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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121-130 订阅

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Context-driven Policies Enforcement for Edge-based IoT data Sharing-as-a-Service

Context-driven Policies Enforcement for Edge-based IoT Data ...

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IEEE international conference on Services computing (IEEE SCC)

作者： Huu-Ha Nguyen Phung, Phu H. Nguyen, Phu H. Hong-Linh Truong Univ Dayton Dept Comp Sci Intelligent Syst Secur Lab Dayton OH 45469 USA SINTEF Oslo Norway Aalto Univ Espoo Finland

ISBN: (数字)9781665481465

ISBN: (纸本)9781665481465

Sharing real-time data originating from connected devices is crucial to real-world intelligent Internet of Things (IoT) applications, i.e., based on artificial intelligence/machine learning (AI/ML). Such IoT data sharing involves multiple parties for different purposes and is usually based on data contracts that might depend on the dynamic change of IoT data variety and velocity. It is still an open challenge to support multiple parties (aka tenants) with these dynamic contracts based on the data value for their specific contextual purposes. This work addresses these challenges by introducing a novel dynamic context-based policy enforcement framework to support IoT data sharing (on-Edge) based on dynamic contracts. Our enforcement framework allows IoT data Hub owners to define extensible rules and metrics to govern the tenants in accessing the shared data on the Edge based on policies defined with static and dynamic contexts. We have developed a proof-of-concept prototype for sharing sensitive data such as surveillance camera videos to illustrate our proposed framework. The experimental results demonstrated that our framework could soundly and timely enforce context-based policies at runtime with moderate overhead. Moreover, the context and policy changes are correctly reflected in the system in nearly real-time.

关键词： IoT Access Control Edge computing IoT Hub data sharing-as-a-service Policy Enforcement Dynamic Context

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A Novel Distributed Task Scheduling Framework for Supporting Vehicular Edge Intelligence 42

A Novel Distributed Task Scheduling Framework for Supporting...

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42nd IEEE international conference on Distributed computing systems (ICDCS)

作者： Yang, Kun Sun, Peng Lin, Jieyu Boukerche, Azzedine Song, Liang Fudan Univ Acad Engn & Amp Technol Shanghai Peoples R China Duke Kunshan Univ Suzhou Peoples R China Univ Toronto Toronto ON Canada Univ Ottawa Ottawa ON Canada

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

In recent years, data-driven intelligent transportation systems (ITS) have developed rapidly and brought various AI-assisted applications to improve traffic efficiency. However, these applications are constrained by their inherent high computing demand and the limitation of vehicular computing power. Vehicular edge computing (VEC) has shown great potential to support these applications by providing computing and storage capacity in close proximity. For facing the heterogeneous nature of in-vehicle applications and the highly dynamic network topology in the Internet-of-Vehicle (IoV) environment, how to achieve efficient scheduling of computational tasks is a critical problem. Accordingly, we design a two-layer distributed online task scheduling framework to maximize the task acceptance ratio (TAR) under various QoS requirements when facing unbalanced task distribution. Briefly, we implement the computation offloading and transmission scheduling policies for the vehicles to optimize the onboard computational task scheduling. Meanwhile, in the edge computing layer, a new distributed task dispatching policy is developed to maximize the utilization of system computing power and minimize the data transmission delay caused by vehicle motion. Through single-vehicle and multi-vehicle simulations, we evaluate the performance of our framework, and the experimental results show that our method outperforms the state-of-the-art algorithms. Moreover, we conduct ablation experiments to validate the effectiveness of our core algorithms.

关键词： Vehicular edge computing mobility-aware distributed scheduling in-vehicle application resource allocation

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Proactive and intelligent Healthcare Management Using IoT

Proactive and Intelligent Healthcare Management Using IoT

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2022 IEEE international conference on advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Kurapati, Keerthana Redmond WA United States

ISBN: (纸本)9781665495295

Healthcare aims at enhancing the quality of life and is an important field that every country invests in. Improving the health care system will result in better health for the general population. A population with better health will result in higher gross domestic happiness and will accelerate the productivity and growth of the country. The traditional health care system has fragmented integration across different related sub-systems, which often rely on proprietary manual and semi-automated approaches of information exchange. The amount of data that is captured and exchanged is limited by the manual processes around these sub-systems. The quality of health care can be significantly enhanced by applying the latest technical advancements like Internet of Things. Internet of Things (IoT) is a system of internet-connected objects that exchange information between each other without human intervention. The use of IoT applications in Health care will allow for the automated capture and exchange of rich information between patients, medical professionals, and hospitals, allowing intelligent high quality medical services to be rendered. The proposed architecture introduces IoT -enabled medical devices embedded across several sub-systems that transmit meaningful data to a central system. This central system hosts the brain of the solution, which analyzes the data, provides insights, and recommends meaningful actions. This system not only defines a model to collect the data and generate insights from it, but it also ties the hospital management and doctors together so that proper care can be delivered comprehensively. Apart from comprehensive integration, this system also has an 'AI powered learning and suggestion system' which takes inputs from the doctors and medical practitioners, noting the effectiveness of the treatment as they treat the patients. the 'AI powered learning and suggestion system' constantly evolves by learning from this input and updates the future recomm

