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11th International Conference on Applied informatics, ICAI 2020

作者： László, Martin Csongor Istenes, Zoltán Tarcsi, Ádám Eötvös Loránd University 3in Research Group Martonvásár Hungary Eötvös Loránd University Faculty of Informatics Data Science and Data Technology Department Hungary

Two of the primary vital signs are breathing and pulse rate. There are various solutions to monitor them, however, all require additional equipment and expertise to use. Smartphones are nowadays at almost every person's arm length, therefore, it could be cost-effective for crowd and personal health screening. Diaphragmatic breathing can be measured with Inertial Measurement Units (IMU). To optimize the breathing detection the smartphone has to be placed in the middle of the epigastric region. The tissue in the region vibrates because of the presence of the abdominal aorta which is also picked up by the IMU. Breathing, which is usually under 1 Hz during sleep can be filtered out with a Bandpass filter. The heartbeat is present as vibrations which can be seen between 1-30 Hz. After filtering, breathing is detectable by a peak detector algorithm and can be differentiated from noises. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Smartphones

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SP3.7 - Patient experiences of using wearable health monitors during cancer treatment: A qualitative study

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British Journal of Surgery 2024年第SUPPLEMENT_8期111卷

作者： Collinson, Sophie Ingram-Walpole, Shannon Jackson, Corie Chan, Anthony Merchant, Zoe Wilson, Anthony Manchester University NHS Foundation Trust Manchester UK Dept of Imaging Informatics and Data Science Faculty of Biology and Health University of Manchester UK

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Matrix Factorization for Cache Optimization in Content Delivery Networks (CDN)

arXiv

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arXiv 2022年

作者： Kamuzora, Adolf Skaf, Wadie Birihanu, Ermiyas Mahmud, Jiyan Salim Kiss, Péter Jursonovics, Tamás Pogrzeba, Peter Lendák, Imre Horváth, Tomáš Telekom Innovation Laboratories Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter str. 1/A Budapest1117 Hungary Deutsche Telekom Berlin Germany

Content delivery networks (CDNs) are key components of high throughput, low latency services on the internet. CDN cache servers have limited storage and bandwidth and implement state-of-the-art cache admission and eviction algorithms to select the most popular and relevant content for the customers served. The aim of this study was to utilize state-of-the-art recommender system techniques for predicting ratings for cache content in CDN. Matrix factorization was used in predicting content popularity which is valuable information in content eviction and content admission algorithms run on CDN edge servers. A custom implemented matrix factorization class and MyMediaLite were utilized. The input CDN logs were received from a European telecommunication service provider. We built a matrix factorization model with that data and utilized grid search to tune its hyper-parameters. Experimental results indicate that there is promise about the proposed approaches and we showed that a low root mean square error value can be achieved on the real-life CDN log data. © 2022, CC BY-NC-ND.

关键词： Recommender systems

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Stress Detection of Children With ASD Using Physiological Signals

Stress Detection of Children With ASD Using Physiological Si...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Sevgi Nur Bilgin Aktaş Pı nar Uluer Buket Coş kun Elif Toprak Duygun Erol Barkana Hatice Kö se Tatjana Zorcec Ben Robins Agnieszka Landowska Department of AI and Data Engineering Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Department of Computer Engineering Faculty of Technology and Engineering Galatasaray University Istanbul Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Yeditepe University Istanbul Turkey University Children&#x2019 s Hospital Skopje North Macedonia School of Computer Science University of Hertfordshire Hatfield UK Department of Software Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

This paper proposes a physiological signal-based stress detection approach for children with autism spectrum disorder (ASD) to be used in social and assistive robot intervention. Electrodermal activity (EDA) and blood volume pulse (BVP) signals are collected with an E4 smart wristband from children with ASD in different countries. The peak count and signal amplitude features are derived from EDA signal and used in order to detect the stress of children based on the previously provided reference baselines. Furthermore, a comparison has been made with the stress values determined using low frequency (LF) and high frequency (HF) values extracted from BVP signal.

