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arXiv 2025年

作者： Cheng, Xinxing Zhang, Tianyang Lu, Wenqi Meng, Qingjie Frangi, Alejandro F. Duan, Jinming School of Computer Science University of Birmingham United Kingdom Department of Computing and Mathematics Manchester Metropolitan University United Kingdom Division of Informatics Imaging and Data Sciences University of Manchester United Kingdom Centre for Computational Imaging and Modelling in Medicine University of Manchester United Kingdom Department of Computing Impeiral College London United Kingdom

Deep learning-based image registration methods have shown state-of-the-art performance and rapid inference speeds. Despite these advances, many existing approaches fall short in capturing spatially varying information in non-local regions of feature maps due to the reliance on spatially-shared convolution kernels. This limitation leads to suboptimal estimation of deformation fields. In this paper, we propose a 3D Spatial-Awareness Convolution Block (SACB) to enhance the spatial information within feature representations. Our SACB estimates the spatial clusters within feature maps by leveraging feature similarity and subsequently parameterizes the adaptive convolution kernels across diverse regions. This adaptive mechanism generates the convolution kernels (weights and biases) tailored to spatial variations, thereby enabling the network to effectively capture spatially varying information. Building on SACB, we introduce a pyramid flow estimator (named SACB-Net) that integrates SACBs to facilitate multi-scale flow composition, particularly addressing large deformations. Experimental results on the brain IXI and LPBA datasets as well as Abdomen CT datasets demonstrate the effectiveness of SACB and the superiority of SACB-Net over the state-of-the-art learning-based registration methods. The code is available at https://***/x-xc/SACB_Net. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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An Ontology for the Social Determinants of Health Domain

arXiv

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arXiv 2022年

作者： Kollapally, Navya Martin Chen, Yan Xu, Julia Geller, James Department of Computer Science New Jersey Institute of Technology NewarkNJ United States Department of Computer Information Systems Borough of Manhattan Community College New York United States JX Consulting Texas United States

Social Determinants of Health (SDOH) are societal factors, such as where a person was born, grew up, works, lives, etc., along with socio-economic and community factors that affect an individual’s health. SDOH are correlated with many clinical outcomes, hence it is desirable to record SDOH data in Electronic Health Records (EHRs). Besides storing images, text, etc., EHRs rely on coded terms available in standard ontologies and terminologies to record observations and analyses. There is a substantial amount of research on understanding the clinical impact of SDOH, ranging from screening tools to practice-based interventions. However, there is no comprehensive collection of terms for recording SDOH observations in EHRs. Our research goal is to develop an ontology that covers the terms describing SDOH. We present a prototype ontology called Social Determinant of Health Ontology (SOHO) that covers relevant concepts and IS-A relationships describing impacts and associations of social determinants. We describe the evaluation techniques that we applied to SOHO, including human experts’ review and algorithmic evaluation. © 2022, CC BY-SA.

关键词： Ontology

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Optimizing Logistics in E-Commerce Using Deep Random Forest for Enhanced User Satisfaction

Optimizing Logistics in E-Commerce Using Deep Random Forest ...

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Automation and Computation (AUTOCOM), International Conference on

作者： G.Anne Nisha Sreetha P Seetha A K. Manickam Sharath Kumar Jagannathan Sekar S D Department of Informatics Kyoto Computer Gakuin Kyoto Japan Department of Biomedical Engineering KPR Institute of Engineering and Technology Tamil Nadu India Department of Information Technology S. A. Engineering College Thiruverkadu Chennai India Department of Mathematics Sona College of Technology Tamil Nadu India Data Science Institute Saint Peter's University New Jersery Department of Mechanical Engineering R.M.K.Engineering College Chennai Tamilnadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains challenging problems. Deep Random Forest (DRF) is a new approach presented in this work to examine and forecast significant logistics metrics including delivery times and satisfaction scores. Development of this approach targeted to solve these issues. The model makes use of e-commerce past performance including order volumes, customer comments, and delivery times. Findings: The suggested approach achieves 10% increase in user satisfaction, 12% cost reduction, and 15% faster delivery times than baseline methods. This is accomplished by means of DRF, applied to examine trends and carry out predictive modifications. Results and Methodology: These results outperform those of the benchmarks established by Gradient Boosting and XGBoost, which displayed respective rises in satisfaction of 9% and 8%. Apart from improving the quality of service, the use of DRF not only strengthened user loyalty but also contributed to improve the transportation efficiency. This approach exposes the transforming opportunities of artificial intelligence in logistics management for e-commerce systems. This approach guarantees operations with a customer-centric concentration benefiting from measurable advantages.

