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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

共 558 条记录，以下是71-80 订阅

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Random Forest Predictive Model for Ventilator-Associated Pneumonia Prediction with IoT data Analytics

Random Forest Predictive Model for Ventilator-Associated Pne...

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2024 international conference on advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Ramadevi, R. Kishoresonti, V.J.K. Jain Jacob, M. Vaidehi, V. Mohankumar, N. Rajmohan, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India Sathyabama Institute Of Science And Technology Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Management Tamil Nadu Chennai India Department Of Computer Science Tamil Nadu Salem India B.V. Raju Institute Of Technology Department Of Electronics And Communication Engineering Telangana India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

To identify and forecast ventilator-associated pneumonia (VAP) early on, this study investigates the use of random forest predictive models in combination with Internet of Things (IoT) data analytics. Patients on mechanical ventilation are particularly vulnerable to VAP, a serious nosocomial infection. It uses Random Forest algorithms to examine a wide range of factors, such as vital signs, patient demographics, and ventilator settings, by using the abundance of healthcare data supplied by the IoT. The goal is to create reliable prediction models that can identify people who are at risk of getting VAP before any symptoms appear. Using data gathered from ventilator-integrated real-time monitoring devices, it creates a complete dataset. The findings reveal that random forest models are reliable and very accurate in predicting VAP. Integrating analytics from the IoT improves prediction accuracy, which in turn helps healthcare providers make better, timelier preventative decisions. Improving patient outcomes and optimizing resource allocation in critical care units may be possible as a result of this study's contributions to the expanding body of work on using sophisticated data-driven methodologies for infectious illness prediction in clinical settings. © 2024 IEEE.

关键词： Internet of things

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The ThirdWorkshop on data-driven intelligent Transportation 22

The ThirdWorkshop on Data-driven Intelligent Transportation

引用

31st ACM international conference on Information and Knowledge Management (CIKM)

作者： Wei, Hua Sheron, Guni Wu, Cathy Chawla, Sanjay Li, Zhenhui New Jersey Inst Technol Newark NJ 07102 USA Texas A&M Univ College Stn TX USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Qatar Comp Res Inst Doha Qatar Yunqi Acad Engn Hangzhou Peoples R China

ISBN: (纸本)9781450392365

Traffic is the pulse of the city. Transportation systems can involve humans, vehicles, shipments, information technology, and the physical infrastructure, all interacting in complex ways. intelligent transportation enables the city to function in a more efficient and effective way. A wide range of city data become increasingly available, such as taxi trips, surveillance camera data, human mobility data from mobile phones or location-based services, events from social media, car accident reports, bike-sharing information, Points-Of-Interest, traffic sensors, public transportation data, and many more. This abundance of data poses a grand challenge to the CIKM research community: How to utilize such data toward city intelligence, across various transportation tasks? The 3rd workshop of "data-driven intelligent Transportation" welcomes articles and presentations in the areas of transportation systems, data mining, and artificial intelligence, conveying new advances and developments in theory, modeling, simulation, testing, case studies, as well as large-scale deployment.

关键词： Transportation spatio-temporal data mining urban computing

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Research on data-driven Virtual Experimental Framework 24

Research on Data-driven Virtual Experimental Framework

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7th international conference on Educational Technology Management, ICETM 2024

作者： Li, Miaomiao He, Xiangchun College of Educational Technology Northwest Normal University Gansu Lanzhou China

ISBN: (纸本)9798400717468

With the rapid development of computing technology, data-driven method has become an indispensable part of scientific research and engineering design. Aiming at the problems of high cost and long cycle of traditional experimental methods, a data-driven virtual experimental framework is proposed. The framework utilizes advanced data analysis techniques and machine learning algorithms to predict and simulate the behavior of complex systems without the need for physical experiments. Based on the research of virtual experiment literature, this paper understands the current status of virtual experiment research, summarizes the key elements of virtual experiment, and proposes a virtual experiment framework including data layer, service layer and user layer by using design-based research method. The framework includes knowledge graph construction of data layer and behavior data construction based on xAPI. Service layer data collection, analysis and application and other basic functions;The design of specific virtual experiments in the application layer and the feedback of experiment reports, and the data collection, analysis and application methods for the overall experiment design through the experiment link. In the part of result report, it not only gives the overall results of the data analysis of the whole experiment process, but also gives the personalized resources and intelligent assistant database resources to provide data feedback for the improvement of the virtual experiment. © 2024 Copyright held by the owner/author(s).

关键词： data driven

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Prediction Strategy Assisted by LSTM Neural Network for Dynamic Multi-objective Optimization 6

Prediction Strategy Assisted by LSTM Neural Network for Dyna...

