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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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Concierge: Towards Accuracy-Driven Bandwidth Allocation for Video Analytics Applications in Edge Network

Concierge: Towards Accuracy-Driven Bandwidth Allocation for ...

引用

IEEE International Conference on Edge Computing (EDGE)

作者： Yuyang Huang Faishal Zharfan Hendrawan Hendrawan Haryadi S. Gunawi Junchen Jiang Department of Computer Science University of Chicago Chicago USA School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798350368499

ISBN: (纸本)9798350368505

When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since important information may not appear all the time. Consequently, each edge AI application’s bandwidth demand is highly dynamic. Thus, a shared edge system should dynamically allocate more bandwidth to the applications in need to reach high accuracy at each moment. However, previous bandwidth allocators are ill-suited because they are agnostic to the timevarying impact of bandwidth on each application’s *** short paper explores a new accuracy-driven approach to bandwidth allocation, which periodically re-allocates bandwidth across edge AI applications based on the sensitivity of each application’s accuracy to its bandwidth share. To examine its practical benefit and technical challenges, we present a concrete accuracy-driven bandwidth allocator called ConciERGE, which exposes a simple yet efficient interface to estimate each application’s sensitivity to a small change in its bandwidth *** run CONCIERGE on state-of-the-art video-analytics applications with real video streams and show its early promise in greatly improving the inference accuracy of video analytics.

关键词： Accuracy Sensitivity Visual analytics Estimation Bandwidth Edge AI Channel allocation

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Machine Learning based Classification Algorithm for Classification of Dengue (Dengue Fever -DF, Dengue Harmonic Fever -DHF & Serve Dengue -SD) 1

Machine Learning based Classification Algorithm for Classifi...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Kapoor, Rajeev Ahuja, Sachin Kadyan, Virender Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9781607685395

Dengue is becoming a burden for society worldwide and become challenge for the world. The main objective of this research paper is to classify dengue at an early stage. The author has adopted a methodology that is divided into five phases namely Data Collection, Transmission of Raw Data in Structural data format, Data Preprocessing, Model building with Train & Test Datasets and Prediction. Further, the proposed prediction model has been trained with the help of 250 data samples from primary sources namely clinical labs and 1000 data sample in the form of synthetic datasets adopted from 20 research papers. The proposed prediction Model has been tested and validated with the help of comparative study of different machine learning algorithms namely Decision Tree, Logistical Regression, Support Vector Machine (SVM), Gaussian Naïve Bayes etc. The proposed model has achieved 97.39% accuracy, 0.98 Precision, 0.97 Recall, 0.97 F1 Score, 0.02 MS and 0.14 RMSE. © The Electrochemical Society

关键词： Dengue Prediction Model Machine Learning Prediction Model Vector Borne Disease

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images

A Systematic Literature Review of XAI-based Approaches on Br...

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Advanced Research in Computing (ICARC), International Conference on

作者： Savin Madapatha Pumudu Fernando Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection.

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Sustainable AI & Agricultural Technologies 4

Sustainable AI & Agricultural Technologies

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4th IEEE International Conference on Automatic Computing and Self-Organizing Systems Companion, ACSOS-C 2023

作者： Sarkadi, Stefan Moraru, Ionut Manning, Louise King's College London Dept. of Informatics London United Kingdom School of Computer Science University of Lincoln Lincoln United Kingdom Lincoln Institute for Agri-food Technology University of Lincoln Lincoln United Kingdom

ISBN: (纸本)9798350337464

Climate change, increase in world population and the war in Ukraine have led nations to put a larger focus on food security, while simultaneously trying to halt declines in biodiversity and reduce risks to human health posed by chemically-reliant farming practices. Achieving these goals simultaneously will require the adoption of novel approaches. One potential solution is accelerating the uptake of AI & Robotic Technologies in the agricultural sector. However, we currently do not have a scientific model for predicting the effects of adopting AI-based agri-tech in complex systems such as the food sector. Self-organising systems offer a holistic and intuitive perspective on complex ecosystems and their resilience to socio-economic pressures at various scales. We propose the application of evolutionary agent-based modelling to explore the emergence of strategies in the food sector considering various levels of abstraction. © 2023 IEEE.

关键词： Biodiversity

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IDeploySmart: A Machine Learning-Based Approach for Predicting the Deployment Type of Identity Servers

IDeploySmart: A Machine Learning-Based Approach for Predicti...

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International Conference on Information technology Research (ICITR)

作者： Mamoru D. L. S. Amjadh Ifthikar Computer Science and Engineering University of Westminster London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Identity servers are integral to modern Information technology (IT) infrastructures, managing secure access to critical applications through authentication and authorization processes. However, determining the optimal deployment strategy for these servers in complex environments remains challenging, especially when considering factors like performance, scalability, and security. This paper introduces IDeploySmart, a machine learning-based system designed to predict the most efficient deployment type of identity servers. By employing Gradient Boosting Regressor, IDeploySmart achieves a prediction accuracy of 92.5%, surpassing industry benchmarks. The system was tested across multiple deployment scenarios, showing significant improvements in system performance, scalability, and security. This paper details the system's design, data preprocessing techniques, model evaluations, and real-world applications, providing insights into how machine learning can enhance identity server deployment strategies in modern IT infrastructure.

