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Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 859-876页

作者： Ahmad Chaddad Qizong Lu Jiali Li Yousef Katib Reem Kateb Camel Tanougast Ahmed Bouridane Ahmed Abdulkadir School of Artificial intelligence Guilin Universiy of Electronic TechnologyGuilin 541004China Laboratory for Imagery Vision and Artificial IntelligenceEcole de Technologie SuperieureMontrealQuebec H3C 1K3Canada College of Medicine Taibah UniversityMadinah 42353Saudi Arabia College of Computer Science and Engineering Taibah UniversityMadinah 42353Saudi Arabia Laboratory of Design Optimization and Modeling SystemsUniversity of LorraineMetz 57070France Center for Data Analytics and Cybersecurity(CDAC) University of SharjahSharjah 27272United Arab Emirates Laboratoire de recherche en neuroimagerie Centre Hospitalier Universitaire VaudoisMont Paisible 161011LausanneSwitzerland

Artificial intelligence(AI)continues to transform data analysis in many *** in each domain is driven by a growing body of annotated data,increased computational resources,and technological *** medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of *** this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each *** models increase confidence if the results appear plausible and match the clinicians ***,the absence of a plausible explanation does not imply an inaccurate *** in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple *** example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health *** centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health *** narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.

关键词： Domain adaptation explainable artificial intelligence federated learning

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SSRN

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SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

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Feature Selection and Classification Using CatBoost Method for Improving the Performance of Predicting Parkinson’s Disease 1st

Feature Selection and Classification Using CatBoost Method f...

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1st International Conference of Advanced Computing and Informatics, ICACIN 2020

作者： Al-Sarem, Mohammed Saeed, Faisal Boulila, Wadii Emara, Abdel Hamid Al-Mohaimeed, Muhannad Errais, Mohammed College of Computer Science and Engineering Taibah University Medina Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Computers and Systems Engineering Department Al-Azhar University Cairo Egypt Research and Computer Innovation Laboratory Hassan II University of Casablanca Casablanca Morocco

ISBN: (纸本)9789811560477

Several studies investigated the diagnosis of Parkinson’s disease (PD), which utilized machine learning methods such as support vector machine, neural network, Naïve Bayes and K-nearest neighbor. In addition, different ensemble methods were used such as bagging, random forest and boosting. On the other hand, different feature ranking methods have been used to reduce the data dimensionality by selecting the most important features. In this paper, the ensemble methods, random forest, XGBoost and CatBoost were used to find the most important features for predicting PD. The effect of these features with different thresholds was investigated in order to obtain the best performance for predicting PD. The results showed that CatBoost method obtained the best results. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Urban Digital Twins for Intelligent Road Inspection

Urban Digital Twins for Intelligent Road Inspection

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Fan, Rui Zhang, Yikang Guo, Sicen Li, Jiahang Feng, Yi Su, Shuai Zhang, Yanting Wang, Wenshuo Jiang, Yu Junaid Bocus, Mohammud Zhu, Xingyi Chen, Qijun Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems State Key Laboratory of Intelligent Autonomous Systems Shanghai201804 China Donghua University Department of Computer Science & Technology Shanghai201620 China McGill University Department of Civil Engineering MontrealH3A 0C3 Canada Cto Office at ClearMotion BillericaMA01821 United States University of Bristol Department of Electrical & Electronic Engineering BristolBS8 1UB United Kingdom Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai200092 China

ISBN: (纸本)9781665480451

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022. © 2022 IEEE.

关键词： Data acquisition

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Computing Power in the Sky: Digital Twin-assisted Collaborative Computing with Multi-UAV Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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Eco-driving Incentive Mechanisms for Mitigating Emissions in Urban Transportation

arXiv

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arXiv 2024年

作者： Umar Niazi, M.B. Cho, Jung-Hoon Dahleh, Munther A. Dong, Roy Wu, Cathy Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

This paper proposes incentive mechanisms that promote eco-driving in transportation networks with the overarching objective of minimizing emissions. The transportation system operator provides the drivers with energy-efficient driving guidance throughout their trips, and their eco-driving levels are measured by how closely they follow this guidance via vehicle telematics. Drivers choose their eco-driving levels to optimize a combination of their travel times and their emissions. To obtain optimal budget allocation and recommendations for the incentive mechanism, the system operator gathers drivers’ preferences, or types, to assess each driver’s trip urgency and natural willingness to eco-drive. In a setting where drivers truthfully report their types, we introduce the first-best incentive mechanism and show that the obedience condition holds (i.e., drivers find it optimal to comply with the system operator’s recommendations) when the recommended eco-driving profile constitutes a Nash equilibrium. Moreover, in a setting where drivers can strategically report their types, we introduce the second-best incentive mechanism and show that the proposed mechanism is incentive-compatible (i.e., drivers find it optimal to be truthful). Under this mechanism, we also show that all equilibrium outcomes are at least as good as the recommended eco-driving profile in terms of the system operator’s objective. Overall, this work offers a framework for designing eco-driving incentive mechanisms while considering both the strategic behavior of individual drivers and the network effects of collective decision-making. © 2024, CC BY.

关键词： Budget control

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Load-Aware Hierarchical Information-Centric Routing for Large-Scale LEO Satellite Networks

Load-Aware Hierarchical Information-Centric Routing for Larg...

