-making in breast cancer management, with the potential for substantial improvements in healthcare practices. © 2024 IEEE.

关键词： Lung cancer

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Optimizing the Use of technology in Efforts to Improve Public Health, Especially with Appropriate Food Calorie Intake 9th

Optimizing the Use of Technology in Efforts to Improve Publi...

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

作者： Lo, Yossy Frenlie Fernandi, Fery Saputri, Hanis Amalia Sari, Azani Cempaka Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

NutriFitPal is a web-based online application developed to help someone monitor and control their calorie intake and maintain a healthy lifestyle. This web application was built using React TypeScript for frontend development and Firebase for backend operations. The main features contained in this web application are user authentication, real-time data storage, an extensive food database, and personalized calorie recommendations based on individual user data. In testing its reliability and functionality, this web application carries out black-box testing and System Usability Scale (SUS). Future improvements will focus on incorporating user feedback and expanding the food database. © 2024 The Authors.

关键词： Food ingredients

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Comparative Analysis of Encoder-Based Pretrained Models: Investigating the Performance of BERT Variants in Indonesian Question-Answering

Comparative Analysis of Encoder-Based Pretrained Models: Inv...

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2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics, ICoABCD 2024

作者： Islamey, Javier Jonathan, Vannes Nurzaki, Muhammad Lucky, Henry School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368451

Question-Answering (QA) is an NLP task designed to answer questions automatically. In Indonesian QA systems, various methods, such as rule-based, semantic-based, and machine-learning approaches with deep learning, have been implemented. The unique linguistic features of Bahasa Indonesia may pose significant challenges for some of these methods. Additionally, research on generative QA systems using pre-trained models in Bahasa Indonesia is underexplored compared to English. To address these issues, this paper aims to develop a deep learning-based question-answering system for Bahasa Indonesia using a sequence-to-sequence approach with pre-trained Bidirectional Encoder Representations from Transformers (BERT) models. The proposed solution implements IndoBERT, IndoRoBERTa, and mBERT in an encoder-decoder architecture with each BERT model as both the encoder and the decoder. The models employ different weight configurations as part of an ablation experiment. The models are then evaluated on the SQuAD-ID dataset. Results have shown that IndoBERT2BERT with pre-trained weights in both encoder and decoder have consistently outperformed every model on BLEU, ROUGE-N, BERTScore, EM, and F1. This proves IndoBERT2BERT's capability to generate texts that are lexically and semantically similar to the target answer while capturing relevant information. Findings also show that IndoBERT2BERT is particularly capable of handling longer answers, showcasing its ability to work with complex passages. © 2024 IEEE.

关键词： Question answering

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Named Entity Recognition Method Based on Boundary-Aware Attention Mechanism and Generative Adversarial Network

Named Entity Recognition Method Based on Boundary-Aware Atte...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Huang, Pengfei Qiu, Chen School of Computer Science and Technology Wuhan University of Science and Technology University Wuhan430000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Named Entity Recognition (NER) aims to locate and identify entities with specific meaning in text. The NER problem can usually be regarded as a type of sequence labeling problem. The key to solving this type of problem lies in determining the boundaries and categories of entities. However, due to the fuzziness of entity boundaries and limitations at the labeling level, most existing NER models introduce vocabulary information loss and The problem of entity boundary recognition error. To this end, a named entity recognition method based on the boundary-aware attention mechanism is proposed. By introducing pointer annotation to construct the boundary position vector, a sequence annotation layer that fuses the boundary position information is established to fully exploit the boundary characteristics of the entity. On the basis of integrating the boundary position vector, the lattice structure of the text information is converted into a planar structure composed of spans, a dynamic position encoding strategy is designed based on pointer annotation, and then the semantics of the label of the entity annotation and the entity boundary position are learned based on the generative adversarial network Similarity, and improve entity recognition performance by introducing information of entity boundary pointers in the weight calculation of the attention mechanism. Experimental results on weibo and Chinese-Literature-NER data sets show that the proposed method has obvious advantages in accuracy and F1 index compared with the baseline method, verifying the effectiveness of the attention mechanism based on boundary awareness in named entity recognition. © 2025 SPIE.

关键词： Generative adversarial networks

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Road Object Detection for Visually Impaired People in Bangladesh 3rd

Road Object Detection for Visually Impaired People in Bang...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Tui, Nazmun Nahar Hamza, Amir Rahman, Mohammad Shahidur Computer Science and Engineering Shahjalal University of Science and Technology Akhalia Sylhet Bangladesh

ISBN: (纸本)9789819739363

Road object detection is a critical aspect of developing assistive technologies to enhance the mobility and safety of visually impaired individuals. In the context of Bangladesh, a densely populated and diverse environment, the need for accurate and contextually relevant road object detection is particularly significant. This paper introduces a novel road object detection dataset tailored specifically for the Bangladeshi context, aimed at facilitating the advancement of computer vision systems for assisting visually impaired pedestrians. The proposed dataset, named "BanROD" (Bangladesh Road Object Detection Dataset), is constructed by capturing images across Sylhet, Bangladesh. We primarily constructed this dataset which is trained on a baseline machine learning model. The dataset is annotated with meticulously labeled bounding boxes around road objects of interest including persons, CNGs, cars, upstairs-downstairs, and other obstacles. To ensure diversity, images are collected under different lighting conditions, weather scenarios, and traffic densities. In addition to dataset creation, we train and evaluate a deep learning-based object detection model "MobileNetV2" on the BanROD dataset. We then evaluate the model’s performance by measuring loss and average precision to find out how well it performs on real-world images. This solution is only for mobile devices as they are more handy for blind people around us. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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QRALadder: QoE and Resource Consumption-Aware Encoding Ladder Optimization for Live Video Streaming 31st