关键词： Accuracy Costs Transportation Boosting Real-time systems Computational efficiency Electronic commerce Random forests Optimization Logistics

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Severity Prediction of COVID-19 Patients Using Machine Learning Classification Algorithms: A Case Study of Small City in Pakistan with Minimal Health Facility 6

Severity Prediction of COVID-19 Patients Using Machine Learn...

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6th IEEE International Conference on computer and Communications, ICCC 2020

作者： Gull, Hina Krishna, Gomathi Aldossary, May Issa Iqbal, Sardar Zafar College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Department of Computer Information Systems Dammam Saudi Arabia

ISBN: (纸本)9781728186351

A bstract-Coronavirus disease has been declared as an infectious pandemic affecting the life and health of millions across the globe. It has caused high number of mortalities giving birth to exceptional state of emergency worldwide. It has not affected the people but also has damaged infrastructure of different countries, especially causing an expectational situation in health care systems globally. Due to unavailability of vaccination and faster human to human transmission of virus, healthcare facilities are at high risk of exceeding their limit and capacity, especially in developing countries like Pakistan. Therefore, it is important to manage resources properly in these countries to control high mortality rate and damage it can cause. In this paper we have taken a case study of small city in Pakistan, where healthcare facilities are not enough to deal with pandemic. Most of the COVID-19 patients have to be refer to big cities based on their severity. We have taken data of COVID-19 positive patients from this small city, developed and applied machine learning classification model to predict the severity of patient, in order to deal with the shortage of resources. Among all seven taken and tested algorithms, we have chosen SVM to predict severity of patients. Model has shown 60% of accuracy and have divided patient's severity into mild, moderate and severe levels. © 2020 IEEE.

关键词： COVID-19

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Optimizing Logistics in E-Commerce Using Deep Random Forest for Enhanced User Satisfaction 3

Optimizing Logistics in E-Commerce Using Deep Random Forest ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Nisha, G.Anne Sreetha, P. Seetha, A. Manickam, K. Jagannathan, Sharath Kumar Sekar, S.D. Kyoto Computer Gakuin Department of Informatics Kyoto Japan Kpr Institute of Engineering and Technology Department of Biomedical Engineering Tamil Nadu India S. A. Engineering College Department of Information Technology Thiruverkadu Chennai India Sona College of Technology Department of Mathematics Tamil Nadu India Data Science Institute Saint Peter's University NJ United States R.M.K.Engineering College Department of Mechanical Engineering TamilNadu Chennai India

ISBN: (纸本)9798331542375

关键词： Marketplaces

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Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization

arXiv

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arXiv 2025年

作者： Wang, Shengbo Li, Ke Yan, Zheng Guo, Zhenyuan Zhu, Song Wen, Guanghui Wen, Shiping School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science University of Exeter ExeterEX4 4RN United Kingdom Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia College of Mathematics and Econometrics Hunan University Changsha410082 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Department of Systems Science School of Mathematics Southeast University Nanjing China

