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arXiv 2023年

作者： Mendez, Carlos dos Santos, Fabio Pereira Gonçalves, Gabriel Universidad Nacional de Asunción Facultad de Ciencias Químicas San Lorenzo Paraguay Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Rio de Janeiro21941-909 Brazil Chemical Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Rio de Janeiro21941-972 Brazil

Bubble columns are present in several applications, such as chemical and biochemical reactors and petrochemical and environmental engineering industries. This variety of applications is why understanding the bubble columns' dynamics is essential. This paper aims to describe a 2D bubble column system by a data-driven method to comprehend its dynamics better. We provided a set of simulations considering different superficial velocities to produce training data. With this data set, we compared two approaches: the Fast Fourier transformation (FFT) and the High-Order Dynamic Mode Decomposition (HODMD). Our results showed that FFT could not adequately describe the system as it has been done for a long time in the industry. However, with a few measurement points, HODMD can well represent and reconstruct the dynamics of this complex dispersed multiphase flow system. Copyright © 2023, The Authors. All rights reserved.

关键词： Bubble columns

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Guest Editorial AI-Generated Content Empowered Healthcare Electronics

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1319-1321页

作者： Gwanggil Jeon Joel Rodrigues Shiping Wen Junxin Chen Nan Ji Abdellah Chehri Department of Embedded Systems Engineering Incheon National University Incheon South Korea Post-Graduation Program in Electrical Engineering Federal University of Piauí Teresina Brazil Instituto de Telecomunicações Universidade de Aveiro Aveiro Portugal Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia School of Software Dalian University of Technology Dalian China Hong Kong Centre for Cerebro-Cardiovascular Health Engineering City University of Hong Kong Hong Kong China Department of Mathematics and Computer Science Military College of Canada Kingston ON Canada

The rapid advancements in AI-Generated Content (AIGC) have revolutionized various domains, including consumer electronics and healthcare technologies. AIGC’s ability to generate high-quality text, images, and videos within seconds has reshaped human-computer interactions, from intelligent customer service to immersive virtual experiences. More importantly, its application in Healthcare Electronics (HE) has opened new frontiers, facilitating automated diagnostics, medical data synthesis, and intelligent healthcare predictions.

关键词： Special issues and sections Generative AI Medical services Artificial intelligence Medical diagnostic imaging Brain modeling Knowledge graphs Electroencephalography Wearable devices Electronic medical records

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An Island-Based Hybrid Evolutionary Algorithm for Caloric-Restricted Diets

Research Square

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Research Square 2021年

作者： Silva, João Gabriel R. Duarte, Grasiele R. Carvalho, Iago A. Goliatt, Leonardo Vieira, Vinicius F. Xavier, Carolina R. IFMT Brazil Computational Modeling Federal University of Juiz de Fora Brazil Civil Engineering Program COPPE Federal University of Rio de Janeiro Brazil Computer Science Program Federal University of Minas Gerais Brazil Computer Science Program Federal University of Sao Joao del Rei Brazil

The most popular and successful way to maintain a healthy body is to have a rich and balanced diet, combined with physical exercise. Since it was proposed the diet dilemma, several works in the literature suggested calculating a diet that respects an individual’s nutritional needs. In the Caloric-Restricted Diet Problem (CRDP), the goal is to find a reduced-calorie diet that meets an individual’s dietary needs aiming for weight loss. This paper proposes a Hybrid Island-Based Evolutionary Algorithm (IBHEA) that combines a Genetic Algorithm (GA) with a Differential Evolution (DE) communicating through a migration policy to solve the CRDP. Computational experiments showed that IBHEA outperforms the non-distributed and non-hybrid implementations, generating a greater variety of diets with a small calorie count. © 2021, CC BY.

关键词： Nutrition

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A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach

arXiv

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arXiv 2025年

作者： Kaur, Navreet Gonzales, Manuel Alcaraz, Cristian Garcia Gong, Jiaqi Wells, Kristen J. Barnes, Laura E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology San DiegoCA United States Department of Computer Science The University of Alabama TuscaloosaAL United States Department of Psychology San Diego State University San DiegoCA United States

Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25% (Precision – 92.04%, Recall – 93.15%, Specificity – 77.50%), and weekly models, an accuracy of 76.04% (Precision – 75.83%, Recall – 85.80%, Specificity – 72.30%). Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns. While our models exhibit strong predictive performance, they are constrained by potential cohort-specific biases, reliance on self-reported adherence data, and a limited understanding of the context around non-adherence. Future research will focus on external validation across diverse populations and explore the real-world implementation of sensor-rich systems for a more compre

关键词： Endocrinology

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Synthetic helicoidal rotation of a quasielectrostatic ring resonator for ultrabroadband magnet-free nonreciprocity

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Physical Review Applied 2024年第6期22卷 064020-064020页

