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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301-310 订阅

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Communication and Energy Efficient Edge Intelligence

Communication and Energy Efficient Edge Intelligence

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Sabtain Ahmad Institute of Information Systems Engineering Vienna University of Technology Vienna Austria

driven by the rapid advances in Artificial Intelligence of Things (AIoT), billions of mobile and IoT devices are connected to the internet, generating huge quantities of data at the network edge. Meanwhile, traditional analytics approaches such as cloud computing and centralized AI are unable to manage these massively distributed heterogeneous data primarily because 1) moving a tremendous amount of data across the network poses severe challenges to network capacity 2) cloud-based analytics can result in prohibitively high transmission delays 3) transporting data containing private information over the network poses serious concerns for the privacy and may not even be possible due to regulations like GDPR. Accelerated by the success of AI and IoT technologies, there is an urgent need to push AI to the network edge to tap the full potential of big data.

关键词： Cloud computing data privacy Distributed databases Big data Regulation Energy efficiency Delays

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Big data computer aided drug design and its application in new pesticide research and development

Big data computer aided drug design and its application in n...

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international conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Tiantian, Chen Laiwu Vocational and Technical College JinanShandong271100 China

ISBN: (纸本)9783030539795

In the era of big data, data-driven new pesticide R&D is expected to significantly improve the success rate of new pesticide R&D, shorten the cycle of new pesticide R&D and reduce the cost of new pesticide R&D. This paper briefly summarizes the latest research overview of new pesticide design and data integration and data mining related to new pesticide information in recent years, the prospect of new pesticide design and new pesticide information research in the era of big data is put forward. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Big data

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Decoupling Observer for Contact Force Estimation of Robot Manipulators Based on Enhanced Gaussian Process Model

Decoupling Observer for Contact Force Estimation of Robot Ma...

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IEEE international conference on Cloud computing and Intelligence systems (CCIS)

作者： Yanran Wei Wenshuo Li Yi Yang Xiang Yu Lei Guo Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Key Laboratory of Intelligent Sensing Materials and Chip Integration Technology of Zhejiang Province Hangzhou Innovation Institute of Beihang University Hangzhou China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781665456579

This paper aims at addressing the challenge of contact force estimation of robot manipulators with incomplete model information. A novel decoupling observer is proposed based on an enhanced Gaussian process (EGP) model. Specifically, the dynamic model of the robot manipulator is decomposed into the nominal part established using Euler-Lagrange theory and a residual dynamics term with no priori information. To improve the model accuracy, the Gaussian process regression (GPR) technique is adopted to develop a data-driven compensation term for the residual dynamics. Compared to the purely data-driven models, the advantage of the proposed EGP model lies in its computational efficiency as the information contained in the nominal model has been exploited. Based on the statistical information of the residual dynamics learned via GPR, a novel decoupling observer is presented for real-time force estimation. Due to its capability of decoupling the contact force from residual dynamics and system noises, the proposed observer approach is no longer dependent on an accurate dynamic model of the robot manipulator. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods.

关键词： Uncertainty Computational modeling Dynamics Force Estimation Gaussian processes Observers

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15th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2020

15th International Conference on Soft Computing Models in In...

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15th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2020

ISBN: (纸本)9783030578015

The proceedings contain 83 papers. The special focus in this conference is on Soft computing Models in Industrial and Environmental Applications. The topics include: A Smart Crutch Tip for Monitoring the Activities of Daily Living Based on a Novel Neural-Network intelligent Classifier;Hourly Air Quality Index (AQI) Forecasting Using Machine Learning Methods;interpretable Deep Learning with Hybrid Autoencoders to Predict Electric Energy Consumption;on the Performance of Deep Learning Models for Time Series Classification in Streaming;An Approach to Forecasting and Filtering Noise in Dynamic systems Using LSTM Architectures;novel Approach for Person Detection Based on Image Segmentation Neural Network;an Adaptive Cognitive Model to Integrate Machine Learning and Visual Streaming data;smart Song Equalization Based on the Classification of Musical Genres;machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution;A Preliminary Study for Automatic Activity Labelling on an Elder People ADL dataset;software Sensors for the Monitoring of Bioprocesses;RGB Images driven Recognition of Grapevine Varieties;discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering;counting Livestock with Image Segmentation Neural Network;smart, Precision or Digital Agriculture and Farming - Current State of Technology;an Automated Platform for Microrobot Manipulation;growth Models of Female Dairy Cattle;a Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops;blocks of Jobs for Solving Two-Machine Flow Shop Problem with Normal Distributed Processing Times;soft computing Analysis of Pressure Decay Leak Test Detection.