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IEEE Conference on Wireless Communications and Networking

作者： Fei Yan Zhiyuan Wang Shan Zhang Qingkai Meng Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beijing China State Key Laboratory of Software Development Environment Beijing China Institute of Artificial Intelligence Beihang University Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The emerging large-scale low earth orbit (LEO) constellation is expected to provide global Internet services. However, large-scale satellite networks meet the challenges of topology dynamics and continuous traffic variation. This paper proposes LoHi, a Load-aware Hierarchical Information-centric (LoHi) Routing Protocol based on constellation Partitioning and logic path identifier (PID), to address the above challenges. Specifically, LoHi divides a constellation into satellite groups and only keeps track of the inter-satellite link (ISL) state within each group instead of the entire constellation, stabilizing global routing. Moreover, LoHi adopts the PID to denote the logic connectivity of adjacent satellite groups, which corresponds to multiple physical ISLs. Accordingly, the hierarchical multipath routing is calculated based on the inner-group and inter-group connectivity information. Furthermore, LoHi adjusts forwarding decisions according to the real-time traffic load on the ISL and group-level inter-group paths to reduce packet drop and queuing delay in a hierarchical pattern. Packet-level experiment results show that LoHi achieves a higher packet delivery ratio than the state-of-the-art mechanisms (up to 132.12%).

关键词： Satellites Web and internet services Low earth orbit satellites Telecommunication traffic Routing Throughput Routing protocols

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A new transformation approach for complex systems modelling and simulation: Application to industrial control system

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International Journal of Simulation and Process Modelling 2021年第1期16卷 34-48页

作者： Seddari, Noureddine Boukelkoul, Sofiane Bouras, Abdelghani Belaoued, Mohamed Redjimi, Mohammed LICUS Laboratory Department of Computer Science University of 20 August 1955-Skikda Skikda21000 Algeria LIRE Laboratory Department of Software Technologies and Information Systems University of Abdelhamid Mehri-Constantine 2 Constantine25000 Algeria Department of Industrial Engineering Alfaisal University Riyadh12714 Saudi Arabia

Due to the increasing importance of complex systems, the problems of modelling and simulation (M&S) of these systems, their approaches and solutions have been studied extensively these last years. In this paper, we present a novel approach to modelling and simulation of discrete event dynamic systems (DEDS), based on the combination of discrete event systems specification (DEVS) and agent group role (AGR). In this study, the DEVS atomic models are transformed into agents and the DEVS coupled models into groups of agents. Thus, we provide a set of procedures and functions that allow the systematic transformation from DEVS into AGR models in order to be implemented in multi-agent platforms. The proposed approach was validated by a case study, an application in the water supply used in natural gas liquefaction process. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Water supply

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A Lightweight Fine-Grained Scheme for Distinguishing the Hotness of Warm Data to Reduce Segment Cleaning Overhead

SSRN

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SSRN 2025年

作者： Yang, Lihua Wang, Fang Lin, Weizhao Zhang, Wei Xiao, Yang Tan, Zhipeng Li, Jiaxin Lu, Kai College of Computer Science and Technology National University of Defense Technology Yanwachi Street Changsha410073 China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Luoyu Road Wuhan430074 China

With the widespread adoption of flash memory, the Flash Friendly File System (F2FS) designed to flash memory characteristics has become widely-used in large data centers. However, F2FS encounters from significant cleaning overheads due to its logging scheme writes. We observe that warm data in F2FS account for a substantial proportion, at least 80%. Nevertheless, the mixed storage of warm data with varying hotness exacerbates segment cleaning challenges. To address this issue, we propose a scheme called M2H, which involves a fine-grained management of warm data hotness identified by the K-means clustering algorithm. M2H determines hotness by considering factors such as file block update distance, most recently used distance, and workload characteristics. M2H facilitates {M}ulti-log delayed writing and {M}odified segment cleaning based on {H}otness. To reduce costs associated with distinguishing data hotness at the file block level, we employ Mini Batch K-means and refer to this variant as M2H_MBK. Moreover, for servers equipped with GPUs, the clustering process can be offloaded to the GPU, known as M2H_GPU. We conduct a comprehensive comparison of traditional F2FS, M2H, M2H_MBK, and M2H_GPU on a real platform. Results show that compared to traditional F2FS, M2H_GPU reduces the number of segment cleanings by 54.41% to 97.93%. © 2025, The Authors. All rights reserved.

关键词： Flash memory

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Identifying the net information flow direction pattern in mutually coupled non-identical chaotic oscillators

arXiv

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arXiv 2025年

作者： Ghosh, Anupam Liang, X. San Manshour, Pouya Paluš, Milan Department of Complex Systems Institute of Computer Science The Czech Academy of Sciences Prague18200 Czech Republic Division of Frontier Research Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519000 China Department of Atmospheric & Oceanic Sciences Fudan University Shanghai200438 China

This paper focuses on a fundamental inquiry in a coupled oscillator model framework. It specifically addresses the direction of net information flow in mutually coupled non-identical chaotic oscillators. Adopting a specific form of conditional mutual information as a model-free and asymmetric index, we establish that if the magnitude of the maximum Lyapunov exponent can be defined as the ‘degree of chaos’ of a given isolated chaotic system, a predominant net information transfer exists from the oscillator exhibiting a higher degree of chaos to the other while they are coupled. We incorporate two distinct categories of coupled ‘non-identical’ oscillators to strengthen our claim. In the first category, both oscillators share identical functional forms, differing solely in one parameter value. We also adopt another measure, the Liang–Kleeman information flow, to support the generality of our results. The functional forms of the interacting oscillators are entirely different in the second category. We further extend our study to the coupled oscillator models, where the interacting oscillators possess different dimensions in phase space. These comprehensive analyses support the broad applicability of our results. © 2025, CC BY-NC-ND.

关键词： Chaotic systems

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