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Crowd Safety Sensing (CroSS) for the Post Pandemic Era

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Crowd Safety Sensing (CroSS) for the Post Pandemic Era

2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Almalki, Khalid J Mohzary, Muhammad Choi, Baek-Young Song, Sejun Chen, Yu School of Computing and Engineering University of Missouri - Kansas City MO United States Dept. of Electrical and Computer Engineering Binghamton University BinghamtonNY United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Dept. of Computer Science Jazan University Jazan Saudi Arabia

ISBN: (纸本)9781665423908

Due to its long incubation period, aggressive asymptomatic transmission, and new mutations of the virus, COVID-19 is causing multiple pandemic waves worldwide. Despite recent vaccination, social distancing, and social restriction efforts, false negatives, and dormant positives can make pandemics challenging to restrain. In addition to rapid vaccination, effective contact tracing, mask-wearing, and social distancing are critical for out-break containment and for achieving herd immunity. However, the existing technology solutions, such as contact tracing apps and social-distance sensing, have been met with suspicion due to privacy and accuracy concerns and have not been widely adopted. Without achieving a critical mass of individual users, these personal technologies have been rendered useless. On the other hand, large-scale policy efforts have been complicated, requiring the coordination of federal, state, and local governments and regulation enforcement logistics. However, local communities balance these approaches and are an unrealized, powerful resource to prevent future *** paper proposes a novel Crowd Safety Sensing (CroSS) for building a sustainable safe community cluster against COVID-19 and beyond using affordable Internet of Things (IoT) technologies. CroSS monitors social distancing policies to small, focused communities for accommodating efficient technology penetration, greater accuracy, effective practices, and privacy policy assistance. We implemented a social distancing method and integrated it into an edge-based IoT system. The experimental results show that CroSS detects false-positive social distancing cases. © 2021 IEEE.

关键词： COVID-19

Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

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arXiv 2023年

作者： Merlo, Elena Lagomarsino, Marta Lamon, Edoardo Ajoudani, Arash Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

关键词： Anomaly detection

Camera-Based Remote Photoplethysmography for Physiological Monitoring in Neonatal Intensive Care 7

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Camera-Based Remote Photoplethysmography for Physiological M...

7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021

作者： Sweely, Benjamin Liu, Ziming Wyatt, Tami Newnam, Katherine Qi, Hairong Zhao, Xiaopeng Dept. of Mechanical Aerospace and Biomedical Eng University of Tennessee KnoxvilleTN United States College of Nursing University of Tennessee KnoxvilleTN United States Dept. of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States

ISBN: (纸本)9781665435925

The ability to measure heart rate (HR) noninvasively is important in both a hospital and a home setting due to the role this vital sign plays in health and wellbeing. Despite great advancements and improvements in recent years, safety remains a challenging issue in the neonatal intensive care unit (NICU). Traditional sensors found in the NICU incubators require adhesives and wires. The objective of this article was to develop a wireless, noncontact monitoring system that measures multiple physiological parameters in human faces from a distance using a camera and a single board computer. Experiments were conducted to estimate heart rate. The current practices of measuring HR involve collecting electrocardiogram (ECG) signals from adhesive electrodes placed on various parts of the body or using a pulse oximeter (PO) typically placed on the ear lobe or finger. We developed a monitoring system and compared its results to that from a PO. The monitoring system is low-cost at less than $200. The system has not been shown to exist in literature thus making it a novel implementation. In conclusion, we were able to estimate HR from a distance using a camera-based system. The developed system may have many useful applications, in both clinical and home health settings. © 2021 IEEE

关键词： Adhesives

DIFFUSION-BASED APPROACHES IN MEDICAL IMAGE GENERATION AND ANALYSIS

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arXiv 2024年

作者： al Nomaan Nafi, Abdullah Hossain, Md. Alamgir Rifat, Rakib Hossain Uz Zaman, Md Mahabub Ahsan, Md Manjurul Raman, Shivakumar Department of Biomedical Engineering Islamic University Kushtia7003 Bangladesh Dept. of Computer Science Texas Tech University LubbockTX79409 United States Department of Industrial and Systems Engineering University of Oklahoma Norman Oklahoma73071 United States

Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about the performance of convolutional neural network (CNN) models on original and synthetic datasets. If diffusion-generated samples can help CNN models perform comparably to those trained on original datasets, reliance on patient-specific data for training CNNs might be reduced. In this study, we investigated the effectiveness of diffusion models for generating synthetic medical images to train CNNs in three domains: Brain Tumor MRI, Acute Lymphoblastic Leukemia (ALL), and SARS-CoV-2 CT scans. A diffusion model was trained to generate synthetic datasets for each domain. Pre-trained CNN architectures were then trained on these synthetic datasets and evaluated on unseen real data. All three datasets achieved promising classification performance using CNNs trained on synthetic data. Local Interpretable Model-Agnostic Explanations (LIME) analysis revealed that the models focused on relevant image features for classification. This study demonstrates the potential of diffusion models to generate synthetic medical images for training CNNs in medical image analysis. © 2024, CC BY.

关键词： Convolutional neural networks

Modeling of Personality Traits based on Demographic Data on Multi-Races Samples of Ages from 13 to 50 Years Old: Investigating the Effect of Race on Model 6

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Modeling of Personality Traits based on Demographic Data on ...