作者： Aravind Nagulu Ahmed Mekawy Mykhailo Tymchenko Dimitrios Sounas Harish Krishnaswamy Andrea Alù Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis Missouri 63130 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Department of Electrical Engineering Wayne State University Detroit Michigan 48202 USA Department of Electrical Engineering Columbia University New York New York 10027 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Suitably tailored forms of spatiotemporal modulation in electronic circuit networks have been recently employed to overcome fundamental challenges in modern electronic systems, including breaking reciprocity, squeezing the footprint of high-Q resonators, and overcoming the delay-bandwidth limit. Rotating patterns of temporal modulations have been used to synthesize angular momentum, which replaces magnetic bias to break reciprocity in integrated circuits. However, this approach is limited by trade-offs between modulation speed, footprint, and bandwidth of operation. Rotating switching patterns in commutated capacitor networks also enables compact filters and quasielectrostatic wave propagation, overcoming the delay-bandwidth limit. In this paper, we combine these mechanisms in an integrated-circuit ring that synthetically rotates in two dimensions, realizing an effective helicoidal motion that provides ultrabroadband quasielectrostatic nonreciprocal responses fitting within a theoretically infinitesimal size. We also analyze the impact of modulation signal noise on time-modulated nonreciprocal components and unveil the role of a dynamic noise mechanism based on which the noise level increases in the presence of a strong signal passing through the component, along with methods to mitigate this effect. We experimentally verify these principles in a three-port integrated circulator based on a 65-nm CMOS process that operates from dc to 1 GHz with a miniaturization factor of 2 × 106.

关键词： Metamaterials Non-reciprocal transmission Photonics Topolectrical circuits

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Trackable Agent-based Evolution Models at Wafer Scale

arXiv

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arXiv 2024年

作者： Moreno, Matthew Andres Yang, Connor Dolson, Emily Zaman, Luis Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data Science University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as transitions in individuality, eco-evolutionary dynamics, and rare evolutionary events. Emerging ML/AI-oriented hardware accelerators, like the 850,000 processor Cerebras Wafer Scale Engine (WSE), hold particular promise. However, many practical challenges remain in conducting informative evolution experiments that efficiently utilize these platforms’ large processor counts. Here, we focus on the problem of extracting phylogenetic information from agent-based evolution on the WSE platform. This goal drove significant refinements to decentralized in silico phylogenetic tracking, reported here. These improvements yield order-of-magnitude performance improvements. We also present an asynchronous island-based genetic algorithm (GA) framework for WSE hardware. Emulated and on-hardware GA benchmarks with a simple tracking-enabled agent model clock upwards of 1 million generations a minute for population sizes reaching 16 million agents. This pace enables quadrillions of agent replication events a day. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction, from wafer-scale simulation, of clear phylometric signals that differentiate runs with adaptive dynamics enabled versus disabled. Together, these benchmark and validation trials reflect strong potential for highly scalable agent-based evolution simulation that is both efficient and observable. Developed capabilities will bring entirely new classes of previously intractable research questions within reach, benefiting further explorations within the evolutionary biology and artificial life communities across a variety of emerging high-performance computing platforms. Copyright © 20

关键词： Genetic algorithms

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IoT Attack Detection and Prevention Through Machine Learning System

IoT Attack Detection and Prevention Through Machine Learning...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Kanishk Agrawal Pavan Kumar Reddy Poli Yash Gupta Garv Juneja Adhip Kishen Bopaiah Dinesh Ashok Arani Department of IET CSE Manglayatan University Aligarh Uttar Pradesh India Managing Information Technology Implementations Program management for fortune 6 Healthcare Company USA Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Delhi India ICT Trainer Trinity College Bangalore India Manglayatan University Aligarh Uttar Pradesh India

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified “Support Vector Machine (SVM)” as well as “Random Forest (RF)” ML techniques showed optimal performance in IoT attack detection and prevention.

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Scientometric Research Assessment of IEEE CSCWD Conference Proceedings: An Exploratory Analysis from 2001 to 2019

Scientometric Research Assessment of IEEE CSCWD Conference P...

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International Conference on computer Supported Cooperative Work in Design

作者： António Correia Dennis Paulino Hugo Paredes Benjamim Fonseca Shoaib Jameel Daniel Schneider Jano Moreira de Souza INESC TEC and University of Trás-os-Montes e Alto Douro UTAD Vila Real Portugal School of Computer Science and Electronic Engineering Colchester Campus University of Essex UK Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil

ISBN: (纸本)9781728165981

It has been a quarter of a century since the publication of the first edition of the IEEE International Conference on computer Supported Cooperative Work in Design (CSCWD) held in 1996 in Beijing, China. Despite some attempts to empirically examine the evolution and identity of the field of CSCW and its related communities and disciplines, the scarcity of scientometric studies on the IEEE CSCWD research productivity is noteworthy. To fill this gap, this study reports on an exploratory quantitative analysis of the literature published in the IEEE CSCWD conference proceedings with the purpose of visualizing and understanding its structure and evolution for the 2001-2019 period. The findings offer valuable insights into the paper and author distribution, country and citation-level productivity indicators, degree of collaboration, and collaboration index. Through this analysis we also expect to get an initial overview of the IEEE CSCWD conference concerning the main topics being presented, most cited papers, and variances in the number of keywords, full-text views, and references.

关键词： Productivity Visualization Statistical analysis Conferences Bibliometrics Collaboration Collaborative work

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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