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PERSISTENT OBSTRUCTION THEORY FOR A MODEL CATEGORY OF MEASURES WITH APPLICATIONS TO data MERGING

Transactions of the American Mathematical Society Series B 2021年第1期8卷 1-38页

作者： Smith, Abraham D. Bendich, Paul Harer, John Department of Mathematics Statistics and Computer Science University of Wisconsin-Stout Menomonie 54751 WI United States Geometric Data Analytics Inc. Durham 27707 NC United States Department of Mathematics Duke University Durham 27708 NC United States

Collections of measures on compact metric spaces form a model category (“data complexes”), whose morphisms are marginalization integrals. The fibrant objects in this category represent collections of measures in which there is a measure on a product space that marginalizes to any measures on pairs of its factors. The homotopy and homology for this category allow measurement of obstructions to finding measures on larger and larger product spaces. The obstruction theory is compatible with a fibrant filtration built from the Wasserstein distance on measures. Despite the abstract tools, this is motivated by a widespread problem in data science. data complexes provide a mathematical foundation for semi-automated data-alignment tools that are common in commercial database software. Practically speaking, the theory shows that database JOIN operations are subject to genuine topological obstructions. Those obstructions can be detected by an obstruction cocycle and can be resolved by moving through a filtration. Thus, any collection of databases has a persistence level, which measures the difficulty of JOINing those databases. Because of its general formulation, this persistent obstruction theory also encompasses multi-modal data fusion problems, some forms of Bayesian inference, and probability couplings. © 2021 by the authors.

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Review on 3D printers

Review on 3D printers

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作者： Hoshang Kolivand Luke Bailey Shiva Asadianfam Amjad Rehman Department of Computer Science Liverpool John Moores University Department of Computer Engineering Islamic Azad University Qom Branch Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University

In this paper of the report,it is all the different research in the that has been done to understand more about the subject of 3D printers and the health *** this paper all the different research that was done was information that is very closely related to subjects previously stated,although these papers are very professional and informative the focus of this part of the paper is to better show all the different gaps in the information and what it could be missing that could possibly be showed in this paper that they have missed out on.

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Comparative Performance of Intensive Care Mortality Prediction Models Based on Manually Curated Versus Automatically Extracted Electronic Health Record data

SSRN

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SSRN 2023年

作者： Jagesar, Ameet R. Otten, Martijn Dam, Tariq A. Biesheuvel, Laurens Dongelmans, Dave A. Brinkman, Sylvia Thoral, Patrick J. Francois-Lavet, Vincent Girbes, Armand R.J. de Keizer, N.F. de Grooth, Harm-Jan S. Elbers, P.W.G. Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Department of Intensive Care Medicine Amsterdam UMC Universiteit van Amsterdam Amsterdam Netherlands Quantitative Data Analytics Group Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam Netherlands foundation Amsterdam Netherlands Netherlands

Introduction: Benchmarking intensive care units for audit and feedback is frequently based on comparing actual mortality versus predicted mortality. Traditionally, mortality prediction models rely on a limited number of input variables and significant manual data entry and curation. Using automatically extracted electronic health record data may be a promising alternative. However, adequate data on comparative performance between these approaches is currently ***: The freely available and highly granular AmsterdamUMCdb intensive care database was used to construct a baseline APACHE IV in-hospital mortality model based on data typically available through manual data curation. Subsequently, new in-hospital mortality models were systematically developed and evaluated. New models differed with respect to the extent of automatic variable extraction, classification method, recalibration usage and the size of collection ***: A total of 13 models were developed based on data from 5,077 admissions divided into a train (80%) and test (20%) cohort. Adding variables or extending collection windows only marginally improved discrimination and calibration. An XGBoost model using only automatically extracted variables, and therefore no acute or chronic diagnoses, was the best performing automated model with an AUC of 0.89 and a Brier score of ***: Performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data is similar. Importantly, our results suggest that variables typically requiring manual curation, such as diagnosis at admission and comorbidities, may not be necessary for accurate mortality prediction. These proof-of-concept results require replication using multi-centre data. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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Solar-Powered IoT Charging System for ESP32 in Organic Rice Field

Solar-Powered IoT Charging System for ESP32 in Organic Rice ...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Jarinya Thaloy Parinya Jansengrat Weena Janratchakool Saranpong Nooyimsai Jaturapith Krohkaew Padma Nyoman Crisnapati Yamin Thwe Department of Big Data Management and Analytics Rajamagala University of Technology Thanyaburi Pathum Thani Thailand Department of Computer Science Rajamagala University of Technology Thanyaburi Pathum Thani Thailand Department of Information Technology Rajamangala University of Technology Srivijaya Nakhon Si Thammarat Thailand Department of Information System Institute of Technology and Business STIKOM Bali Bali Indonesia Department of Big Data Management and Analytics Rajamangala University of Technology Thanyaburi Pathum Thani Thailand

