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Moment closure approximations of susceptible-infected-susceptible epidemics on adaptive networks

Physical Review E 2022年第1期106卷 014308-014308页

作者： Massimo A. Achterberg Piet Van Mieghem Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology P.O. Box 5031 2600 GA Delft The Netherlands

The influence of people's individual responses to the spread of contagious phenomena, like the COVID-19 pandemic, is still not well understood. We investigate the Markovian Generalized Adaptive Susceptible-Infected-Susceptible (G-ASIS) epidemic model. The G-ASIS model comprises many contagious phenomena on networks, ranging from epidemics and information diffusion to innovation spread and human brain interactions. The connections between nodes in the G-ASIS model change adaptively over time, because nodes make decisions to create or break links based on the health state of their neighbors. Our contribution is fourfold. First, we rigorously derive the first-order and second-order mean-field approximations from the continuous-time Markov chain. Second, we illustrate that the first-order mean-field approximation fails to approximate the epidemic threshold of the Markovian G-ASIS model accurately. Third, we show that the second-order mean-field approximation is a qualitative good approximation of the Markovian G-ASIS model. Finally, we discuss the Adaptive Information Diffusion (AID) model in detail, which is contained in the G-ASIS model. We show that, similar to most other instances of the G-ASIS model, the AID model possesses a unique steady state, but that in the AID model, the convergence time toward the steady state is very large. Our theoretical results are supported by numerical simulations.

关键词： Continuous phase transition Discontinuous phase transition Epidemic evolution Epidemic prevalence Evolving networks Epidemic spreading Rumor spreading SIS model

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Decision Tree and Naïve Bayes for Sentiment Analysis in Smoking Perception 12

Decision Tree and Naïve Bayes for Sentiment Analysis in Smo...

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12th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2022

作者： Mizan Khairul Anwar, Muhammad Khairul Yusoff, Marina Kassim, Murizah Menara Multi Purpose Capital Square Alliance Bank Malaysia Berhad Kuala Lumpur Malaysia Shah Alam Malaysia Universiti Teknologi Mara Faculty of Computer and Mathematical Science Shah Alam Malaysia Universiti Teknologi Mara School of Electrical Engineering College of Engineering Shah Alam Malaysia

ISBN: (纸本)9781665487030

The number of smokers is still significant in many countries. The trend of smokers has seemed to keep changing in time, especially in Malaysia. However, a smoke-free nation is one of the aims by 2045. Thus, there is a need to see the trend of smoking as it can be one the assistances to the relevant agencies to track the smoking trend. Perceiving the sentiment on smoking is now possible instead of only utilizing the traditional approaches like surveys, interviews, and questionnaires are popular methods for identifying current smoking trends. These approaches acquire time and cost, and fieldwork. In parallel with digitization and IR 4.0, the trend analysis methods are established in many forms, such as Twitter, web site, and Facebook. This paper's focal point is to see the utilization of the Decision Tree and Naive Bayes to perform sentiment analysis on smoking perceptions in three states in Malaysia. The methods are employed to classify the about 4500 tweets into positive, negative and neutral based on one-word and two-word search terms. The decision tree performs better when applied to the one-word search term data set, while Naïve Bayes is better for the two-word tweets search term data set. The finding demonstrates that this work is beneficial to obtain a quick and efficient result of the current smoker's perception, which will help the authorities develop new solutions to support the smoke-free generation initiative. © 2022 IEEE.

关键词： Decision trees

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Integral probability metrics PAC-bayes bounds 22

Integral probability metrics PAC-bayes bounds

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Ron Amit Baruch Epstein Shay Moran Ron Meir Technion - Israel Institute of Technology The Viterbi Faculty of Electrical Engineering Technion - Israel Institute of Technology The Viterbi Faculty of Electrical Engineering Technion - Israel Institute of Technology Faculty of Mathematics The Taub Faculty of Computer Science Google Research Israel

ISBN: (纸本)9781713871088

We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM). We provide instances of this bound with the IPM being the total variation metric and the Wasserstein distance. A notable feature of the obtained bounds is that they naturally interpolate between classical uniform convergence bounds in the worst case (when the prior and posterior are far away from each other), and improved bounds in favorable cases (when the posterior and prior are close). This illustrates the possibility of reinforcing classical generalization bounds with algorithm- and data-dependent components, thus making them more suitable to analyze algorithms that use a large hypothesis space.

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SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding (Extended Abstract)

SoulMate: Short-Text Author Linking Through Multi-Aspect Tem...

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International Conference on Data engineering

作者： Saeed Najafipour Saeid Hosseini Wen Hua Mohammad Reza Kangavari Xiaofang Zhou School of Information Technology and Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Computing and Information Technology Sohar University Sohar Oman Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors.

