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Optimizing Predictive Models in Healthcare Using Artificial Intelligence: A Comprehensive Approach with a COVID-19 Case Study 12th

Optimizing Predictive Models in Healthcare Using Artificial ...

引用

12th Ecuadorian Conference on information and Communication Technologies

作者： Astudillo Leon, Juan Pablo Chamorro, Kevin Ballaz, Santiago J. Yachay Tech Univ Sch Math & Computat Sci Urcuqui 100115 Ecuador Yachay Tech Univ Commun Networks & Intelligent Serv Res Grp ComNet Urcuqui 100115 Ecuador Yachay Tech Univ Sch Biol Sci & Engn Urcuqui 100115 Ecuador

ISBN: (纸本)9783031754302;9783031754319

The ongoing global healthcare challenges underscore the need for accurate and efficient predictive tools capable of assessing disease severity. This study introduces a broadly applicable artificial intelligence (AI) framework designed to enhance disease severity predictions, focusing on analyzing feature importance and optimizing hyperparameters using the Hyperband method. Utilizing a dataset of 1215 patients, we employed L1 regularization to pinpoint a minimal yet highly informative set of biomarkers-oxygen saturation (O2SAT), partial pressure of carbon dioxide (PCO2), age, percentage of lymphocytes, and percentage of neutrophils. These biomarkers were found sufficient to predict disease severity with 95% accuracy. The Hyperband method facilitated efficient and effective tuning of our neural network models, enhancing their predictive capabilities. Although initially developed for COVID-19, our streamlined model can be adapted to other diseases with similar data characteristics, thereby improving predictive efficiency and impacting healthcare resource allocation and patient management during pandemics. Our findings demonstrate the potential of targeted AI applications to not only refine response strategies during health crises but also provide insights that could lead to more informed and effective healthcare practices globally.

关键词： COVID-19 Prognosis Artificial intelligence Feature importance analysis Hyperparameter optimization

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Evaluation of the Tool Visualization of Interaction Tracking in Collaborative Work in a Forum Activity in Moodle 10th

Evaluation of the Tool Visualization of Interaction Tracking...

引用

10th International Conference on Technologies and Innovation

作者： Vasquez-Bermudez, Mitchell Sanz, Cecilia Alejandra Zangara, Maria Hidalgo, Jorge Univ Agr Ecuador Fac Ciencias Agr Dr Jacobo Bucaram Ortiz Ave 25 Julio & Pio Jaramillo Guayaquil Ecuador Univ Guayaquil Fac Ciencias Matemat & Fis Guayaquil Ecuador Univ Nacl La Plata Fac Informat Inst Invest Informat LIDI La Plata Argentina Comis Invest Cient Prov Buenos Aires Buenos Aires DF Argentina

ISBN: (纸本)9783031757013;9783031757020

Collaborative work has become crucial in contemporary classrooms, especially with the integration of technology. However, teachers face the challenge of managing and evaluating these interactions efficiently. This study evaluates the VIST tool forum, which combines indicators based on the CoI Model with data visualization through graphs in Moodle, considered a teaching and learning environment. VIST underwent evaluation to measure its effectiveness in visualizing CoI indicators of participation, using metrics such as precision, recall, and F-measure. Results obtained indicate promising performance in tracking collaborative work, suggesting significant progress in the effective management of collaborative activities in virtual educational environments.

关键词： Inquiry Community Model (CoI) CoI indicators Data visualization

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IoT Application Development: A Comprehensive Narrowband Connectivity Device Solution

IoT Application Development: A Comprehensive Narrowband Conn...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Moreno, Martin Nunez, Ariel Sandoval, Jorge Azurdia-Meza, Cesar A. Carrasco, Eduardo Franulic, Frane Univ Chile Dept Elect Engn Santiago Chile Entel Ocean Things Santiago Chile

ISBN: (纸本)9783031834318;9783031834325

The Internet of Things (IoT) has revolutionized the way we interact with our surroundings, offering many applications across various domains. This paper presents a novel IoT development kit that uses Narrowband technology to establish reliable and efficient connectivity to the Internet. The kit addresses key challenges in the current IoT implementations and architectures by using the current LTE infrastructure for connectivity, offering a comprehensive solution for seamless integration into diverse use cases and prototypes. The main focus of the paper is on the exploration of the development of four pivotal aspects: hardware, software, connectivity, and data cloud;all used in the kit for a fully fledged solution. To demonstrate the kit's versatility, three use cases are presented: a temperature/humidity monitor, a device tracker, and a Narrowband coverage monitor.

关键词： Development kit Internet of Things narrowband systems low power wide area networks

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An Orchestrator for the Dynamic Extension of Automotive E/E Architectures to the Cloud 18th

An Orchestrator for the Dynamic Extension of Automotive E/E ...

