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The Digital Twin City in Enhancing Flood Evacuation Systems: Future Opportunities and Challenges

IEEE Access 2025年 13卷 32371-32383页

作者： Setijadi Prihatmanto, Ary Prasetyadi, Abdurrakhman Yoganingrum, Ambar Sutriadi, Ridwan Hadiana, Ana Bandung Institute of Technology School of Electrical and Informatics Engineering Bandung40132 Indonesia National Research and Innovation Agency Research Center for Data and Information Science Bandung40231 Indonesia Bandung Institute of Technology School of Architecture Planning and Policy Development Bandung40132 Indonesia

In recent decades, the increasing frequency and severity of natural disasters have necessitated the development of more effective disaster management systems. This paper explores the potential of digital twin technology to enhance flood evacuation systems. A digital twin-city model can simulate urban environments, predict flood inundation, and identify optimal evacuation routes in real-time. The study conducts a systematic literature review to assess current advancements, evaluating the integration of 3D city models, artificial intelligence, and Internet of things sensors in flood evacuation planning. Results indicate that digital twin technology significantly improves predictive capabilities and evacuation efficiency, although data integration, model accuracy, and computational demands remain. Future research should focus on enhancing data integration, refining simulation models, improving predictive analytics, developing advanced visualization tools, and validating models across diverse environments to ensure reliability and effectiveness. © 2025 The Authors.

关键词： Digital elevation model

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FAIRifying STEM data Ecosystem to Enhance data Reuse 53

FAIRifying STEM Data Ecosystem to Enhance Data Reuse

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Namgay, Phub Wangdi, Pema Thinley, Sangay Namgyel, Thinley Uppsala University Department of Informatics and Media Uppsala Sweden School of Data Science and Data Analytics Sherubtse College Royal University of Bhutan Bhutan

ISBN: (纸本)9798350336429

In the science, technology, engineering, and mathematics (STEM) community, academic and research workflows and work practices are increasingly mediated and informed by data. However, making digital materials and resources findable, accessible, interoperable, and reusable (FAIR) for teaching, learning, and research is an under-researched area. Thus, it is vital to examine the current data practices of STEM students and faculties and acquaint them with the FAIR data concept. FAIR data Principles is a set of guidelines that underscore metadata, vocabularies, licences, and standards to enhance data reuse. A study was conducted among students and faculties in the STEM community of the Royal University of Bhutan (RUB) to unpack their current data practices and explore areas for improvement using the FAIR data Principles. The STEM students and faculties of the RUB share and reuse digital materials and resources for teaching, learning, and research. Nevertheless, their data practice is not as widespread or desired in the literature on optimum data reuse. Moreover, the compliance of current data practices to the tenets of FAIR data Principles is not satisfactory. A pragmatic solution is complementing data practices with policies and infrastructural systems that underscore FAIR data Principles. A sensitisation programme such as seminars and hands-on exercises on data FAIRification is crucial to familiarise people with the essentialness of FAIR data, and doing so will provide a platform to develop their repertoire for FAIRifying data and encourage systematic sharing and reuse of data. An in-depth account of the FAIRifying STEM data ecosystem in the study contributes to the growing knowledge base on adopting FAIR data Principles in other areas of data-informed work and life. © 2023 IEEE.

关键词： Bhutan data Reuse FAIR data Principles FAIRifying data STEM data Ecosystem

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MAML^(2):meta reinforcement learning via meta-learning for task categories

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Frontiers of Computer science 2023年第4期17卷 57-67页

作者： Qiming FU Zhechao WANG Nengwei FANG Bin XING Xiao ZHANG Jianping CHEN School of Electronics and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Chongqing Industrial Big Data Innovation Center Co.Ltd. Chongqing 404100China School of Medical Informatics Xuzhou Medical UniversityXuzhou 221004China School of Architecture and Urban Planning Suzhou University of Science and TechnologySuzhou 215009China

Meta-learning has been widely applied to solving few-shot reinforcement learning problems,where we hope to obtain an agent that can learn quickly in a new ***,these algorithms often ignore some isolated tasks in pursuit of the average performance,which may result in negative adaptation in these isolated tasks,and they usually need sufficient learning in a stationary task *** this paper,our algorithm presents a hierarchical framework of double meta-learning,and the whole framework includes classification,meta-learning,and ***,in the classification process,we classify tasks into several task subsets,considered as some categories of tasks,by learned parameters of each task,which can separate out some isolated tasks ***,in the meta-learning process,we learn category parameters in all subsets via ***,based on the gradient of each category parameter in each subset,we use meta-learning again to learn a new metaparameter related to the whole task set,which can be used as an initial parameter for the new ***,in the re-adaption process,we adapt the parameter of the new task with two steps,by the meta-parameter and the appropriate category parameter ***,we demonstrate our algorithm prevents the agent from negative adaptation without losing the average performance for the whole task ***,our algorithm presents a more rapid adaptation process within ***,we show the good performance of our algorithm with fewer samples as the agent is exposed to an online meta-learning setting.

