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International Conference on Machine Learning and Applications (ICMLA)

作者： Andre Gustavo Hochuli Jean Paul Barddal Gillian Cezar Palhano Leonardo Matheus Mendes Paulo Ricardo Lisboa de Almeida Graduate Program in Informatics (PPGIa) Pontificia Universidade Catolica do Parana Curitiba PR Brazil Pontificia Universidade Catolica do Parana Curitiba PR Brazil Department of Informatics (DInf) Universidade Federal do Parana Curitiba PR Brazil

Searching for available parking spots in high-density urban centers is a stressful task for drivers that can be mitigated by systems that know in advance the nearest parking space available. To this end, image-based systems offer cost advantages over other sensor-based alternatives (e.g., ultrasonic sensors), requiring less physical infrastructure for installation and maintenance. Despite recent deep learning advances, de-ploying intelligent parking monitoring is still a challenge since most approaches involve collecting and labeling large amounts of data, which is laborious and time-consuming. Our study aims to uncover the challenges in creating a global framework, trained using publicly available labeled parking lot images, that performs accurately across diverse scenarios, enabling the parking space monitoring as a ready-to-use system to deploy in a new environment. Through exhaustive experiments involving different datasets and deep learning architectures, including fusion strategies and ensemble methods, we found that models trained on diverse datasets can achieve 95% accuracy without the burden of data annotation and model training on the target parking lot.

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Patient Engagement through Mobile Health Interventions of Diabetes: A Systematic Literature Review 33

Patient Engagement through Mobile Health Interventions of Di...

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33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022

作者： Alhubayshi, Arwa Almutairi, Nawaf Win, Khin Than Information Technology Department Medical Cities Program – MOI Riyadh Saudi Arabia School of Computing and Information Technology University of Wollongong Wollongong Australia Faculty of Public Health and Health Informatics University of Hail Hail Saudi Arabia

This paper aims to review the applicability of the Healthcare Information and Management Systems Society (HIMSS) patient engagement framework and to recognise the patient engagement features presented in current mHealth interventions for improving engagement in diabetes self-management. Scopus, Web of Science and PubMed databases were searched for relevant studies. The selected studies were then analysed using the Healthcare Information and Management Systems Society (HIMSS) patient engagement framework. Twenty engagement features were identified from 29 studies reviewed. Half of the HIMSS patient engagement framework categories were seen in the studies. This review shows that mHealth interventions have achieved the third and fourth phases of the HIMSS patient engagement framework. In addition, this review suggests improvements in patient engagement facilitation, including integrating patient-generated data into electronic health records, collaboration access to the health records, and peer support through online community forums. Finally, the future patient engagement framework is discussed for mHealth technology. Copyright © 2022 Arwa Alhubayshi, Nawaf Almutairi, Khin Than Win.

关键词： mHealth

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Intelligent Traffic Management System using Internet of Things: A Systematic Literature Review 8

Intelligent Traffic Management System using Internet of Thin...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Marcos, Hendra Gernowo, Rahmat Rosyida, Isnaini Nurhayati, Oky Dwi Diponegoro University Doctoral Program of Information System School of Postgraduate Studies Semarang Indonesia Universitas Amikom Purwokerto Department of Informatics Indonesia Universitas Negeri Semarang Faculty of Mathematics and Natural Sciences Department of Mathematics Semarang Indonesia

ISBN: (纸本)9781665453899

The number of vehicles on the road will continue to expand as cities worldwide grow in population. This increases transportation congestion, especially in major cities and especially during rush hour. This phenomenon continuously pushes academics, city authorities, and urban planners to enhance traffic control systems for safety and efficiency. The application of Internet of Things (IoT) in the field of intelligent transportation systems is getting the attention of many parties. A well-controlled transportation management system can reduce congestion, especially at intersections and roads. This study was conducted to observe and study the development of research in transportation management, especially the use of IoT technology. There are 743 documents according to the keywords given related to IMS and ITS, the period 2012 until September 2021. Mapping of these articles, both in terms of publishers, the relationship between authors, and the methods used from keywords, abstracts, and titles, is expected to help the author determine research gaps and future research directions. © 2022 IEEE.

关键词： Internet of things

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Sentiment Analysis using Recurrent Neural Network 1

Sentiment Analysis using Recurrent Neural Network

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1st Bukittinggi International Conference on Education, BICED 2019

作者： Kurniasari, Lilis Setyanto, Arif Program of Informatics Engineering Universitas AMIKOM Yogyakarta Indonesia

This study aims to measure the accuracy of the sentiment analysis classification model using deep learning and neural networks. This study used the algorithm Recurrent Neural Network (RNN) and Word2vec. No previous research has used this model to analyze sentiments written using Indonesian language so that the level of accuracy is unknown. The research began by making a classification model of sentiment analysis. Then, the model was tested through experiments. In this study, They used two classifications (positive and negative). Experiments are carried out using training data sets and the test used data sets sourced from Traveloka theybsite. The result shows that the model presents outstanding results and reaches about 91.9%. © Published under licence by IOP Publishing Ltd.

