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Fusing clinical and image data for detecting the severity level of hospitalized symptomatic COVID-19 patients using hierarchical model

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Research on Biomedical engineering 2023年第1期39卷 209-232页

作者： Ershadi, Mohammad Mahdi Rise, Zeinab Rahimi Department of Industrial Engineering and Management Systems Amirkabir University of Technology No. 350 Hafez Ave Valiasr Square Tehran*** Iran

Purpose: Based on medical reports, it is hard to find levels of different hospitalized symptomatic COVID-19 patients according to their features in a short time. Besides, there are common and special features for COVID-19 patients at different levels based on physicians’ knowledge that make diagnosis difficult. For this purpose, a hierarchical model is proposed in this paper based on experts’ knowledge, fuzzy C-mean (FCM) clustering, and adaptive neuro-fuzzy inference system (ANFIS) classifier. Methods: Experts considered a special set of features for different groups of COVID-19 patients to find their treatment plans. Accordingly, the structure of the proposed hierarchical model is designed based on experts’ knowledge. In the proposed model, we applied clustering methods to patients’ data to determine some clusters. Then, we learn classifiers for each cluster in a hierarchical model. Regarding different common and special features of patients, FCM is considered for the clustering method. Besides, ANFIS had better performances than other classification methods. Therefore, FCM and ANFIS were considered to design the proposed hierarchical model. FCM finds the membership degree of each patient’s data based on common and special features of different clusters to reinforce the ANFIS classifier. Next, ANFIS identifies the need of hospitalized symptomatic COVID-19 patients to ICU and to find whether or not they are in the end-stage (mortality target class). Two real datasets about COVID-19 patients are analyzed in this paper using the proposed model. One of these datasets had only clinical features and another dataset had both clinical and image features. Therefore, some appropriate features are extracted using some image processing and deep learning methods. Results: According to the results and statistical test, the proposed model has the best performance among other utilized classifiers. Its accuracies based on clinical features of the first and second datasets are 92%

关键词： COVID-19

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Joint Optimization of Production, Distribution and Maintenance for Multi-Site Refinery System with Single Machine and Multiple Parallel Storage Tanks 8

Joint Optimization of Production, Distribution and Maintenan...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Diaz, Kamar Sefiani, Naoufal Kammoun, Mohamed Ali Hajej, Zied Department of industrial Engineering and Maintenance University of Lorraine France Department of Mechanical Engineering Abdelmalek Essaâdi University Computer Engineering Production and Maintenance Laboratory Morocco Research Laboratory in Engineering Innovation and Management of Industrial Systems Metz France Department of Mechanical Engineering Abdelmalek Essaâdi University Research Laboratory in Engineering Innovation and Management of Industrial Systems Tangier Morocco Department of industrial Engineering and Maintenance University of Lorraine Computer Engineering Production and Maintenance Laboratory Metz France

ISBN: (纸本)9798350382150

This paper deals with a problem of optimizing production, logistics and maintenance in a multi-site, multi-demand fuel refinery. Each site is equipped with one refining machine and parallel storage tanks. The objective is to meet customer demand while maintaining a given service level. The process starts with an economic production plan for each site during each production period. A distribution plan is then implemented to improve cooperation between sites and reduce the risk of shortages in the case of unsatisfied demand. Given the impact of production rates on the refining machines degradation, a preventive maintenance strategy is developed for each machine. In addition, a preventive maintenance strategy for storage tanks is established based on experimental results showing the correlation between production rates and storage tank failure rates due to corrosion. A numerical example is presented to illustrate the robustness of the proposed model. ©2024 IEEE.

