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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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IoT-Enhanced Airborne Pathogen Surveillance in Healthcare Environments using Deep Generative Networks

IoT-Enhanced Airborne Pathogen Surveillance in Healthcare En...

引用

visual Analytics and data visualization (ICvADv), International conference on

作者： Prabhu v S Satheeshkumar Sekar S. Dhanalakshmi N. Mohankumar B. Gopi S. Murugan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Mphasis Corporation Project Manager Chandler Arizona USA Department of Information Technology Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Biomedical Engineering Innovation Center Sona College of Technology Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In hospital settings, airborne pathogens present significant risks to the health and safety of workers and patients. This research proposes an enhanced method for airborne pathogen detection by integrating deep generative networks (DGNs) with Internet of Things (IoT) technologies. The system uses a network of IoT sensors placed in hospitals to continuously monitor environmental factors, including humidity, temperature, and particulate matter, to identify pathogens. By analyzing the data collected by the sensors, DGNs can accurately detect the amount and kind of airborne pathogens by modeling complicated patterns. The system's deep generative method enhances the model's capacity to identify minute variations in pathogen levels by producing artificial data that complements sensor data. The possibility of spreading infection may be greatly reduced with real-time data processing and analysis, which enables quick alerts and responses. The proposed system's capacity to enhance infection control measures and sustain a safer hospital environment is shown via simulations and real implementations, proving its efficiency. Integrating IoT real-time monitoring capabilities with DGNs' complex analytical capability signifies a notable leap forward in pathogen surveillance.

关键词： Temperature sensors Temperature measurement Pathogens Hospitals Atmospheric modeling Surveillance Real-time systems data models Sensor systems Internet of Things

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An Effectual Forecasting of Oil Production Performance Using Advanced Adaptive Recurrent Neural Network Classifier

An Effectual Forecasting of Oil Production Performance Using...

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International conference on Communication Systems and Network Technologies (CSNT)

作者： K. Siva Kumar v. R. Nagalakshmi Nethi Korupula Siva Kumar Yagati Sai Chanikhya Mohammad Yousf Basha B P M Harshavardhan Department of Petroleum Engineering Godavari Institute of Engineering and Technology (A) Rajahmundry Andhra Pradesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Oil is the primary source of energy worldwide, accounting for a large percentage of global energy consumption, with significant impacts on economic, industrial, geopolitical, and social dimensions. It enables the movement of transport, driving globalization and economic integration because oil is a backbone of international trade. However, various reasons, including political, technical, natural, economic, and strategic ones, lead to the reduction of oil production. Hence, this paper proposed a comprehensive analysis and prediction of oil production using an advanced adaptive Recurrent Neural Network (RNN). Initially, diverse datasets are collected to ensure a robust foundation for analysis. Following this, preprocessing techniques, including data cleaning and filtering, are employed to enhance data quality and relevance. The exploration phase focuses on evaluating the data, identifying trends, and uncovering underlying patterns. Feature selection is critical to optimize the predictive model's performance. The Adaptive RNN is then utilized to model the relationships within the dataset, leading to the generation of predicted values. Overall, this work is implemented by python language. Finally, predicted the error value, such as the Root Mean Square Error (RMSE) of 72.9% and the Mean Absolute Error (MAE) of 26.1%, are attained. This structured approach facilitates effective data analysis and contributes to the advancement of predictive analytics in various domains.

关键词： Adaptation models Recurrent neural networks Adaptive systems Oils Production Predictive models Feature extraction Reservoirs Topology Python

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Space exploration Using Sensors Based on Island Nanostructures

Space Exploration Using Sensors Based on Island Nanostructur...

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Svetlana v. Sidorova Sergey v. Kiryanov Sofia A. Aksenova Electronic Technologies in Mechanical Engineering Department Bauman Moscow State Technical University Moscow Russian Federation Space-rocket Launch Complex Department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

In this paper the relevance of using weak magnetic field sensors for space research is shown. The analysis of the potential integration of spin-tunnel magnetoresistive (STMR) sensors with island nanostructures into various spacecraft systems has been conducted. A possible application of the sensor in inertial navigation systems of spacecraft for long-duration missions was considered, specifically for calibrating data in regions with low Earth's magnetic field intensity. The feasibility of using the sensor in space missions to investigate remanent magnetization and measure the magnetic field of Mars at various distances from the planetary surface was explored. Through multiphysics modeling, the configuration and geometric parameters of the functional layer consisting of island nanostructures were determined for their use in designing components for spacecraft and navigation systems.

关键词： Space vehicles Mars Sensitivity Magnetic sensors Magnetization Sensor systems Nanostructures Magnetic fields Magnetoresistance Magnetosphere

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IoT-Enhanced Airborne Pathogen Surveillance in Healthcare Environments using Deep Generative Networks

IoT-Enhanced Airborne Pathogen Surveillance in Healthcare En...

