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International eConference on computer and Knowledge Engineering (ICCKE)

作者： Rasoul Jafari Gohari Laya Aliahmadipour Ezat Valipour Department of Computer Science Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Shahid Bahonar University of Kerman Kerman Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

Brain is one the most complex organs in the human body. Due to its complexity, classification of brain tumors still poses a significant challenge, making brain tumors a particularly serious medical issue. Techniques such as Machine Learning (ML) coupled with Magnetic Resonance Imaging (MRI) have paved the way for doctors and medical institutions to classify different types of tumors. However, these techniques suffer from limitations that violate patients' privacy. Federated Learning (FL) has recently been introduced to solve such an issue, but the FL itself suffers from limitations like communication costs and dependencies on model architecture, forcing all models to have identical architectures. In this paper, we propose FedBrain-Distill, an approach that leverages Knowledge Distillation (KD) in an FL setting that maintains the users privacy and ensures the independence of FL clients in terms of model architecture. FedBrain-Distill uses an ensemble of teachers that distill their knowledge to a simple student model. The evaluation of FedBrain-Distill demonstrated high-accuracy results for both Independent and Identically Distributed (IID) and non-IID data with substantial low communication costs on the real-world Figshare brain tumor dataset. It is worth mentioning that we used Dirichlet distribution to partition the data into IID and non-IID data. All the implementation details are accessible through our Github repository 1 1 https://***/russelljeffrey/FedBrain-Distill.

关键词： Training Privacy Costs Magnetic resonance imaging Computational modeling Brain tumors computer architecture Brain modeling Data models Biomedical imaging

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Global-Local Graph Neural Networks for Node-Classification

arXiv

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arXiv 2024年

作者： Eliasof, Moshe Treister, Eran Ben-Gurion University of the Negev Department of Computer Science Beer Sheva Israel University of Cambridge Department of Applied Mathematics Cambridge United Kingdom

The task of graph node classification is often approached by utilizing a local Graph Neural Network (GNN), that learns only local information from the node input features and their adjacency. In this paper, we propose to improve the performance of node classification GNNs by utilizing both global and local information, specifically by learning label- and node- features. We therefore call our method Global-Local-GNN (GLGNN). To learn proper label features, for each label, we maximize the similarity between its features and nodes features that belong to the label, while maximizing the distance between nodes that do not belong to the considered label. We then use the learnt label features to predict the node classification map. We demonstrate our GLGNN using three different GNN backbones, and show that our approach improves baseline performance, revealing the importance of global information utilization for node classification. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Certifying IoT Data with Verifiable Credentials

Certifying IoT Data with Verifiable Credentials

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IEEE International Symposium on Network Computing and Applications

作者： Carlo Mazzocca Stefano Allevi Rebecca Montanari Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Salerno Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331510183

ISBN: (纸本)9798331510190

The Internet of Things (IoT) is a major contributor to the vast amount of data generated worldwide, significantly impacting the big data market. However, this data holds value only when utilized for insights and applications. Many organizations hesitate to use third-party data due to concerns about accuracy, reliability, and integrity. Enhancing trustworthiness in data is crucial to unlock their full potential, especially in critical domains such as healthcare where erroneous data can have severe consequences. The Identity of Things (IDoT) paradigm addresses this need by identifying trustworthy devices using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), offering promising solutions for digital identification. Although DIDs and VCs have primarily been used for mutual trust and access control, their potential for data certification in IoT remains underexplored. This paper is the first to investigate the feasibility of IoT devices to use VCs for certifying data. We evaluated devices with varying capabilities, focusing on the latency, computing, and storage requirements for issuing VCs. Our findings demonstrate that IoT devices can issue VCs with up to 100 claims in less than 90 ms, with storage requirements growing linearly and remaining below 4 KB. Using VCs for data certification does not introduce significant computational overhead, suggesting its practicality for IoT environments.