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Self-adjusted data-driven System for Prediction of Human Performance 3rd

Self-adjusted Data-Driven System for Prediction of Human Per...

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3rd international conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Burov, Oleksandr Lavrov, Evgeniy Pasko, Nadiia Hlazunova, Olena Lavrova, Olga Kyzenko, Vasyl Dolgikh, Yana Inst Informat Technol & Learning Tools 9 Berlinskoho St UA-04060 Kiev Ukraine Sumy State Univ Sumy Ukraine Sumy Natl Agr Univ Sumy Ukraine Natl Univ Life & Environm Sci Ukraine Kiev Ukraine Kyiv Natl Univ Kiev Ukraine Natl Acad Educ Sci Ukraine Inst Pedag Kiev Ukraine

ISBN: (纸本)9783030395124;9783030395117

Design principles and the data-driven system to assess and to predict an operator readiness-to-perform are discussed in the article. Principles of construction and performance of the system are formulated. The main focus is on data organization (time line, date set for the model construction) and adaptive algorithm construction. High level of the prediction accuracy for an operator readiness-to-perform (85-90%) was achieved because of use data stored (parameters of time and cognitive tasks performance by user) the system to control its performance, as well as its self-adjusted algorithm of functioning.

关键词： data-driven Cognitive performance Self-adjustment Adaptive models

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A Comparative Study of Recent advances in Big data Analytics in Vehicular Ad Hoc Networks

A Comparative Study of Recent Advances in Big Data Analytics...

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international Wireless Communications and Mobile computing conference, IWCMC

作者： Kawthar Zaraket Ismail Bennis Hassan Harb Ali Jaber Abedalhafid Abouaissa MERCEL Lab Doctoral School of Science and Technology Lebanese University Beirut Lebanon IRIMAS Universit&#x00E9 de Haute Alsace Mulhouse France

Big data is becoming a research focus in intelligent Transportation systems (ITS), which can be seen in many projects worldwide. intelligent transportation systems will produce a large amount of data. The produced big data will profoundly impact the design and application of the ITS, which makes them safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper aims to provide a comparative study of some recent works in Big data analytics for Vehicular Ad Hoc Networks. The study includes reviewing some frameworks that conduct big data analytics in ITS while discussing the data source and collection methods, data analytics methods, platforms and big data analytics application categories. The comparison and implementation of five recent works, with a focus on the data collection and application layers, are followed to pick up the best approach based on the Friedman test results.

关键词： Wireless communication Measurement data analysis Soft sensors Vehicular ad hoc networks Computer architecture Big data

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AI for Information Tecchnology Operation (AIOps): A Review of IT Incident Risk Prediction

AI for Information Tecchnology Operation (AIOps): A Review o...

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international conference on Soft computing and Machine Intelligence (ISCMI)

作者： Salman Ahmed Muskaan Singh Brendan Doherty Effirul Ramlan Kathryn Harkin Damien Coyle Intelligent Systems Research Centre Ulster University Londonderry Northern Ireland Data and Intelligent Systems Allstate NI Belfast Northern Ireland UCD School of Medicine University College Dublin Dublin 4 Ireland

The advancement of Artificial Intelligence has led to a surge in its application in Information Technology (IT) Operations, often termed Artificial Intelligence for IT Operations (AIOPS). One of the most challenging problems in AIOPS is IT Service Management (ITSM), which deals with incidents and anomalies of users, often referred to as tickets. These tickets are resolved by the IT firm support system, which plays a significant role in the company's user experiences, productivity, and profit. Recent advances have been made to automate the prediction of IT incidents and resolve them in a minimal time, utilizing AI models. In this paper, we take stock of the work in this domain and review the challenges. We also highlight the open topics that require further investigation for the advancement of the field.