Safety is of paramount importance in control systems to avoid costly risks and catastrophic damages. The control barrier function (CBF) method, a promising solution for safety-critical control, poses a new challenge of enhancing control performance due to its direct modification of original control design and the introduction of uncalibrated parameters. In this work, we shed light on the crucial role of configurable parameters in the CBF method for performance enhancement with a systematical categorization. Based on that, we propose a novel framework combining the CBF method with Bayesian optimization (BO) to optimize the safe control performance. Considering feasibility/safety-critical constraints, we develop a safe version of BO using the barrier-based interior method to efficiently search for promising feasible configurable parameters. Furthermore, we provide theoretical criteria of our framework regarding safety and optimality. An essential advantage of our framework lies in that it can work in model-agnostic environments, leaving sufficient flexibility in designing objective and constraint functions. Finally, simulation experiments on swing-up control and high-fidelity adaptive cruise control are conducted to demonstrate the effectiveness of our framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Adaptive cruise control

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Flexible Control of the Interline DC Power Flow Controller Using Virtual Capacitor in VSC-HVDC Grids

Flexible Control of the Interline DC Power Flow Controller U...

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Power Electronic and Drive systems and Technologies Conference (PEDSTC)

作者： Mirhamed Pourmirasghariyan Mirsajed Pourmirasghariyan Seyed Saeid Heidari Yazdi Seyyed Yousef Mousazadeh Mousavi Kumars Rouzbehi G. B. Gharehpetian Departement of Electrical and Computer Engineering Amirkabir University of Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical & Computer Engineering School of Engineering and Digital Science Nazarbayev University Nur-Sultan Kazakhstan Faculty of Engineering and Technology University of Mazandaran Sari Iran Department of Systems and Automatic Control Engineering University of Seville Seville Spain

The performance of Interline DC Power Flow Controllers (IDC-PFCs) in Voltage Source Converters (VSC)-based High Voltage Direct Current (HVDC) grids, can be affected due to different issues. The current limitation of HVDC lines, the voltage limitation of HVDC buses, and DC voltage of the IDC-PFC intermediary capacitor prevent effective and efficient operation of IDC-PFCs. In this paper, it is shown that this issue can be overcome by using a virtual capacitor in parallel with the IDC-PFC intermediary capacitor. Also, an energy control-based scheme is proposed for the operation of IDC-PFCs in VSC-HVDC grid. The benefits of using the virtual capacitor are: widening the operational area of the IDC-PFCs for the determined duty cycle and injecting more voltage in series to the interconnected HVDC line to control the related HVDC line current. The proposed solution is successfully evaluated on a CIGRE three-terminal VSC-HVDC grid which is modeled by linearized space-state equations.

关键词： Fluctuations HVDC transmission Capacitors High-voltage techniques Boosting Mathematical models Power conversion

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Deep Dynamic Epidemiological Modelling for COVID-19 Forecasting in Multi-level Districts

arXiv

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arXiv 2023年

作者： Liu, Ruhan Li, Jiajia Wen, Yang Li, Huating Zhang, Ping Sheng, Bin Feng, David Dagan The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China The Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai200233 China The Department of Computer Science and Engineering The Ohio State University ColumbusOH43210 United States The Department of Biomedical Informatics The Ohio State University ColumbusOH43210 United States The School of Computer Science The University of Sydney SydneyNSW2006 Australia

Objective: COVID-19 has spread worldwide and made a huge influence across the world. Modeling the infectious spread situation of COVID-19 is essential to understand the current condition and to formulate intervention measurements. Epidemiological equations based on the SEIR model simulate disease development. The traditional parameter estimation method to solve SEIR equations could not precisely fit real-world data due to different situations, such as social distancing policies and intervention strategies. Additionally, learning-based models achieve outstanding fitting performance, but cannot visualize mechanisms. Methods: Thus, we propose a deep dynamic epidemiological (DDE) method that combines epidemiological equations and deep-learning advantages to obtain high accuracy and visualization. The DDE contains deep networks to fit the effect function to simulate the ever-changing situations based on the neural ODE method in solving variants’ equations, ensuring the fitting performance of multi-level areas. Results: We introduce four SEIR variants to fit different situations in different countries and regions. We compare our DDE method with traditional parameter estimation methods (Nelder-Mead, BFGS, Powell, Truncated Newton Conjugate-Gradient, Neural ODE) in fitting the real-world data in the cases of countries (the USA, Columbia, South Africa) and regions (Wuhan in China, Piedmont in Italy). Our DDE method achieves the best Mean Square Error and Pearson coefficient in all five areas. Further, compared with the state-of-art learning-based approaches, the DDE outperforms all techniques, including LSTM, RNN, GRU, Random Forest, Extremely Random Trees, and Decision Tree. Conclusion: DDE presents outstanding predictive ability and visualized display of the changes in infection rates in different regions and countries. © 2023, CC BY-NC-SA.