关键词： meta-learning reinforcement learning few-shot learning negative adaptation

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Enhancing Protein Language Model With Feature Integration for Anticancer Peptide Prediction

Enhancing Protein Language Model With Feature Integration fo...

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2024 IEEE-EMBS International Conference on Biomedical and Health informatics, BHI 2024

作者： Binte Sayuti, Tiara Natasha Cheng, Shen Ajith, Santhisenan Bin Abdul Samad, Abdul Hadi Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Health Informatics Lab Singapore College of Computing and Data Science Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350351552

In the fight against cancer, anticancer peptides (ACP) hold promising therapeutic potential due to their selective cytotoxicity and lower side effects compared to traditional treatments. However, identifying novel ACP is challenged by high costs and labor-intensive processes. Protein language models (PLMs), such as ESM-2 and ProtBERT, have revolutionized peptide prediction by leveraging vast datasets to capture complex biological patterns through pre-training. However, they often struggle to accurately model specific biochemical interactions. To address this limitation, we integrated four sequence-based features: amino acid composition (AAC), dipeptide composition (DPC), composition of k-spaced amino acid group pairs (CKS), and k-mer sparse matrix (k-mer) through a cross-attention mechanism. These features infuse biochemical insights that PLM alone may overlook, enabling a more detailed prediction of anti-cancer properties. This integration enhances biochemical insights, improving prediction accuracy by 15.8% for ProtBERT and 2.9% for ESM-2, with ESM-2 achieving the highest accuracy at 77.8%. SHapley Additive exPlanations (SHAP) analysis confirms the importance of these features, demonstrating that incorporating amino acid features into PLMs enhances ACP prediction. © 2024 IEEE.

关键词： Prediction models

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Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation 23

Document-level Clinical Entity and Relation Extraction via K...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Bhattarai, Kriti Oh, Inez Y. Abrams, Zachary B. Lai, Albert M. Institute for Informatics Data Science & Biostatistics Washington University School of Medicine United States Department of Computer Science Washington University in St. Louis United States

ISBN: (纸本)9798891761308

Generative pre-trained transformer (GPT) models have shown promise in clinical entity and relation extraction tasks because of their precise extraction and contextual understanding capability. In this work, we further leverage the Unified Medical Language System (UMLS) knowledge base to accurately identify medical concepts and improve clinical entity and relation extraction at the document level. Our framework selects UMLS concepts relevant to the text and combines them with prompts to guide language models in extracting entities. Our experiments demonstrate that this initial concept mapping and the inclusion of these mapped concepts in the prompts improves extraction results compared to few-shot extraction tasks on generic language models that do not leverage UMLS. Further, our results show that this approach is more effective than the standard Retrieval Augmented Generation (RAG) technique, where retrieved data is compared with prompt embeddings to generate results. Overall, we find that integrating UMLS concepts with GPT models significantly improves entity and relation identification, outperforming the baseline and RAG models. By combining the precise concept mapping capability of knowledge-based approaches like UMLS with the contextual understanding capability of GPT, our method highlights the potential of these approaches in specialized domains like healthcare.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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An LLM-Enhanced Adversarial Editing System for Lexical Simplification 30

An LLM-Enhanced Adversarial Editing System for Lexical Simpl...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Tan, Keren Luo, Kangyang Lan, Yunshi Yuan, Zheng Shu, Jinlong School of Data Science & Engineering East China Normal University Shanghai China Department of Informatics King's College London United Kingdom