关键词： Internet of things

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intelligent and Automated Fault Detection and Diagnosis Strategy for HVAC systems Based on Maintainability Rules for Construction 4.0 23

Intelligent and Automated Fault Detection and Diagnosis Stra...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chew, Michael Yit Lin Yan, Ke National University of Singapore Department of the Built Environment Singapore117566 Singapore

ISBN: (纸本)9781665494571

intelligent and automated fault detection and diagnosis (IAFDD) of heating ventilation air conditioning (HVAC) systems is one important technique in the development process of construction 4.0. data-driven IAFDD adopts various rules analyzing sensor data collected by the Internet of things (IoT) system for detecting and diagnosing potential faults for facilities. A rule-based system, named, a three-layer diagnostic Bayesian network (DBN) is developed in this study. A set of maintainability rules have been identified through an interview and survey process, supplementing the development of the DBN. The outcome of this study concretizes the development and implementation of the diagnostic Bayesian network for HVAC IAFDD. © 2021 IEEE.

关键词： HVAC Smart buildings Smart cities Fault detection Ventilation Cognition Bayes methods

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Traffic Prediction for intelligent Transportation System Using Machine Learning 2

Traffic Prediction for Intelligent Transportation System Usi...

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2nd IEEE international conference on advances in Electrical, computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Swathi, V Yerraboina, Sirisha Mallikarjun, G. JhansiRani, M. Sreyas Inst Engn & Technol Comp Sci & Engn Hyderabad India St Martins Engn Coll Comp Sci & Engn Hyderabad India

ISBN: (纸本)9781665411202

It is commonly believed that ITS can assist relieve urban transportation congestion. Traffic forecasting is the most important function of an ITS. An accurate and timely traffic flow forecasting tool is the goal of this project. Every factor that can affect traffic flow on the road, from accidents to rallies to road repairs, is included in the Traffic Environment. A motorist or passenger can make an informed decision if they have prior information that is close to accurate about all of the above and many more everyday life conditions that can affect traffic. The development of self-driving cars will also benefit from this research. In the previous couple decades, we've seen a shift toward big data approaches for transportation as traffic data has risen tremendously. For real-world applications, the available prediction algorithms all use some form of traffic flow model, although this model is inadequate. Using a traffic forecasting and estimation system can assist reduce traffic congestion and increase the capacity of roadways. Smart cities' traffic forecasts are detailed in detail, as well as the issues and constraints that these forecasting models confront. This will be the subject of our discussion in this essay. The multiparameter integration theory. Co integration theory is a novel modelling method that studies time series data and long- term equilibrium links between non stationary variables. Random errors in traffic patterns gathered over the same period of time show a strong correlation. If speed, density, and occupancy rate are all taken into account together, it is possible to improve short-term projections of traffic flow.

关键词： short-term traffic flow intelligent transportation systems machine Learning

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Multi-corpus Affect Recognition with Emotion Embeddings and Self-Supervised Representations of Speech 10

Multi-corpus Affect Recognition with Emotion Embeddings and ...

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10th international conference on Affective computing and intelligent Interaction (ACII)

作者： Alisamir, Sina Ringeval, Fabien Portet, Francois Univ Grenoble Alpes Atos LIG Grenoble France Univ Grenoble Alpes CNRS INRIA Grenoble INPLIG Grenoble France

ISBN: (数字)9781665459082

ISBN: (纸本)9781665459082

Speech emotion recognition systems use data-driven machine learning techniques that rely on annotated corpora. To achieve a usable performance in real-life, we need to exploit multiple different datasets since each one can shed the light on some specific expression of affect. However, different corpora use subjectively defined annotation schemes, which poses a challenge to train a model that can sense similar emotions across different corpora. Here, we propose a method that can relate similar emotions across corpora without being explicitly trained for it. Our method relies on self-supervised representations, which can provide us with highly contextualised speech representations, and multi-task learning paradigms. This allows to train on different corpora without changing their labelling schemes. The results show that by fine-tuning self-supervised representations on each corpus separately, we can significantly improve the state of the art within-corpus performance. We further demonstrate that by using multiple corpora during the training of the same model, we can improve the cross-corpus performance, and show that our emotion embeddings can effectively recognise the same emotions across different corpora.

