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2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2021

作者： Roy, Andrea Farshadmanesh, Pegah Sakurahara, Tatsuya Kee, Ernie Mohaghegh, Zahra Research Laboratory Department of Nuclear Plasma and Radiological Engineering Grainger College of Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois Urbana-Champaign United States Illinois Informatics Institute University of Illinois at Urbana-Champaign United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States

Organizational factors have been identified as key contributors to accidents and incidents in nuclear power plants (NPPs). The Data-Theoretic (DT) methodology, previously developed by some of the authors of this paper, helps quantify the impacts of organizational factors on plant risk scenarios in Probabilistic Risk Assessment (Pra). In the DT methodology, "data analytics" are guided by "theory," enhancing the completeness of "causality" derived from data and helping to avoid potentially misleading results from solely data-driven approaches. This paper advances the quantification of uncertainties (e.g., uncertainty associated with the Pra analyst's assessment of organizational factors) in the DT methodology. The DT methodology includes two submodules: (I) DT-BASE, which focuses on the development of detailed causal relationships based on a theory-building process and creates a baseline quantification utilizing analyst interpretation of information extracted from theoretical references and standards;(II) DT-SITE, which conducts data analytics (text mining) to update (add or delete links and nodes) the causal model structure developed in DT-BASE, quantify causal relationships using historical event databases, and update the results of baseline quantification in DT-BASE using Bayesian analysis. This paper adds uncertainty analysis to the DT-BASE submodule. The updated DT approach is implemented in a case study targeting NPP organizational "training quality" that influences the "training" performance shaping factor (PSF) in Human Reliability Analysis (Hra) associated with the plant Pra. © 2021 PSA. All Rights Reserved.

关键词： Nuclear power plants

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FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition

arXiv

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arXiv 2025年

作者： Wu, Linshan Zhuang, Jiaxin Zhou, Yanning He, Sunan Ma, Jiabo Luo, Luyang Wang, Xi Ni, Xuefeng Zhong, Xiaoling Wu, Mingxiang Zhao, Yinghua Duan, Xiaohui Vardhanabhuti, Varut rajpurkar, Pranav Chen, Hao Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Tencent AI Lab Shenzhen China Department of Biomedical Informatics Harvard University Boston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Radiology Shenzhen People’s Hospital Shenzhen China Department of Radiology The Third Affiliated Hospital of Southern Medical University Guangzhou China Department of Radiology Sun Yat-Sen Memorial Hospital Sun Yat-Sen University Guangzhou China Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Division of Life Science The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Hong Kong Shenzhen-Hong Kong Collaborative Innovation Research Institute The Hong Kong University of Science and Technology Shenzhen China

Tumor is a leading cause of death worldwide, with an estimated 10 million deaths attributed to tumor-related diseases every year. AI-driven tumor recognition unlocks new possibilities for more precise and intelligent tumor screening and diagnosis. However, the progress is heavily hampered by the scarcity of annotated datasets, which demands extensive annotation efforts by radiologists. To tackle this challenge, we introduce FreeTumor, an innovative Generative AI (GAI) framework to enable large-scale tumor synthesis for mitigating data scarcity. Specifically, FreeTumor effectively leverages a combination of limited labeled data and large-scale unlabeled data for tumor synthesis training. Unleashing the power of large-scale data, FreeTumor is capable of synthesizing a large number of realistic tumors on images for augmenting training datasets. To this end, we create the largest training dataset for tumor synthesis and recognition by curating 161,310 publicly available Computed Tomography (CT) volumes from 33 sources, with only 2.3% containing annotated tumors. To validate the fidelity of synthetic tumors, we engaged 13 board-certified radiologists in a Visual Turing Test to discern between synthetic and real tumors. Rigorous clinician evaluation validates the high quality of our synthetic tumors, as they achieved only 51.1% sensitivity and 60.8% accuracy in distinguishing our synthetic tumors from real ones. Through high-quality tumor synthesis, FreeTumor scales up the recognition training datasets by over 40 times, showcasing a notable superiority over state-of-the-art AI methods including various synthesis methods and foundation models. On average, FreeTumor improves the segmentation Dice scores by 6.7% and early tumor detection sensitivity by 16.4%. These findings indicate promising prospects of FreeTumor in clinical applications, potentially advancing tumor treatments and improving the survival rates of patients. Copyright © 2025, The Authors. All rights reserved.

