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IEEE International Conference on Big Data

作者： Marin Wada Miho Adachi Ryusuke Miyamoto Dept. of Computer Science Graduate School of Science and Technology Dept. of Computer Science School of Science and Technology Meiji University Kawasaki Japan

A visual navigation method based on results of semantic segmentation showed interesting results in previous researches. The most significant problem of the method is that the moving performance is affected by the segmentation accuracy, which strongly depends on the training data even though SOTA methods are adopted. To create high-quality dataset for this application without huge human efforts, the authors tries to generate datasets for semantic segmentation from a 3D scanned data composed of colored point clouds. In this study, we investigate what kinds of variations are effective to construct a classifier: variation of augmentation considering shadows, shooting angles, and shooting locations. Experimental results using actual images for evaluation and generated dataset for training captured at the course of Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that adding shadows in training datasets improved the mIoU but random changes to the shooting angle and location did not always work well. By the result, it is shown that augmentation considering the characteristic of the target environment becomes important for practical use.

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WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans

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Multimedia Tools and Applications 2024年第28期83卷 71885-71908页

作者： Das, Suchismita Dubey, Rajni Jena, Biswajit Tsai, Lung-Wen Saxena, Sanjay Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei111031 Taiwan Department of Computer Science and Engineering Institute of Technical Education and Research SOA Deemed to Be University Bhubaneswar India Department of Medicine Research Taipei Medical University Hospital Taipei111031 Taiwan Department of Information Technology Office Taipei Medical University Hospital Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798331505530

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. technology also plays an essential role in supporting runners and running races. technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential. © 2024 IEEE.

关键词： artificial intelligence aspects marathon performance

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GBT-CHAIN: A System Framework for Solving the General Trilemma in Permissioned Blockchains

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Distributed Ledger Technologies: Research and Practice 2024年第2期3卷 1-15页

作者： Han Wang Hui Li Abla Smahi Mingrui Xiao Shuo-Yen Robert Li School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China National Key Laboratory of Science and Technology on Communications University of Electronic Science & Technology of China Chengdu China

As a subset of distributed systems, blockchain technology is equally bound by the CAP trilemma. Specifically, no distributed system can satisfy consistency, availability, and partition tolerance simultaneously. Informally, the innovative blockchain trilemma has been theorized to include security, scalability, and decentralization. While many software approaches on blockchains claim to solve the trilemma problem, almost all known approaches have been proven to involve tradeoffs and fail to reach the upper bounds, meaning they do not achieve the theoretical limit value in each term of a trilemma. In this article, we propose a general trilemma based on the above two trilemmas. For permissioned blockchains, we then describe a system framework called GBT-CHAIN to solve the General Blockchain Trilemma. In the proposed framework, the consensus and data layer, the network layer, and the physical layer work in cooperation. According to our theoretical proof and experimental analysis, the three properties of the trilemma consisting of consistency, scalability, and partition tolerance are perfectly satisfied without tradeoffs. Our results demonstrate that the general blockchain trilemma is resolved by combining consensus algorithms and physical topologies, in particular for permissioned blockchains.

关键词： Permissioned blockchain trilemma scalability partition tolerance consistency

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Cloud-Based Mixed Reality System in Machinery Vocational school: ICT Blueprint

Cloud-Based Mixed Reality System in Machinery Vocational Sch...

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2022 International Conference on science and technology, ICOSTECH 2022

作者： Suryodiningrat, Satrio Pradono Gaol, Ford L. Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta Indonesia Bina Nusantara University Jakarta Doctor of Computer Science Graduate Program Jakarta Indonesia Smart City and Community Innovation Center Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781665479851

Education is always the most important part of our lives. To make education make a significant impact on today's learners, teaching and learning must evolve to match with today's learner's profile. Any education including vocational education has an important role in economic development and one of the ways to maximize the contribution of vocational educations is strengthening the link and match with the world of work [1]. As the technology also keeps evolving and it is getting more and more advanced, one technology that has significant traction lately is the mixed reality system and is often used for education purposes. Most big ICT companies like Microsoft, and HTC are racing to develop mixed reality systems to help the teaching and learning process to be more effective and efficient. Where everything is cloud-based, having a cloud-based mixed reality system is just makes sense but will have a great challenge and considered as a big ICT project. One of the ways to make the ICT project successful is to make a blueprint before it is started. The objective of this paper is how to develop an ICT Blueprint for implementing a cloud-based mixed reality system for machinery vocational schools to make the project have a higher success rate. © 2022 IEEE.

关键词： Blueprints

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Dataset Genreratoin for Semantic Segmentation from 3D Scanned Data Considering Domain Gap

Dataset Genreratoin for Semantic Segmentation from 3D Scanne...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Marin Wada Miho Adachi Yuriko Ueda Ryusuke Miyamoto Dept. of Computer Science Graduate School of Science and Technology Dept. of Computer Science School of Science and Technology Meiji University Kawasaki Japan

An autonomous moving scheme with semantic information extracted from images captured by the monocular camera was proposed, providing accurate results of semantic segmentation. A training dataset must accommodate the moving environment to train a classifier for autonomous moving. However, preparing a large-scale dataset composed of images having pixel-wise manually-annotated class labels is impractical. We generated datasets automatically from 3D point clouds, for reducing manpower. The dataset had significant problems: domain gap and shadows. Therefore, style transfer is incorporated in the proposed scheme to bridge the gap to resolve this problem. Moreover, pseudo shadows were added to improve the classification accuracy of testing images with shadows. Experimental results using 3D point clouds and testing images taken around Tsukuba City showed that classification accuracy was improved by the proposed scheme. The classification accuracy of the sidewalk, the most significant class for autonomous moving at the Tsukuba Challenge, was 95.7%.