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6th international conference on data-driven Optimization of Complex systems (DOCS)

作者： Li, Suixun Zou, Feng Chen, Debao Huang, Huimei Huaibei Normal Univ Sch Phys & Elect Informat Anhui Prov Key Lab Intelligent Comp & Applicat Huaibei Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Dynamic multi-objective optimization problems (DMOPs) exist widely in real life, and the algorithm needs to track changing Pareto optima accurately and quickly after the environment changes when dealing with these DMOPs. In response to the above issues, this paper proposes a prediction strategy assisted by long short-term memory recurrent neural network (PS-LSTM) for DMOPs. In the proposed method, when environmental changes occur, the difference of the center point of Pareto solutions is predicted by long short-term memory recurrent neural network and then the evolutionary direction of Pareto solutions can be calculated to better track changing Pareto optima by selecting excellent individuals in a new environment. Moreover, the next initial population is predicted based on the difference of the center point and the evolutionary direction of Pareto solutions. Finally, the PS-LSTM strategy is compared with four representative strategies on 11 benchmark test problems. Experimental results indicate that the strategy has better performance.

关键词： Dynamic multi-objective optimization LSTM New environmental information

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Cricket Team Selection and Player Analysis using data Analytics 6

Cricket Team Selection and Player Analysis using Data Analyt...

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6th IEEE international conference on Recent advances in intelligent Computational systems, RAICS 2024

作者： Raajesh, Sanjay Martin, Noel Jiji, Jyothsna Nair, Aakash Haritha, H. Muthoot Institute of Technology and Science Department of Computer Science and Engineering India

ISBN: (纸本)9798350381689

The inception of the 'Cricket Team Selection and Player Analysis using data Analytics' project arose from the imperative to introduce precision through data-driven methods in the process of selecting cricket teams. Recognizing the pivotal role of team composition in the sport, our project employs the K-means clustering algorithm to categorize players based on their historical performance data with the strike rate. This approach facilitates the streamlined creation of well-balanced teams, targeting cricket enthusiasts, coaches, and team selectors. The ultimate goal is to enhance their decision-making processes, optimizing team performance and strategy. The project adopts a comprehensive approach, heralding a new era in cricket by leveraging data. It enables the formation of playing elevens derived from historical player performance metrics, ensuring a more resilient and strategic selection process. The project also has a selection of top players based on the attributes provided by the user. The tangible outcome of this project is a tool designed to serve cricket stakeholders, aiding them in making informed decisions for improved performance on the field. Representing a significant stride towards a more data-driven approach to cricket, the 'Cricket Team Selection and Player Analysis using data Analytics' project is poised to redefine team selection and strategy in the sport. Its applicability extends to a broad audience, promising to be a transformative force in the realm of cricket decision-making. © 2024 IEEE.

关键词： Support vector machines

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XAI for U: Explainable AI for Ubiquitous, Pervasive and Wearable computing 24

XAI for U: Explainable AI for Ubiquitous, Pervasive and Wear...

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ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable Computers (UbiComp/ISWC)

作者： Gjoreski, Martin Hassan, Teena Vered, Mor Houben, Sebastian Kopp, Stefan Univ Svizzera Italiana Lugano Switzerland Bonn Rhein Sieg Univ Appl Sci St Augustin Germany Monash Univ Melbourne Vic Australia Bielefeld Univ Bielefeld Germany

ISBN: (纸本)9798400710582

The workshop XAI for U aims to address the critical need for transparency in Artificial Intelligence (AI) systems that integrate into our daily lives through mobile systems, wearables, and smart environments. Despite advances in AI, many of these systems remain opaque, making it difficult for users, developers, and stakeholders to verify their reliability and correctness. This workshop addresses the pressing need for enabling Explainable AI (XAI) tools within Ubiquitous and Wearable computing and highlights the unique challenges that come with it, such as XAI that deals with time-series and multimodal data, XAI that explains interconnected machine learning (ML) components, and XAI that provides user-centered explanations. The workshop aims to foster collaboration among researchers in related domains, share recent advancements, address open challenges, and propose future research directions to improve the applicability and development of XAI in Ubiquitous Pervasive and Wearable computing - and with that seeks to enhance user trust, understanding, interaction, and adoption, ensuring that AI-driven solutions are not only more explainable but also more aligned with ethical standards and user expectations.

关键词： XAI explainable AI Ubiquitous Wearable computing Wearable computing User Studies

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Entropy-based Sentence-level Hallucination Score in Large Language Models

Entropy-based Sentence-level Hallucination Score in Large La...