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18th Symposium and Summer School on Service Oriented Computing

作者： Sommer, Martin Guissouma, Houssem Schindewolf, Marc Sax, Eric Karlsruhe Inst Technol KIT Inst Tech Informat Verarbeitung ITIV Kaiserstr 12 D-76131 Karlsruhe Germany

ISBN: (纸本)9783031725777;9783031725784

Service orientation finds its way into automotive electrical/-electronic (E/E) architectures. The greater flexibility and adaptability of a service-oriented architecture (SOA) requires a central module, called an orchestrator, which manages the communication structure and updates the available functions. Such a software orchestrator in the automotive sector is part of scientific research, especially because of the strict safety and availability requirements of automated and connected vehicles. In this paper, our own requirements for an orchestrator are compared with existing requirements and implementations from the scientific community. This leads to a focus of this work on extending the E/E architecture into the cloud to produce a control-over-the-air (COTA) approach. Such an approach is implemented using a non-safety-critical function and carried out on the institute's own demonstrator platform representing a basic modern E/E architecture with cloud connection. The resulting orchestrator is able to dynamically switch between different controllers, one deployed in the vehicle and one in the cloud, and close the loop of a heating, ventilation and air conditioning (HVAC) control system of a city bus passenger compartment model executed on our demonstrator platform. Finally, we compare and evaluate the requirements that we have identified as open in the community to see if they have been implemented.

关键词： E/E architecture Service orientation Cloud computing

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Predictive Modeling of Shading Effects on Photovoltaic Panels Using Regression Analysis 8th

Predictive Modeling of Shading Effects on Photovoltaic Panel...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Olivares, Jonathan Depe, Tyler Sood, Kanika Mahto, Rakeshkumar Calif State Univ Fullerton Dept Elect & Comp Engn Fullerton CA 92831 USA Calif State Univ Fullerton Dept Comp Sci Fullerton CA 92831 USA

ISBN: (纸本)9783031856273;9783031856280

Drones have become indispensable assets during human-made and natural disasters, offering damage assessment, aid delivery, and communication restoration capabilities. However, most drones rely on batteries that require frequent recharging, limiting their effectiveness in continuous missions. Photovoltaic (PV) powered drones are an ideal alternative. However, their performance degrades in variable lighting conditions. Hence, machine learning (ML) controlled PV cells present a promising solution for extending the endurance of a drone. This work evaluates five regression models, linear regression, lasso regression, ridge regression, random forest regression, and XGBoost regression, to predict shading percentages on PV panels. Accurate prediction of shading is crucial for improving the performance and efficiency of ML-controlled PV panels in varying conditions. By achieving a lower MSE and higher R-2 Scores, XGBoost and random forest methods were the best-performing regression models. Notably, XGBoost showed superior performance with an R-2 Score of 0.926. These findings highlight the possibility of utilizing the regression model to enhance PV-powered drones' efficiency, prolong flight time, reduce maintenance costs, and improve disaster response capabilities.

关键词： Photovoltaics (PV) regression analysis XGBoost random forest ensemble method machine learning (ML)

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3D Pose Estimation of Markerless Fish on Deep Learning 5th

3D Pose Estimation of Markerless Fish on Deep Learning

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Wang, Yuanchang Cao, Jianrong Wang, Ming Zhao, Qianchuan Gao, He Shandong Jianzhu Univ Sch Informat & Elect Engn Key Lab Intelligent Bldg Technol Shandong Prov Jinan 250101 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Shandong Zhengchen Technol Co Ltd Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Posture estimation is a significant area of research in the realm of computer science. The objective is to precisely determine human posture information by conducting computer analysis on photos or videos of the human body. Posture estimation has made considerable advancements and advances because to the increasing popularity of deep learning technologies. The paper presents a robust and accurate technique, based on deep learning, for estimating the 3D pose of fish without the need for markers. Initially, through the analysis of labels in 2D posture estimation and the assessment of the effectiveness of various neural network variations, an ideal 2D pose estimation model is achieved. The 3D posture estimation utilizes the Bundle Adjustment optimization approach and the precise key point triangulation methodology for camera calibration. The 3D pose estimate is achieved by a multi-dimensional optimization method, both at the 2D and 3D levels. The simultaneous presentation of 2D and 3D orientation from various viewpoints demonstrates that this approach may effectively accomplish the estimation of fish's 3D orientation. Furthermore, we also compute several kinematic properties of fish, which serves as a basis for future improvements in pose estimation accuracy.

关键词： 3D pose estimation Markerless Deep learning Motion parameter calculation

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Cognition in Digital Twins for Cyber-Physical Systems and Humans: Where and Why? 5th

Cognition in Digital Twins for Cyber-Physical Systems and Hu...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Naudet, Yannick Ali, Jana Al Haj Ben Gaffinet Panetto, Herve Luxembourg Inst Sci & Technol LIST Luxembourg Luxembourg Univ Lorraine CNRS CRAN Nancy France

ISBN: (纸本)9783031807749;9783031807756

In modern Cyber-Physical Enterprises, the place of cognition to build more flexible, autonomous, self-adaptive and ultimately more intelligent systems remains unclear. However it could be an essential element to ensure interoperability, as highlighted in recent works on cognitive interoperability. Assuming an environment where cyber-physical systems and humans are coupled with their individual digital twins, we discuss where cognition can exist in the couple and how Cognitive Digital Twin and Cyber-Physical Systems can emerge. This is supported by a proposition for a high-level architecture for Entity- Digital Twin coupling. The place of Cognitive Architectures as a special model of cognition is discussed. Exploring the integration with the ISO23247 Digital Twin framework for manufacturing, we also highlight its lacks to include cognition and humans.