关键词： Preventive maintenance

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Intelligent Turning Cyber-Physical systems Modeling using SysML 50

Intelligent Turning Cyber-Physical Systems Modeling using Sy...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Dey, Prithbey Raj Enke, David Buchely, Mario Department of Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla United States Department of Materials Science and Engineering Missouri University of Science and Technology Rolla United States

ISBN: (纸本)9781665464543

Cyber-Physical systems (CPS) support industrial automation that incorporates people, hardware, signal, computation, and control using networking to achieve desired results. The complex automated CPS design demands a standard and comprehensive approach to appropriately identify the system requirements, define the architecture, and model the relationships among the software and hardware components. systems Modeling Language (SysML) provides the capability for a comprehensive modeling to capture the desired design requirements in the systems architecture. SysML enables performance estimation of the model by analyzing constraints while identifying interactions among the components through various behavioral diagrams. In this paper, SysML is applied to the design and development of an intelligent cyber-physical system to automate an industrial turning operation to facilitate optimal operating conditions, maintainability, and scalability. © 2024 IEEE.

关键词： Intelligent systems

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Minimizing D.C. Metrorail Disturbances: A Multi-Objective Decision Making Analysis Framework for Strategic Resilience

Minimizing D.C. Metrorail Disturbances: A Multi-Objective De...

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2024 systems and Information engineering Design Symposium, SIEDS 2024

作者： Foley, Claire Walker, Amaya Yang, Ziqian Mohammed, Salah Dano, Eric Jachimowicz, Adam George Washington University Department of Engineering Management and Systems Engineering United States Washington Metropolitan Area Transit Authority United States

ISBN: (纸本)9798350385144

The Washington Metropolitan Area Transit Authority (WMATA) faces frequent disruptions in its metro system, impacting operational efficiency and rider satisfaction. WMATA's disruptions are categorized into 12 incident levels from A1 (most severe) to D3 (least severe). This research introduces an analytical framework to determine which incident level's disturbances should be mitigated to minimize both downtime and costs due to disturbances. The number of occurrences and total downtime for each incident level were found through data processing on the 18 months of logged disturbances. Multiple WMATA Subject Matter Experts (SMEs) conducted an Analytic Hierarchy Process (AHP) assessment to determine the relative disturbance costs. These costs were subsequently converted into actual costs for all incident levels, leveraging known D2 incident costs. An exploratory interview was performed with WMATA SMEs to obtain weights for the importance of disturbance downtime relative to the cost. The resulting weights were applied to separate normalized downtime and cost scores to yield a ranked list of incident levels that need to be prioritized to best reduce disturbance downtime and cost. These findings directly allow WMATA to prioritize disruption mitigation and ensure operational continuity, benefiting WMATA and its stakeholders. © 2024 IEEE.

关键词： Data handling

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Assessing Blockchain-Based Platforms for Organic Food Supplychain

Assessing Blockchain-Based Platforms for Organic Food Supply...

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2024 IEEE International Conference on Technology management, Operations and Decisions, ICTMOD 2024

作者： Moustapha, Mariem Mohamed Masmoudi, Malek Elhoseny, Mohamed University of Sharjah Department of Industrial Engineering and Engineering Management Sharjah United Arab Emirates IEEM University of Sharjah Department of Industrial Engineering and Engineering Management Sharjah United Arab Emirates University of Sharjah Department of Information Security and Intelligent Systems Sharjah United Arab Emirates

ISBN: (纸本)9798350367355

This research aims to find the best open-source blockchain platforms for use in agriculture, providing businesses with a solid foundation and helping them avoid the challenges of starting from scratch. The study began by reviewing 18 blockchain platforms and narrowing them down to 9 open-source options. It then used a combination of the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate the platforms. Expert opinions and a thorough literature review focused on four key criteria: Transparency and Traceability, Security and Data Integrity, Cost Efficiency, and User Experience and Accessibility. The AHP analysis showed that Transparency and Traceability were the most important factors, while the TOPSIS evaluation highlighted Hyperledger Sawtooth as the top platform, excelling in all areas. The study underscores the importance of a multi-criteria decision-making approach in selecting blockchain solutions for agriculture, showing how open-source platforms can enhance efficiency, ensure data integrity, and build trust with consumers. © 2024 IEEE.