关键词： Sentiment analysis

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Gene Name Recognition in Gene Pathway Figures Using Siamese Networks

Gene Name Recognition in Gene Pathway Figures Using Siamese ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Micheal Olaolu Arowolo Muhammad Azam Fei He Mihail Popescu Dong Xu Department of Public Health Sciences Health Informatics Program Xavier University of Louisiana New Orleans Louisiana USA Department of Electrical Engineering and Computer Science Bond Life Sciences Center University of Missouri Columbia Missouri USA Department of Health Management and Informatics University of Missouri Columbia Missouri USA

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

The number of scientific papers related to biology is rapidly increasing, and with it, the number of pathway figures containing crucial biological information. However, manually annotating these figures is a time-consuming process, and current optical character recognition (OCR) methods are not effective in identifying gene names within these figures. To improve gene name recognition from pathway figures, we utilized a Siamese network that maps image segments to a library of pictures containing known gene name, like object recognition in many photo applications. We combined the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net) to create a CNN triple loss function. We trained and tested the developed TSPP-Net network using 563 gene images from 45 pathway pictures, 1000 images of alphanumeric characters and gene names from HUGO and KEGG. To benchmark against major available OCR technologies, we used 20 randomly selected curated pathway genes. Additionally, we compared several models, such as VGG19, VGG16, Resnet-50 Resnet-18, Densnet-121, and Xception for the Siamese backbone network model. VGG16 achieved the best performance with an accuracy of 93%, which is much higher than the results obtained using OCR. Our code is publicly available at https://***/MuhammadAzam636/Siamese-Network.

关键词： Image recognition Accuracy Sensitivity Text recognition Biological system modeling Optical character recognition Neural networks Libraries Object recognition Biological information theory

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Fuzzy-Valued Distance Between Fuzzy Numbers Based on a Generalized Extension Principle 19th

Fuzzy-Valued Distance Between Fuzzy Numbers Based on a Gen...

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19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022

作者： Araújo, Juscelino Bedregal, Benjamin Santiago, Regivan Postgraduate Program in Systems and Computation Federal University of Rio Grande do Norte Natal59072-970 Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal59072-970 Brazil

ISBN: (纸本)9783031089701

This paper presents a generalization of the extension principle for fuzzy numbers. The minimum is substituted by a general binary aggregation function. It is used to extend the usual metric for real numbers to fuzzy numbers, generating a new family of fuzzy-valued distances between fuzzy numbers. Then, some conditions on these aggregation functions are studied to hold the fuzzy number properties of the generated distances. © 2022, Springer Nature Switzerland AG.

关键词： Fuzzy rules

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Longmambattn: A Novel Architecture for Enhanced Breast Cancer Risk Prediction Using Variable-Length Longitudinal Mammograms

Longmambattn: A Novel Architecture for Enhanced Breast Cance...

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IEEE International Symposium on Biomedical Imaging

作者： Zhengbo Zhou Dooman Arefan Margarita Zuley Jules Sumkin Shandong Wu Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA Department of Radiology University of Pittsburgh Pittsburgh PA USA Department of Biomedical Informatics and Department of Bioengineering University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Recent advancements in deep learning have shown significant potential in predicting breast cancer risk from mammograms. While leveraging longitudinal changes in mammograms is crucial for accurate risk prediction, existing models often face limitations in capturing these temporal relationships effectively, and they tend to be computationally intensive. To address these challenges, we introduce LongMambAttn, a novel architecture designed to handle variable-length, multitemporal mammograms, thereby enhancing breast cancer risk prediction accuracy. LongMambAttn models the temporal changes of mammograms taken over periods ranging from 1 to 8 years. In an internal case-control dataset of 590 patients, our model demonstrates superior performance in predicting future cancer incidence, surpassing methods that rely only on the most recent prior mammogram as well as other models that incorporate mammograms taken at varying time intervals. Our results show that incorporating longitudinal mammograms via LongMambAttn leads to improved predictive accuracy for breast cancer risk.