In order to develop an integrated power system module for IoT ESP32 monitoring applications, this research technique explores the crucial elements of solar panels, solar charger modules, voltage dividers, DC step-up converters, and PCB layout integration. By lowering dependency on grid electricity, the careful selection and integration of these components enable the dependable and ecologically friendly functioning of monitoring systems, especially in remote or off-grid sites. data gathering, voltage monitoring, and energy usage analysis are all highlighted in the thorough agricultural case study. The outcomes show how well the proposed PCB design satisfies the need for sustainable electricity and offers insight into its potential to enhance energy management in agricultural settings. For IoT-based monitoring systems, this study sets the groundwork for future developments and enhancements in sustainable energy solutions.

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Gamification and Serious Games for Cybersecurity Awareness and First Responders Training: An overview

TechRxiv

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TechRxiv 2023年

作者： Batzos, Zisis Saoulidis, Theocharis Margounakis, Dimitrios Fountoukidis, Eleftherios Grigoriou, Elisavet Moukoulis, Achilleas Sarigiannidis, Antonios Liatifis, Athanasios Karypidis, Paris-Alexandros Bibi, Stamatia Filippidis, Adam Kazanidis, Ioannis Nifakos, Sokratis Kasig, Timo Heydari, Mohammad Mouratidis, Haralambos Sidroco Holdings Ltd. Nicosia Cyprus Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Computer Science International Hellenic University Kavala Campus Greece Massive Dynamic Sweden Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Institute for Analytics and Data Science University of Essex United Kingdom

Gamification has been characterized as the usage of video game elements in non-game environments. Gamification in cybersecurity awareness training can lead to more engaging interactions, greater pleasure, and increased security. Serious games are becoming increasingly popular since they allow learning in a natural setting. Although many of the principles covered in cybersecurity awareness training are universal, many forms of training fail because they rely on rote learning and do not require users to think about security concepts. The primary goal of this research is to conduct a review of the literature on cybersecurity awareness training methodologies and frameworks, as well as serious games that may be used to raise awareness among people, organizations, and first responders. © 2023, CC BY.

关键词： Human computer interaction

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Domain-Enhanced Attention Enabled Deep Network for Behavioral Health Identification from 911 Narratives

Domain-Enhanced Attention Enabled Deep Network for Behaviora...

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IEEE International Conference on Big data

作者： Abm Adnan Azmee Martin Brown Md Abdullah Al Hafiz Khan Dominic Thomas Yong Pei Monica Nandan Department of Computer Science Kennesaw State University Marietta USA Data Science and Analytics Kennesaw State University Marietta USA Information Systems Kennesaw State University Marietta USA Social Work and Human Services Kennesaw State University Marietta USA

Appropriately addressing behavioral health challenges is a problem in the United States. Behavioral health challenges can manifest in the form of crimes committed by individuals with mental illness, social and domestic violence, etc., which may result in emergency calls to first responders. For example, in domestic violence situations, involved parties may need various supports and resources if no criminal charges are filed. In these instances, when a 911 call is made, it mobilizes the entire first responder system (e.g., police, fire, EMT), each of whom write their respective incident reports. A majority of the calls that first responders are addressing are social and behavioral in nature, which they are often ill-equipped to handle. The recurring nature of these calls and limited available resources, first responders encounter challenges in providing appropriate responses to community residents. Therefore, early identification of these cases through incident reports could guide service providers and first responders to take appropriate actions on behalf of the community resident. However, the presence of noise and contextual variability within these unstructured public narrative reports makes it challenging for identifying social and behavioral health based cases. To address this challenge, in this study, we propose a novel model to analyze the first responders’ public narratives: the model learns contextual features automatically from the public narratives by employing self-attention techniques and incorporates automatically extracted domain knowledge by comparing expert-given keywords to represent document features to identify behavioral health cases. Unlike conventional approaches, integrating domain expert knowledge with the contextual representation of the public narrative report enriches the model’s understanding of social and behavioral health cues and enhances its overall performance. Extensive evaluation showcases the efficacy and effectiveness of our pro

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Intelligent Energy Management for Electric Vehicles: Fuzzy Logic and Neural Network Integration