关键词： Industries Scalability System performance Data preprocessing Predictive models Boosting Data models Servers Security Information technology

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Composite metamorphic relations for integration testing 22

Composite metamorphic relations for integration testing

引用

8th International Conference on computer technology Applications, ICCTA 2022

作者： Iakusheva, Sofia Khritankov, Anton Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Russia Faculty of Computer Science Dept. of Software Engineering Hse University Russia

ISBN: (纸本)9781450396226

Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper, we propose a novel approach that applies metamorphic testing in integration testing. The main idea is to develop a composite metamorphic relation for the system represented as an acyclic graph. This relation is a logical function of metamorphic relations for the parts of the system (vertices of the graph). It takes into account the features of the parts. Also, it can simplify the search for failure by identifying the subsystem with error. In this paper's theoretical part, we describe an algorithm of relation design. Then, we apply our method to a bioinformatics system for comparative genetic analysis of tissues using production tools. This experiment proves our method can be applied to real-life pipelines and find errors in them. © 2022 ACM.

关键词： Integration testing

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Real-time testing of vision-based systems for AGVs with ArUco markers

Real-time testing of vision-based systems for AGVs with ArUc...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Filus, Katarzyna Sobczak, Lukasz Domanska, Joanna Domanski, Adam Cupek, Rafal Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland

ISBN: (纸本)9781665480451

Automated Ground Vehicles (AGVs) use deep-learning-based vision systems to perceive the surrounding environment and extract relevant information about it. Although deep learning models offer high capabilities, they require large amounts of data to be properly trained and tested. Testing is especially important when off-the-shelf models are used by the AGVs - to examine whether they can meet the demands of complex environments such as the production halls of automated factories. One area of such perception algorithms is object recognition. To test such systems, we propose a solution based on ArUco fiducial markers used for automatic labeling of objects. Our solution can be used to test deep learning systems in real time directly on a robot. Our solution requires minimal interference with the environment and additional infrastructure - the desired objects only need to be marked with a marker printed on a home printer. Therefore, the presented testing procedure can be used for testing of AGVs in real-life environments during a real ride from an actual robot perspective. Data gathered during the online testing can be used for the offline comparison of the accuracy of different deep learning models. Although we focus on the online and offline testing in our study, we also incorporated a marker masking procedure. Therefore, the resulting datasets may also be used for training. © 2022 IEEE.

关键词： Object recognition

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SSANet: Side-by-Side Additive Network for Knee Osteoarthritis Severity Detection from X-Ray Images 4th

SSANet: Side-by-Side Additive Network for Knee Osteoarthrit...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Tewari, Rakhi Bhattacharjee, Debotosh Roy, Hiranmoy Krejcar, Ondrej Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Information Technology RCC Institute of Information Technology Canal South Road Beliaghata Kolkata700015 India Faculty of Informatics and Management Center for Basic and Applied Science University of Hradec Kralove Rokitanskeho 62 Hradec Kralove50003 Czech Republic

ISBN: (纸本)9789819726103

Knee Osteoarthritis (KOA) is becoming one of the most frequent degenerative and irreversible diseases in elderly people. Its early detection and diagnosis is also a difficult and time-intensive task. The recent advances in Machine Learning (ML) and computer Vision (CV) created new paths to automatically detect KOA in the early stages. Detecting and grading the severity of KOA requires a dedicated ML model. This paper proposes a Side-by-Side Additive Network (SSANet)-based model for detecting and grading the severity of KOA. Since KOA affects the knee joint space by narrowing the gap between the joints, we preprocess the X-ray images to detect the Region of Interest (ROI) before the training of the model. From experimental results, it is evidenced that the ROI selection is really improving the detection accuracy of the model. Unlike sequentially connected convolution layer-based deep learning models, the proposed SSANet is based on a parallel convolution layer to reduce the degradation of feature quality. Again, to enhance the edge feature maps, we apply an additive layer that couples the previous layers’ convolutional output. SSANet captures both high-level and low-level features to predict the KOA severity effectively. The presence of a smaller number of layers and fewer numbers of learnable parameters than the existing popular deep learning models, make SSANet a time and space-efficient network. Moreover, the performance of the proposed SSANet in detecting KOA severity is significantly superior to that of other popular state-of-the-art networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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Method of optimizing delay in IoT system using fog calculations 1

Method of optimizing delay in IoT system using fog calculati...

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1st International Workshop on Intelligent and CyberPhysical Systems, ICyberPhyS 2024

作者： Lysenko, Sergii Bondaruk, Oleh Gaj, Piotr Sachenko, Anatoliy Lysenko, Olena Khmelnitskyi National University Instytutska street 11 Khmelnitsky29016 Ukraine Silesian University of Technology ul. Akademicka 2A Gliwice44-100 Poland Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

The article presents the improved the method and means of task planning in IoT infrastructure using fog computing. For the purpose of improvement, the task planning method based on the ant colony algorithm (ACO) was chosen. The concept of priority of task execution by fog nodes was introduced. Formulas were also developed to determine the node that will perform calculations with the least time and delay. The environment for conducting DISSECT-CF experimental studies was also considered. Its modules, abstractions and their settings were described. According to the results of experimental studies, it was determined that the use of an improved method of optimizing the IoT infrastructure with the use of fog computing improved the quality of service (QoS) of the system by reducing the delay of processing tasks. © 2023 Copyright for this paper by its authors.

关键词： Fog

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