关键词： Deep learning Accuracy Computational modeling Biological system modeling Computer architecture Predictive models Mammography Breast cancer Distance measurement Faces

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Finite-time analysis of globally nonstationary multi-armed bandits

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 5481-5536页

作者： Junpei Komiyama Edouard Fouché Junya Honda Stern School of Business New York University Institute for Program Structures and Data Organization Karlsruhe Institute of Technology Germany Department of Systems Science Graduate School of Informatics Kyoto University Japan Center for Advanced Intelligence Project RIKEN Japan

We consider nonstationary multi-armed bandit problems where the model parameters of the arms change over time. We introduce the adaptive resetting bandit (ADR-bandit), a bandit algorithm class that leverages adaptive windowing techniques from literature on data streams. We first provide new guarantees on the quality of estimators resulting from adaptive windowing techniques, which are of independent interest. Furthermore, we conduct a finite-time analysis of ADR-bandit in two typical environments: an abrupt environment where changes occur instantaneously and a gradual environment where changes occur progressively. We demonstrate that ADR-bandit has nearly optimal performance when abrupt or gradual changes occur in a coordinated manner that we call global changes. We demonstrate that forced exploration is unnecessary when we assume such global changes. Unlike the existing nonstationary bandit algorithms, ADR-bandit has optimal performance in stationary environments as well as nonstationary environments with global changes. Our experiments show that the proposed algorithms outperform the existing approaches in synthetic and real-world environments.

关键词： multi-armed bandits adaptive windows nonstationary bandits changepoint detection sequential learning

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Usability, Acceptability, and Implementation of Artificial Intelligence (AI) and Machine Learning (ML) Techniques in Surgical Coaching and Training: A Scoping Review

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Journal of Surgical Education 2024年第7期81卷 994-1003页

作者： Isaac, Samuel Phillips, Michael R. Chen, Kevin A. Carlson, Rebecca Greenberg, Caprice C. Khairat, Saif Carolina Health Informatics Program University of North Carolina at Chapel Hill (UNC) Chapel Hill NC United States School of Nursing University of North Carolina Chapel Hill NC United States Department of Surgery University of North Carolina at Chapel Hill Chapel Hill NC United States Health Sciences Library The University of North Carolina at Chapel Hill Chapel Hill NC United States

OBJECTIVE: To define the current state of peer-reviewed literature demonstrating the usability, acceptability, and implementation of artificial intelligence (AI) and machine learning (ML) techniques in surgical coaching and training. DESIGN: We conducted a literature search with defined inclusion and exclusion criteria. We searched five scholarly databases: MEDLINE via PubMed, Embase via Elsevier, Scopus via Elsevier, Cochrane Central Register of Controlled Trials, and the Healthcare Administration Database via ProQuest. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. RESULTS: Only 4 articles met the inclusion criteria and used standardized methods for performance evaluation with expert observation. We found no literature examining the impact on performance, user acceptance, or implementation of AI/ML techniques used for surgical coaching and training. We highlight the need for qualitative and quantitative research demonstrating these techniques' effectiveness before broad implementation. CONCLUSION AND RELEVANCE: We emphasize the need for research to specifically evaluate performance, impact, user acceptance, and implementation of AI/ML techniques. Incorporating these facets of research when developing AI/ML techniques for surgical training is crucial to ensure emerging technology meets user needs without increasing cognitive burden or frustrating users. © 2024 Association of program Directors in Surgery

关键词： artificial intelligence competency-based education machine learning usability user-centered design

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The association between urinary tract infections and diet: a literature review

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Discover Medicine 2025年第1期2卷 1-17页

作者： Çelik, Haluk Kozan, Esra Caf, Balım Bengisu Çebi, Gizem Koç, Murat Vivosens Inc. San Francisco USA Program of Stem Cell and Tissue Engineering Institute of Graduate Education Istinye University Istanbul Türkiye Department of Health Informatics University of Health Science Hamidiye Institute of Health Sciences Istanbul Türkiye

Urinary tract infections (UTIs) are a significant global health concern, affecting millions worldwide, with particular prevalence among specific vulnerable populations. Women of sexually active age, post-menopausal women, pregnant women, individuals with diabetes, catheterized patients, childs and older indivisuals face heightened risk due to various physiological, hormonal, and anatomical factors. While anatomical abnormalities and behavioral factors contribute to UTI susceptibility, the growing challenge of antibiotic resistance has necessitated exploration of alternative treatment approaches. Non-antibiotic interventions, including probiotics, cranberry products, and natural remedies, are gaining attention for their potential role in UTI prevention and management. Recent studies suggest the efficacy of probiotics and plant extracts in promoting urinary tract health and preventing bacterial colonization. Additionally, research highlights the importance of dietary factors, such as vitamin D and probiotic intake, in reducing UTI susceptibility. This review examines the emerging shift toward non-drug strategies, offering a more comprehensive approach to UTI management while addressing the pressing concern of antibiotic resistance.

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