关键词： computerized tomography

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Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs

arXiv

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arXiv 2021年

作者： Han, Zhen Ding, Zifeng Ma, Yunpu Gu, Yujia Tresp, Volker Institute of Informatics LMU Munich Germany Corporate Technology Siemens AG Germany Department of Electrical and Computer Engineering Technical University of Munich Germany

There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary differential equations (ODEs) to multi-relational graph convolutional networks. The proposed model preserves the continuous nature of dynamic multi-relational graph data and encodes both temporal and structural information into continuous-time dynamic embeddings. In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i.e., edge formation and dissolution. We perform extensive experiments on five benchmark datasets for temporal KG reasoning, showing our model's superior performance on the future link forecasting task. Copyright © 2021, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Self-Sovereign Identity Empowered Non-Fungible Patient Tokenization for Patient-Centric Health Information Exchange Using Blockchain technology

SSRN

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SSRN 2022年

作者： Zhuang, Yan Shyu, Chi-Ren Hong, Shenda Li, Pengfei Zhang, Luxia National Institute of Health Data Science Peking University Beijing China Instititue for Data Science and Informatics Electrical Engineering and Computer Science Department University of Missouri ColumbiaMO United States Advanced Institute of Information Technology Peking University Hangzhou China

Background: Patient tokenization is a novel approach that allows anonymous patient-level linkage across healthcare facilities, minimizing the risk of breaching protected health information which is one of the major persistent challenges in health information exchange (HIE). Since most patient tokenization is developed and managed by third parties, centralized tokenization is fundamentally unable to address data security concerns. Non-Fungible Tokens (NFT), which are non-transferable cryptographic assets developed by leveraging blockchain’s unique properties, have the potential to provide secure, decentralized, and trustworthy patient tokenization. To verify the ownership of NFTs in a decentralized manner, Self-Sovereign Identity (SSI) is a user-centric approach that allows individuals full control over their *** procedures: We have developed a blockchain architecture that contains four modules: (1) Creation module, which permits users to create NFTs through blockchain, (2) Linkage module, which links the patient account from each health system to the patient NFT, (3) Authentication Module, which allows patients to permit healthcare providers to access their token to retrieve their patient’s permitted medical history from multiple healthcare facilities, and (4) Exchange Module, which involves validation of the legitimacy of the token through SSI and the HIE *** findings: A case study has been conducted on our proposed architecture from NFT creation to final HIE. Over 3 million transactions have been completed successfully with a blockchain validation and written time of 1.17 seconds on average. A stability test has also been conducted with a higher throughput of 200 transactions per second running for an hour with an average transaction processing time of 1.42 *** Conclusions: This study proposed a blockchain architecture that achieves SSI-enabled NFT-based patient tokenization. Our architecture design, implementation, and case stu

关键词： Smart contract

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Opinion Mining using Machine Learning Approaches: A Critical Study

Opinion Mining using Machine Learning Approaches: A Critical...