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Neural Machine Translation Approach for Low-resource Languages using Long Short-term Memory Model

Neural Machine Translation Approach for Low-resource Languag...

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2023 International Conference on computer science, Information technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Murad, Dina Fitria Tho, Cuk Hashimoto, Kiyota Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

Communication is a form of translation that human learns naturally since early childhood. Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. One example of high-impact language translation is translating health protocols from the World Health Organization to many national languages to prevent the spread of the Covid-19 virus. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of the Long Short-term Memory model as a machine translation involving Bahasa Indonesia and the Sundanese languages. Performance of the Sundanese language to Indonesian language translation is 0.93 average training and validation accuracy and 0.91 average testings BLEU score. Whilst, Indonesian language to Sundanese language translation is 0.92 average training and validation accuracy and 0.88 average testings BLEU score. Both models achieve 0.39 average training loss and 0.38 average validation loss. © 2023 IEEE.

关键词： Viruses

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Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction

Can Language Beat Numerical Regression? Language-Based Multi...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Inhwan Bae Junoh Lee Hae-Gon Jeon AI Graduate School School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju South Korea

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Language models have demonstrated impressive ability in context understanding and generative performance. Inspired by the recent success of language foundation models, in this paper, we propose LMTraj (Language-based Multimodal Trajectory predictor), which recasts the trajectory prediction task into a sort of question-answering problem. Departing from traditional numerical regression models, which treat the trajectory coordinate sequence as continuous signals, we consider them as discrete signals like text prompts. Specially, we first transform an input space for the trajectory coordinate into the natural language space. Here, the entire timeseries trajectories of pedestrians are converted into a text prompt, and scene images are described as text information through image captioning. The transformed numerical and image data are then wrapped into the question-answering template for use in a language model. Next, to guide the language model in understanding and reasoning high-level knowledge, such as scene context and social relationships between pedestrians, we introduce an auxiliary multi-task question and answering. We then train a numerical tokenizer with the prompt data. We encourage the tokenizer to separate the integer and decimal parts well, and leverage it to capture correlations between the consecutive numbers in the language model. Lastly, we train the language model using the numerical tokenizer and all of the question-answer prompts. Here, we propose a beam-search-based most-likely prediction and a temperature-based multimodal prediction to implement both deterministic and stochastic inferences. Applying our LMTraj, we show that the language-based model can be a powerful pedestrian trajectory predictor, and outperforms existing numerical-based predictor methods. Extensive experiments show that our LMTraj can successfully understand social relationships and accurately extrapolate the multimodal futures on the public pedestrian trajectory prediction benchm

关键词： Pedestrians Computational modeling Stochastic processes Predictive models Multitasking Data models Cognition

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Novel machine learning investigation for Buongiorno fluidic model with Sutterby nanomaterial

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Tribology International 2024年 199卷

作者： Khan, Maryam Pervaiz Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Shoaib, Muhammad Department of Computer Science and Information Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Data Science and AI Applications Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan AI center Yuan Ze University Taoyuan320 Taiwan

The nanomaterials are frequently employed in a variety of heat transfer applications arising in energy generation, engine cooling, extrusion procedures, heat exchanger, thermos-chemical systems, manufacturing structures, powered plants etc. Experts and researchers in these fields often encounter such materials, leading to discernible impacts on velocity, temperature and concentration profiles. The objective of this study is to represent the mathematical structures for Sutterby nanomaterial fluidic model involving porous medium BFM-SNM using nonlinear autoregressive exogeneous networks backpropagated with Levenberg-Marquardt technique (NARX-LMT). The mathematical design of the model is originally presented by PDEs, which are converted into ODEs system through suitable modifications with alternative transformation incorporating numbers, i.e., Deborah, Prandtl and Reynold, as well as parameters, i.e., magnetic thermophoresis, radiation and temperature. The synthetic data is produced numerically by simulating Adams numerical method for BFM-SNM and the supervised computing paradigm of NARX-LMT is applied to the obtained datasets, and the results of NARX-LMT consistently align with numerical observations for each variant of the presented model, exhibiting negligible errors. The NARX-LMT performance on exhaustive experimentation is effectively illustrated through iterative convergence curves on mean squared error, control metrics of the optimization, distribution of error on histograms and regression outputs for Sutterby nanomaterial fluidic model. © 2024 Elsevier Ltd

关键词： Ordinary differential equations

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Multimodal Information Extraction:A Systematic Review of Subtask, Modal Types and Applications Based on Deep Learning in Banking Sector

Multimodal Information Extraction:A Systematic Review of Sub...

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Emerging technology (INCET), International Conference for

作者： Samundiswary Srinivsan R Sathya Bama Krishna Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai SIES Graduate School of Technology Navi Mumbai

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Deep Learning has been widely applied in all application domains namely Healthcare, Entertainment, Banking & finance, Agriculture, Aerospace & defence etc. Due to stupendous achievement of deep learning as a data processing technique has fascinated in all research areas. Handling of Deep learning in Banking and Financial sector is pervasive. Advancement of Information extraction especially because of different formats of information such as text, image, sound, video can be extracted simultaneously by the usage of deep learning techniques. This study reviews and analyses multimodal information extraction based on deep learning techniques especially on banking and financial sector. It aims to enhance the efficiency of document-intensive business tasks in the financial sector. Deep review analysis will be carried out to understand subtasks of Information extraction and techniques involved in Banking domain. Finally, how multimodal information extraction using deep learning enhances the efficiency than unimodal and its use cases in current AI based automation world will be highlighted.

关键词： Deep learning Surveys Systematics Reviews Semantics Finance Banking

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