关键词： Pediatrics Autism Social robots Signal processing Feature extraction Assistive robots Physiology

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Client Error Clustering Approaches in Content Delivery Networks (CDN)

arXiv

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arXiv 2022年

作者： Belachew, Ermiyas Birihanu Mahmud, Jiyan Kiss, Péter Kamuzora, Adolf Skaf, Wadie Horváth, Tomáš Jursonovics, Tamás Pogrzeba, Peter Lendák, Imre Telekom Innovation Laboratories Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter str. 1/A Budapest1117 Hungary Deutsche Telekom Berlin Germany

Content delivery networks (CDNs) are the backbone of the Internet and are key in delivering high quality video on demand (VoD), web content and file services to billions of users. CDNs usually consist of hierarchically organized content servers positioned as close to the customers as possible. CDN operators face a significant challenge when analyzing billions of web server and proxy logs generated by their systems. The main objective of this study was to analyze the applicability of various clustering methods in CDN error log analysis. We worked with real-life CDN proxy logs, identified key features included in the logs (e.g., content type, HTTP status code, time-of-day, host) and clustered the log lines corresponding to different host types offering live TV, video on demand, file caching and web content. Our experiments were run on a dataset consisting of proxy logs collected over a 7-day period from a single, physical CDN server running multiple types of services (VoD, live TV, file). The dataset consisted of 2.2 billion log lines. Our analysis showed that CDN error clustering is a viable approach towards identifying recurring errors and improving overall quality of service. © 2022, CC BY-NC-SA.

关键词： Errors

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Hybrid ACO and PSO to minimize makespan in job shop scheduling problems

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AIP Conference Proceedings 2024年第1期3222卷

作者： Priljen Habeahan Erna Budhiarti Nababan Mahyuddin K. M. Nasution Informatics Engineering Master Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The lowest time search in the dataset that E. Taillard utilized employs a heuristic approach based on tabu search techniques to get the predicted solution. Glover’s study gives a broad description of tabu search, which is commonly encountered in Taillard’s job shop scheduling difficulties and Widmer et al.’s flow shop sorting challenges. Although tabu search is relatively simple to use and typically yields excellent results, it takes a long time to complete. In this research a hybrid ACO and PSO was carried out to minimize makespan in the Job Shop Scheduling Problem which was used as sourced from benchmark data which is secondary data obtained from E. Taillard “Benchmarks for basic scheduling problems” which consists of job shop matrix data (job × machine) measuring 4 × 4, 5 × 5, 7 × 7, 10 × 10, 15 × 15, 20 × 20, 30 × 15, 30 × 20, 50 × 15 and 50 × 20. Hybrid is carried out by calculating the Pbest value, namely the process position of each job on the machine to get the best solution using the PSO algorithm. Next, calculate the Gbest (Global best) value for the position of each job on the best machine on the entire machine using the PSO algorithm and initialize the ACO parameters using the PBest and Gbest values. The results of research on datasets with sizes 10×10, 15×15, 20×20, 30×15, 30×20, 50×15 and 50×20 produce smaller makespan compared to the lower bound on the dataset with an average minimum makespan improvement value of 1.184.

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Classification of COVID-19 Symptoms Using Multilayer Perceptron

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for Computer science and Mathematics 2023年第4期4卷 99-110页

作者： Azam, Nurulain Nusrah Mohd Ismail, Mohd Arfian Mohamad, Mohd Saberi Ibrahim, Ashraf Osman Jeba, Shermina Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan 26600 Malaysia Center of Excellence for Artificial Intelligence & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak Gambang26300 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medicine and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah 88400 Malaysia Department of Computing Muscat College Muscat Oman

The COVID-19 virus had easily affected people worldwide through direct contact. Individuals diagnosed with positive COVID-19 virus may be affected with many symptoms, such as fever, tiredness, dry cough, difficulty in breathing, sore throat, chest pain, nasal congestion, runny nose, and diarrhea. An individual can also be diagnosed with COVID-19 even when he does not have any symptoms or be in contact with an infected person. data classification was required due to the size of COVID-19 data that will be analyzed for future countermeasures determination. Some problems in data classification occurred due to unorganized data, such as time consumption, human error in complexity of symptom features and the diagnosis process data needed expert knowledge. This study aimed to use the artificial neural network (ANN) approach, which was multilayer perceptron (MLP) to classify the COVID-19 data by using patient symptom data. The MLP process involved data collection, data normalization, MLP design, MLP training, testing, and MLP verification. From the experiments, the MLP method was able to obtain an accuracy rate of 77.10%. In conclusion, the MLP method could classify the COVID-19 data and achieve a high accuracy rate. © 2023 College of Education, Al-Iraqia University. All rights reserved.

