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2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Ismail, Leila Materwala, Huned United Arab Emirates University College of Information Technology Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu-Dhabi Al-Ain15551 United Arab Emirates

ISBN: (纸本)9781728176246

Diabetes is one of the top 10 causes of death worldwide. Health professionals are aiming for machine learning models to support the prognosis of diabetes for better healthcare and to put in place an effective prevention plan. In this paper, we conduct a comparative analysis of the most used machine learning models in the literature to predict the prevalence of diabetes mellitus type 2. We evaluate the models in terms of accuracy, F-measure and execution time with and without feature selection using a real-life diabetes dataset. The detailed analysis is in the paper. © 2020 IEEE.

关键词： Medical informatics

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A Generalized Binary Tree Mechanism for Differentially Private Approximation of All-Pair Distances

arXiv

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arXiv 2025年

作者： Dinitz, Michael Fan, Chenglin Liu, Jingcheng Upadhyay, Jalaj Zou, Zongrui Department of Computer Science Johns Hopkins University United States Department of Computer Science and Engineering Seoul National University Korea Republic of State Key Laboratory for Novel Software Technology New Cornerstone Science Laboratory Nanjing University China Management Science & Information Systems Department Rutgers University United States

We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected graph, we treat the weights of edges as sensitive information, and two graphs are neighbors if their edge weights differ in one edge by at most one. We obtain efficient algorithms with significantly improved bounds on a broad class of graphs which we refer to as recursively separable. In particular, for any n-vertex Kh-minor-free graph, our algorithm achieve an additive error of Õ(h(nW)1/3), where W represents the maximum edge weight;For grid graphs, the same algorithmic scheme achieve additive error of Õ(n1/4√W). Our approach can be seen as a generalization of the celebrated binary tree mechanism for range queries, as releasing range queries is equivalent to computing all-pair distances on a path graph. In essence, our approach is based on generalizing the binary tree mechanism to graphs that are recursively separable. Copyright © 2025, The Authors. All rights reserved.

关键词： Binary trees

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Independent and Identically Distributed (IID) Data Assessment in Federated Learning

Independent and Identically Distributed (IID) Data Assessmen...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Mohamad Arafeh Ahmad Hammoud Hadi Otrok Azzam Mourad Chamseddine Talhi Zbigniew Dziong Department of Software Engineering Ecole de Technologie Superieure (ETS) Montreal QC Canada Department of Electrical Engineering Ecole de Technologie Superieure (ETS) Montreal Canada Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Department of Computer science and Mathematics Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE

ISBN: (纸本)9781665435413

Federated learning extends the centralized machine learning architecture by enabling data privacy for its providers. The distributed structure of the emerged federated architecture imposes a problem of the data being not independent and identically distributed (non-IID), which drastically affects the performance of the learning process. While the majority of the recent works in the federated learning domain have accepted this limitation, only a few scholars addressed the non-IID problem straightforwardly. Nevertheless, these works lack the fundamental analysis of the data’ IIDness, and/or contradict the privacy feature of the federated learning paradigm. In this paper, we focus on evaluating the harmony of the participants by studying their data distribution and calculating their level of compatibility. The devised tool, in this work, is an assessment technique integrated within the federated learning framework to analyze the data distribution among the trainers. Our proposed method is proven by experimenting with several scenarios, and results show that our utility can fairly assess the selected participants before initiating the learning process.

关键词： Data privacy Federated learning Distributed databases Coherence Global communication Convergence