关键词： Accuracy Focusing Organizations Medical services Big Data Internet of Things Object recognition Stakeholders Reliability Certification

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Analysis of How Artificial Intelligence and Machine Learning are Employed in the Field of Marketing 8th

Analysis of How Artificial Intelligence and Machine Learning...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Abdullayev, Ilyos Ljubimova, Elena Sychanina, Svetlana Laxmi Lydia, E. Vijaya Kumar, K. Department of Business and Management Urgench State University Urgench Uzbekistan Department of Mathematics and Applied Computer Science of Elabuga Institute Kazan Federal University Kazan Russia Department of Management Kuban State Agrarian University Named After I.T. Trubilin Krasnodar Russia Vijayawada520007 India Visakhapatnam Campus Visakhapatnam India

ISBN: (纸本)9789819786657

Artificial Intelligence (AI) permeates many aspects of daily life through intelligent gadgets and programs that are made to understand user preferences, behavior, and options in order to provide personalized narratives. Artificial Intelligence has emerged as a major force behind marketing innovation. Marketers are already taking advantage of AI's benefits to gain invaluable information about their target audience, competitors, and marketplaces. On the other hand, AI streamlines processes, lowers costs, and automates operations. This study provides a thorough analysis of current academic research while examining the potential and current uses of AI in marketing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Predicting Student Success in mathematics Using Machine Learning Techniques: A Comparative Study of Algorithms

Predicting Student Success in Mathematics Using Machine Lear...

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Innovative and Intelligent Information Technologies (IC3IT), International Conference on

作者： Zouaoui Samia Zouaoui Nour El Houda Mestour Hareth Computer Science Department University of Batna 2 Batna Algeria LINFI Laboratory of Biskra University Mathematics Department Laboratory of Applied Mathematics University of Biskra Algeria Mathematics Department University of Batna 2 Batna Algeria

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

Achieving low results in mathematics is a real and urgent concern, as mathematics is the foundation of science and technology, and any weakness in it reflects negatively on the progress of society in various ways. This study evaluates how different mathematics study methods affect students educational success and achievement. We employed three different machine learning models to analyze data related to mathematics study techniques and student outcomes. These methods included: support vector machine (SVM), A K-nearest neighbors (KNN), and Linear Regression (LR). The results showed that KNN provided the best accuracy rate with 97.6% and an F1-score of 96.7%. These results highlight the significant contribution of forming study groups and trying to cooperate with each other, this factor is considered important and influential in the success of students in mathematics.

关键词： Support vector machines Logistic regression Machine learning algorithms Education Machine learning Nearest neighbor methods Predictive models Prediction algorithms Mathematical models Object recognition

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AGFA-Net: Attention-Guided Feature-Aggregated Network for Coronary Artery Segmentation Using Computed Tomography Angiography 36

AGFA-Net: Attention-Guided Feature-Aggregated Network for Co...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Liu, Xinyun Xiao, Pengcheng Esposito, Michele Raavi, Manohar Zhao, Chen Michigan Technological University Department of Applied Computing HoughtonMI49931 United States Kennesaw State University Department of Mathematics MariettaGA30060 United States Medical University of South Carolina Department of Cardiology CharlestonSC29425 United States Kennesaw State University Department of Computer Science MariettaGA30062 United States

ISBN: (纸本)9798331527235

coronary artery disease (CAD) remains a prevalent cardiovascular condition, posing significant health risks worldwide. Accurate segmentation of coronary arteries from coronary computed tomography angiography (CCTA) images is crucial for diagnosis and treatment planning. Despite advancements, coronary artery segmentation (CAS) remains a significant challenge due to the considerable variability in the scale of coronary arteries, the complex anatomical structure and morphology, and the low contrast between blood vessels and the surrounding tissue. To effectively tackle these issues, we propose an attention-guided feature-aggregated network (AGFA-Net) specifically designed for CAS using CCTA images. AGFA-Net leverages attention mechanisms, feature refinement and hierarchical feature aggregation to capture salient features, acquire a more distinctive semantic representation and enhance segmentation accuracy. Evaluation on a dataset comprising 1,000 CCTAs demonstrates AGFA-Net's superior performance, achieving an average Dice coefficient similarity of 86.74 % and a 95% Hausdorff distance of 0.82 mm during 5-fold cross-validation. Ablation studies further validate the effectiveness of the proposed modules, highlighting their contributions to improved segmentation accuracy. Overall, AGFA-Net offers a robust and reliable solution for CAS, addressing challenges posed by varying arterial sizes, complex anatomies, and low image contrast. © 2024 IEEE.

