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Enhancing Septic Shock Detection through Interpretable Machine Learning

computer Modeling in engineering & Sciences 2024年第12期141卷 2501-2525页

作者： Md Mahfuzur Rahman Md Solaiman Chowdhury Mohammad Shorfuzzaman Lutful Karim Md Shafiullah Farag Azzedin Department of Information and Computer Science King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Science Taif UniversityTaif21974Saudi Arabia College of Applied Arts and Technology Seneca PolytechnicTorontoM2J 2X5Canada Department of Control&Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health *** traditional methods,which often lack transparency in decision-making,our approach focuses on early detection,offering a proactive strategy to mitigate the risks of *** integrating advanced machine learning algorithms with interpretability techniques,our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s ***,we introduce a preference-based matching algorithm to evaluate disease severity,enabling timely interventions guided by the analysis *** innovative integration significantly enhances the effectiveness of our *** leverage a clinical health dataset comprising 1,552,210 Electronic Health Records(EHR)to train our interpretable machine learning models within a cloud computing *** techniques like feature importance analysis and model-agnostic interpretability tools,we aim to clarify the crucial indicators contributing to septic shock *** transparency not only assists healthcare professionals in comprehending the model’s predictions but also facilitates the integration of our system into existing clinical *** validate the effectiveness of our interpretable models using the same dataset,achieving an impressive accuracy rate exceeding 98%through the application of oversampling *** findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management.

关键词： Sepsis prediction machine learning cloud computing

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Fall Detection using NAO Robot Pose Estimation in RoboCup SPL Matches 9

Fall Detection using NAO Robot Pose Estimation in RoboCup SP...

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9th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2022

作者： Zampino, Cristian Biancospino, Flavio Brienza, Michele Laus, Francesco Di Stefano, Gianluca Romano, Rocchina Pennisi, Andrea Suriani, Vincenzo Bloisi, Domenico Daniele Department of Mathematics Computer Science and Economics University of Basilicata Potenza Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

RoboCup is an International robotics initiative whose aim is to promote robotics and AI research. RoboCup's long-term goal is to create a fully autonomous humanoid robot team capable of competing and winning a soccer game against the human World champion team, in compliance with the official rules of FIFA, by 2050. In this paper, we describe a two-step method for action recognition. In the first step, we extract the pose of the robots using a pose detector trained on a novel dataset for pose estimation called UNIBAS NAO Pose Dataset, which is a contribution of this work. In the second step, a Spatial-Temporal Graph Convolutional Network is used for modeling the gameplay, with particular regard to fall-down detection. Experimental results show the effectiveness of our approach in detecting falls for humanoid robots. © 2022 Copyright for this paper by its authors.

关键词： Sports

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Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

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computers, Materials & Continua 2024年第9期80卷 4475-4496页

作者： Xiwang Guo Liangbo Zhou Zhiwei Zhang Liang Qi Jiacun Wang Shujin Qin Jinrui Cao College of Information and Control Engineering Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal UniversityShangqiu476000China Department of Computer Science New Jersey City UniversityJersey CityNJ 07305USA

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle *** on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is *** enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular *** established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,*** a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

关键词： Parallel disassembly line balancing problem multi-product multiskilled workers discrete chemical reaction optimization algorithm

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Natural language processing for the automation of operations in an online bookstore 19

Natural language processing for the automation of operations...

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19th IEEE International Conference on Intelligent computer Communication and Processing Conference, ICCP 2023

作者： Iorga, Elena Dumitriu, Tiberius Gheorghe Asachi' Technical University of Iaşi Faculty of Automatic Control and Computer Engineering Iaşi Romania

ISBN: (纸本)9798350370355

The use of Natural Language Processing Algorithms (NLP) for automation purposes in various applications is frequently encountered recently. Some research managed to identify the dominant emotion from a text using neural networks (ANN), Random Forest (RF) and Support Vectors Machines (SVM) while other studies classified documents with the aim of mechanizing the extraction process of *** paper presents a study on different Natural Language Processing Algorithms (NLP) used for automation of a virtual bookstore. The objective was the application of artificial intelligence algorithms to streamline the necessary processes of an online bookstore or library, taking into consideration the short descriptions and summaries of the books. The aim is to improve the results obtained by supervised algorithms using various techniques such as aggregating unsupervised classifications. In order to boost their understanding of natural language, GPT is used to enhance the dataset by adding additional context. Finally, some of the methods utilized in improving accuracy can be used to create a personalized recommendation system that suits each reader's needs. © 2023 IEEE.

关键词： Automation

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Development of High Accurate Family-use Digital Refractometer Based on CMOS

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Instrumentation 2023年第3期10卷 12-22页

作者： WANG Zhenxing JIA Zhenyuan School of Computer and Information Engineering Shanghai Polytechnic UniversityShanghai 201209China School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic UniversityShanghai 201209China

This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice,which is an important factor affecting human *** laboratory-grade refractometers are expensive and unsuitable for personal use,existing low-cost commercial options lack stability and *** address this gap,we propose a refractometer that replaces the expensive CCD sensor and light source with a conventional LED and a reasonably priced CMOS *** analyzing the output waveform pattern of the CMOS sensor,we achieve high precision with a personal-use-appropriate accuracy of 0.1%.We tested the proposed refractometer by conducting 100 repeated measurements on various fruit juice samples,and the results demonstrate its reliability and *** on a 48 MHz ARM processor,the algorithm can acquire data within 0.2 *** low-cost refractometer is suitable for personal health management and small-scale production,providing an affordable and reliable method for measuring sucrose concentration in fruit *** improves upon the existing low-cost options by offering better stability and *** accessible tool has potential applications in optimizing the sucrose content of fruit juice for better health and quality control.