关键词： agriculture AHP blockchain organic farming industry TOPSIS

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QoS-Aware Decentralized Trustworthy Forensic Evidence management Framework Using the Internet of Vehicle Things (IoVT) 3rd

QoS-Aware Decentralized Trustworthy Forensic Evidence Manage...

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3rd International Conference on Advanced Computing and Applications, ICACA 2024

作者： Das, Puja Singh, Moutushi Roy, Deepsubhra Guha Department of Computer Science and Business Systems KPRIET Uthupalayam641407 India Department of Information Technology Institute of Engineering and Management IEM Centre of Excellence for InnovAI University of Engineering and Management Kolkata India Institute of Engineering and Management IEM Centre of Excellence for Cloud Computing and IoT University of Engineering and Management Kolkata700091 India

ISBN: (纸本)9789819747986

Vehicles are becoming physical computer systems that connect with other cars and collect data from numerous pieces of equipment. These improvements contribute to developing smart and connected automobiles that will supply valuable data to owners, producers, insurance firms, and repair network operators for various uses. In post-accident circumstances, especially for auto vehicles (self-driving cars), using transportation data might help identify the guilty party. Verification, accessibility, secrecy, and dependability are all privacy needs for a vehicular ad hoc network. These criteria are linked to critical attacks that make operating a network difficult. However, to access different types of information in automobiles, we proposed a permissioned blockchain framework to organize the acquired transportation information among the numerous parts concerned. We incorporate the public key exchange protocol on the vehicle into the projected blockchain-based forensic system to ensure participation identification and anonymity. The proposed system offers a deep learning-based predictive incident modeling with blockchain for the incident data stored in the blockchain. Moreover, several methods have been combined in forensic management with blockchain to establish an auto-evaluation scheme. As a result, we extensively evaluated, analyzed, and discussed the works and contributions of skilled researchers to uncover new avenues in an ongoing and prospective study. The main goal of this research might be to explore the future potential for smart and connected automobiles using blockchain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vehicular ad hoc networks

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Fully Interpretable Optimized Weights-Based Methodology for Adaptive Machine Fault Components Extraction

Fully Interpretable Optimized Weights-Based Methodology for ...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Hou, Bingchang Wang, Dong Xie, Min Department of Industrial Engineering and Management School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Systems Engineering City University of Hong Kong Hong Kong

ISBN: (纸本)9783031494208

Rotating machines are widely used in various domains and its condition monitoring can help to gain more economic profits and prevent unexpected accidents. Vibration signals usually contain sufficient health information, but they are also severely contaminated by heavy background noises including random noise and fixed fundamental vibration components. Therefore, it is of vital importance to effectively extract fault components for effective machine condition monitoring. Considering that the fault components of rotating machines usually exist in some narrow frequency bands, blind bandpass filtering methods such as fast kurtogram, blind deconvolution, and adaptive signal decomposition have been successively proposed. However, it is observed that these methods might be easily influenced by random impulsive noise and low-frequency components. Moreover, these methods also cannot simultaneously extract fault components distributed in different frequency bands. To solve these problems, a new machine fault components extraction methodology based on fully interpretable optimized weights is proposed. The interpretable optimized weights are generated by convex optimization by using healthy and faulty vibration signals, and they could provide sufficient frequency information of fault components. Simulated and experimental vibration signals demonstrate the effectiveness and superiority of the proposed method in solving the aforementioned problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convex optimization

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Image Semantic Segmentation Used in Automatics 17

Image Semantic Segmentation Used in Automatics

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17th International Conference on engineering of Modern Electric systems, EMES 2023

作者： Spoiala, Viorica University of Oradea Control Systems Engineering and Management Department Oradea Romania

ISBN: (纸本)9798350310634

This paper presents the importance of segmentation methods used in the object analysis in computer vision and image proccessing field, in the automated processes. Image segmentation is a crucial stage of image processing in the context of industrial automation. The detection of objects is one of the main purposes of image segmentation, and in order to do this, it is necessary to perform the segmentation and extraction of the image's significant elements in advance. Putting pixels into semantic classifications is the foundation of semantic segmentation, this being a deep learning method. In order to apply segmentation in computer vision of automated processes the first step could be to label the objects and then to train a neural network to apply this labels to all the similar objects detected in similar images. In this paper some images were segmented by using labels and deep learning methods. After the theoretical aspects are presented, then some examples are made in Matlab environment. © 2023 IEEE.