关键词： computerized tomography

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FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data

arXiv

引用

arXiv 2024年

作者： Gohari, Rasoul Jafari Aliahmadipour, Laya Valipour, Ezat Department of Computer Science Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Shahid Bahonar University of Kerman Kerman Iran

Brain is one the most complex organs in the human body. Due to its complexity, classification of brain tumors still poses a significant challenge, making brain tumors a particularly serious medical issue. Techniques such as Machine Learning (ML) coupled with Magnetic Resonance Imaging (MRI) have paved the way for doctors and medical institutions to classify different types of tumors. However, these techniques suffer from limitations that violate patients' privacy. Federated Learning (FL) has recently been introduced to solve such an issue, but the FL itself suffers from limitations like communication costs and dependencies on model architecture, forcing all models to have identical architectures. In this paper, we propose FedBrain- Distill, an approach that leverages Knowledge Distillation (KD) in an FL setting that maintains the users privacy and ensures the independence of FL clients in terms of model architecture. FedBrain-Distill uses an ensemble of teachers that distill their knowledge to a simple student model. The evaluation of FedBrain-Distill demonstrated high-accuracy results for both Independent and Identically Distributed (IID) and non-IID data with substantial low communication costs on the real-world Figshare brain tumor dataset. It is worth mentioning that we used Dirichlet distribution to partition the data into IID and non- IID data. All the implementation details are accessible through our Github repository. © 2024, CC BY.

关键词： Federated learning

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Splinets 1.5.0 – Periodic Splinets

arXiv

引用

arXiv 2023年

作者： Nassar, Hiba Podgórski, Krzysztof Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Statistics Lund University Sweden

Periodic splines are a special kind of splines that are defined over a set of knots over a circle and are adequate for solving interpolation problems related to closed curves. This paper presents a method of implementing the objects representing such splines and describes how an efficient orthogonal basis can be obtained. The proposed orthonormalized basis is called a periodic splinet in the reference to earlier work where analogous concepts and tools have been introduced for splines on an interval. Based on this methodology, the periodic splines and splinets are added to the earlier version of the R package Splinets. Moreover, the developed computational tools have been applied to functionally analyze a standard example of functional circular data of wind directions and speeds. © 2023, CC BY.

关键词： Interpolation

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A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks 7th

A Novel Nash-Based Matching Approach for Multirobot Task All...

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7th EAI International Conference on Robotics and Networks, ROSENET 2023

作者： Hamidoğlu, Ali Gül, Ömer Melih Gültekin, Gökhan Koray Kadry, Seifedine Nimer Department of Mathematics Bahcesehir University İstanbul Turkey Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Electrical-Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates

ISBN: (纸本)9783031644948

Efficiency is paramount in distributed robotic networks, where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (MRTA) challenges. This study presents a novel cooperative Nash game framework that serves as a distributed matching method for addressing the task allocation problem in a distributed robotic network consisting of robots and tasks. A particular MRTA problem is investigated where each robot moves at a constant speed determined by maximizing energy harvesting while minimizing energy consumption during the motion. In this framework, Nash equilibrium is established as a near-optimal approach for matching based on distance. In the numerical experiments, the performances are assessed for various scenarios involving 10 robots and 20 robots with the same number of tasks. Here, the Hungarian algorithm is used as an optimal benchmark algorithm to demonstrate the reliability of the theoretical findings and the robustness of the proposed model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Distributed systems Energy harvesting Game theory Multirobot systems Task allocation

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Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models

arXiv

引用

arXiv 2024年

作者： Ciudad, Javier García Mørup, Morten Kornum, Birgitte Rahbek Zahid, Alexander Neergaard Department of Neuroscience University of Copenhagen Copenhagen Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

In human sleep staging models, augmenting the temporal context of the input to the range of tens of minutes has recently demonstrated performance improvement. In contrast, the temporal context of mouse sleep staging models is typically in the order of tens of seconds. While long-term time patterns are less clear in mouse sleep, increasing the temporal context further than that of the current mouse sleep staging models might still result in a performance increase, given that the current methods only model very short term patterns. In this study, we examine the influence of increasing the temporal context in mouse sleep staging up to 15 minutes in three mouse cohorts using two recent and high-performing human sleep staging models that account for long-term dependencies. These are compared to two prominent mouse sleep staging models that use a local context of 12 s and 20 s, respectively. An increase in context up to 28 s is observed to have a positive impact on sleep stage classification performance, especially in REM sleep. However, the impact is limited for longer context windows. One of the human sleep scoring models, L-SeqSleepNet, outperforms both mouse models in all cohorts. This suggests that mouse sleep staging can benefit from more temporal context than currently used. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrophysiology

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