关键词： COVID-19

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Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research

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IEEE Communications Surveys and Tutorials 2024年

作者： Brik, Bouziane Chergui, Hatim Zanzi, Lanfranco Devoti, Francesco Ksentini, Adlen Siddiqui, Muhammad Shuaib Costa-Perez, Xavier Verikoukis, Christos University of Sharjah Computer Science Department College of Computing and Informatics United Arab Emirates 2CAT Foundation Barcelona Spain NEC Laboratories Europe Heidelberg69115 Germany EURECOM France NEC Laboratories Europe ICREA Barcelona08034 Spain University of Patras ATHENA/ISI IQUADRAT Informatica Barcelona08006 Spain

The recent o-ran specifications promote the evolution of ranran architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by ric entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by ai and ml, novel solutions targeting traditionally unsolved ran management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such ai/ml solutions, affecting their trust in such novel tools. xai aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-To-machine barrier. In this survey, we provide a summary of the xai methods and metrics before studying their deployment over the o-ran Alliance ran architecture along with its main building blocks. We then present various use-cases and discuss the automation of xai pipelines for o-ran as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of ai/ml decision entities in this context, focusing on how xai can help to interpret, understand, and improve trust in o-ran operational networks. © 1998-2012 IEEE.

关键词： Information management

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Effective Intrusion Detection and Classification using Fuzzy Rule based Classifier in Cloud Environment

Effective Intrusion Detection and Classification using Fuzzy...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： C. Veena S. Ramalakshmi V. Bhoopathy Minakshi Dattatraya Bhosale C. G. Magadum Abirami. S. K. Department of CSE Kommuri Pratap Reddy Institute of Technology Ghanpur (V) Hyderabad Telangana India Department of Computer Science and Applications Don Bosco College (Arts and Science) Puducherry India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana India Faculty of MCA Yashoda Technical Campus Satara Maharashtra India Smt. Kasturbai Walchand College Sangli Maharashtra India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamilnadu India

ISBN: (纸本)9781665460859

Cloud computing (CC) is the demonstration of the technology that makes use of the substructure for computing in a proficient fashion. This sort of computing offers great quantity of consequences in augmenting the productivity that verifies the risk handling management and decreases the cost. Intrusion detection system (IDS) is widely applied for detecting malicious actions in its host and the communication network. IDS is a procedure of discovering activities that take place in a network and attempts to fulfill the confidentiality, network, or security accessibility to smear the trust procedure. This article introduces an Effective Intrusion Detection and Classification using Fuzzy Rule based Classifier (EIDC-FRC) model in Cloud Environment. The goal of the presented EIDC-FRC model is to determine the occurrence of intrusions and normal data traffic in the cloud environment. In order to achieve this, the presented EIDC-FRC model applies FRC for data classification process. The parameter tuning of the FRC model is performed using enhanced bird swarm algorithm (EBSA). The FRC is an effective model in pattern recognition that offers effective outcomes by the use of linguistic labels in the antecedents of the rules. The simulation analysis of the EIDC-FRC model ensured the enhancements of the presented approach compared to recent state of art approaches.

关键词： Cloud computing Analytical models Art Computational modeling Intrusion detection Linguistics Data models

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