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2025 IEEE international conference on Big data and Smart computing, BigComp 2025

作者： Joo, Eojin Lee, Young-Jun Choi, Ho-Jin KAIST School of Computing Daejeon Korea Republic of

Large Language Models (LLMs) excel at generating human-like text but are prone to hallucinations-fabricated outputs that deviate from reality. Addressing this is critical for ensuring trustworthy AI, especially in fields like medicine, law, and education. This paper introduces an entropy-based metric for detecting sentence-level hallucinations by assessing token prediction uncertainty. High entropy correlates with hallucinatory outputs, and we validate this metric's effectiveness across different LLMs. Applications include hallucination flagging and improving user trust through uncertainty-driven feedback. This approach advances the reliability and transparency of LLMs, contributing to safer AI systems. © 2025 IEEE.

关键词： Deep learning

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Proceedings - 11th IEEE international conference on intelligent computing and Information systems, ICICIS 2023

Proceedings - 11th IEEE International Conference on Intellig...

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11th IEEE international conference on intelligent computing and Information systems, ICICIS 2023

ISBN: (纸本)9798350322101

The proceedings contain 85 papers. The topics discussed include: traditional and deep learning techniques in image steganography: recent advances;deep learning for extracting human movement patterns from spatio-temporal data;speech emotion recognition: enhancing human-computer interaction;a combined ELM and CNN model architecture for accurate viral family DNA classification;ML driven priority-based constrained routing for pollution management in ITS;on development of computer aided system for detecting brain neurological disorders;CalorieMe: an image-based calorie estimator system;deepfake detection using EfficientNet and XceptionNet;dental disease detection based on CNN for panoramic dental radiographs;and AI-based detection of myocardial infarction through electrocardiogram signals: a review.

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Real-Time House Price Predictions with Regression Analysis 6

Real-Time House Price Predictions with Regression Analysis

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6th IEEE international conference on Recent advances in intelligent Computational systems, RAICS 2024

作者： Purushotham, K. Bangarappa Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350381689

Accurate property assessment is essential in the dynamic and ever-changing world of real estate so that both buyers and sellers can make well-informed choices. We are aware that setting the appropriate asking price for a house can be a difficult undertaking. Property values are greatly influenced by factors including location, size, amenities, market trends, and numerous economic indicators. This system supports in determining a property's price based on geographic regions factors, the number of bathrooms, bedrooms, and area (square feet). Forecasting future costs relies on examining historical market trends and value ranges. The primary focus of this analysis is to efficiently utilize regression models in combination with other methodologies to achieve the highest possible accuracy in predictions. This study aims to forecast housing values in Bangalore. Our website is designed to give you reliable and data-driven insights on property values when you're a potential homebuyer looking to identify the best property. © 2024 IEEE.

关键词： Houses

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AI-Enhanced Predictive Maintenance in intelligent systems for Industries

AI-Enhanced Predictive Maintenance in Intelligent Systems fo...

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2024 international conference on advances in computing Research on Science Engineering and Technology, ACROSET 2024

作者： Sharma, Shiv Shankar Vivek, V. Malviya, Ashwini Vivekananda Global University Department of Mathematics Jaipur India Faculty of Engineering and Technology Department of Computer Science Engineering Bangalore India Atlas SkillTech University Department of UGDX Mumbai India

ISBN: (纸本)9798350388800

A innovative approach to AI-enhanced predictive maintenance in AI-powered industrial systems is presented here. The suggested technique creates an all-encompassing predictive maintenance framework by fusing three powerful algorithms: Random Forest, Long Short-Term Memory (LSTM) networks, and XGBoost. The goal is to improve equipment failure prediction accuracy while simultaneously optimizing maintenance schedules to facilitate productive and economical manufacturing processes. Random Forest is the first step in the process, and it is responsible for feature selection and the first failure predictions. Using an ensemble learning strategy, Random Forest can efficiently process high-dimensional, complicated information. Following this, long short-term memory (LSTM) networks are presented as a means of capturing temporal relationships in the data, making them well-suited for the investigation of failures in equipment over time. Finally, XGBoost improves predictive accuracy by decreasing the number of incorrect predictions. Each algorithm has its own merits, and by combining them, we may create a more robust framework. The suggested technique excels over the status quo because of its flexibility to adapt to new circumstances, high accuracy in failure prediction, low cost, and high sustainability. It enables real-time data-driven optimization of maintenance plans, cutting down on unplanned downtime and wasted resources. The suggested method for AI-enhanced predictive maintenance is a state-of-the-art solution for businesses, as it replaces outdated methods with cutting-edge AI and machine learning strategies. In addition to improving the accuracy and efficiency of intelligent industrial systems, it also helps create a greener and more sustainable industrial landscape. © 2024 IEEE.

关键词： Predictive maintenance

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