6th International Conference on science and Technology, ICST 2020

作者： Paryudi, Iman Winarko, Edi Priyanta, Sigit Nursari, Sri Rezeki Candra Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia Universitas Pancasila Dept. of Informatics Jakarta Indonesia

ISBN: (纸本)9781728194721

The current method to predict personality in personality-based recommender systems is by using Personality Extraction from Text (PET). Since this method has a flexibility weakness, a new method that is based on demographic data is proposed. The objective of this paper is to study the effect of race on the resulted model. In this study, we compare models obtained from International data, which comprise many races, and SE Asian data containing only one race. The results of the study reveal that races do influence the accuracy of the model. The International models are less accurate than those of SE Asian models are. We suspect that this happens because each race has its own personality level. This claim is supported by previous studies on personality differences across nations. These studies have found that personality differences across nations do exist. Therefore, we hypothesize that the more homogenous the data in terms of race, the more accurate the model. © 2020 IEEE.

关键词： Recommender systems

Evolutionary algorithms in biomedical data mining: Challenges, solutions, and frontiers 20

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Evolutionary algorithms in biomedical data mining: Challenge...

2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Urbanowicz, Ryan J. Sipper, Moshe Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA19104-6021 United States Dept. of Computer Science Ben-Gurion University BeerSheva84105 Israel

Quality Controlled Stock Prediction Model 23

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Quality Controlled Stock Prediction Model

23rd International Conference on computer Theory and Applications, ICCTA 2013

作者： Guirguis, Shawket Zada, Fatma Khattab, Tawfik Institute of Graduate Studies Research Dept. Computer Science and Informatics Alexandria Egypt Arab Academy for Science Technology Maritime Transport Admission Registration Alexandria Egypt Arab Academy for Science Technology Maritime Transport Integrated Simulator Complex Alexandria Egypt

ISBN: (纸本)9781479924158

This paper attempts to improve the quality of investing in the highly volatile Egyptian Stock Exchange by combining the concepts of statistical process control and artificial intelligence. Control charts were used to construct a statistically controlled stock market prediction model to support the decision of stock investors. The suggested model is mainly based on the concepts of Case-based Reasoning which is an artificial intelligent methodology that imitates the human problem-solving and reasoning behavior. Hit rate was applied as a performance measure of the quality of prediction for the suggested model. Results of predicting 900 next day stock predictions during January 2012 had a mean absolute prediction error of 2.096 LE and a hit ratio of 67%. After using the quality controlled process, the mean absolute prediction error was reduced to 1.92 L.E. and the hit ratio increased to 72%. © 2013 IEEE.

关键词： Control charts

Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion

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arXiv 2025年

作者： Zeinoddin, Mona Sheikh Islam, Mobarakol Tandogdu, Zafer Shaw, Greg Clarkson, Mathew J. Mazomenos, Evangelos Stoyanov, Danail UCL Hawkes Institute University College London United Kingdom Institute of Health Informatics University College London United Kingdom Dept of Urology University College London Hospitals United Kingdom Dept of Medical Physics & Biomedical Engineering University College London United Kingdom Dept of Computer Science University College London United Kingdom

Accurate dept. and camera pose estimation is essential for achieving high-quality 3D visualisations in robotic-assisted surgery. Despite recent advancements in foundation model adaptation to monocular dept. estimation of endoscopic scenes via self-supervised learning (SSL), no prior work has explored their use for pose estimation. These methods rely on low rank-based adaptation approaches, which constrain model updates to a low-rank space. We propose Endo-FASt3r, the first monocular SSL dept. and pose estimation framework that uses foundation models for both tasks. We extend the Reloc3r relative pose estimation foundation model by designing Reloc3rX, introducing modifications necessary for convergence in SSL. We also present DoMoRA, a novel adaptation technique that enables higher-rank updates and faster convergence. Experiments on the SCARED dataset show that Endo-FASt3r achieves a substantial 10% improvement in pose estimation and a 2% improvement in dept. estimation over prior work. Similar performance gains on the Hamlyn and StereoMIS datasets reinforce the generalisability of EndoFASt3r across different datasets. Our code is available at: Endo-FASt3r. Copyright © 2025, The Authors. All rights reserved.

关键词： Endoscopy

Improved Prediction Of Ligand-Protein Binding Affinities By Meta-Modeling

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arXiv 2023年

作者： Lee, Ho-Joon Emani, Prashant S. Gerstein, Mark B. Dept. of Genetics and Yale University New HavenCT06510 United States Yale Center for Genome Analysis Yale University New HavenCT06510 United States Dept. of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Dept. of Computer Science Yale University New HavenCT06520 United States Dept. of Statistics & Data Science Yale University New HavenCT06520 United States Dept. of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over base models. Our best meta-models achieve comparable performance to state-of-the-art deep learning tools exclusively based on 3D structures, while allowing for improved database scalability and flexibility through the explicit inclusion of features such as physicochemical properties or molecular descriptors. We further demonstrate improved generalization capability by our models using a large-scale benchmark of affinity prediction as well as a virtual screening application benchmark. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain meaningful improvement in binding affinity prediction. © 2023, CC BY-NC-SA.

关键词： Prediction models