Intelligent Energy Management for Electric Vehicles: Fuzzy L...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： T Thirumalaikumari D Lita Princy S Uma R Sadaieswaran P Malathi N Legapriyadharshini Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Master of Computer Applications Meenakshi College of Engineering Chennai Tamil Nadu India Department of Computer Science Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mahabalipuram Tamil Nadu India Department of Computer Applications Tagore College of arts and science Chromepet Chennai India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Application Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This study suggests a novel methodology for intelligent energy management in electric vehicles (EVs) through the integration of neural networks and fuzzy logic. Achieving enhanced energy efficiency for electric vehicles is a critical concern that is tackled in this research through the integration of neural networks' decision-making and pattern recognition functionalities with fuzzy logic's adaptability and learning capabilities. The proposed method seeks to optimize energy consumption, enhance overall performance, and extend driving range. The integrated system is subjected to rigorous testing using a meticulously planned experimental setup and simulation environment, which enables it to surpass the performance of more conventional approaches to energy management. The results emphasize the potential of the novel methodology to improve environmentally sustainable transportation options and propel the development of electric vehicle technology. This study not only offers valuable insights into efficient energy management but also establishes the groundwork for subsequent developments in the domain.

关键词： Fuzzy logic Roads Neural networks Transportation Propulsion Electric vehicles Energy efficiency Pollution measurement Energy management systems Testing

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Long-term mortality in ICU patients with hematological malignancies: Impact of organ support duration and ICU length of stay

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Journal of Critical Care 2025年 89卷 155122-155122页

作者： Otten, Martijn van Kempen, Bob J.H. van der Woude, Brittney Dam, Tariq A. Gigengack, Rolf K. Müller, Marcella C.A. Girbes, Armand R.J. Biemond, Bart J. de Grooth, Harm-Jan Elbers, Paul W.G. Department of Intensive Care Medicine Center for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam Public Health Amsterdam institute for Immunology and Infectious Diseases Amsterdam UMC Vrije Universiteit University of Amsterdam Amsterdam Netherlands Quantitative Data Analytics Group Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam Netherlands Department of Intensive Care Medicine UMC Utrecht Utrecht Netherlands Department of Hematology Amsterdam UMC Netherlands

Background: Patients with hematological malignancies face unique challenges due to immunosuppression and treatment-related complications. This study aims to evaluate long-term survival probabilities for ICU patients with hematological malignancy as a function of length of stay in the ICU and organ support duration, compared to non-hematological ICU patients. Methods: In this retrospective multicenter cohort study, patients with hematological malignancies were matched with four controls based on APACHE IV scores and year of admission. Mortality at 1 year following ICU admission was assessed using landmark analyses, stratified by length-of-stay and organ support duration. Results: A total of 2.775 admissions were included (555 hematological malignancy and 2.220 matched controls). One-year mortality for patients with hematological malignancy (matched controls) with ICU length of stay of at least 1, 7 and 14 days was 64 % (43 %), 72 % (42 %), and 74 % (34 %). For those receiving 1, 7 and 14 days of renal replacement therapy 1-year mortality was 79 % (60 %), 79 % (52 %), and 85 % (55 %). For those receiving blood transfusion for 1, 7 and 14 days 1-year mortality was 74 % (47 %), 81 % (51 %), and 86 % (50 %). For those receiving triple organ support for 1,7 and 14 days 1-year mortality was 84 % (54 %), 84 % (51 %), and 94 % (56 %). Conclusions: One-year mortality among patients with hematological malignancy compared to matched controls was higher at the first day of admission and stayed higher for patients who remained in the ICU. The observed one-year mortality seemed to increase with initiation of prolonged renal replacement therapy, blood transfusion and combination of at least 3 organs supported, but not for the other forms of organ support. © 2025

关键词： Hematology Intensive care medicine Length of stay Mortality Organ support Survival

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Detection of Behavioral Health Cases from Sensitive Police Officer Narratives

Detection of Behavioral Health Cases from Sensitive Police O...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Martin Brown Md Abdullah Al Hafiz Khan Dominic Thomas Yong Pei Monica Nandan Department of Data Science and Analytics Kennesaw State University Marietta USA Computer Science Kennesaw State University Marietta USA Information Systems Kennesaw State University Marietta USA Social Work and Human Services Kennesaw State University Marietta USA

Early detection of and intervention in behavioral health cases, including mental health, is crucial to prevent harm to one’s self and others. Police reports generated by officers on duty or in response to 911 calls remain an untapped resource for identifying such incidents. To expedite the detection process, we propose a workflow that involves collaboration between experts to manually annotate cases and correct model predictions. This approach can improve both initial annotation and model performance. Therefore, we advocate for the incorporation of manual annotations from experts, natural language processing (NLP), active learning, and advanced machine learning techniques to detect behavioral health cases within police reports. The experimentation suggests that a CNN-LSTM model achieves the best performance with an accuracy of 86.67% and an F1-score of 0.82 in detecting behavioral health issues.

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