关键词： Brix Meter High-precision Refractometer Oversampling 1D CMOS Sensor

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Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition (Extended Abstract) 39

Tensor Canonical Correlation Analysis Networks for Multi-vie...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Yang, Xinghao Liu, Weifeng Liu, Wei College of Control Science and Engineering Qingdao China University of Technology Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350322279

Remote sensing (RS) images are frequently observed from multiviews. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle the multiview RS recognition problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition. © 2023 IEEE.

关键词： Convolution

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A Framework for Anything-as-a-Service on a Cloud platform 27

A Framework for Anything-as-a-Service on a Cloud platform

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Alexandrescu, Adrian Mironeanu, Cătălin Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

ISBN: (纸本)9798350337983

There is an increasing need for computational and storage capabilities for complex distributed applications. Existing solutions need to be deployed in an environment that allows for an increase in performance, scalability, and availability. This paper takes looks at the state-of-the-art regarding methods that take existing applications and make them more efficient by using Cloud services. The novelty of the paper consists of a proposed framework for deploying applications on three major Cloud providers (i.e., Amazon's AWS, Google Cloud and Microsoft Azure) and on the OpenStack open-source Cloud. After the main services from the four Cloud providers are identified, different deployment methods are described depending on the Cloud services and on the requirements of the application. Also, some examples of migrations are discussed with reference to specific Cloud provider services. The proposed solution for Anything-as-a-Service (YaaS) is a straightforward framework for taking different types of applications and migrating them to the Cloud. Therefore, the deployed applications benefit from Cloud features such as resource pooling, availability or scalability, while also being wary of the incurring costs. © 2023 IEEE.

关键词： Web services

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UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles

UPB at IberLEF-2023 AuTexTification: Detection of Machine-Ge...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Preda, Andrei-Alexandru Cercel, Dumitru-Clementin Rebedea, Traian Chiru, Costin-Gabriel Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest060042 Romania

This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep learning

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Learning the Dynamic Environment of an Original Game Using Hierarchical Reinforcement Learning Methods 27

Learning the Dynamic Environment of an Original Game Using H...

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Batalan, Vlad Leon, Florin Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iași Iași Romania

ISBN: (纸本)9798350337983

This paper compares the performance of two reinforcement learning algorithms, Q-Learning and MAXQ-0, in learning to play an original game. An extension of MAXQ-0 algorithm, MAXQ-P is introduced, which enhances the variety of the tree nodes with simple, ordered and repetitive nodes. The hierarchical approach provided by MAXQ-P finds the optimal solution faster than the flat Q-Learning approach but converges more slowly. Furthermore, the performance of the MAXQ-P algorithm decreases after a certain number of episodes due to representation error in the weights of the model. To address this issue, the model is periodically tested with an exploration value of 0, and if the model successfully finds the solution, it is stored for future use. This study provides insights into the benefits and drawbacks of using hierarchical reinforcement learning algorithms for complex tasks and highlights the importance of carefully designing and training such algorithms for optimal performance. © 2023 IEEE.

关键词： Reinforcement learning

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Understanding Deep RL agent decisions: a novel interpretable approach with trainable prototypes 4

Understanding Deep RL agent decisions: a novel interpretable...

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4th Italian Workshop on Explainable Artificial Intelligence, XAI.it 2023

作者： Borzillo, Caterina Ragno, Alessio Capobianco, Roberto Department of Computer Control and Management Engineering Sapienza University of Rome RM Rome00185 Italy Sony AI

Deep reinforcement learning (DRL) models have shown great promise in various applications, but their practical adoption in critical domains is limited due to their opaque decision-making processes. To address this challenge, explainable AI (XAI) techniques aim to enhance transparency and interpretability of black-box models. However, most current interpretable systems focus on supervised learning problems, leaving reinforcement learning relatively unexplored. This paper extends the work of PW-Net, an interpretable wrapper model for DRL agents inspired by image classification methodologies. We introduce Shared-PW-Net, an interpretable deep learning model that features a fully trainable prototype layer. Unlike PW-Net, Shared-PW-Net does not rely on pre-existing prototypes. Instead, it leverages the concept of ProtoPool to automatically learn general prototypes assigned to actions during training. Additionally, we propose a novel prototype initialization method that significantly improves the model's performance. Through extensive experimentation, we demonstrate that our Shared-PW-Net achieves the same reward performance as existing methods without requiring human intervention. Our model's fully trainable prototype layer, coupled with the innovative prototype initialization approach, contributes to a clearer and more interpretable decision-making process. The code for this work is publicly available for further exploration and applications. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Reinforcement learning

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