关键词： Accuracy Computational modeling Data engineering Vectors Inference algorithms Data models

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Designing of an Autonomous System for Electric Vehicle

Designing of an Autonomous System for Electric Vehicle

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International Conference on Robotics,electrical and Signal Processing Techniques (ICREST)

作者： Towfiq Mahmud Mridul Abidur Rahman Sagor Faisal Ahmad Mridha Md. Mosaraf Hosen Bhuiyan Md. Jahid Hasan Md. Abdur Rahman Department of Electrical and Electronics Engineering American International University-Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh

The creation of an autonomous vehicle has two major goals: to minimize the amount of labor that must be performed manually and to provide the automobile with superior intelligence. In order to turn a standard automobile into an autonomous vehicle that can drive itself around a predetermined map (in this case, a university campus), several modifications need to be made. The outcomes of this project will also contribute to the creation of a more technologically advanced campus atmosphere. Additional responsibilities include taking control of the horn system and being in charge of the headlights while also receiving the response signal from the station. In order to guarantee that everything functions correctly, we utilized components such as the Raspberry Pi, light sensors, motors, Arduino Uno, ultrasonic sensors, buzzers, and cameras. A temporary model is built to ensure that this project is successful right from the start. The training model trains the deep learning approach using images captured by a camera in manual mode, which enables the vehicle to operate in autonomous mode using the multilayered neural network that was taught. The final prototype utilizes image processing methods to track lanes. A few image processing technologies, such as intelligent edge detection, provide autonomous movement capabilities. These algorithms were built and tested on the vehicle. The system operated without a hitch, and the data gathered was within reasonable bounds of expectations. As well as demonstrating how the autonomous system of an electric car works.

关键词： Training Automation Tracking Cameras Sonar navigation Robot sensing systems Electric vehicles

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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computer Systems science & engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

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Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems

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PeerJ computer science 2025年 11卷 e2582-e2582页

作者： Ali, Shoukath K. Philip, Sajan P. Khan, Arfat Ahmad Moses, Leeban Cengiz, Korhan Akleylek, Sedat Ivković, Nikola Department of Electronics and Communication Engineering Presidency University Karnataka Bengaluru India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Electrical-Electronics Engineering Istinye University Istanbul Turkey Institute of Computer Science University of Tartu Tartu Estonia Faculty of Organization and Informatics University of Zagreb Pavlinska Varaždin Croatia

Channel estimation poses a main challenge in intelligent reflecting surface (IRS)assisted millimeter wave (mmWave) multi-user multiple-input multiple-output (MIMO) systems due to the substantial number of antennas at the base station (BS) and the passive reflective elements within the IRS lacking sufficient signal processing capabilities. This article addresses this challenge by proposing a channel estimation technique for IRS-assisted mmWave MIMO systems. The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). EM-VAMP demonstrates better performance only when a Gaussian mixture (GM) distribution is chosen as the prior for the sparse channel, especially at high signal-to-noise ratios (SNRs). To address this, the article introduces the application of generalized linear models (GLMs), extensions of standard linear models, providing increased flexibility in modeling data that deviates from Gaussian distribution. Numerical results unveil that the proposed Its EM-VAMP-GLM is much more robust to the existing OMP, GAMP and EM-LAMP algorithms. Copyright 2025 K et al. Distributed under Creative Commons CC-BY 4.0

关键词： Gaussian distribution

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Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging

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IEEE Journal of Biomedical and Health Informatics 2024年第6期29卷 3853-3863页

作者： Yusheng Zhou Hao Li Jianan Liu Zhengmin Kong Tao Huang Euijoon Ahn Zhihan Lv Jinman Kim David Dagan Feng School of Electrical Engineering and Automation Wuhan University Hubei China Department of Neuroradiology University Hospital Heidelberg Heidelberg Germany Vitalent Consulting Gothenburg Sweden College of Science and Engineering James Cook University Cairns QLD Australia Department of Game Design Faculty of Arts Uppsala University Uppsala Sweden School of Computer Science University of Sydney Sydney NSW Australia

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting with various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.

关键词： Magnetic resonance imaging Training Motion artifacts Task analysis Image reconstruction Generators Feature extraction

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ALGEBRAIC PROPERTIES OF INDIGENOUS SEMIRINGS

arXiv

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arXiv 2024年

作者： Behzadipour, Hussein Koppelaar, Henk Nasehpour, Peyman Department of Mathematics Sharif University of Technology Tehran Iran Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Education Department The New York Academy of Sciences United States

In this paper, we introduce Indigenous semirings and show that they are examples of information algebras. We also attribute a graph to them and discuss their diameters, girths, and clique numbers. Then, we proceed to investigate their algebraic properties (including ideal theory). We also characterize units and idempotent elements of formal power series over Indigenous *** Codes 16Y60, 13A15 © 2024, CC BY.

关键词： Algebra

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