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2025 International conference on visual Analytics and data visualization, ICvADv 2025

作者： Prabhu, v.S. Sekar, Satheeshkumar Dhanalakshmi, S. Mohankumar, N. Gopi, B. Murugan, S. R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Mphasis Corporation Project Manager ChandlerAZ United States Sri Muthukumaran Institute of Technology Department of Information Technology Tamil Nadu Chennai India Nagpur Campus Pune India Innovation Center Sona College of Technology Department of Biomedical Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

关键词： Deep Generative Networks Environmental Sensors Infection Control Internet of Things (IoT) Pathogen Detection Real-Time Monitoring

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Effect of Gaze visualization on Task Efficiency and User Behavior for Guidance Scenarios in Co-Located AR Collaboration

Effect of Gaze Visualization on Task Efficiency and User Beh...

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virtual Reality and 3D User Interfaces Abstracts and Workshops (vRW), IEEE conference on

作者： Froso Sarri Katerina Mania School of Electrical & Computer Engineering Technical University of Crete Greece

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Augmented Reality (AR)-enabled collaboration enhances contextual guidance in shared environments, where unambiguous cues are essential, for swift and accurate task completion. Gaze-based cues offer clear, non-verbal indication of intent, reducing interaction ambiguity. While prior research has studied the effectiveness of gaze-based visualization in scenarios where collaborators share the same level of knowledge, less attention has been shown to guidance scenarios, where one user provides task-related information to another. Existing studies often focus on task outcomes (e.g., completion time, error rate) without analyzing gaze data to gain further insight into user behavior and attention. This study leverages gaze-based cues as an active means of communication in guidance scenarios, assessing their impact on both task performance and user behavior through gaze data analysis. The developed system incorporates gaze-based visualizations, including dynamic representations of gaze behavior, to deliver task-critical guidance. These are compared to verbal instructions using quantitative metrics for task effectiveness and gaze analysis, alongside subjective feedback, to determine whether they reduce ambiguity and enhance collaboration. Our study showed that gaze-based visualizations significantly reduced task related errors, with contextual triggered cues offering the best balance between speed and correctness. Gaze analysis revealed that gaze-based visualizations effectively directed participants’ attention to task-relevant areas, reducing unnecessary visual exploration. Participants reported lower perceived workload in gaze-based conditions, further supporting their effectiveness in enhancing collaboration and reducing ambiguity.

关键词： Human computer interaction Measurement Hands visualization Three-dimensional displays Error analysis Collaboration data visualization Information sharing Augmented reality

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Preliminary exploration of Geotechnical data Interpretation and GIS Integration: Toward AI Implementation

Preliminary Exploration of Geotechnical Data Interpretation ...

引用

Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International conference for

作者： Danah S. Al-Enezi Kuwait Institute for Scientific Research Kuwait

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Kuwait's rapid urban expansion, driven by large-scale projects, such as AI-Mutlaa City and Silk City, highlights the need for comprehensive geotechnical data to support sustainable development. However, detailed studies on the subsurface geotechnical characteristics of the northern regions of Kuwait are lacking. This study focuses on establishing a preliminary geotechnical database, addressing challenges encountered during its development, and conducting a pilot analysis for AI-Mutlaa City. A questionnaire was distributed to geology, environmental, geotechnical, and engineering professionals to identify the challenges associated with creating the database. The survey revealed issues such as inconsistent data formats, limited access to borehole logs, and the hesitancy of private firms to share data due to security concerns. Despite these challenges, borehole data from ministries and private organizations were collected and organized using Microsoft Excel. These data formed the foundation of the geotechnical database, which was analyzed and visualized using Geographic Information System (GIS) tools. The pilot study in AI-Mutlaa City revealed predominantly dense soil layers, with significant variations in soil strength at a depth of 19.5 m, where standard penetration resistance ranged from 10 to 50 blows. These findings emphasize the variability of soil properties in the area and underscore the importance of site-specific investigations. The established geotechnical database provides valuable insights for construction planning, risk assessment, and geohazard management. Moving forward, the integration of artificial intelligence into the database is planned to enhance data processing, automate soil classification, and predict subsurface conditions in an effort to contribute to advanced geotechnical practices in Kuwait.

关键词： Surveys Technological innovation Accuracy databases Urban areas Soil properties visual databases Artificial intelligence Sustainable development Geographic information systems

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On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study 25

On the Hyperparameter Loss Landscapes of Machine Learning Mo...

引用

Proceedings of the 31st ACM SIGKDD conference on Knowledge Discovery and data Mining v.1

作者： Mingyu Huang Ke Li University of Electronic Science and Technology of China Chengdu China University of Exeter Exeter United Kingdom

ISBN: (纸本)9798400712456

Previous efforts on hyperparameter optimization (HPO) of machine learning (ML) models predominately focus on algorithmic advances, yet little is known about the topography of the underlying hyperparameter (HP) loss landscape, which plays a fundamental role in governing the search process of HPO. While several works have conducted fitness landscape analysis (FLA) on various ML systems, they are limited to properties of isolated landscape without interrogating the potential structural similarities among landscapes induced on different scenarios. The exploration of such similarities can provide a novel perspective for understanding the mechanism behind modern HPO methods, but has been missing. In this paper, we mapped 1,500 HP loss landscapes of 6 representative ML models on 63 datasets across different fidelity levels, with 11M+ configurations. By conducting exploratory analysis on these landscapes with fine-grained visualizations and dedicated FLA metrics, we observed a similar landscape topography across a wide range of models, datasets, and fidelities, and shed light on the mechanism behind the success of several popular methods in HPO. The artifacts associated with this paper is available at https://***/COLA-Laboratory/GraphFLA.