ISBN: (纸本)9782493814104

Lexical Simplification (LS) aims to simplify text at the lexical level. Existing methods rely heavily on annotated data, making it challenging to apply in low-resource scenarios. In this paper, we propose a novel LS method without parallel corpora. This method employs an Adversarial Editing System with guidance from a confusion loss and an invariance loss to predict lexical edits in the original sentences. Meanwhile, we introduce an innovative LLM-enhanced loss to enable the distillation of knowledge from Large Language Models (LLMs) into a small-size LS system. From that, complex words within sentences are masked and a Difficulty-aware Filling module is crafted to replace masked positions with simpler words. At last, extensive experimental results and analyses on three benchmark LS datasets demonstrate the effectiveness of our proposed method. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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Sliced Average Variance Estimation for Tensor data

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Acta Mathematicae Applicatae Sinica 2024年第3期40卷 630-655页

作者： Chuan-quan LI Pei-wen XIAO Chao YING Xiao-hui LIU School of Statistics Jiangxi University of Finance and EconomicsNanchang 330013China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and EconomicsNanchang 330013China Biostatistics and Medical Informatics University of Wisconsin MadisonMadison 53726USA

Tensor data have been widely used in many fields,e.g.,modern biomedical imaging,chemometrics,and economics,but often suffer from some common issues as in high dimensional *** to find their low-dimensional latent structure has been of great interest for *** this end,we develop two efficient tensor sufficient dimension reduction methods based on the sliced average variance estimation(SAVE)to estimate the corresponding dimension reduction *** first one,entitled tensor sliced average variance estimation(TSAVE),works well when the response is discrete or takes finite values,but is not■consistent for continuous response;the second one,named bias-correction tensor sliced average variance estimation(CTSAVE),is a de-biased version of the TSAVE *** asymptotic properties of both methods are derived under mild *** and real data examples are also provided to show the superiority of the efficiency of the developed methods.

关键词： tensor data sliced average variance estimation sufficient dimension reduction central subspace

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Yale University Section for Biomedical Informatics and Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350372120

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing. © 2024 IEEE.

关键词： Ophthalmology

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Mobile Islamic Banking Consumer Behaviour in Indonesia 12

Mobile Islamic Banking Consumer Behaviour in Indonesia

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12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Hidayah, Nur Haryatih Mutiara, Tira Salim, Agus Maulana, Ahmad Ihsan Syarif Hidayatullah State Islamic University Faculty of Economics and Business Jakarta Indonesia School of Data Science Mathematics and Informatics Ipb University Bogor Indonesia

ISBN: (纸本)9798331528799

The challenge of managing digital banks is in managing technology and convincing consumers to use it. The purpose of this study is to analyze the intention and behavior of using Islamic mobile banking by adopting the UTAUT 2 variables. data were collected by distributing online questionnaires using Google Form. The data collected from 117 respondents were then analyzed using the Structural Equation Model Partial Least Square (SEM - PLS) technique using SmartPLS Software version 3.3.0. The results of the study show that social influence, experience and habits have a significant influence on the intention to use Islamic mobile banking. Performance expectancy, effort expectancy, and price values have a positive relationship, but their influence is insignificant. While facilitating conditions and hedonic motivation have a negative relationship, their influence is insignificant. The intention to use mobile banking has a significant effect on the behavior of using Islamic mobile banking. Facilitating conditions have a positive relationship but the effect is not significant. Islamic banks need to focus on setting strategies to increase social influence and user experience to encourage customer intentions to use Islamic mobile banking, which will ultimately affect Islamic mobile banking behavior. © 2024 IEEE.

关键词： Mobile commerce

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Enhancing Diabetic Retinopathy Screening with Sequential Deep Learning Models 7

Enhancing Diabetic Retinopathy Screening with Sequential Dee...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Chennai India Yale University Section For Biomedical Informatics And Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350371406

Diabetic retinopathy is a serious medical disorder that, if left untreated, can result in visual impairment or blindness. The precise and timely categorization of its severity is critical for appropriate medical management. This research describes an automated method for determining the severity of diabetic retinopathy using deep learning algorithms. The suggested methodology adopts a two-step process for identifying diabetic retinopathy and then categorizing its severity. In this technique, two pre-trained deep learning models, InceptionResNetV2 and DenseNet121, are used. For training and assessment purposes, the 'APTOS 2019 Blindness Detection' dataset which consists of five severity classes is used as the foundation. To overcome class imbalance, data augmentation approaches are used to improve the model's performance. The results of the experiment show that the strategy is effective, with an accuracy of 83.10 % and an F1 score of 82.77 %. This study contributes to the development of automated systems for assessing diabetic retinopathy, perhaps enhancing early detection and treatment. © 2023 IEEE.

关键词： Medical imaging

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