关键词： affective computing emotion recognition emotion embedding multi-task learning self-supervised representation

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An Empirical Evaluation and Comparison of the Impact of MVVM and MVC GUI driven Application Architectures on Maintainability and Testability 3

An Empirical Evaluation and Comparison of the Impact of MVVM...

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3rd international conference on intelligent data Science Technologies and Applications, IDSTA 2022

作者： Wilson, Amy Wedyan, Fadi Omari, Safwan Lewis University Engineering Computing and Mathematical Sciences RomeovilleIL60446 United States

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

Model View Controller (MVC) and Main-View-ViewModel (MVVM) are two similar, but different, architectural frameworks that utilize differing sets of components to produce a graphical user interface driven application. The primary difference between these two architectures resides in MVC's use of a controller, MVVM's use of a viewmodel, and how these two components interact with their respective views. These differing usages are factors that effect how tightly coupled their layered systems are and effects the ability to test and maintain systems built using these architectures. This paper seeks to explain both frameworks, evaluate a sample code base, collect metrics, and then compare the testability and maintainability of both architectures. © 2022 IEEE.

关键词： Maintainability

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Distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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作者： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. This paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 IEEE.

关键词： Smart cities Soft sensors Transportation data integration Process control Distributed databases data models

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Enhancing Dry Eye Disease Detection Through the Application of an Ophthalmic Knowledge Graph 1

Enhancing Dry Eye Disease Detection Through the Application ...

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1st international conference on intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Cui, Jiazheng Lin, Zhiyuan Liu, Haoyang Liu, Yunxiao Wang, Mini Han Beijing Normal University Hong Kong Baptist University United International College Zhuhai China Digital Healthcare and Artificial Intelligence Association China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China Perspective Technology Group Zhuhai China Beijing Institute of Technology Zhuhai China Macao Polytechnic University China The Faculty of Medicine Chinese University of Hong Kong Hong Kong Zhuhai People's Hospital Zhuhai Clinical Medical College Jinan University Zhuhai China

ISBN: (纸本)9781643685779

Dry eye disease is a multifactorial condition that significantly impacts the quality of life for millions worldwide. Accurate diagnosis and treatment are often challenging due to the complex interplay of symptoms, risk factors, and underlying causes. This study presents an innovative approach to enhance dry eye detection through the development of an ophthalmic knowledge graph. By curating and organizing data from peer-reviewed literature, the knowledge graph captures key entities such as symptoms, treatments, and diagnostic methods, and systematically maps their relationships. The study employs natural language processing (NLP) and deep learning techniques to extract entities and relationships, which are then integrated into the knowledge graph using the Neo4j platform. This structured representation provides clinicians with an intuitive and comprehensive tool for improving diagnostic accuracy and developing personalized treatment strategies. The knowledge graph not only strengthens the connections between diverse clinical data but also facilitates the integration of AI-driven diagnostic methods. This research demonstrates the potential of knowledge graphs in advancing the field of ophthalmology, specifically in the diagnosis and management of dry eye disease. © 2025 The Authors.

关键词： Knowledge graph

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A Statistical Technique to Construct data-driven Decision Rules Based on Prediction Intervals of Nonlinear Regressions 3

A Statistical Technique to Construct Data-Driven Decision Ru...

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3rd IEEE international conference on System Analysis and intelligent computing, SAIC 2022

作者： Prykhodko, Sergiy Prykhodko, Natalia Admiral Makarov National University of Shipbuilding Department of Software of Automated Systems Mykolaiv Ukraine Admiral Makarov National University of Shipbuilding Finance Department Mykolaiv Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

A statistical technique to build data-driven decision rules based on prediction intervals of nonlinear regressions created using multivariate normalizing transformations is proposed. In the general case, the proposed technique is iterative since there may be outliers in the process of building the decision-making rule based on prediction intervals of nonlinear regression. To construct nonlinear regressions and their prediction intervals we use the appropriate techniques based on multivariate normalizing transformations that take into account the correlation between dependent and independent variables. We give examples of constructing data-driven decision rules based on prediction intervals of nonlinear regression with two predictors created using the Johnson three-variate normalizing transformation for the SB family for three-dimensional non-Gaussian data from 33 auto companies, 32 of which were from the S&P 500 Index in 2020. The above rules are constructed to make a decision in which stocks it is better to invest in. We have found that as of December 2020, the shares of two auto companies were overvalued, and the shares of one, on the contrary, were undervalued, so the shares of this company could be bought. © 2022 IEEE.

关键词： Decision making

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