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Any-to-Any Vision-language Model for Multimodal X-ray Imaging and Radiological Report Generation

arXiv

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arXiv 2025年

作者： Molino, Daniele di Feola, Francesco Shen, Linlin Soda, Paolo Guarrasi, Valerio Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Diagnostics and Intervention Biomedical Engineering and Radiation Physics Umeå University Umeå Sweden College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Generative models have revolutionized Artificial Intelligence (AI), particularly in multimodal applications. However, adapting these models to the medical domain poses unique challenges due to the complexity of medical data and the stringent need for clinical accuracy. In this work, we introduce a framework specifically designed for multimodal medical data generation. By enabling the generation of multi-view chest X-rays and their associated clinical report, it bridges the gap between general-purpose vision-language models and the specialized requirements of healthcare. Leveraging the MIMIC-CXR dataset, the proposed framework shows superior performance in generating high-fidelity images and semantically coherent reports. Our quantitative evaluation reveals significant results in terms of FID and BLEU scores, showcasing the quality of the generated data. Notably, our framework achieves comparable or even superior performance compared to real data on downstream disease classification tasks, underlining its potential as a tool for medical research and diagnostics. This study highlights the importance of domain-specific adaptations in enhancing the relevance and utility of generative models for clinical applications, paving the way for future advancements in synthetic multimodal medical data generation. © 2025, CC BY-NC-ND.

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Level of Trust and Privacy Management in 6G Intent-based Networks for Vertical Scenarios

Level of Trust and Privacy Management in 6G Intent-based Net...

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6G Networking (6GNet), International Conference on

作者： Jesú s A. Alonso-Lupez Luis Alberto Martí nez Herná ndez Sandra Pé rez Arteaga Ana Lucila Sandoval Orozco Luis Javier Garcí a Villalba Antonio Pastor Diego Ló pez Garcí a Department of Software Engineering and Artificial Intelligence (DISIA) Faculty of Computer Science and Engineering Office 431 Group of Analysis Security and Systems (GASS) Universidad Complutense de Madrid (UCM) Madrid Spain CTIO Unit Telef&#x00F3 nica I+D Ronda de la Comunicacion S/N Madrid Spain

Security is one of the main concerns when designing future 6G networks. Most of the components of service chains are software-based and their security must be ensured. In this work we propose a system that will check the level of trust of 6G services following 3 complementary approaches: attestation, generation of proofs of transit and usage of smart contracts whose execution will be published via Distributed Ledger Technology in order to enable their traceability. The service orchestration function will use reinforcement learning to formulate optimised service orchestration decisions. Services will be specified in a declarative model, following a privacy-aware flavour of intent-based networking. The proposed architecture will take into account privacy of every stakeholder: final users, service providers and infrastructure providers.

关键词： 6G mobile communication Privacy Distributed ledger Network topology Smart contracts Reinforcement learning Service-oriented architecture

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Data distribution aware clustering for parallel split learning in healthcare applications

引用

Future Generation computer systems 2025年

作者： Md. Tanvir Arafat Md. Abdur Razzaque Abdulhamed Alelaiwi Md. Zia Uddin Mohammad Mehedi Hassan Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh Department of Software Engineering King Saud University Riyadh Saudi Arabia SINTEF Digital Oslo Norway Department of Information Systems King Saud University Riyadh Saudi Arabia

Split learning, a promising approach in privacy-preserving machine learning, decentralizes model training by dividing it among client devices and a central server. However, split learning has exhibited a certain level of slowness in its vanilla approach, mainly due to the serial processing of devices. Recent research endeavours have addressed this challenge by introducing parallelism and thus accelerating the split learning process. However, the existing split learning methodologies often overlook the critical aspect of data distribution among client devices. This paper introduces a Data Distribution Aware Clustering-based Parallel Split Learning (DCSL), a scheme purposefully crafted to address the complexities stemming from non-identically and non-independently distributed (non-IID) data among client devices engaged in the split learning paradigm. In healthcare applications, comprehending the intricacies of data distribution is imperative, particularly given the non-IID nature of medical datasets, to ensure accurate analysis and decision-making. The DCSL leverages a novel clustering technique to create clusters of medical client devices, considering the data distributions of their local datasets, and employs parallel model training within the device clusters. It enhances model convergence and reduces training latency by optimizing the cluster formation. Extensive experiments demonstrate that DCSL outperforms traditional split learning approaches, significantly improving accuracy and reducing training latency across various applications.

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Modeling and Simulation of Total Attenuation of P2P NLOS 5G and Beyond Wireless Communication systems Operating in FR2 Bands in Nigeria

Modeling and Simulation of Total Attenuation of P2P NLOS 5G ...