关键词： Cognitive cyber-physical system Cognitive digital twin Cognitive architecture Cyber-physical enterprise Digital twin framework

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How Does Games User Research Measure and Guide Haptic Experience Design? A Critical Review 1

引用

15th Indian Conference on Human-computer Interaction Design and Research

作者： Samanta, Saptarshi Banerjee, Sharmistha Upadhyay, Pankaj Indian Inst Technol Guwahati Gauhati India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

Games User Research (GUR) is an established but relatively young field that explores and evaluates the interaction between players and digital games. Several elements in a digital game affect the player experience. Engagement of the sensory modalities is one such element that can affect the player experience. Digital game developers are moving beyond just engaging the visual and aural modalities. They are striving to provide players with a multimodal experience, such as engaging the sense of touch through haptics or, more recently, the sense of smell. It remains to be understood if the various methods in GUR can evaluate or guide the design of multimodal player experiences. In this paper, we critically review GUR methods and assess if these methods can determine the effect of the haptic modality on player experience. We reviewed literature from the Scopus database to identify the various GUR methods, the intent of those methods, key features, types of sensory modalities evaluated and if there is a scope to assess or guide the effects of the haptic modality on player experience. We observe that GUR has not significantly assessed the impact of the haptic modality on player experience. There is also a lack of haptic experience evaluation tools and methods in the context of digital games. For GUR, this presents an opportunity to utilize the insights from existing player experience evaluation toolkits and methods and expand the player experience evaluation process by developing new haptic experience evaluation tools or methods for digital games.

关键词： Games User Research GUR haptic experience game design

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Design and Optimization in SPI Master at the RTL Level 5th

Design and Optimization in SPI Master at the RTL Level

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5th International Conference on Computing science, Communication and Security

作者： Sahu, Rajat R. Patel, Mitur Soni, Bhavesh Patoliya, Jignesh Ganpat Univ Mehsana 384012 India Einfochips Pvt Ltd Ahmadabad 380060 India

ISBN: (纸本)9783031751691;9783031751707

This paper presents the architecture design, verification, and optimization of a SPI (Serial Peripheral Interface) Master, based on the specifications stated in SPI-block guide V03.06 by Motorola. Transferring serial data between a master and a slave device is possible via the synchronous protocol known as SPI. This work proposes high-speed SPI Master Block Design, Verification, and Optimization in terms of power. Reliability and manufacturing yield are greatly impacted by power. For ASIC/FPGA designers, power minimization is increasingly becoming a crucial design requirement. By addressing power at the RTL and system levels, power can be reduced. At these levels, sequential adjustments such as voltage/frequency scaling, power gating, sequential clock gating, and other micro-architectural approaches can be applied to reduce power and energy consumption. The primary goal of this paper is power reduction at the RTL using Clock gating technique. The whole design is created in Verilog using Xilinx Vivado software is used to verify the design Simulation and performance. Implemented design has 26.05% reduction in power dissipation and Slight increment in logic resources due to Area and power trade off. It also provides all the requirements of the SPI protocol in terms of modularity and functionality.

关键词： spi-protocol 3-Pin spi-master clock-gating serial data transmission Verilog

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Enhanced Traffic Forecasting for Urban Planning Using DeepWalk Embeddings and Spatio-Temporal Graph Ordinary Differential Equation Model 4th

Enhanced Traffic Forecasting for Urban Planning Using DeepWa...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Choudhary, Aniket Mendhiratta, Anubhav Vinayak, Divyansh Arya, Arti PES Univ Dept CSE Bangalore Karnataka India

ISBN: (纸本)9783031837951;9783031837968

Enhancing the computational efficiency of traffic forecasting is important for urban planning and congestion management. Traditional methods often face challenges in handling the complex variable nature of the traffic flow. A novel approach has been introduced to enhance the traffic prediction method by executing and comparing the node generation methods such as random walk, DeepWalk, and Node2Vec using performance metrics. Nodes are converted into time series data and are used to construct adjacency matrices, which are processed by a GraphConvolutionalNetwork (GCN) to acquire any form of spatial features. Long ShortTerm Memory network then models temporal dependencies. We identify Deepwalk as the most effective technique. As a novel contribution, we apply Deepwalk to an existing Spatio-Temporal Graph Ordinary Differential Equation model. We compare our method with baseline and best in class algorithms by (MAE) Mean Absolute Error and (RMSE) Root Mean Squared Error and realized that our offered approach is performing better than existing approaches.

关键词： Traffic Forecasting GCN LSTM Random Walk DeepWalk Node2Vec STG-ODE

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