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2020 International Conference on Computing and Information technology, ICCIT 2020

作者： ramakrishnan, Jayabrabu Mavaluru, DInesh Srinivasan, Karthik Mubarakali, Azath Narmatha, C. Malathi, G. Jazan University College of Computer Science and Information Technology Department of Information Technology and Security Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Information Technology Saudi Arabia King Khalid University College of Computer Science Department of CNE Saudi Arabia University of Tabuk Faculty of Computer and Information Technology Department of Computer Science Tabuk Saudi Arabia Vellore Institute of Technology School of Computing Science and Engineering Chennai India

ISBN: (纸本)9781728126807

With the advancement of internet technology, there is a large volume of information exists on the web for users of the internet. These users not just utilize the accessible resources on the web yet, in addition, give their feedback, consequently creating more useful data. Opinion mining (OM) is contemplated as a subfield of NLP, information retrieval, and data mining. It is the way toward extricating people's thoughts and observations from unformed content, which with respect to the development of online social media and the huge amount of user feedback, has gotten an effective, attractive and furthermore complex issue. The fundamental in OM is classifying the polarity of text as far as positive (good), neutral (surprise) or negative (bad). This paper is proposed to review the methods and applications of opinion mining. This paper presents a review of OM and related strategies and additionally discusses the applications and difficulties for OM with knowledge into previous researchers. © 2020 IEEE.

关键词： Sentiment analysis

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Optimal Sizing of Security Constrained Unit Commitment Problem Integrated with Renewable Energy Sources and PEVs

Optimal Sizing of Security Constrained Unit Commitment Probl...

引用

International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Pravin G. Dhawale Vikram Kumar S.K. Bath Chaman Verma Maria Simona raboaca Constantin Filote Deepak Kumar School of Electrical and Electronics Engineering Lovely Professional University Phagwara University of Calgary Calgary Canada Department of Electrical Engineering Maharaja Ranjit Singh Punjab Technical University Bhatinda India Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Universitatii Street Suceava Romania Apex Institute of Technology Chandigarh Univerity Mohali Punjab India

Optimal sizing of SCUCP with solar energy and PEVs is crucial for electric power system operation and planning. This paper presents a Chaotic Arithmetic Optimization Algorithm to minimize cost while optimizing the sizing of solar energy and PEV systems. The proposed framework integrates probabilistic forecasting models for solar power generation and realistic EV charging profiles based on travel patterns and infrastructure characteristics. It aims to find optimal sizes for Solar power plants and EV charging infrastructure, considering system demand, cost, emissions, and reliability. The Chaotic Arithmetic Optimization Algorithm efficiently solves the optimization problem by accounting for the complexities of SCUCP. The research evaluates the proposed approach using benchmark test systems of varying dimensions and power systems. The results enable informed decision-making by identifying optimal capacities for solar power plants and charging infrastructure, leading to improved system reliability, reduced emissions, and minimized costs.

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Few-shot learning based histopathological image classification of colorectal cancer

引用

Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms

Enhancing Security and Scalability of Metaverse with Blockch...

引用

International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Anand Singh rajawat S.B. Goyal Aarti Goyal Kavita rajawat Maria Simona raboaca Chaman Verma Traian Candin Mihaltan School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Mind Intellect Technology Indore India ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world.

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive rate (0.016), and the False Negative rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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Investigation of the Impact of Height and Bandwidth on Path Loss Exponent of 5G FR1 Wireless Networks at 800, 3500, and 5900 MHz

Investigation of the Impact of Height and Bandwidth on Path ...

引用

Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Yusuf Olayinka Imam-Fulani Nasir Faruk Aliyu D. Usman Abubakar Abdulkarim Abdoulie M.S Tekanyi Abdulmalik S. Yaro Emmanuel Alozie Salisu Garba Kayode S. Adewole Abdulkarim A. Oloyede Olugbenga A. Sowande Bashir Abdullahi Baba Abdulwaheed Musa Agbotiname Lucky Imoize Yinusa A. Adediran Hawau I. Olagunju Lawan S. Taura Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Electronics and Telecommunication Engineering Ahmadu Bello University Zaria Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Directorate of Information and Communication Technology Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunication Engineering Kampala International University Kampala Uganda Department of Informatics and Qualitative Methods Faculty of Informatics and Management University of Hradec Králové Hradec Králové Czech Republic Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Computer Engineering Kwara State University Malete Nigeria Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Electrical and Electronics Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Electrical and Electronics Engineering University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

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