QRALadder: QoE and Resource Consumption-Aware Encoding Ladd...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Zhu, Yingqian Gao, Guanyu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9789819620630

Live video streaming demands high user Quality of Experience (QoE) and requires significant computing power and bandwidth for video encoding and transmission. The standard adaptive live streaming approach encodes the source video content at multiple predefined bitrates, allowing users to select the most appropriate bitrate for optimal rate adaptation. The current methods for encoding ladder design primarily focus on optimizing user QoE. However, the relationship between the encoding ladder and user QoE is complex due to the dynamic nature of live streaming, where the number of users fluctuates, and each user experiences varying and evolving bandwidth conditions. On the other hand, resource consumption in live streaming significantly impacts operational costs, as it requires substantial resources for both video encoding and delivery. To address this challenge, we propose QRALadder, a QoE and resource consumption-aware approach to encoding ladder design. Our method simultaneously optimizes user QoE while considering the impact of the encoding ladder on resource consumption in live streaming, and it can dynamically select the most suitable encoding ladder based on the current system state. We use the Dueling Double Deep Q Networks (Dueling DDQN) algorithm to learn the optimal encoding ladder, maximizing overall performance by analyzing user QoE feedback and resource consumption about system states. We conduct comparative experiments using various types of video content and real-world bandwidth traces to validate our approach. The results show that QRALadder can achieve higher overall performance compared to baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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Analysis of Assessment Factors of Consensus Algorithms in Supply Chain Management

Analysis of Assessment Factors of Consensus Algorithms in Su...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Rani, Shikha Kushwaha, Vandana Pandey, Divya Banaras Hindu University Department of Computer Science Varanasi India School of Computer Science Engineering Technology Bennett University Greater Noida India

ISBN: (纸本)9798350379716

Blockchain technology has transformed supply chain management due to its unprecedented security, transparency, and efficiency. This technology has also enabled businesses to track their products in real-time and make informed decisions about inventory levels. Anyone who involved in the supplychain needs to ensure that they provide reliable information about the product's handling, provenance, and quality. Supply chain operations have emerged as a complex and challenging business environment, but blockchain technology and consensus algorithms offer potential solutions. This study examines the intricate nature of consensus algorithms and their applications in supply chain management. The elementary objective of this study is to provide an extensive analysis of consensus algorithms, focusing on their important assessment factors, like trust, security, cost, and others, in supply chain processes using blockchain. By employing a systematic approach, we evaluate the fundamental consensus algorithms, including proof of authority, proof of stake, and some others. This study also assesses the respective platforms on which the consensus algorithms have been executed and the integration with the IoT's suitability for different domains of supply chain management. © 2024 IEEE.

关键词： Blockchain Consensus Protocols Supply chain Management

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Introducing Tourism Spot in Indonesia Using Adventure Video Game 1

Introducing Tourism Spot in Indonesia Using Adventure Video ...

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1st International Conference of Adisutjipto on Aerospace Electrical Engineering and Informatics, ICAAEEI 2024

作者： Prayudi, Nicholas Putra Aditya Jason, Johanes Widianto, Mochammad Haldi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331507930

Video games have been quickly rising to become one of the most popular entertainment media. The relation between a video game and a location can promote or introduce the location to the game's players. On the other hand, Indonesia is a country that has benefited from the tourism industry. The objective of the research itself is to find out if an adventure game can introduce a tourism place in Indonesia effectively and identify features needed for the game for it to be successful in introducing a tourism place. A user requirement survey was conducted and there was a warm reception for using adventure games to introduce a tourism place and according to the respondents, graphics and visuals was the most important aspect of the game. A video game was developed using the Game Development Life Cycle method and the Unity game engine to introduce one tourism place in Indonesia, Tebing Keraton. Using a survey to gather feedback from players, this research found that the game that was developed even though it is generally successful in introducing the tourism place, according to the feedback there are also many things that could be further improved like accessibility of the game, change the game to be an open world setting and adding the introduction of culture as well using non-playable characters or game objects and decorations. © 2024 IEEE.

关键词： Leisure industry

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OCRBench: on the hidden mystery of OCR in large multimodal models

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science China(Information sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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Evolutionary Algorithms and Fuzzy Systems for Enabling Smart Technologies across Industries 1

Evolutionary Algorithms and Fuzzy Systems for Enabling Smart...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Refonaa, J. Poornima, D. Shabu, S.L. Jany Kanipriya, M. Surya, V. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India Srmist Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Smart technology have end up an increasing number of vital in today's rapidly evolving generation panorama. Automation, records-pushed decision-making, and streamlined operations are all being revolutionized by means of the usage of clever era. However, there are some of boundaries that save you their effective deployment, which include as converting conditions, complicated device interactions, and the requirement for instantaneous flexibility. Fuzzy Logic Control for Smart technology Optimization (FLC-STO) is a way that makes use of the complementary nature of Evolutionary Algorithms and Fuzzy Systems (EA-FS) to achieve optimization in smart era. FLC-STO is a dynamic and adaptive technique for optimizing smart generation for maximum performance over a great form of situations. FLC-STO has proven to be powerful via simulation evaluation in optimizing smart generation in practical settings. It demonstrates how the proposed method works in workout, demonstrating how common performance, performance, and flexibility are all improved in the face of dynamic surroundings and complex interactions. The FLC-STO method gives a effective framework for permitting and optimizing clever generation throughout industries due to its fusion of Evolutionary Algorithms and Fuzzy Systems. The studies presented here allows to enhance clever technology and encourage their fashionable reputation with the aid of addressing the difficulties confronted with the aid of converting contexts, complex gadget interconnections, and the need for actual-time variation. © 2023 IEEE.

关键词： Evolutionary algorithms

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