关键词： Multilayer neural networks

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PHYSICS-INSPIRED GENERATIVE MODELS IN MEDICAL IMAGING: A REVIEW

arXiv

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arXiv 2024年

作者： Hein, Dennis Bozorgpour, Afshin Merhof, Dorit Wang, Ge Department of Physics KTH Royal Institute of Technology Stockholm Sweden MedTechLabs Karolinska University Hospital Stockholm Sweden Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Department of Biomedical Engineering School of Engineering Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States

Physics-inspired Generative Models (GMs), in particular Diffusion Models (DMs) and Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in medical imaging. This review examines the transformative role of such generative methods. First, a variety of physics-inspired GMs, including Denoising Diffusion Probabilistic Models (DDPMs), Score-based Diffusion Models (SDMs), and Poisson Flow Generative Models (PFGMs and PFGM++), are revisited, with an emphasis on their accuracy, robustness as well as acceleration. Then, major applications of physics-inspired GMs in medical imaging are presented, comprising image reconstruction, image generation, and image analysis. Finally, future research directions are brainstormed, including unification of physics-inspired GMs, integration with Vision-Language Models (VLMs), and potential novel applications of GMs. Since the development of generative methods has been rapid, this review will hopefully give peers and learners a timely snapshot of this new family of physics-driven generative models and help capitalize their enormous potential for medical imaging. Copyright © 2024, The Authors. All rights reserved.

关键词： Medical imaging

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Application of data lake approach for data analytics in e-health issues of architectures and organization 12

Application of data lake approach for data analytics in e-he...

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12th IADIS International Conference e-Health 2020, EH 2020, Part of the 14th Multi Conference on Computer science and Information Systems, MCCSIS 2020

作者： Molnár, Bálint Tarcsi, Ádám Eötvös Loránd University Department of Information Systems Budapest Hungary Eötvös Loránd University Faculty of Informatics Data Science and Engineering Department IoT&Data Innovation Lab Hungary

ISBN: (纸本)9789898704184

By a case study approach, the paper analyzes a project and information ecosystem of the Clinical Research environment within a Medical School at a University. There are two types of data sources, a medical information system based on a transactional database for recording patients and serving as the basis of nursing and treatments of patients and medical and other medical IoT systems (like ECGs, etc). There exists neither data Warehouse solutions nor any other data collection solutions dedicated to utilizing data science methods and tools. The emerging technologies provide opportunities for synergy between the traditional data Warehouse and the most recent data Lake approaches. Therefore, it seems feasible and reasonable to integrate these two architecture approaches to support data analytics on the research of medicine and biomedical areas. © Proceedings of the 12th IADIS International Conference e-Health 2020, EH 2020 - Part of the 14th Multi Conference on Computer science and Information Systems, MCCSIS 2020. All rights reserved.

关键词： data warehouses

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Image-to-Graph Transformation via Superpixel Clustering to Build Nodes in Deep Learning for Graph

Image-to-Graph Transformation via Superpixel Clustering to B...

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IEEE EMBS Conference on Biomedical Engineering and sciences (IECBES)

作者： Hong-Seng Gan Muhammad Hanif Ramlee Asnida Abdul Wahab Wan Mahani Hafizah Wan Mahmud De Rosal Ignatius Moses Setiadi Department of Data Science Universiti Malaysia Kelantan Malaysia School of Biomedical Engineering Universiti Teknologi Malaysia Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Malaysia Informatics Engineering Department Dian Nuswantoro University Indonesia

In recent years, convolutional neural network (CNN) becomes the mainstream image processing techniques for numerous medical imaging tasks such as segmentation, classification and detection. Nonetheless, CNN is limited to processing of fixed size input and demonstrates low generalizability to unseen features. Graph deep learning adopts graph concept and properties to capture rich information from complex data structure. Graph can effectively analyze the pairwise relationship between the target entities. Implementation of graph deep learning in medical imaging requires the conversion of grid-like image structure into graph representation. To date, the conversion mechanism remains underexplored. In this work, image-to-graph conversion via clustering has been proposed. Locally group homogeneous pixels have been grouped into a superpixel, which can be identified as node. Simple linear iterative clustering (SLIC) emerged as the suitable clustering technique to build superpixels as nodes for subsequent graph deep learning computation. The method was validated on knee, call and membrane image datasets. SLIC has reported Rand score of 0.92±0.015 and Silhouette coefficient of 0.85±0.02 for cell dataset, 0.62±0.02 (Rand score) and 0.61±0.07 (Silhouette coefficient) for membrane dataset and 0.82±0.025 (Rand score) and 0.67±0.02 (Silhouette coefficient) for knee dataset. Future works will investigate the performance of superpixel with enforcing connectivity as the prerequisite to develop graph deep learning for medical image segmentation.

关键词： Deep learning Image segmentation Three-dimensional displays Computed tomography data structures Biomembranes Convolutional neural networks

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