关键词： exploratory data analysis

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Improving Brain Tumor Diagnosis with Deep Learning Algorithms

Improving Brain Tumor Diagnosis with Deep Learning Algorithm...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： v varun Rahul Palanivel Sumathi D Karthika Natarjanv VIT-AP University Guntur India School of Engineering (Cyber Security) School of Computer Science and Engineering (SCOPE)

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This paper proposes an innovative approach for brain tumour assessment employing deep learning procedures, specifically integrating the Segment Anything Module (SAM) with the vGG (visual Geometry Group) architecture. SAM is utilized to segment various components within brain tumour images, enhancing the localization and differentiation of tumour regions from surrounding tissues. By leveraging SAM's capability to segment diverse structures, including tumours and adjacent anatomical features, the proposed framework aims to enhance the accuracy of tumour analysis. The vGG architecture is employed to get high-level features, while SAM refines the segmentation masks, facilitating more precise feature representation. Experimental evaluations on standard brain tumour data exhibit the effectiveness of the proposed one, showcasing improved segmentation accuracy compared to conventional approaches. This research contributes to advancing automated brain tumour analysis, potentially enhancing diagnostic accuracy and treatment planning in clinical settings.

关键词： Deep learning Location awareness Geometry Image segmentation visualization Accuracy Planning Standards Collaborative intelligence Tumors

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A Comprehensive analysis on Refined BiLSTM-RNN Models for Accurate Forecasting of Bottom Hole Pressure in Managed Pressure Drilling Operations

A Comprehensive Analysis on Refined BiLSTM-RNN Models for Ac...

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International conference on Communication Systems and Network Technologies (CSNT)

作者： v.R. Nagalakshmi Nethi Md Ahammad Sharif Bora Hymavathi Nitish Raj Banka Akhil Muppidi Daniel Ashirvad Department of Petroleum Engineering Godavari Institute of Engineering and Technology (A) Rajahmundry Andhra Pradesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Managed Pressure Drilling (MPD) is a crucial technique in the oil and gas industry, ensuring optimal pressure conditions to reduce risks like bore instability and blowouts. Accurate Bottom Hole Pressure (BHP) forecasting is essential for real-time decision-making, but traditional methods often fail to capture the complex dynamics of drilling environments. This paper introduces a Bidirectional Long Short-Term Memory Recurrent Neural Network (BiLSTM-RNN) model for BHP forecasting in MPD. The process includes data exploration, cleaning, pattern identification, feature engineering, and dimensionality reduction to enhance computational efficiency. The BiLSTM-RNN model addresses the vanishing gradient problem and passes in time-series analysis. Simulations in Python achieved reliable error metrics, including MSE of 31942.15, MAE of 136.71, MAPE of 13.02, RMSE of 178.72, and an R2-score of 0.6, demonstrating its effectiveness in improving drilling performance and safety.

关键词： Drilling Analytical models Accuracy Atmospheric measurements Computational modeling Atmospheric modeling Time series analysis Predictive models Security Forecasting

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A Bibliometric analysis of visualizing Knowledge Mapping for Learning Management System

A Bibliometric Analysis of Visualizing Knowledge Mapping for...

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International conference on Educational and Information Technology (ICEIT)

作者： Xiquan Ye Jiaming Wang Danya Liu Qinggui Qin Guangdong Business and Technology University Zhaoqing China GXNU School of Design Guangxi Normal University Guilin China Foreign Language School Guangzhou Institute of Science and Technology Guangzhou China School of Art and Design Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

With the advancement of technology and methodologies, Learning Management System (LMS) has seen a profound and extensive application in the field of education, thereby rendering scientific and systematic research on it particularly significant. The primary objective of this study is to comprehensively explore the current application status and developmental trajectory of LMS in educational practice. To this end, the authors screened approximately 1,347 high-quality related articles from the Web of Science (WOS) and conducted a visual analysis using the CiteSpace. By analyzing countries, institutions, journals, and keywords, this study reveals the research hotspots and evolving trends of LMS over the past two decades. The results show that the research focus of LMS has gradually expanded from the initial exploration of basic theories on system design to a wide range of applications in educational practice. Furthermore, it has increasingly centered on personalized learning experiences, the improvement of learning efficiency, and the enhancement of learning outcomes. Additionally, the analysis reveals that different countries and regions exhibit diversity and variability in their research emphasis and developmental trends regarding LMS. The conclusions and findings of this study provide some references and insights for future research endeavors and practical implementations of LMS in the field of education. These contributions have the potential to promote ongoing innovation and facilitate the development of LMS in education.

关键词： Learning management systems visualization Systematics databases Soft sensors Education Bibliometrics data visualization Market research Trajectory

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