关键词： Automation

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From Crisis to Strategy and Action: A Qualitative Study to Understand the Impact of COVID-19 on Implementing Knowledge management in Organizations

From Crisis to Strategy and Action: A Qualitative Study to U...

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IISE Annual Conference and Expo 2024

作者： Naderi, Iman Dickerson, Deborah E. Ashrafi, Maryam Grado Department of Industrial and Systems Engineering Virginia Tech BlacksburgVA24061 United States Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Polytechnic Tehran Iran

ISBN: (纸本)9781713877851

As Knowledge management (KM) offers significant benefits for organizations, its implementation is crucial for them to make sure they take advantage of those benefits. However, risks and uncertainties are undeniable when working towards a goal on almost every project. As a result, when approached as a project, Knowledge management Implementation (KMI) would require taking Risk management (RM) into account. With the changes that organizations underwent during the Coronavirus (COVID-19) Pandemic (CP), it is clear that managing risks when it comes to such a situation is exceptionally important. To address this issue, the paper utilizes an RM approach for KMI projects within organizations, with special attention to the risks particularly caused by the CP. More specifically, according to the RM standard by Project management Institute (PMI), to address each identified risk of the project, this paper suggests possible effective strategies and actions to deal with it. As a result, organizations will have a better understanding of how CP can affect KMI in their organizations and what strategies and actions would be helpful in dealing with them. Doing so can help organizations ensure that they can implement KM in their organizations more smoothly and use the KM benefits provided for them. Accordingly, it has been concluded that under the CP environment, KMI project managers should embrace a proactive approach to RM. To accomplish this, they should strive to minimize the negative repercussions of the threats they face and maximize the benefits of the opportunities they are provided. © IISE Annual Conference and Expo *** rights reserved.

关键词： Coronavirus

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Informative Path Planning for Nano-Surgical Robot Adaptive Drug Delivery 59

Informative Path Planning for Nano-Surgical Robot Adaptive D...

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59th Annual Conference on Information Sciences and systems, CISS 2025

作者： Alla, Sujatha Bheesetty, Nagesh Park, Hyoshin John Old Dominion University Engineering Management and Systems Engineering Norfolk United States Old Dominion University Department of Data Science Norfolk United States

ISBN: (纸本)9798331513269

Nano-surgical robots are transforming precision medicine by enabling minimally invasive procedures at a microscopic scale. Informative path planning for these robots is critical for ensuring safety, efficiency, and high precision in navigating sensitive anatomical and uncertain environments. This study adapts the RRT*-IT (Rapidly Exploring Random Tree-Information Theoretic) algorithm, traditionally utilized in robotics, to address the unique challenges faced by nano-surgical robots. The proposed methodology offers significant advancements in sub-millimeter precision navigation, reducing collision risks and enhancing procedural efficiency. The adaptive path planning system optimizes a trade-off between collision avoidance and information gain while minimizing surgical trajectory. The algorithm dynamically adjusts paths by incorporating probabilistic models for obstacle detection and uncertainty reduction, accommodating changes in tissue density, obstacle dimensions, and operational constraints. The performance of proposed approach are validated through various scenarios involving dynamic uncertain environments. The findings hold promise for broader applications in automated drug delivery setting a foundation for intelligent nano-robotic systems in healthcare. © 2025 IEEE.

关键词： Robot programming

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