关键词： Support vector machines Productivity Bibliographies Predictive models Prediction algorithms Transformers Libraries

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A Big data Semantic driven Context Aware Recommendation Method

A Big Data Semantic Driven Context Aware Recommendation Meth...

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international conference on intelligent and Fuzzy systems, INFUS 2020

作者： Barranco, Manuel J. Sanchez, Pedro J. Castro, Jorge Yera, Raciel University of Jaen Jaen Spain University of Granada Granada Spain University of Ciego de Avila Ciego de Avila Cuba

ISBN: (纸本)9783030511555

Classical content-based recommender systems (CB) help users to find preferred items in overloaded search spaces, comparing items descriptions with user profiles. However, classical CBs do not take into account that user preferences may change over time influenced by the user context. This paper propounds to consider context-awareness (CA) in order to improve the quality of recommendations, using contextual information obtained from streams of status updates in microblogging platforms. A novel CA-CB approach is proposed, which provides context awareness recommendations based on topic detection within the current trend interest in Twitter. Finally, some guidelines for the implementation, using the Map Reduce paradigm, are given. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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A data-driven Approach to Constraint Optimization

A Data-Driven Approach to Constraint Optimization

引用

international conference on Automation (AUTOMATION)

作者： Wikarek, Jaroslaw Sitek, Pawel Kielce Univ Technol Dept Informat Syst Kielce Poland

ISBN: (纸本)9783030132736;9783030132729

Many problems occurring in production, transport, supply chains and everyday life problems can be formulated in the form of constraint optimization problems (COPs). Most often these are issues related to planning and scheduling, distribution of resources, fleet selection, route and network optimization, configuration of machines and manufacturing systems, timetabling, etc. In the vast majority of cases, these are discrete problems of a combinatorial nature. Significant difficulties in modelling and solving COPs are usually the magnitude of real problems, which translates into a large number of variables and constraints as well as high computational complexity (usually NP-hard problems). The article proposes a data-driven approach, which allows a significant reduction in the magnitude of modelled problems and, consequently, the possibility of solving many real problems in an acceptable time.

关键词： data-driven Constraint optimization Mathematical programing Problem modeling

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Alignment of Visual Senses and Acoustical Senses based on Emotion Recognitions

Alignment of Visual Senses and Acoustical Senses based on Em...

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international Symposium on Soft computing and intelligent systems (SCIS)

作者： Ja-Hwung Su Yi-Wen Liao Ming-Hung Kao Yung-Wen Tsai Chih-Jui Chang Hsiu-Wei Wu Cheng-Wei Chen Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Intelligent Commerce National Kaohsiung University of Science and Technology Taiwan

Recently, due to the rapid advances on multimedia technology, multimedia data such as pictures and music has heavily invaded our daily life. By the mobile camera, users capture the interested scenes as the preferred pictures, and then browse these pictures in either computers or mobile devices. This incurs an interesting issue that, how to provide the users with a better browsing/listening atmosphere and experience by music. Actually, it can be viewed as the problem for aligning photos with music and also as an exciting research topic for cross-media retrieval. However, very few studies focus their attention on this topic. To aim at this issue, in this paper, we propose a novel cross-media alignment method that bridges visual pictures to harmonic music by visual emotions and acoustical emotions. In this method, the pictures and music are projected onto the emotion space first. Next, the emotion features are fuzzed to be near the human senses. Finally, the effective alignment method for associating pictures with music is performed. The results of subjective and objective evaluations on the real datasets reveal that, our proposed method can successfully provide the users with a rich experience on audiovisual presentations. Also, the proposed alignment algorithm is shown to be effective in terms of Normalized Discounted Cumulative Gain.

关键词： Computers Bridges Visualization Emotion recognition Music Atmosphere Information retrieval

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