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

作者： Emmanuel Alozie Nasir Faruk Abubakar Abdulkarim Aliyu D. Usman Yusuf Olayinka Imam-Fulani 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 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 Nigeria Department of Electrical and Telecommunications Engineering Kampala International University Kansanga Kampala Uganda Department of Electronics and Telecommunication Engineering Ahmadu Bello University Nigeria Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Cyber Security Sule Lamido University Kafin Hausa 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 Akoka 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

Signal propagation in a particular region differs from another due to differences in atmospheric, climatic and environmental properties, distinct terrain and clutter features. Adequate analysis is essential to understand the radio propagation behavior in a particular region. The ITU-R designated four rain regions, M, N, P, and Q, for Nigeria representing the rain rate distribution and also provided further classifications based on ground conductivity, among other salient parameters. Based on these classifications, this paper utilized the EDX Signal Pro software® to model and simulate a typical Point-to-Point (P2P), Non-Line of Sight (NLOS) link scenarios for 5G networks. The objective was to estimate and compare the total loss, excess loss, and flat fade margins for each rain region in Nigeria. Results obtained from the comparison showed that signals propagating in region N experience the highest level of losses and fading, while, region Q has the least losses and fading.

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Signal Attenuation Modeling and Simulation for 5G and Beyond P2P Wireless Communication Networks in FR2 Bands: Comparison within M-Region in Nigeria

Signal Attenuation Modeling and Simulation for 5G and Beyond...

引用

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

作者： Emmanuel Alozie Nasir Faruk Abubakar Abdulkarim Aliyu D. Usman Yusuf Olayinka Imam-Fulani 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 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 Kafin Hausa Nigeria Department of Electrical and Telecommunications Engineering Kampala International University Kampala Uganda Department of Electronics and Telecommunication Engineering Ahmadu Bello University Zaria Nigeria Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Cyber Security Sule Lamido University Kafin Hausa 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

ITU-R categorized Nigeria into four rain regions (i.e., M, N, P, and Q) depending on the atmospheric conditions. Previous works that have conducted rain and attenuation modeling and simulations have assumed similar signal propagation behavior and parameters within locations categorized under the same region. This paper aims to explore these assumptions by conducting an extensive radio propagation simulations of point-to-point microwave links with a clear line-of-sight in order to estimate the total path loss (attenuation), excess path loss, and flat fade margins for each of the locations within the M-regions, i.e., Kano, Sokoto, and Adamawa. Results obtained showed that in all the locations, the loss monotonically increases with distance, with Sokoto having the highest level of signal losses and fading, while, Adamawa has the lowest. The deviation for both total loss, excess loss, and flat fading margin was found to be between 15 dB across the region.

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A Novel Motion Accumulation and Selection Network for Rgb-Based Action Recognition

SSRN

引用

SSRN 2022年

作者： Wang, Huafeng Li, Hanlin Liu, Wanquan Gu, Xianfeng School of Information Technology North China University of Technology Beijing China School of Software Beihang University Beijing China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China Department of Computer Science Stony BrookNY11794-2424 United States

A large number of existing methods have demonstrated the significance of motion information for action recognition in video. In the literature, most of the previous methods rely heavily on the temporal differences of features extracted by convolutional networks (CNN) to represent the motion. However, this type of motion representation approach may have two potential drawbacks: 1) The difference operation may cause the incompleteness of the extracted moving target contour;2) Treating all the extracted motion features equally may lead to situations where some motion features will not contribute to the classification or even produce negative incentives. Inspired by the fact that the human eye is often a cumulative and selective process of visual attention when observing a video sequence, we propose a motion accumulation and selection network (MAS-Net) based on a novel embedded 2D CNN. According to the experimental results on typical video datasets such as Something-Something V1&V2 and Kinetics-400, it is shown that MAS-Net has achieved the state-of-the-arts on Something-Something V1&V2 and the competitive results on Kinetics-400, while the computational load is kept at a relatively low level. © 2022, The Authors. All rights reserved.

关键词： Motion estimation

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